Interface SatParametersOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

SatParameters, SatParameters.Builder

@Generated
public interface SatParametersOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method	Description
double	getAbsoluteGapLimit()	Stop the search when the gap between the best feasible objective (O) and our best objective bound (B) is smaller than a limit.
boolean	getAddCgCuts()	Whether we generate and add Chvatal-Gomory cuts to the LP at root node.
boolean	getAddCliqueCuts()	Whether we generate clique cuts from the binary implication graph.
boolean	getAddLinMaxCuts()	For the lin max constraints, generates the cuts described in "Strong mixed-integer programming formulations for trained neural networks" by Ross Anderson et.
boolean	getAddLpConstraintsLazily()	If true, we start by an empty LP, and only add constraints not satisfied by the current LP solution batch by batch.
boolean	getAddMirCuts()	Whether we generate MIR cuts at root node.
boolean	getAddObjectiveCut()	When the LP objective is fractional, do we add the cut that forces the linear objective expression to be greater or equal to this fractional value rounded up?
boolean	getAddRltCuts()	Whether we generate RLT cuts.
boolean	getAddZeroHalfCuts()	Whether we generate Zero-Half cuts at root node.
boolean	getAlsoBumpVariablesInConflictReasons()	When this is true, then the variables that appear in any of the reason of the variables in a conflict have their activity bumped.
int	getAlternativePoolSize()	In order to not get stuck in local optima, when this is non-zero, we try to also work on "older" solutions with a worse objective value so we get a chance to follow a different LS/LNS trajectory.
int	getAtMostOneMaxExpansionSize()	All at_most_one constraints with a size <= param will be replaced by a quadratic number of binary implications.
boolean	getAutoDetectGreaterThanAtLeastOneOf()	If true, then the precedences propagator try to detect for each variable if it has a set of "optional incoming arc" for which at least one of them is present.
SatParameters.BinaryMinizationAlgorithm	getBinaryMinimizationAlgorithm()	optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FROM_UIP_AND_DECISIONS];
int	getBinarySearchNumConflicts()	If non-negative, perform a binary search on the objective variable in order to find an [min, max] interval outside of which the solver proved unsat/sat under this amount of conflict.
double	getBlockingRestartMultiplier()	optional double blocking_restart_multiplier = 66 [default = 1.4];
int	getBlockingRestartWindowSize()	optional int32 blocking_restart_window_size = 65 [default = 5000];
int	getBooleanEncodingLevel()	A non-negative level indicating how much we should try to fully encode Integer variables as Boolean.
boolean	getCatchSigintSignal()	Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals when calling solve.
boolean	getCheckDratProof()	If true, and if the problem is UNSAT, a DRAT proof of this UNSAT property is checked after the solver has finished.
boolean	getCheckLratProof()	If true, inferred clauses are checked with an LRAT checker as they are learned, in presolve (reduced to trivial simplifications if cp_model_presolve is false), and in each worker.
boolean	getCheckMergedLratProof()	If true, and if output_lrat_proof is true and the problem is UNSAT, check that the merged proof file is valid, i.e., that clause sharing between workers is correct.
int	getChronologicalBacktrackMinConflicts()	If chronological backtracking is enabled, this is the minimum number of conflicts before we will consider backjumping.
double	getClauseActivityDecay()	Clause activity parameters (same effect as the one on the variables).
int	getClauseCleanupLbdBound()	All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept.
int	getClauseCleanupLbdTier1()	All the clause with a LBD lower or equal to this will be kept except if its activity hasn't been bumped in the last 32 cleanup phase.
int	getClauseCleanupLbdTier2()	All the clause with a LBD lower or equal to this will be kept except if its activity hasn't been bumped since the previous cleanup phase.
SatParameters.ClauseOrdering	getClauseCleanupOrdering()	optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];
int	getClauseCleanupPeriod()	Trigger a cleanup when this number of "deletable" clauses is learned.
int	getClauseCleanupPeriodIncrement()	Increase clause_cleanup_period by this amount after each cleanup.
double	getClauseCleanupRatio()	During a cleanup, if clause_cleanup_target is 0, we will delete the clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed target of clauses to keep.
int	getClauseCleanupTarget()	During a cleanup, we will always keep that number of "deletable" clauses.
boolean	getConvertIntervals()	Temporary flag util the feature is more mature.
int	getCoreMinimizationLevel()	If positive, we spend some effort on each core: - At level 1, we use a simple heuristic to try to minimize an UNSAT core. - At level 2, we use propagation to minimize the core but also identify literal in at most one relationship in this core.
boolean	getCountAssumptionLevelsInLbd()	Whether or not the assumption levels are taken into account during the LBD computation.
boolean	getCoverOptimization()	If true, when the max-sat algo find a core, we compute the minimal number of literals in the core that needs to be true to have a feasible solution.
boolean	getCpModelPresolve()	Whether we presolve the cp_model before solving it.
int	getCpModelProbingLevel()	How much effort do we spend on probing. 0 disables it completely.
boolean	getCpModelUseSatPresolve()	Whether we also use the sat presolve when cp_model_presolve is true.
boolean	getCreate1UipBooleanDuringIcr()	If true, and during integer conflict resolution (icr) the 1-UIP is an integer literal for which we do not have an associated Boolean.
double	getCutActiveCountDecay()	optional double cut_active_count_decay = 156 [default = 0.8];
int	getCutCleanupTarget()	Target number of constraints to remove during cleanup.
int	getCutLevel()	Control the global cut effort.
double	getCutMaxActiveCountValue()	These parameters are similar to sat clause management activity parameters.
boolean	getDebugCrashIfLratCheckFails()	Crash if the LRAT UNSAT proof is invalid.
boolean	getDebugCrashIfPresolveBreaksHint()	Crash if presolve breaks a feasible hint.
boolean	getDebugCrashOnBadHint()	Crash if we do not manage to complete the hint into a full solution.
int	getDebugMaxNumPresolveOperations()	If positive, try to stop just after that many presolve rules have been applied.
boolean	getDebugPostsolveWithFullSolver()	We have two different postsolve code.
boolean	getDecisionSubsumptionDuringConflictAnalysis()	Try even more subsumption options during conflict analysis.
String	getDefaultRestartAlgorithms()	optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];
com.google.protobuf.ByteString	getDefaultRestartAlgorithmsBytes()	optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];
boolean	getDetectLinearizedProduct()	Infer products of Boolean or of Boolean time IntegerVariable from the linear constrainst in the problem.
boolean	getDetectTableWithCost()	If true, we detect variable that are unique to a table constraint and only there to encode a cost on each tuple.
boolean	getDisableConstraintExpansion()	If true, it disable all constraint expansion.
boolean	getDiversifyLnsParams()	If true, registers more lns subsolvers with different parameters.
int	getEagerlySubsumeLastNConflicts()	If >=0, each time we have a conflict, we try to subsume the last n learned clause with it.
boolean	getEncodeComplexLinearConstraintWithInteger()	Linear constraint with a complex right hand side (more than a single interval) need to be expanded, there is a couple of way to do that.
boolean	getEncodeCumulativeAsReservoir()	Encore cumulative with fixed demands and capacity as a reservoir constraint.
boolean	getEnumerateAllSolutions()	Whether we enumerate all solutions of a problem without objective.
boolean	getExpandAlldiffConstraints()	If true, expand all_different constraints that are not permutations.
boolean	getExpandReservoirConstraints()	If true, expand the reservoir constraints by creating booleans for all possible precedences between event and encoding the constraint.
boolean	getExpandReservoirUsingCircuit()	Mainly useful for testing.
boolean	getExploitAllLpSolution()	If true and the Lp relaxation of the problem has a solution, try to exploit it.
boolean	getExploitAllPrecedences()	optional bool exploit_all_precedences = 220 [default = false];
boolean	getExploitBestSolution()	When branching on a variable, follow the last best solution value.
boolean	getExploitIntegerLpSolution()	If true and the Lp relaxation of the problem has an integer optimal solution, try to exploit it.
boolean	getExploitObjective()	When branching an a variable that directly affect the objective, branch on the value that lead to the best objective first.
boolean	getExploitRelaxationSolution()	When branching on a variable, follow the last best relaxation solution value.
String	getExtraSubsolvers(int index)	A convenient way to add more workers types.
com.google.protobuf.ByteString	getExtraSubsolversBytes(int index)	A convenient way to add more workers types.
int	getExtraSubsolversCount()	A convenient way to add more workers types.
List<String>	getExtraSubsolversList()	A convenient way to add more workers types.
boolean	getExtraSubsumptionDuringConflictAnalysis()	It is possible that "intermediate" clauses during conflict resolution subsumes some of the clauses that propagated.
double	getFeasibilityJumpBatchDtime()	How much dtime for each LS batch.
double	getFeasibilityJumpDecay()	On each restart, we randomly choose if we use decay (with this parameter) or no decay.
boolean	getFeasibilityJumpEnableRestarts()	When stagnating, feasibility jump will either restart from a default solution (with some possible randomization), or randomly pertubate the current solution.
int	getFeasibilityJumpLinearizationLevel()	How much do we linearize the problem in the local search code.
int	getFeasibilityJumpMaxExpandedConstraintSize()	Maximum size of no_overlap or no_overlap_2d constraint for a quadratic expansion.
int	getFeasibilityJumpRestartFactor()	This is a factor that directly influence the work before each restart.
double	getFeasibilityJumpVarPerburbationRangeRatio()	Max distance between the default value and the pertubated value relative to the range of the domain of the variable.
double	getFeasibilityJumpVarRandomizationProbability()	Probability for a variable to have a non default value upon restarts or perturbations.
boolean	getFillAdditionalSolutionsInResponse()	If true, the final response addition_solutions field will be filled with all solutions from our solutions pool.
boolean	getFillTightenedDomainsInResponse()	If true, add information about the derived variable domains to the CpSolverResponse.
boolean	getFilterSatPostsolveClauses()	Internal parameter.
String	getFilterSubsolvers(int index)	repeated string filter_subsolvers = 293;
com.google.protobuf.ByteString	getFilterSubsolversBytes(int index)	repeated string filter_subsolvers = 293;
int	getFilterSubsolversCount()	repeated string filter_subsolvers = 293;
List<String>	getFilterSubsolversList()	repeated string filter_subsolvers = 293;
boolean	getFindBigLinearOverlap()	Try to find large "rectangle" in the linear constraint matrix with identical lines.
boolean	getFindClausesThatAreExactlyOne()	By propagating (or just using binary clauses), one can detect that all literal of a clause are actually in at most one relationship.
boolean	getFindMultipleCores()	Whether we try to find more independent cores for a given set of assumptions in the core based max-SAT algorithms.
boolean	getFixVariablesToTheirHintedValue()	If true, variables appearing in the solution hints will be fixed to their hinted value.
SatParameters.FPRoundingMethod	getFpRounding()	optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];
double	getGlucoseDecayIncrement()	optional double glucose_decay_increment = 23 [default = 0.01];
int	getGlucoseDecayIncrementPeriod()	optional int32 glucose_decay_increment_period = 24 [default = 5000];
double	getGlucoseMaxDecay()	The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until 0.95.
int	getHintConflictLimit()	Conflict limit used in the phase that exploit the solution hint.
boolean	getIgnoreNames()	If true, we don't keep names in our internal copy of the user given model.
String	getIgnoreSubsolvers(int index)	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem or only keep specific ones for testing.
com.google.protobuf.ByteString	getIgnoreSubsolversBytes(int index)	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem or only keep specific ones for testing.
int	getIgnoreSubsolversCount()	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem or only keep specific ones for testing.
List<String>	getIgnoreSubsolversList()	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem or only keep specific ones for testing.
boolean	getInferAllDiffs()	Run a max-clique code amongst all the x !
SatParameters.Polarity	getInitialPolarity()	optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];
double	getInitialVariablesActivity()	The initial value of the variables activity.
double	getInprocessingDtimeRatio()	Proportion of deterministic time we should spend on inprocessing.
double	getInprocessingMinimizationDtime()	Parameters for an heuristic similar to the one described in "An effective learnt clause minimization approach for CDCL Sat Solvers", https://www.ijcai.org/proceedings/2017/0098.pdf This is the amount of dtime we should spend on this technique during each inprocessing phase.
boolean	getInprocessingMinimizationUseAllOrderings()	optional bool inprocessing_minimization_use_all_orderings = 298 [default = false];
boolean	getInprocessingMinimizationUseConflictAnalysis()	optional bool inprocessing_minimization_use_conflict_analysis = 297 [default = true];
double	getInprocessingProbingDtime()	The amount of dtime we should spend on probing for each inprocessing round.
boolean	getInprocessingUseCongruenceClosure()	Whether we use the algorithm described in "Clausal Congruence closure", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2024.
boolean	getInprocessingUseSatSweeping()	Whether we use the SAT sweeping algorithm described in "Clausal Equivalence Sweeping", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2025.
boolean	getInstantiateAllVariables()	If true, the solver will add a default integer branching strategy to the already defined search strategy.
int	getInterleaveBatchSize()	optional int32 interleave_batch_size = 134 [default = 0];
boolean	getInterleaveSearch()	Experimental.
boolean	getKeepAllFeasibleSolutionsInPresolve()	If true, we disable the presolve reductions that remove feasible solutions from the search space.
boolean	getKeepSymmetryInPresolve()	Experimental.
int	getLbRelaxNumWorkersThreshold()	Only use lb-relax if we have at least that many workers.
int	getLinearizationLevel()	A non-negative level indicating the type of constraints we consider in the LP relaxation.
int	getLinearSplitSize()	Linear constraints that are not pseudo-Boolean and that are longer than this size will be split into sqrt(size) intermediate sums in order to have faster propation in the CP engine.
double	getLnsInitialDeterministicLimit()	optional double lns_initial_deterministic_limit = 308 [default = 0.1];
double	getLnsInitialDifficulty()	Initial parameters for neighborhood generation.
boolean	getLoadAtMostOnesInSatPresolve()	If we try to load at most ones and exactly ones constraints when running the pure SAT presolve.
String	getLogPrefix()	Add a prefix to all logs.
com.google.protobuf.ByteString	getLogPrefixBytes()	Add a prefix to all logs.
boolean	getLogSearchProgress()	Whether the solver should log the search progress.
boolean	getLogSubsolverStatistics()	Whether the solver should display per sub-solver search statistics.
boolean	getLogToResponse()	Log to response proto.
boolean	getLogToStdout()	Log to stdout.
double	getLpDualTolerance()	optional double lp_dual_tolerance = 267 [default = 1e-07];
double	getLpPrimalTolerance()	The internal LP tolerances used by CP-SAT.
int	getMaxAllDiffCutSize()	Cut generator for all diffs can add too many cuts for large all_diff constraints.
int	getMaxAlldiffDomainSize()	Max domain size for all_different constraints to be expanded.
int	getMaxBackjumpLevels()	If chronological backtracking is enabled, this is the maximum number of levels we will backjump over, otherwise we will backtrack.
double	getMaxClauseActivityValue()	optional double max_clause_activity_value = 18 [default = 1e+20];
int	getMaxConsecutiveInactiveCount()	If a constraint/cut in LP is not active for that many consecutive OPTIMAL solves, remove it from the LP.
int	getMaxCutRoundsAtLevelZero()	Max number of time we perform cut generation and resolve the LP at level 0.
double	getMaxDeterministicTime()	Maximum time allowed in deterministic time to solve a problem.
int	getMaxDomainSizeForLinear2Expansion()	Max domain size for expanding linear2 constraints (ax + by ==/!
int	getMaxDomainSizeWhenEncodingEqNeqConstraints()	When loading a*x + b*y ==/!
double	getMaxDratTimeInSeconds()	The maximum time allowed to check the DRAT proof (this can take more time than the solve itself).
int	getMaximumRegionsToSplitInDisconnectedNoOverlap2D()	Detects when the space where items of a no_overlap_2d constraint can placed is disjoint (ie., fixed boxes split the domain).
int	getMaxIntegerRoundingScaling()	In the integer rounding procedure used for MIR and Gomory cut, the maximum "scaling" we use (must be positive).
int	getMaxLinMaxSizeForExpansion()	If the number of expressions in the lin_max is less that the max size parameter, model expansion replaces target = max(xi) by linear constraint with the introduction of new booleans bi such that bi => target == xi.
long	getMaxMemoryInMb()	Maximum memory allowed for the whole thread containing the solver.
long	getMaxNumberOfConflicts()	Maximum number of conflicts allowed to solve a problem.
int	getMaxNumCuts()	The limit on the number of cuts in our cut pool.
int	getMaxNumDeterministicBatches()	Stops after that number of batches has been scheduled.
int	getMaxNumIntervalsForTimetableEdgeFinding()	Max number of intervals for the timetable_edge_finding algorithm to propagate.
int	getMaxPairsPairwiseReasoningInNoOverlap2D()	If the number of pairs to look is below this threshold, do an extra step of propagation in the no_overlap_2d constraint by looking at all pairs of intervals.
int	getMaxPresolveIterations()	In case of large reduction in a presolve iteration, we perform multiple presolve iterations.
SatParameters.MaxSatAssumptionOrder	getMaxSatAssumptionOrder()	optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];
boolean	getMaxSatReverseAssumptionOrder()	If true, adds the assumption in the reverse order of the one defined by max_sat_assumption_order.
SatParameters.MaxSatStratificationAlgorithm	getMaxSatStratification()	optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];
int	getMaxSizeToCreatePrecedenceLiteralsInDisjunctive()	Create one literal for each disjunction of two pairs of tasks.
double	getMaxTimeInSeconds()	Maximum time allowed in seconds to solve a problem.
double	getMaxVariableActivityValue()	optional double max_variable_activity_value = 16 [default = 1e+100];
double	getMergeAtMostOneWorkLimit()	optional double merge_at_most_one_work_limit = 146 [default = 100000000];
double	getMergeNoOverlapWorkLimit()	During presolve, we use a maximum clique heuristic to merge together no-overlap constraints or at most one constraints.
SatParameters.ConflictMinimizationAlgorithm	getMinimizationAlgorithm()	optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];
boolean	getMinimizeReductionDuringPbResolution()	A different algorithm during PB resolution.
boolean	getMinimizeSharedClauses()	Minimize and detect subsumption of shared clauses immediately after they are imported.
double	getMinOrthogonalityForLpConstraints()	While adding constraints, skip the constraints which have orthogonality less than 'min_orthogonality_for_lp_constraints' with already added constraints during current call.
boolean	getMipAutomaticallyScaleVariables()	If true, some continuous variable might be automatically scaled.
double	getMipCheckPrecision()	As explained in mip_precision and mip_max_activity_exponent, we cannot always reach the wanted precision during scaling.
boolean	getMipComputeTrueObjectiveBound()	Even if we make big error when scaling the objective, we can always derive a correct lower bound on the original objective by using the exact lower bound on the scaled integer version of the objective.
double	getMipDropTolerance()	Any value in the input mip with a magnitude lower than this will be set to zero.
int	getMipMaxActivityExponent()	To avoid integer overflow, we always force the maximum possible constraint activity (and objective value) according to the initial variable domain to be smaller than 2 to this given power.
double	getMipMaxBound()	We need to bound the maximum magnitude of the variables for CP-SAT, and that is the bound we use.
double	getMipMaxValidMagnitude()	Any finite values in the input MIP must be below this threshold, otherwise the model will be reported invalid.
int	getMipPresolveLevel()	When solving a MIP, we do some basic floating point presolving before scaling the problem to integer to be handled by CP-SAT.
boolean	getMipScaleLargeDomain()	If this is false, then mip_var_scaling is only applied to variables with "small" domain.
boolean	getMipTreatHighMagnitudeBoundsAsInfinity()	By default, any variable/constraint bound with a finite value and a magnitude greater than the mip_max_valid_magnitude will result with a invalid model.
double	getMipVarScaling()	All continuous variable of the problem will be multiplied by this factor.
double	getMipWantedPrecision()	When scaling constraint with double coefficients to integer coefficients, we will multiply by a power of 2 and round the coefficients.
String	getName()	In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose.
com.google.protobuf.ByteString	getNameBytes()	In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose.
int	getNewConstraintsBatchSize()	Add that many lazy constraints (or cuts) at once in the LP.
boolean	getNewLinearPropagation()	The new linear propagation code treat all constraints at once and use an adaptation of Bellman-Ford-Tarjan to propagate constraint in a smarter order and potentially detect propagation cycle earlier.
int	getNoOverlap2DBooleanRelationsLimit()	If less than this number of boxes are present in a no-overlap 2d, we create 4 Booleans per pair of boxes: - Box 2 is after Box 1 on x. - Box 1 is after Box 2 on x. - Box 2 is after Box 1 on y. - Box 1 is after Box 2 on y.
int	getNumConflictsBeforeStrategyChanges()	After each restart, if the number of conflict since the last strategy change is greater that this, then we increment a "strategy_counter" that can be use to change the search strategy used by the following restarts.
int	getNumFullSubsolvers()	We distinguish subsolvers that consume a full thread, and the ones that are always interleaved.
int	getNumSearchWorkers()	optional int32 num_search_workers = 100 [default = 0];
int	getNumViolationLs()	This will create incomplete subsolvers (that are not LNS subsolvers) that use the feasibility jump code to find improving solution, treating the objective improvement as a hard constraint.
int	getNumWorkers()	Specify the number of parallel workers (i.e. threads) to use during search.
boolean	getOnlyAddCutsAtLevelZero()	For the cut that can be generated at any level, this control if we only try to generate them at the root node.
boolean	getOnlySolveIp()	If one try to solve a MIP model with CP-SAT, because we assume all variable to be integer after scaling, we will not necessarily have the correct optimal.
boolean	getOptimizeWithCore()	The default optimization method is a simple "linear scan", each time trying to find a better solution than the previous one.
boolean	getOptimizeWithLbTreeSearch()	Do a more conventional tree search (by opposition to SAT based one) where we keep all the explored node in a tree.
boolean	getOptimizeWithMaxHs()	This has no effect if optimize_with_core is false.
boolean	getOutputDratProof()	If true, a DRAT proof that all the clauses inferred by the solver are valid is output to a file.
boolean	getOutputLratProof()	If true, an LRAT proof that all the clauses inferred by the solver are valid is output to several files (one for presolve -- reduced to trivial simplifications if cp_model_presolve is false, one per worker, and one for the merged proof).
int	getPbCleanupIncrement()	Same as for the clauses, but for the learned pseudo-Boolean constraints.
double	getPbCleanupRatio()	optional double pb_cleanup_ratio = 47 [default = 0.5];
boolean	getPermutePresolveConstraintOrder()	optional bool permute_presolve_constraint_order = 179 [default = false];
boolean	getPermuteVariableRandomly()	This is mainly here to test the solver variability.
boolean	getPolarityExploitLsHints()	If true and we have first solution LS workers, tries in some phase to follow a LS solutions that violates has litle constraints as possible.
int	getPolarityRephaseIncrement()	If non-zero, then we change the polarity heuristic after that many number of conflicts in an arithmetically increasing fashion.
boolean	getPolishLpSolution()	Whether we try to do a few degenerate iteration at the end of an LP solve to minimize the fractionality of the integer variable in the basis.
SatParameters.VariableOrder	getPreferredVariableOrder()	optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];
boolean	getPresolveBlockedClause()	Whether we use an heuristic to detect some basic case of blocked clause in the SAT presolve.
int	getPresolveBvaThreshold()	Apply Bounded Variable Addition (BVA) if the number of clauses is reduced by stricly more than this threshold.
int	getPresolveBveClauseWeight()	During presolve, we apply BVE only if this weight times the number of clauses plus the number of clause literals is not increased.
int	getPresolveBveThreshold()	During presolve, only try to perform the bounded variable elimination (BVE) of a variable x if the number of occurrences of x times the number of occurrences of not(x) is not greater than this parameter.
boolean	getPresolveExtractIntegerEnforcement()	If true, we will extract from linear constraints, enforcement literals of the form "integer variable at bound => simplified constraint".
long	getPresolveInclusionWorkLimit()	A few presolve operations involve detecting constraints included in other constraint.
double	getPresolveProbingDeterministicTimeLimit()	optional double presolve_probing_deterministic_time_limit = 57 [default = 30];
int	getPresolveSubstitutionLevel()	How much substitution (also called free variable aggregation in MIP litterature) should we perform at presolve.
boolean	getPresolveUseBva()	Whether or not we use Bounded Variable Addition (BVA) in the presolve.
double	getProbingDeterministicTimeLimit()	The maximum "deterministic" time limit to spend in probing.
int	getProbingNumCombinationsLimit()	How many combinations of pairs or triplets of variables we want to scan.
double	getPropagationLoopDetectionFactor()	Some search decisions might cause a really large number of propagations to happen when integer variables with large domains are only reduced by 1 at each step.
long	getPseudoCostReliabilityThreshold()	The solver ignores the pseudo costs of variables with number of recordings less than this threshold.
boolean	getPushAllTasksTowardStart()	Experimental code: specify if the objective pushes all tasks toward the start of the schedule.
double	getRandomBranchesRatio()	A number between 0 and 1 that indicates the proportion of branching variables that are selected randomly instead of choosing the first variable from the given variable_ordering strategy.
boolean	getRandomizeSearch()	Randomize fixed search.
double	getRandomPolarityRatio()	The proportion of polarity chosen at random.
int	getRandomSeed()	At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed.
double	getRelativeGapLimit()	optional double relative_gap_limit = 160 [default = 0];
boolean	getRemoveFixedVariablesEarly()	If cp_model_presolve is true and there is a large proportion of fixed variable after the first model copy, remap all the model to a dense set of variable before the full presolve even starts.
boolean	getRepairHint()	If true, the solver tries to repair the solution given in the hint.
SatParameters.RestartAlgorithm	getRestartAlgorithms(int index)	The restart strategies will change each time the strategy_counter is increased.
int	getRestartAlgorithmsCount()	The restart strategies will change each time the strategy_counter is increased.
List<SatParameters.RestartAlgorithm>	getRestartAlgorithmsList()	The restart strategies will change each time the strategy_counter is increased.
double	getRestartDlAverageRatio()	In the moving average restart algorithms, a restart is triggered if the window average times this ratio is greater that the global average.
double	getRestartLbdAverageRatio()	optional double restart_lbd_average_ratio = 71 [default = 1];
int	getRestartPeriod()	Restart period for the FIXED_RESTART strategy.
int	getRestartRunningWindowSize()	Size of the window for the moving average restarts.
int	getRootLpIterations()	Even at the root node, we do not want to spend too much time on the LP if it is "difficult".
double	getRoutingCutDpEffort()	The amount of "effort" to spend in dynamic programming for computing routing cuts.
int	getRoutingCutMaxInfeasiblePathLength()	If the length of an infeasible path is less than this value, a cut will be added to exclude it.
int	getRoutingCutSubsetSizeForBinaryRelationBound()	If the size of a subset of nodes of a RoutesConstraint is less than this value, use linear constraints of size 1 and 2 (such as capacity and time window constraints) enforced by the arc literals to compute cuts for this subset (unless the subset size is less than routing_cut_subset_size_for_tight_binary_relation_bound, in which case the corresponding algorithm is used instead).
int	getRoutingCutSubsetSizeForExactBinaryRelationBound()	Similar to above, but with an even stronger algorithm in O(n!).
int	getRoutingCutSubsetSizeForShortestPathsBound()	Similar to routing_cut_subset_size_for_exact_binary_relation_bound but use a bound based on shortest path distances (which respect triangular inequality).
int	getRoutingCutSubsetSizeForTightBinaryRelationBound()	Similar to above, but with a different algorithm producing better cuts, at the price of a higher O(2^n) complexity, where n is the subset size.
boolean	getSaveLpBasisInLbTreeSearch()	Experimental.
SatParameters.SearchBranching	getSearchBranching()	optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];
long	getSearchRandomVariablePoolSize()	Search randomization will collect the top 'search_random_variable_pool_size' valued variables, and pick one randomly.
boolean	getShareBinaryClauses()	Allows sharing of new learned binary clause between workers.
int	getSharedTreeBalanceTolerance()	How much deeper compared to the ideal max depth of the tree is considered "balanced" enough to still accept a split.
int	getSharedTreeMaxNodesPerWorker()	In order to limit total shared memory and communication overhead, limit the total number of nodes that may be generated in the shared tree.
int	getSharedTreeNumWorkers()	Enables shared tree search.
double	getSharedTreeOpenLeavesPerWorker()	How many open leaf nodes should the shared tree maintain per worker.
double	getSharedTreeSplitMinDtime()	How much dtime a worker will wait between proposing splits.
SatParameters.SharedTreeSplitStrategy	getSharedTreeSplitStrategy()	optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];
boolean	getSharedTreeWorkerEnablePhaseSharing()	If true, shared tree workers share their target phase when returning an assigned subtree for the next worker to use.
boolean	getSharedTreeWorkerEnableTrailSharing()	If true, workers share more of the information from their local trail.
int	getSharedTreeWorkerMinRestartsPerSubtree()	Minimum restarts before a worker will replace a subtree that looks "bad" based on the average LBD of learned clauses.
boolean	getShareGlueClauses()	Allows sharing of short glue clauses between workers.
double	getShareGlueClausesDtime()	The amount of dtime between each export of shared glue clauses.
boolean	getShareLevelZeroBounds()	Allows sharing of the bounds of modified variables at level 0.
boolean	getShareLinear2Bounds()	Allows sharing of the bounds on linear2 discovered at level 0.
boolean	getShareObjectiveBounds()	Allows objective sharing between workers.
double	getShavingDeterministicTimeInProbingSearch()	Add a shaving phase (where the solver tries to prove that the lower or upper bound of a variable are infeasible) to the probing search
double	getShavingSearchDeterministicTime()	Specifies the amount of deterministic time spent of each try at shaving a bound in the shaving search.
long	getShavingSearchThreshold()	Specifies the threshold between two modes in the shaving procedure.
int	getSolutionPoolDiversityLimit()	If solution_pool_size is <= this, we will use DP to keep a "diverse" set of solutions (the one further apart via hamming distance) in the pool.
int	getSolutionPoolSize()	Size of the top-n different solutions kept by the solver.
boolean	getStopAfterFirstSolution()	For an optimization problem, stop the solver as soon as we have a solution.
boolean	getStopAfterPresolve()	Mainly used when improving the presolver.
boolean	getStopAfterRootPropagation()	optional bool stop_after_root_propagation = 252 [default = false];
double	getStrategyChangeIncreaseRatio()	The parameter num_conflicts_before_strategy_changes is increased by that much after each strategy change.
SatParameters	getSubsolverParams(int index)	It is possible to specify additional subsolver configuration.
int	getSubsolverParamsCount()	It is possible to specify additional subsolver configuration.
List<SatParameters>	getSubsolverParamsList()	It is possible to specify additional subsolver configuration.
SatParametersOrBuilder	getSubsolverParamsOrBuilder(int index)	It is possible to specify additional subsolver configuration.
List<? extends SatParametersOrBuilder>	getSubsolverParamsOrBuilderList()	It is possible to specify additional subsolver configuration.
String	getSubsolvers(int index)	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
com.google.protobuf.ByteString	getSubsolversBytes(int index)	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
int	getSubsolversCount()	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
List<String>	getSubsolversList()	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
boolean	getSubsumeDuringVivification()	If we remove clause that we now are "implied" by others.
boolean	getSubsumptionDuringConflictAnalysis()	At a really low cost, during the 1-UIP conflict computation, it is easy to detect if some of the involved reasons are subsumed by the current conflict.
double	getSymmetryDetectionDeterministicTimeLimit()	Deterministic time limit for symmetry detection.
int	getSymmetryLevel()	Whether we try to automatically detect the symmetries in a model and exploit them.
int	getTableCompressionLevel()	How much we try to "compress" a table constraint.
int	getTransitivePrecedencesWorkLimit()	At root level, we might compute the transitive closure of "precedences" relations so that we can exploit that in scheduling problems.
boolean	getUseAbslRandom()	optional bool use_absl_random = 180 [default = false];
boolean	getUseAllDifferentForCircuit()	Turn on extra propagation for the circuit constraint.
boolean	getUseAreaEnergeticReasoningInNoOverlap2D()	When this is true, the no_overlap_2d constraint is reinforced with an energetic reasoning that uses an area-based energy.
boolean	getUseBlockingRestart()	Block a moving restart algorithm if the trail size of the current conflict is greater than the multiplier times the moving average of the trail size at the previous conflicts.
boolean	getUseChronologicalBacktracking()	If true, try to backtrack as little as possible on conflict and re-imply the clauses later.
boolean	getUseCombinedNoOverlap()	This can be beneficial if there is a lot of no-overlap constraints but a relatively low number of different intervals in the problem.
boolean	getUseConservativeScaleOverloadChecker()	Enable a heuristic to solve cumulative constraints using a modified energy constraint.
boolean	getUseDisjunctiveConstraintInCumulative()	When this is true, the cumulative constraint is reinforced with propagators from the disjunctive constraint to improve the inference on a set of tasks that are disjunctive at the root of the problem.
boolean	getUseDualSchedulingHeuristics()	When set, it activates a few scheduling parameters to improve the lower bound of scheduling problems.
boolean	getUseDynamicPrecedenceInCumulative()	optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];
boolean	getUseDynamicPrecedenceInDisjunctive()	Whether we try to branch on decision "interval A before interval B" rather than on intervals bounds.
boolean	getUseEnergeticReasoningInNoOverlap2D()	When this is true, the no_overlap_2d constraint is reinforced with energetic reasoning.
boolean	getUseErwaHeuristic()	Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as described in "Learning Rate Based Branching Heuristic for SAT solvers", J.H.Liang, V.
boolean	getUseExactLpReason()	The solver usually exploit the LP relaxation of a model.
boolean	getUseExtendedProbing()	Use extended probing (probe bool_or, at_most_one, exactly_one).
boolean	getUseFeasibilityJump()	Parameters for an heuristic similar to the one described in the paper: "Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation.
boolean	getUseFeasibilityPump()	Adds a feasibility pump subsolver along with lns subsolvers.
boolean	getUseHardPrecedencesInCumulative()	If true, detect and create constraint for integer variable that are "after" a set of intervals in the same cumulative constraint.
boolean	getUseImpliedBounds()	Stores and exploits "implied-bounds" in the solver.
boolean	getUseLbRelaxLns()	Turns on neighborhood generator based on local branching LP.
boolean	getUseLinear3ForNoOverlap2DPrecedences()	When set, this activates a propagator for the no_overlap_2d constraint that uses any eventual linear constraints of the model in the form `{start interval 1} - {end interval 2} + c*w <= ub` to detect that two intervals must overlap in one dimension for some values of `w`.
boolean	getUseLns()	Testing parameters used to disable all lns workers.
boolean	getUseLnsOnly()	Experimental parameters to disable everything but lns.
boolean	getUseLsOnly()	Disable every other type of subsolver, setting this turns CP-SAT into a pure local-search solver.
boolean	getUseNewIntegerConflictResolution()	This should be better on integer problems.
boolean	getUseObjectiveLbSearch()	If true, search will search in ascending max objective value (when minimizing) starting from the lower bound of the objective.
boolean	getUseObjectiveShavingSearch()	This search differs from the previous search as it will not use assumptions to bound the objective, and it will recreate a full model with the hardcoded objective value.
boolean	getUseOptimizationHints()	For an optimization problem, whether we follow some hints in order to find a better first solution.
boolean	getUseOptionalVariables()	If true, we automatically detect variables whose constraint are always enforced by the same literal and we mark them as optional.
boolean	getUseOverloadCheckerInCumulative()	When this is true, the cumulative constraint is reinforced with overload checking, i.e., an additional level of reasoning based on energy.
boolean	getUsePbResolution()	Experimental.
boolean	getUsePhaseSaving()	If this is true, then the polarity of a variable will be the last value it was assigned to, or its default polarity if it was never assigned since the call to ResetDecisionHeuristic().
boolean	getUsePrecedencesInDisjunctiveConstraint()	When this is true, then a disjunctive constraint will try to use the precedence relations between time intervals to propagate their bounds further.
boolean	getUseProbingSearch()	If true, search will continuously probe Boolean variables, and integer variable bounds.
boolean	getUseRinsLns()	Turns on relaxation induced neighborhood generator.
boolean	getUseSatInprocessing()	Enable or disable "inprocessing" which is some SAT presolving done at each restart to the root level.
boolean	getUseSharedTreeSearch()	Set on shared subtree workers.
boolean	getUseStrongPropagationInDisjunctive()	Enable stronger and more expensive propagation on no_overlap constraint.
boolean	getUseSymmetryInLp()	When we have symmetry, it is possible to "fold" all variables from the same orbit into a single variable, while having the same power of LP relaxation.
boolean	getUseTimetableEdgeFindingInCumulative()	When this is true, the cumulative constraint is reinforced with timetable edge finding, i.e., an additional level of reasoning based on the conjunction of energy and mandatory parts.
boolean	getUseTimetablingInNoOverlap2D()	When this is true, the no_overlap_2d constraint is reinforced with propagators from the cumulative constraints.
boolean	getUseTryEdgeReasoningInNoOverlap2D()	optional bool use_try_edge_reasoning_in_no_overlap_2d = 299 [default = false];
double	getVariableActivityDecay()	Each time a conflict is found, the activities of some variables are increased by one.
int	getVariablesShavingLevel()	This search takes all Boolean or integer variables, and maximize or minimize them in order to reduce their domain. -1 is automatic, otherwise value 0 disables it, and 1, 2, or 3 changes something.
double	getViolationLsCompoundMoveProbability()	Probability of using compound move search each restart.
int	getViolationLsPerturbationPeriod()	How long violation_ls should wait before perturbating a solution.
boolean	hasAbsoluteGapLimit()	Stop the search when the gap between the best feasible objective (O) and our best objective bound (B) is smaller than a limit.
boolean	hasAddCgCuts()	Whether we generate and add Chvatal-Gomory cuts to the LP at root node.
boolean	hasAddCliqueCuts()	Whether we generate clique cuts from the binary implication graph.
boolean	hasAddLinMaxCuts()	For the lin max constraints, generates the cuts described in "Strong mixed-integer programming formulations for trained neural networks" by Ross Anderson et.
boolean	hasAddLpConstraintsLazily()	If true, we start by an empty LP, and only add constraints not satisfied by the current LP solution batch by batch.
boolean	hasAddMirCuts()	Whether we generate MIR cuts at root node.
boolean	hasAddObjectiveCut()	When the LP objective is fractional, do we add the cut that forces the linear objective expression to be greater or equal to this fractional value rounded up?
boolean	hasAddRltCuts()	Whether we generate RLT cuts.
boolean	hasAddZeroHalfCuts()	Whether we generate Zero-Half cuts at root node.
boolean	hasAlsoBumpVariablesInConflictReasons()	When this is true, then the variables that appear in any of the reason of the variables in a conflict have their activity bumped.
boolean	hasAlternativePoolSize()	In order to not get stuck in local optima, when this is non-zero, we try to also work on "older" solutions with a worse objective value so we get a chance to follow a different LS/LNS trajectory.
boolean	hasAtMostOneMaxExpansionSize()	All at_most_one constraints with a size <= param will be replaced by a quadratic number of binary implications.
boolean	hasAutoDetectGreaterThanAtLeastOneOf()	If true, then the precedences propagator try to detect for each variable if it has a set of "optional incoming arc" for which at least one of them is present.
boolean	hasBinaryMinimizationAlgorithm()	optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FROM_UIP_AND_DECISIONS];
boolean	hasBinarySearchNumConflicts()	If non-negative, perform a binary search on the objective variable in order to find an [min, max] interval outside of which the solver proved unsat/sat under this amount of conflict.
boolean	hasBlockingRestartMultiplier()	optional double blocking_restart_multiplier = 66 [default = 1.4];
boolean	hasBlockingRestartWindowSize()	optional int32 blocking_restart_window_size = 65 [default = 5000];
boolean	hasBooleanEncodingLevel()	A non-negative level indicating how much we should try to fully encode Integer variables as Boolean.
boolean	hasCatchSigintSignal()	Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals when calling solve.
boolean	hasCheckDratProof()	If true, and if the problem is UNSAT, a DRAT proof of this UNSAT property is checked after the solver has finished.
boolean	hasCheckLratProof()	If true, inferred clauses are checked with an LRAT checker as they are learned, in presolve (reduced to trivial simplifications if cp_model_presolve is false), and in each worker.
boolean	hasCheckMergedLratProof()	If true, and if output_lrat_proof is true and the problem is UNSAT, check that the merged proof file is valid, i.e., that clause sharing between workers is correct.
boolean	hasChronologicalBacktrackMinConflicts()	If chronological backtracking is enabled, this is the minimum number of conflicts before we will consider backjumping.
boolean	hasClauseActivityDecay()	Clause activity parameters (same effect as the one on the variables).
boolean	hasClauseCleanupLbdBound()	All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept.
boolean	hasClauseCleanupLbdTier1()	All the clause with a LBD lower or equal to this will be kept except if its activity hasn't been bumped in the last 32 cleanup phase.
boolean	hasClauseCleanupLbdTier2()	All the clause with a LBD lower or equal to this will be kept except if its activity hasn't been bumped since the previous cleanup phase.
boolean	hasClauseCleanupOrdering()	optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];
boolean	hasClauseCleanupPeriod()	Trigger a cleanup when this number of "deletable" clauses is learned.
boolean	hasClauseCleanupPeriodIncrement()	Increase clause_cleanup_period by this amount after each cleanup.
boolean	hasClauseCleanupRatio()	During a cleanup, if clause_cleanup_target is 0, we will delete the clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed target of clauses to keep.
boolean	hasClauseCleanupTarget()	During a cleanup, we will always keep that number of "deletable" clauses.
boolean	hasConvertIntervals()	Temporary flag util the feature is more mature.
boolean	hasCoreMinimizationLevel()	If positive, we spend some effort on each core: - At level 1, we use a simple heuristic to try to minimize an UNSAT core. - At level 2, we use propagation to minimize the core but also identify literal in at most one relationship in this core.
boolean	hasCountAssumptionLevelsInLbd()	Whether or not the assumption levels are taken into account during the LBD computation.
boolean	hasCoverOptimization()	If true, when the max-sat algo find a core, we compute the minimal number of literals in the core that needs to be true to have a feasible solution.
boolean	hasCpModelPresolve()	Whether we presolve the cp_model before solving it.
boolean	hasCpModelProbingLevel()	How much effort do we spend on probing. 0 disables it completely.
boolean	hasCpModelUseSatPresolve()	Whether we also use the sat presolve when cp_model_presolve is true.
boolean	hasCreate1UipBooleanDuringIcr()	If true, and during integer conflict resolution (icr) the 1-UIP is an integer literal for which we do not have an associated Boolean.
boolean	hasCutActiveCountDecay()	optional double cut_active_count_decay = 156 [default = 0.8];
boolean	hasCutCleanupTarget()	Target number of constraints to remove during cleanup.
boolean	hasCutLevel()	Control the global cut effort.
boolean	hasCutMaxActiveCountValue()	These parameters are similar to sat clause management activity parameters.
boolean	hasDebugCrashIfLratCheckFails()	Crash if the LRAT UNSAT proof is invalid.
boolean	hasDebugCrashIfPresolveBreaksHint()	Crash if presolve breaks a feasible hint.
boolean	hasDebugCrashOnBadHint()	Crash if we do not manage to complete the hint into a full solution.
boolean	hasDebugMaxNumPresolveOperations()	If positive, try to stop just after that many presolve rules have been applied.
boolean	hasDebugPostsolveWithFullSolver()	We have two different postsolve code.
boolean	hasDecisionSubsumptionDuringConflictAnalysis()	Try even more subsumption options during conflict analysis.
boolean	hasDefaultRestartAlgorithms()	optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];
boolean	hasDetectLinearizedProduct()	Infer products of Boolean or of Boolean time IntegerVariable from the linear constrainst in the problem.
boolean	hasDetectTableWithCost()	If true, we detect variable that are unique to a table constraint and only there to encode a cost on each tuple.
boolean	hasDisableConstraintExpansion()	If true, it disable all constraint expansion.
boolean	hasDiversifyLnsParams()	If true, registers more lns subsolvers with different parameters.
boolean	hasEagerlySubsumeLastNConflicts()	If >=0, each time we have a conflict, we try to subsume the last n learned clause with it.
boolean	hasEncodeComplexLinearConstraintWithInteger()	Linear constraint with a complex right hand side (more than a single interval) need to be expanded, there is a couple of way to do that.
boolean	hasEncodeCumulativeAsReservoir()	Encore cumulative with fixed demands and capacity as a reservoir constraint.
boolean	hasEnumerateAllSolutions()	Whether we enumerate all solutions of a problem without objective.
boolean	hasExpandAlldiffConstraints()	If true, expand all_different constraints that are not permutations.
boolean	hasExpandReservoirConstraints()	If true, expand the reservoir constraints by creating booleans for all possible precedences between event and encoding the constraint.
boolean	hasExpandReservoirUsingCircuit()	Mainly useful for testing.
boolean	hasExploitAllLpSolution()	If true and the Lp relaxation of the problem has a solution, try to exploit it.
boolean	hasExploitAllPrecedences()	optional bool exploit_all_precedences = 220 [default = false];
boolean	hasExploitBestSolution()	When branching on a variable, follow the last best solution value.
boolean	hasExploitIntegerLpSolution()	If true and the Lp relaxation of the problem has an integer optimal solution, try to exploit it.
boolean	hasExploitObjective()	When branching an a variable that directly affect the objective, branch on the value that lead to the best objective first.
boolean	hasExploitRelaxationSolution()	When branching on a variable, follow the last best relaxation solution value.
boolean	hasExtraSubsumptionDuringConflictAnalysis()	It is possible that "intermediate" clauses during conflict resolution subsumes some of the clauses that propagated.
boolean	hasFeasibilityJumpBatchDtime()	How much dtime for each LS batch.
boolean	hasFeasibilityJumpDecay()	On each restart, we randomly choose if we use decay (with this parameter) or no decay.
boolean	hasFeasibilityJumpEnableRestarts()	When stagnating, feasibility jump will either restart from a default solution (with some possible randomization), or randomly pertubate the current solution.
boolean	hasFeasibilityJumpLinearizationLevel()	How much do we linearize the problem in the local search code.
boolean	hasFeasibilityJumpMaxExpandedConstraintSize()	Maximum size of no_overlap or no_overlap_2d constraint for a quadratic expansion.
boolean	hasFeasibilityJumpRestartFactor()	This is a factor that directly influence the work before each restart.
boolean	hasFeasibilityJumpVarPerburbationRangeRatio()	Max distance between the default value and the pertubated value relative to the range of the domain of the variable.
boolean	hasFeasibilityJumpVarRandomizationProbability()	Probability for a variable to have a non default value upon restarts or perturbations.
boolean	hasFillAdditionalSolutionsInResponse()	If true, the final response addition_solutions field will be filled with all solutions from our solutions pool.
boolean	hasFillTightenedDomainsInResponse()	If true, add information about the derived variable domains to the CpSolverResponse.
boolean	hasFilterSatPostsolveClauses()	Internal parameter.
boolean	hasFindBigLinearOverlap()	Try to find large "rectangle" in the linear constraint matrix with identical lines.
boolean	hasFindClausesThatAreExactlyOne()	By propagating (or just using binary clauses), one can detect that all literal of a clause are actually in at most one relationship.
boolean	hasFindMultipleCores()	Whether we try to find more independent cores for a given set of assumptions in the core based max-SAT algorithms.
boolean	hasFixVariablesToTheirHintedValue()	If true, variables appearing in the solution hints will be fixed to their hinted value.
boolean	hasFpRounding()	optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];
boolean	hasGlucoseDecayIncrement()	optional double glucose_decay_increment = 23 [default = 0.01];
boolean	hasGlucoseDecayIncrementPeriod()	optional int32 glucose_decay_increment_period = 24 [default = 5000];
boolean	hasGlucoseMaxDecay()	The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until 0.95.
boolean	hasHintConflictLimit()	Conflict limit used in the phase that exploit the solution hint.
boolean	hasIgnoreNames()	If true, we don't keep names in our internal copy of the user given model.
boolean	hasInferAllDiffs()	Run a max-clique code amongst all the x !
boolean	hasInitialPolarity()	optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];
boolean	hasInitialVariablesActivity()	The initial value of the variables activity.
boolean	hasInprocessingDtimeRatio()	Proportion of deterministic time we should spend on inprocessing.
boolean	hasInprocessingMinimizationDtime()	Parameters for an heuristic similar to the one described in "An effective learnt clause minimization approach for CDCL Sat Solvers", https://www.ijcai.org/proceedings/2017/0098.pdf This is the amount of dtime we should spend on this technique during each inprocessing phase.
boolean	hasInprocessingMinimizationUseAllOrderings()	optional bool inprocessing_minimization_use_all_orderings = 298 [default = false];
boolean	hasInprocessingMinimizationUseConflictAnalysis()	optional bool inprocessing_minimization_use_conflict_analysis = 297 [default = true];
boolean	hasInprocessingProbingDtime()	The amount of dtime we should spend on probing for each inprocessing round.
boolean	hasInprocessingUseCongruenceClosure()	Whether we use the algorithm described in "Clausal Congruence closure", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2024.
boolean	hasInprocessingUseSatSweeping()	Whether we use the SAT sweeping algorithm described in "Clausal Equivalence Sweeping", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2025.
boolean	hasInstantiateAllVariables()	If true, the solver will add a default integer branching strategy to the already defined search strategy.
boolean	hasInterleaveBatchSize()	optional int32 interleave_batch_size = 134 [default = 0];
boolean	hasInterleaveSearch()	Experimental.
boolean	hasKeepAllFeasibleSolutionsInPresolve()	If true, we disable the presolve reductions that remove feasible solutions from the search space.
boolean	hasKeepSymmetryInPresolve()	Experimental.
boolean	hasLbRelaxNumWorkersThreshold()	Only use lb-relax if we have at least that many workers.
boolean	hasLinearizationLevel()	A non-negative level indicating the type of constraints we consider in the LP relaxation.
boolean	hasLinearSplitSize()	Linear constraints that are not pseudo-Boolean and that are longer than this size will be split into sqrt(size) intermediate sums in order to have faster propation in the CP engine.
boolean	hasLnsInitialDeterministicLimit()	optional double lns_initial_deterministic_limit = 308 [default = 0.1];
boolean	hasLnsInitialDifficulty()	Initial parameters for neighborhood generation.
boolean	hasLoadAtMostOnesInSatPresolve()	If we try to load at most ones and exactly ones constraints when running the pure SAT presolve.
boolean	hasLogPrefix()	Add a prefix to all logs.
boolean	hasLogSearchProgress()	Whether the solver should log the search progress.
boolean	hasLogSubsolverStatistics()	Whether the solver should display per sub-solver search statistics.
boolean	hasLogToResponse()	Log to response proto.
boolean	hasLogToStdout()	Log to stdout.
boolean	hasLpDualTolerance()	optional double lp_dual_tolerance = 267 [default = 1e-07];
boolean	hasLpPrimalTolerance()	The internal LP tolerances used by CP-SAT.
boolean	hasMaxAllDiffCutSize()	Cut generator for all diffs can add too many cuts for large all_diff constraints.
boolean	hasMaxAlldiffDomainSize()	Max domain size for all_different constraints to be expanded.
boolean	hasMaxBackjumpLevels()	If chronological backtracking is enabled, this is the maximum number of levels we will backjump over, otherwise we will backtrack.
boolean	hasMaxClauseActivityValue()	optional double max_clause_activity_value = 18 [default = 1e+20];
boolean	hasMaxConsecutiveInactiveCount()	If a constraint/cut in LP is not active for that many consecutive OPTIMAL solves, remove it from the LP.
boolean	hasMaxCutRoundsAtLevelZero()	Max number of time we perform cut generation and resolve the LP at level 0.
boolean	hasMaxDeterministicTime()	Maximum time allowed in deterministic time to solve a problem.
boolean	hasMaxDomainSizeForLinear2Expansion()	Max domain size for expanding linear2 constraints (ax + by ==/!
boolean	hasMaxDomainSizeWhenEncodingEqNeqConstraints()	When loading a*x + b*y ==/!
boolean	hasMaxDratTimeInSeconds()	The maximum time allowed to check the DRAT proof (this can take more time than the solve itself).
boolean	hasMaximumRegionsToSplitInDisconnectedNoOverlap2D()	Detects when the space where items of a no_overlap_2d constraint can placed is disjoint (ie., fixed boxes split the domain).
boolean	hasMaxIntegerRoundingScaling()	In the integer rounding procedure used for MIR and Gomory cut, the maximum "scaling" we use (must be positive).
boolean	hasMaxLinMaxSizeForExpansion()	If the number of expressions in the lin_max is less that the max size parameter, model expansion replaces target = max(xi) by linear constraint with the introduction of new booleans bi such that bi => target == xi.
boolean	hasMaxMemoryInMb()	Maximum memory allowed for the whole thread containing the solver.
boolean	hasMaxNumberOfConflicts()	Maximum number of conflicts allowed to solve a problem.
boolean	hasMaxNumCuts()	The limit on the number of cuts in our cut pool.
boolean	hasMaxNumDeterministicBatches()	Stops after that number of batches has been scheduled.
boolean	hasMaxNumIntervalsForTimetableEdgeFinding()	Max number of intervals for the timetable_edge_finding algorithm to propagate.
boolean	hasMaxPairsPairwiseReasoningInNoOverlap2D()	If the number of pairs to look is below this threshold, do an extra step of propagation in the no_overlap_2d constraint by looking at all pairs of intervals.
boolean	hasMaxPresolveIterations()	In case of large reduction in a presolve iteration, we perform multiple presolve iterations.
boolean	hasMaxSatAssumptionOrder()	optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];
boolean	hasMaxSatReverseAssumptionOrder()	If true, adds the assumption in the reverse order of the one defined by max_sat_assumption_order.
boolean	hasMaxSatStratification()	optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];
boolean	hasMaxSizeToCreatePrecedenceLiteralsInDisjunctive()	Create one literal for each disjunction of two pairs of tasks.
boolean	hasMaxTimeInSeconds()	Maximum time allowed in seconds to solve a problem.
boolean	hasMaxVariableActivityValue()	optional double max_variable_activity_value = 16 [default = 1e+100];
boolean	hasMergeAtMostOneWorkLimit()	optional double merge_at_most_one_work_limit = 146 [default = 100000000];
boolean	hasMergeNoOverlapWorkLimit()	During presolve, we use a maximum clique heuristic to merge together no-overlap constraints or at most one constraints.
boolean	hasMinimizationAlgorithm()	optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];
boolean	hasMinimizeReductionDuringPbResolution()	A different algorithm during PB resolution.
boolean	hasMinimizeSharedClauses()	Minimize and detect subsumption of shared clauses immediately after they are imported.
boolean	hasMinOrthogonalityForLpConstraints()	While adding constraints, skip the constraints which have orthogonality less than 'min_orthogonality_for_lp_constraints' with already added constraints during current call.
boolean	hasMipAutomaticallyScaleVariables()	If true, some continuous variable might be automatically scaled.
boolean	hasMipCheckPrecision()	As explained in mip_precision and mip_max_activity_exponent, we cannot always reach the wanted precision during scaling.
boolean	hasMipComputeTrueObjectiveBound()	Even if we make big error when scaling the objective, we can always derive a correct lower bound on the original objective by using the exact lower bound on the scaled integer version of the objective.
boolean	hasMipDropTolerance()	Any value in the input mip with a magnitude lower than this will be set to zero.
boolean	hasMipMaxActivityExponent()	To avoid integer overflow, we always force the maximum possible constraint activity (and objective value) according to the initial variable domain to be smaller than 2 to this given power.
boolean	hasMipMaxBound()	We need to bound the maximum magnitude of the variables for CP-SAT, and that is the bound we use.
boolean	hasMipMaxValidMagnitude()	Any finite values in the input MIP must be below this threshold, otherwise the model will be reported invalid.
boolean	hasMipPresolveLevel()	When solving a MIP, we do some basic floating point presolving before scaling the problem to integer to be handled by CP-SAT.
boolean	hasMipScaleLargeDomain()	If this is false, then mip_var_scaling is only applied to variables with "small" domain.
boolean	hasMipTreatHighMagnitudeBoundsAsInfinity()	By default, any variable/constraint bound with a finite value and a magnitude greater than the mip_max_valid_magnitude will result with a invalid model.
boolean	hasMipVarScaling()	All continuous variable of the problem will be multiplied by this factor.
boolean	hasMipWantedPrecision()	When scaling constraint with double coefficients to integer coefficients, we will multiply by a power of 2 and round the coefficients.
boolean	hasName()	In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose.
boolean	hasNewConstraintsBatchSize()	Add that many lazy constraints (or cuts) at once in the LP.
boolean	hasNewLinearPropagation()	The new linear propagation code treat all constraints at once and use an adaptation of Bellman-Ford-Tarjan to propagate constraint in a smarter order and potentially detect propagation cycle earlier.
boolean	hasNoOverlap2DBooleanRelationsLimit()	If less than this number of boxes are present in a no-overlap 2d, we create 4 Booleans per pair of boxes: - Box 2 is after Box 1 on x. - Box 1 is after Box 2 on x. - Box 2 is after Box 1 on y. - Box 1 is after Box 2 on y.
boolean	hasNumConflictsBeforeStrategyChanges()	After each restart, if the number of conflict since the last strategy change is greater that this, then we increment a "strategy_counter" that can be use to change the search strategy used by the following restarts.
boolean	hasNumFullSubsolvers()	We distinguish subsolvers that consume a full thread, and the ones that are always interleaved.
boolean	hasNumSearchWorkers()	optional int32 num_search_workers = 100 [default = 0];
boolean	hasNumViolationLs()	This will create incomplete subsolvers (that are not LNS subsolvers) that use the feasibility jump code to find improving solution, treating the objective improvement as a hard constraint.
boolean	hasNumWorkers()	Specify the number of parallel workers (i.e. threads) to use during search.
boolean	hasOnlyAddCutsAtLevelZero()	For the cut that can be generated at any level, this control if we only try to generate them at the root node.
boolean	hasOnlySolveIp()	If one try to solve a MIP model with CP-SAT, because we assume all variable to be integer after scaling, we will not necessarily have the correct optimal.
boolean	hasOptimizeWithCore()	The default optimization method is a simple "linear scan", each time trying to find a better solution than the previous one.
boolean	hasOptimizeWithLbTreeSearch()	Do a more conventional tree search (by opposition to SAT based one) where we keep all the explored node in a tree.
boolean	hasOptimizeWithMaxHs()	This has no effect if optimize_with_core is false.
boolean	hasOutputDratProof()	If true, a DRAT proof that all the clauses inferred by the solver are valid is output to a file.
boolean	hasOutputLratProof()	If true, an LRAT proof that all the clauses inferred by the solver are valid is output to several files (one for presolve -- reduced to trivial simplifications if cp_model_presolve is false, one per worker, and one for the merged proof).
boolean	hasPbCleanupIncrement()	Same as for the clauses, but for the learned pseudo-Boolean constraints.
boolean	hasPbCleanupRatio()	optional double pb_cleanup_ratio = 47 [default = 0.5];
boolean	hasPermutePresolveConstraintOrder()	optional bool permute_presolve_constraint_order = 179 [default = false];
boolean	hasPermuteVariableRandomly()	This is mainly here to test the solver variability.
boolean	hasPolarityExploitLsHints()	If true and we have first solution LS workers, tries in some phase to follow a LS solutions that violates has litle constraints as possible.
boolean	hasPolarityRephaseIncrement()	If non-zero, then we change the polarity heuristic after that many number of conflicts in an arithmetically increasing fashion.
boolean	hasPolishLpSolution()	Whether we try to do a few degenerate iteration at the end of an LP solve to minimize the fractionality of the integer variable in the basis.
boolean	hasPreferredVariableOrder()	optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];
boolean	hasPresolveBlockedClause()	Whether we use an heuristic to detect some basic case of blocked clause in the SAT presolve.
boolean	hasPresolveBvaThreshold()	Apply Bounded Variable Addition (BVA) if the number of clauses is reduced by stricly more than this threshold.
boolean	hasPresolveBveClauseWeight()	During presolve, we apply BVE only if this weight times the number of clauses plus the number of clause literals is not increased.
boolean	hasPresolveBveThreshold()	During presolve, only try to perform the bounded variable elimination (BVE) of a variable x if the number of occurrences of x times the number of occurrences of not(x) is not greater than this parameter.
boolean	hasPresolveExtractIntegerEnforcement()	If true, we will extract from linear constraints, enforcement literals of the form "integer variable at bound => simplified constraint".
boolean	hasPresolveInclusionWorkLimit()	A few presolve operations involve detecting constraints included in other constraint.
boolean	hasPresolveProbingDeterministicTimeLimit()	optional double presolve_probing_deterministic_time_limit = 57 [default = 30];
boolean	hasPresolveSubstitutionLevel()	How much substitution (also called free variable aggregation in MIP litterature) should we perform at presolve.
boolean	hasPresolveUseBva()	Whether or not we use Bounded Variable Addition (BVA) in the presolve.
boolean	hasProbingDeterministicTimeLimit()	The maximum "deterministic" time limit to spend in probing.
boolean	hasProbingNumCombinationsLimit()	How many combinations of pairs or triplets of variables we want to scan.
boolean	hasPropagationLoopDetectionFactor()	Some search decisions might cause a really large number of propagations to happen when integer variables with large domains are only reduced by 1 at each step.
boolean	hasPseudoCostReliabilityThreshold()	The solver ignores the pseudo costs of variables with number of recordings less than this threshold.
boolean	hasPushAllTasksTowardStart()	Experimental code: specify if the objective pushes all tasks toward the start of the schedule.
boolean	hasRandomBranchesRatio()	A number between 0 and 1 that indicates the proportion of branching variables that are selected randomly instead of choosing the first variable from the given variable_ordering strategy.
boolean	hasRandomizeSearch()	Randomize fixed search.
boolean	hasRandomPolarityRatio()	The proportion of polarity chosen at random.
boolean	hasRandomSeed()	At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed.
boolean	hasRelativeGapLimit()	optional double relative_gap_limit = 160 [default = 0];
boolean	hasRemoveFixedVariablesEarly()	If cp_model_presolve is true and there is a large proportion of fixed variable after the first model copy, remap all the model to a dense set of variable before the full presolve even starts.
boolean	hasRepairHint()	If true, the solver tries to repair the solution given in the hint.
boolean	hasRestartDlAverageRatio()	In the moving average restart algorithms, a restart is triggered if the window average times this ratio is greater that the global average.
boolean	hasRestartLbdAverageRatio()	optional double restart_lbd_average_ratio = 71 [default = 1];
boolean	hasRestartPeriod()	Restart period for the FIXED_RESTART strategy.
boolean	hasRestartRunningWindowSize()	Size of the window for the moving average restarts.
boolean	hasRootLpIterations()	Even at the root node, we do not want to spend too much time on the LP if it is "difficult".
boolean	hasRoutingCutDpEffort()	The amount of "effort" to spend in dynamic programming for computing routing cuts.
boolean	hasRoutingCutMaxInfeasiblePathLength()	If the length of an infeasible path is less than this value, a cut will be added to exclude it.
boolean	hasRoutingCutSubsetSizeForBinaryRelationBound()	If the size of a subset of nodes of a RoutesConstraint is less than this value, use linear constraints of size 1 and 2 (such as capacity and time window constraints) enforced by the arc literals to compute cuts for this subset (unless the subset size is less than routing_cut_subset_size_for_tight_binary_relation_bound, in which case the corresponding algorithm is used instead).
boolean	hasRoutingCutSubsetSizeForExactBinaryRelationBound()	Similar to above, but with an even stronger algorithm in O(n!).
boolean	hasRoutingCutSubsetSizeForShortestPathsBound()	Similar to routing_cut_subset_size_for_exact_binary_relation_bound but use a bound based on shortest path distances (which respect triangular inequality).
boolean	hasRoutingCutSubsetSizeForTightBinaryRelationBound()	Similar to above, but with a different algorithm producing better cuts, at the price of a higher O(2^n) complexity, where n is the subset size.
boolean	hasSaveLpBasisInLbTreeSearch()	Experimental.
boolean	hasSearchBranching()	optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];
boolean	hasSearchRandomVariablePoolSize()	Search randomization will collect the top 'search_random_variable_pool_size' valued variables, and pick one randomly.
boolean	hasShareBinaryClauses()	Allows sharing of new learned binary clause between workers.
boolean	hasSharedTreeBalanceTolerance()	How much deeper compared to the ideal max depth of the tree is considered "balanced" enough to still accept a split.
boolean	hasSharedTreeMaxNodesPerWorker()	In order to limit total shared memory and communication overhead, limit the total number of nodes that may be generated in the shared tree.
boolean	hasSharedTreeNumWorkers()	Enables shared tree search.
boolean	hasSharedTreeOpenLeavesPerWorker()	How many open leaf nodes should the shared tree maintain per worker.
boolean	hasSharedTreeSplitMinDtime()	How much dtime a worker will wait between proposing splits.
boolean	hasSharedTreeSplitStrategy()	optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];
boolean	hasSharedTreeWorkerEnablePhaseSharing()	If true, shared tree workers share their target phase when returning an assigned subtree for the next worker to use.
boolean	hasSharedTreeWorkerEnableTrailSharing()	If true, workers share more of the information from their local trail.
boolean	hasSharedTreeWorkerMinRestartsPerSubtree()	Minimum restarts before a worker will replace a subtree that looks "bad" based on the average LBD of learned clauses.
boolean	hasShareGlueClauses()	Allows sharing of short glue clauses between workers.
boolean	hasShareGlueClausesDtime()	The amount of dtime between each export of shared glue clauses.
boolean	hasShareLevelZeroBounds()	Allows sharing of the bounds of modified variables at level 0.
boolean	hasShareLinear2Bounds()	Allows sharing of the bounds on linear2 discovered at level 0.
boolean	hasShareObjectiveBounds()	Allows objective sharing between workers.
boolean	hasShavingDeterministicTimeInProbingSearch()	Add a shaving phase (where the solver tries to prove that the lower or upper bound of a variable are infeasible) to the probing search
boolean	hasShavingSearchDeterministicTime()	Specifies the amount of deterministic time spent of each try at shaving a bound in the shaving search.
boolean	hasShavingSearchThreshold()	Specifies the threshold between two modes in the shaving procedure.
boolean	hasSolutionPoolDiversityLimit()	If solution_pool_size is <= this, we will use DP to keep a "diverse" set of solutions (the one further apart via hamming distance) in the pool.
boolean	hasSolutionPoolSize()	Size of the top-n different solutions kept by the solver.
boolean	hasStopAfterFirstSolution()	For an optimization problem, stop the solver as soon as we have a solution.
boolean	hasStopAfterPresolve()	Mainly used when improving the presolver.
boolean	hasStopAfterRootPropagation()	optional bool stop_after_root_propagation = 252 [default = false];
boolean	hasStrategyChangeIncreaseRatio()	The parameter num_conflicts_before_strategy_changes is increased by that much after each strategy change.
boolean	hasSubsumeDuringVivification()	If we remove clause that we now are "implied" by others.
boolean	hasSubsumptionDuringConflictAnalysis()	At a really low cost, during the 1-UIP conflict computation, it is easy to detect if some of the involved reasons are subsumed by the current conflict.
boolean	hasSymmetryDetectionDeterministicTimeLimit()	Deterministic time limit for symmetry detection.
boolean	hasSymmetryLevel()	Whether we try to automatically detect the symmetries in a model and exploit them.
boolean	hasTableCompressionLevel()	How much we try to "compress" a table constraint.
boolean	hasTransitivePrecedencesWorkLimit()	At root level, we might compute the transitive closure of "precedences" relations so that we can exploit that in scheduling problems.
boolean	hasUseAbslRandom()	optional bool use_absl_random = 180 [default = false];
boolean	hasUseAllDifferentForCircuit()	Turn on extra propagation for the circuit constraint.
boolean	hasUseAreaEnergeticReasoningInNoOverlap2D()	When this is true, the no_overlap_2d constraint is reinforced with an energetic reasoning that uses an area-based energy.
boolean	hasUseBlockingRestart()	Block a moving restart algorithm if the trail size of the current conflict is greater than the multiplier times the moving average of the trail size at the previous conflicts.
boolean	hasUseChronologicalBacktracking()	If true, try to backtrack as little as possible on conflict and re-imply the clauses later.
boolean	hasUseCombinedNoOverlap()	This can be beneficial if there is a lot of no-overlap constraints but a relatively low number of different intervals in the problem.
boolean	hasUseConservativeScaleOverloadChecker()	Enable a heuristic to solve cumulative constraints using a modified energy constraint.
boolean	hasUseDisjunctiveConstraintInCumulative()	When this is true, the cumulative constraint is reinforced with propagators from the disjunctive constraint to improve the inference on a set of tasks that are disjunctive at the root of the problem.
boolean	hasUseDualSchedulingHeuristics()	When set, it activates a few scheduling parameters to improve the lower bound of scheduling problems.
boolean	hasUseDynamicPrecedenceInCumulative()	optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];
boolean	hasUseDynamicPrecedenceInDisjunctive()	Whether we try to branch on decision "interval A before interval B" rather than on intervals bounds.
boolean	hasUseEnergeticReasoningInNoOverlap2D()	When this is true, the no_overlap_2d constraint is reinforced with energetic reasoning.
boolean	hasUseErwaHeuristic()	Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as described in "Learning Rate Based Branching Heuristic for SAT solvers", J.H.Liang, V.
boolean	hasUseExactLpReason()	The solver usually exploit the LP relaxation of a model.
boolean	hasUseExtendedProbing()	Use extended probing (probe bool_or, at_most_one, exactly_one).
boolean	hasUseFeasibilityJump()	Parameters for an heuristic similar to the one described in the paper: "Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation.
boolean	hasUseFeasibilityPump()	Adds a feasibility pump subsolver along with lns subsolvers.
boolean	hasUseHardPrecedencesInCumulative()	If true, detect and create constraint for integer variable that are "after" a set of intervals in the same cumulative constraint.
boolean	hasUseImpliedBounds()	Stores and exploits "implied-bounds" in the solver.
boolean	hasUseLbRelaxLns()	Turns on neighborhood generator based on local branching LP.
boolean	hasUseLinear3ForNoOverlap2DPrecedences()	When set, this activates a propagator for the no_overlap_2d constraint that uses any eventual linear constraints of the model in the form `{start interval 1} - {end interval 2} + c*w <= ub` to detect that two intervals must overlap in one dimension for some values of `w`.
boolean	hasUseLns()	Testing parameters used to disable all lns workers.
boolean	hasUseLnsOnly()	Experimental parameters to disable everything but lns.
boolean	hasUseLsOnly()	Disable every other type of subsolver, setting this turns CP-SAT into a pure local-search solver.
boolean	hasUseNewIntegerConflictResolution()	This should be better on integer problems.
boolean	hasUseObjectiveLbSearch()	If true, search will search in ascending max objective value (when minimizing) starting from the lower bound of the objective.
boolean	hasUseObjectiveShavingSearch()	This search differs from the previous search as it will not use assumptions to bound the objective, and it will recreate a full model with the hardcoded objective value.
boolean	hasUseOptimizationHints()	For an optimization problem, whether we follow some hints in order to find a better first solution.
boolean	hasUseOptionalVariables()	If true, we automatically detect variables whose constraint are always enforced by the same literal and we mark them as optional.
boolean	hasUseOverloadCheckerInCumulative()	When this is true, the cumulative constraint is reinforced with overload checking, i.e., an additional level of reasoning based on energy.
boolean	hasUsePbResolution()	Experimental.
boolean	hasUsePhaseSaving()	If this is true, then the polarity of a variable will be the last value it was assigned to, or its default polarity if it was never assigned since the call to ResetDecisionHeuristic().
boolean	hasUsePrecedencesInDisjunctiveConstraint()	When this is true, then a disjunctive constraint will try to use the precedence relations between time intervals to propagate their bounds further.
boolean	hasUseProbingSearch()	If true, search will continuously probe Boolean variables, and integer variable bounds.
boolean	hasUseRinsLns()	Turns on relaxation induced neighborhood generator.
boolean	hasUseSatInprocessing()	Enable or disable "inprocessing" which is some SAT presolving done at each restart to the root level.
boolean	hasUseSharedTreeSearch()	Set on shared subtree workers.
boolean	hasUseStrongPropagationInDisjunctive()	Enable stronger and more expensive propagation on no_overlap constraint.
boolean	hasUseSymmetryInLp()	When we have symmetry, it is possible to "fold" all variables from the same orbit into a single variable, while having the same power of LP relaxation.
boolean	hasUseTimetableEdgeFindingInCumulative()	When this is true, the cumulative constraint is reinforced with timetable edge finding, i.e., an additional level of reasoning based on the conjunction of energy and mandatory parts.
boolean	hasUseTimetablingInNoOverlap2D()	When this is true, the no_overlap_2d constraint is reinforced with propagators from the cumulative constraints.
boolean	hasUseTryEdgeReasoningInNoOverlap2D()	optional bool use_try_edge_reasoning_in_no_overlap_2d = 299 [default = false];
boolean	hasVariableActivityDecay()	Each time a conflict is found, the activities of some variables are increased by one.
boolean	hasVariablesShavingLevel()	This search takes all Boolean or integer variables, and maximize or minimize them in order to reduce their domain. -1 is automatic, otherwise value 0 disables it, and 1, 2, or 3 changes something.
boolean	hasViolationLsCompoundMoveProbability()	Probability of using compound move search each restart.
boolean	hasViolationLsPerturbationPeriod()	How long violation_ls should wait before perturbating a solution.

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Details

hasName

boolean hasName()

In some context, like in a portfolio of search, it makes sense to name a
given parameters set for logging purpose.

optional string name = 171 [default = ""];

Returns:

Whether the name field is set.

getName

String getName()

In some context, like in a portfolio of search, it makes sense to name a
given parameters set for logging purpose.

optional string name = 171 [default = ""];

Returns:

The name.

getNameBytes

com.google.protobuf.ByteString getNameBytes()

In some context, like in a portfolio of search, it makes sense to name a
given parameters set for logging purpose.

optional string name = 171 [default = ""];

Returns:

The bytes for name.

hasPreferredVariableOrder

boolean hasPreferredVariableOrder()

optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];

Returns:

Whether the preferredVariableOrder field is set.

getPreferredVariableOrder

SatParameters.VariableOrder getPreferredVariableOrder()

optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];

Returns:

The preferredVariableOrder.

hasInitialPolarity

boolean hasInitialPolarity()

optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];

Returns:

Whether the initialPolarity field is set.

getInitialPolarity

SatParameters.Polarity getInitialPolarity()

optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];

Returns:

The initialPolarity.

hasUsePhaseSaving

boolean hasUsePhaseSaving()

If this is true, then the polarity of a variable will be the last value it
was assigned to, or its default polarity if it was never assigned since the
call to ResetDecisionHeuristic().

Actually, we use a newer version where we follow the last value in the
longest non-conflicting partial assignment in the current phase.

This is called 'literal phase saving'. For details see 'A Lightweight
Component Caching Scheme for Satisfiability Solvers' K. Pipatsrisawat and
A.Darwiche, In 10th International Conference on Theory and Applications of
Satisfiability Testing, 2007.

optional bool use_phase_saving = 44 [default = true];

Returns:

Whether the usePhaseSaving field is set.

getUsePhaseSaving

boolean getUsePhaseSaving()

If this is true, then the polarity of a variable will be the last value it
was assigned to, or its default polarity if it was never assigned since the
call to ResetDecisionHeuristic().

Actually, we use a newer version where we follow the last value in the
longest non-conflicting partial assignment in the current phase.

This is called 'literal phase saving'. For details see 'A Lightweight
Component Caching Scheme for Satisfiability Solvers' K. Pipatsrisawat and
A.Darwiche, In 10th International Conference on Theory and Applications of
Satisfiability Testing, 2007.

optional bool use_phase_saving = 44 [default = true];

Returns:

The usePhaseSaving.

hasPolarityRephaseIncrement

boolean hasPolarityRephaseIncrement()

If non-zero, then we change the polarity heuristic after that many number
of conflicts in an arithmetically increasing fashion. So x the first time,
2 * x the second time, etc...

optional int32 polarity_rephase_increment = 168 [default = 1000];

Returns:

Whether the polarityRephaseIncrement field is set.

getPolarityRephaseIncrement

int getPolarityRephaseIncrement()

If non-zero, then we change the polarity heuristic after that many number
of conflicts in an arithmetically increasing fashion. So x the first time,
2 * x the second time, etc...

optional int32 polarity_rephase_increment = 168 [default = 1000];

Returns:

The polarityRephaseIncrement.

hasPolarityExploitLsHints

boolean hasPolarityExploitLsHints()

If true and we have first solution LS workers, tries in some phase to
follow a LS solutions that violates has litle constraints as possible.

optional bool polarity_exploit_ls_hints = 309 [default = false];

Returns:

Whether the polarityExploitLsHints field is set.

getPolarityExploitLsHints

boolean getPolarityExploitLsHints()

If true and we have first solution LS workers, tries in some phase to
follow a LS solutions that violates has litle constraints as possible.

optional bool polarity_exploit_ls_hints = 309 [default = false];

Returns:

The polarityExploitLsHints.

hasRandomPolarityRatio

boolean hasRandomPolarityRatio()

The proportion of polarity chosen at random. Note that this take
precedence over the phase saving heuristic. This is different from
initial_polarity:POLARITY_RANDOM because it will select a new random
polarity each time the variable is branched upon instead of selecting one
initially and then always taking this choice.

optional double random_polarity_ratio = 45 [default = 0];

Returns:

Whether the randomPolarityRatio field is set.

getRandomPolarityRatio

double getRandomPolarityRatio()

The proportion of polarity chosen at random. Note that this take
precedence over the phase saving heuristic. This is different from
initial_polarity:POLARITY_RANDOM because it will select a new random
polarity each time the variable is branched upon instead of selecting one
initially and then always taking this choice.

optional double random_polarity_ratio = 45 [default = 0];

Returns:

The randomPolarityRatio.

hasRandomBranchesRatio

boolean hasRandomBranchesRatio()

A number between 0 and 1 that indicates the proportion of branching
variables that are selected randomly instead of choosing the first variable
from the given variable_ordering strategy.

optional double random_branches_ratio = 32 [default = 0];

Returns:

Whether the randomBranchesRatio field is set.

getRandomBranchesRatio

double getRandomBranchesRatio()

A number between 0 and 1 that indicates the proportion of branching
variables that are selected randomly instead of choosing the first variable
from the given variable_ordering strategy.

optional double random_branches_ratio = 32 [default = 0];

Returns:

The randomBranchesRatio.

hasUseErwaHeuristic

boolean hasUseErwaHeuristic()

Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as
described in "Learning Rate Based Branching Heuristic for SAT solvers",
J.H.Liang, V. Ganesh, P. Poupart, K.Czarnecki, SAT 2016.

optional bool use_erwa_heuristic = 75 [default = false];

Returns:

Whether the useErwaHeuristic field is set.

getUseErwaHeuristic

boolean getUseErwaHeuristic()

Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as
described in "Learning Rate Based Branching Heuristic for SAT solvers",
J.H.Liang, V. Ganesh, P. Poupart, K.Czarnecki, SAT 2016.

optional bool use_erwa_heuristic = 75 [default = false];

Returns:

The useErwaHeuristic.

hasInitialVariablesActivity

boolean hasInitialVariablesActivity()

The initial value of the variables activity. A non-zero value only make
sense when use_erwa_heuristic is true. Experiments with a value of 1e-2
together with the ERWA heuristic showed slighthly better result than simply
using zero. The idea is that when the "learning rate" of a variable becomes
lower than this value, then we prefer to branch on never explored before
variables. This is not in the ERWA paper.

optional double initial_variables_activity = 76 [default = 0];

Returns:

Whether the initialVariablesActivity field is set.

getInitialVariablesActivity

double getInitialVariablesActivity()

The initial value of the variables activity. A non-zero value only make
sense when use_erwa_heuristic is true. Experiments with a value of 1e-2
together with the ERWA heuristic showed slighthly better result than simply
using zero. The idea is that when the "learning rate" of a variable becomes
lower than this value, then we prefer to branch on never explored before
variables. This is not in the ERWA paper.

optional double initial_variables_activity = 76 [default = 0];

Returns:

The initialVariablesActivity.

hasAlsoBumpVariablesInConflictReasons

boolean hasAlsoBumpVariablesInConflictReasons()

When this is true, then the variables that appear in any of the reason of
the variables in a conflict have their activity bumped. This is addition to
the variables in the conflict, and the one that were used during conflict
resolution.

optional bool also_bump_variables_in_conflict_reasons = 77 [default = false];

Returns:

Whether the alsoBumpVariablesInConflictReasons field is set.

getAlsoBumpVariablesInConflictReasons

boolean getAlsoBumpVariablesInConflictReasons()

When this is true, then the variables that appear in any of the reason of
the variables in a conflict have their activity bumped. This is addition to
the variables in the conflict, and the one that were used during conflict
resolution.

optional bool also_bump_variables_in_conflict_reasons = 77 [default = false];

Returns:

The alsoBumpVariablesInConflictReasons.

hasMinimizationAlgorithm

boolean hasMinimizationAlgorithm()

optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];

Returns:

Whether the minimizationAlgorithm field is set.

getMinimizationAlgorithm

SatParameters.ConflictMinimizationAlgorithm getMinimizationAlgorithm()

optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];

Returns:

The minimizationAlgorithm.

hasBinaryMinimizationAlgorithm

boolean hasBinaryMinimizationAlgorithm()

optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FROM_UIP_AND_DECISIONS];

Returns:

Whether the binaryMinimizationAlgorithm field is set.

getBinaryMinimizationAlgorithm

SatParameters.BinaryMinizationAlgorithm getBinaryMinimizationAlgorithm()

optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FROM_UIP_AND_DECISIONS];

Returns:

The binaryMinimizationAlgorithm.

hasSubsumptionDuringConflictAnalysis

boolean hasSubsumptionDuringConflictAnalysis()

At a really low cost, during the 1-UIP conflict computation, it is easy to
detect if some of the involved reasons are subsumed by the current
conflict. When this is true, such clauses are detached and later removed
from the problem.

optional bool subsumption_during_conflict_analysis = 56 [default = true];

Returns:

Whether the subsumptionDuringConflictAnalysis field is set.

getSubsumptionDuringConflictAnalysis

boolean getSubsumptionDuringConflictAnalysis()

At a really low cost, during the 1-UIP conflict computation, it is easy to
detect if some of the involved reasons are subsumed by the current
conflict. When this is true, such clauses are detached and later removed
from the problem.

optional bool subsumption_during_conflict_analysis = 56 [default = true];

Returns:

The subsumptionDuringConflictAnalysis.

hasExtraSubsumptionDuringConflictAnalysis

boolean hasExtraSubsumptionDuringConflictAnalysis()

It is possible that "intermediate" clauses during conflict resolution
subsumes some of the clauses that propagated. This is quite cheap to detect
and result in more subsumption/strengthening of clauses.

optional bool extra_subsumption_during_conflict_analysis = 351 [default = true];

Returns:

Whether the extraSubsumptionDuringConflictAnalysis field is set.

getExtraSubsumptionDuringConflictAnalysis

boolean getExtraSubsumptionDuringConflictAnalysis()

It is possible that "intermediate" clauses during conflict resolution
subsumes some of the clauses that propagated. This is quite cheap to detect
and result in more subsumption/strengthening of clauses.

optional bool extra_subsumption_during_conflict_analysis = 351 [default = true];

Returns:

The extraSubsumptionDuringConflictAnalysis.

hasDecisionSubsumptionDuringConflictAnalysis

boolean hasDecisionSubsumptionDuringConflictAnalysis()

Try even more subsumption options during conflict analysis.

optional bool decision_subsumption_during_conflict_analysis = 353 [default = true];

Returns:

Whether the decisionSubsumptionDuringConflictAnalysis field is set.

getDecisionSubsumptionDuringConflictAnalysis

boolean getDecisionSubsumptionDuringConflictAnalysis()

Try even more subsumption options during conflict analysis.

optional bool decision_subsumption_during_conflict_analysis = 353 [default = true];

Returns:

The decisionSubsumptionDuringConflictAnalysis.

hasEagerlySubsumeLastNConflicts

boolean hasEagerlySubsumeLastNConflicts()

If >=0, each time we have a conflict, we try to subsume the last n learned
clause with it.

optional int32 eagerly_subsume_last_n_conflicts = 343 [default = 4];

Returns:

Whether the eagerlySubsumeLastNConflicts field is set.

getEagerlySubsumeLastNConflicts

int getEagerlySubsumeLastNConflicts()

If >=0, each time we have a conflict, we try to subsume the last n learned
clause with it.

optional int32 eagerly_subsume_last_n_conflicts = 343 [default = 4];

Returns:

The eagerlySubsumeLastNConflicts.

hasSubsumeDuringVivification

boolean hasSubsumeDuringVivification()

If we remove clause that we now are "implied" by others. Note that this
might not always be good as we might loose some propagation power.

optional bool subsume_during_vivification = 355 [default = true];

Returns:

Whether the subsumeDuringVivification field is set.

getSubsumeDuringVivification

boolean getSubsumeDuringVivification()

If we remove clause that we now are "implied" by others. Note that this
might not always be good as we might loose some propagation power.

optional bool subsume_during_vivification = 355 [default = true];

Returns:

The subsumeDuringVivification.

hasUseChronologicalBacktracking

boolean hasUseChronologicalBacktracking()

If true, try to backtrack as little as possible on conflict and re-imply
the clauses later.
This means we discard less propagation than traditional backjumping, but
requites additional bookkeeping to handle reimplication.
See: https://doi.org/10.1007/978-3-319-94144-8_7

optional bool use_chronological_backtracking = 330 [default = false];

Returns:

Whether the useChronologicalBacktracking field is set.

getUseChronologicalBacktracking

boolean getUseChronologicalBacktracking()

If true, try to backtrack as little as possible on conflict and re-imply
the clauses later.
This means we discard less propagation than traditional backjumping, but
requites additional bookkeeping to handle reimplication.
See: https://doi.org/10.1007/978-3-319-94144-8_7

optional bool use_chronological_backtracking = 330 [default = false];

Returns:

The useChronologicalBacktracking.

hasMaxBackjumpLevels

boolean hasMaxBackjumpLevels()

If chronological backtracking is enabled, this is the maximum number of
levels we will backjump over, otherwise we will backtrack.

optional int32 max_backjump_levels = 331 [default = 50];

Returns:

Whether the maxBackjumpLevels field is set.

getMaxBackjumpLevels

int getMaxBackjumpLevels()

If chronological backtracking is enabled, this is the maximum number of
levels we will backjump over, otherwise we will backtrack.

optional int32 max_backjump_levels = 331 [default = 50];

Returns:

The maxBackjumpLevels.

hasChronologicalBacktrackMinConflicts

boolean hasChronologicalBacktrackMinConflicts()

If chronological backtracking is enabled, this is the minimum number of
conflicts before we will consider backjumping.

optional int32 chronological_backtrack_min_conflicts = 332 [default = 1000];

Returns:

Whether the chronologicalBacktrackMinConflicts field is set.

getChronologicalBacktrackMinConflicts

int getChronologicalBacktrackMinConflicts()

If chronological backtracking is enabled, this is the minimum number of
conflicts before we will consider backjumping.

optional int32 chronological_backtrack_min_conflicts = 332 [default = 1000];

Returns:

The chronologicalBacktrackMinConflicts.

hasClauseCleanupPeriod

boolean hasClauseCleanupPeriod()

Trigger a cleanup when this number of "deletable" clauses is learned.

optional int32 clause_cleanup_period = 11 [default = 10000];

Returns:

Whether the clauseCleanupPeriod field is set.

getClauseCleanupPeriod

int getClauseCleanupPeriod()

Trigger a cleanup when this number of "deletable" clauses is learned.

optional int32 clause_cleanup_period = 11 [default = 10000];

Returns:

The clauseCleanupPeriod.

hasClauseCleanupPeriodIncrement

boolean hasClauseCleanupPeriodIncrement()

Increase clause_cleanup_period by this amount after each cleanup.

optional int32 clause_cleanup_period_increment = 337 [default = 0];

Returns:

Whether the clauseCleanupPeriodIncrement field is set.

getClauseCleanupPeriodIncrement

int getClauseCleanupPeriodIncrement()

Increase clause_cleanup_period by this amount after each cleanup.

optional int32 clause_cleanup_period_increment = 337 [default = 0];

Returns:

The clauseCleanupPeriodIncrement.

hasClauseCleanupTarget

boolean hasClauseCleanupTarget()

During a cleanup, we will always keep that number of "deletable" clauses.
Note that this doesn't include the "protected" clauses.

optional int32 clause_cleanup_target = 13 [default = 0];

Returns:

Whether the clauseCleanupTarget field is set.

getClauseCleanupTarget

int getClauseCleanupTarget()

During a cleanup, we will always keep that number of "deletable" clauses.
Note that this doesn't include the "protected" clauses.

optional int32 clause_cleanup_target = 13 [default = 0];

Returns:

The clauseCleanupTarget.

hasClauseCleanupRatio

boolean hasClauseCleanupRatio()

During a cleanup, if clause_cleanup_target is 0, we will delete the
clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed
target of clauses to keep.

optional double clause_cleanup_ratio = 190 [default = 0.5];

Returns:

Whether the clauseCleanupRatio field is set.

getClauseCleanupRatio

double getClauseCleanupRatio()

During a cleanup, if clause_cleanup_target is 0, we will delete the
clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed
target of clauses to keep.

optional double clause_cleanup_ratio = 190 [default = 0.5];

Returns:

The clauseCleanupRatio.

hasClauseCleanupLbdBound

boolean hasClauseCleanupLbdBound()

All the clauses with a LBD (literal blocks distance) lower or equal to this
parameters will always be kept.

Note that the LBD of a clause that just propagated is 1 + number of
different decision levels of its literals. So that the "classic" LBD of a
learned conflict is the same as its LBD when we backjump and then propagate
it.

optional int32 clause_cleanup_lbd_bound = 59 [default = 5];

Returns:

Whether the clauseCleanupLbdBound field is set.

getClauseCleanupLbdBound

int getClauseCleanupLbdBound()

All the clauses with a LBD (literal blocks distance) lower or equal to this
parameters will always be kept.

Note that the LBD of a clause that just propagated is 1 + number of
different decision levels of its literals. So that the "classic" LBD of a
learned conflict is the same as its LBD when we backjump and then propagate
it.

optional int32 clause_cleanup_lbd_bound = 59 [default = 5];

Returns:

The clauseCleanupLbdBound.

hasClauseCleanupLbdTier1

boolean hasClauseCleanupLbdTier1()

All the clause with a LBD lower or equal to this will be kept except if
its activity hasn't been bumped in the last 32 cleanup phase. Note that
this has no effect if it is <= clause_cleanup_lbd_bound.

optional int32 clause_cleanup_lbd_tier1 = 349 [default = 0];

Returns:

Whether the clauseCleanupLbdTier1 field is set.

getClauseCleanupLbdTier1

int getClauseCleanupLbdTier1()

All the clause with a LBD lower or equal to this will be kept except if
its activity hasn't been bumped in the last 32 cleanup phase. Note that
this has no effect if it is <= clause_cleanup_lbd_bound.

optional int32 clause_cleanup_lbd_tier1 = 349 [default = 0];

Returns:

The clauseCleanupLbdTier1.

hasClauseCleanupLbdTier2

boolean hasClauseCleanupLbdTier2()

All the clause with a LBD lower or equal to this will be kept except if its
activity hasn't been bumped since the previous cleanup phase. Note that
this has no effect if it is <= clause_cleanup_lbd_bound or <=
clause_cleanup_lbd_tier1.

optional int32 clause_cleanup_lbd_tier2 = 350 [default = 0];

Returns:

Whether the clauseCleanupLbdTier2 field is set.

getClauseCleanupLbdTier2

int getClauseCleanupLbdTier2()

All the clause with a LBD lower or equal to this will be kept except if its
activity hasn't been bumped since the previous cleanup phase. Note that
this has no effect if it is <= clause_cleanup_lbd_bound or <=
clause_cleanup_lbd_tier1.

optional int32 clause_cleanup_lbd_tier2 = 350 [default = 0];

Returns:

The clauseCleanupLbdTier2.

hasClauseCleanupOrdering

boolean hasClauseCleanupOrdering()

optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];

Returns:

Whether the clauseCleanupOrdering field is set.

getClauseCleanupOrdering

SatParameters.ClauseOrdering getClauseCleanupOrdering()

optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];

Returns:

The clauseCleanupOrdering.

hasPbCleanupIncrement

boolean hasPbCleanupIncrement()

Same as for the clauses, but for the learned pseudo-Boolean constraints.

optional int32 pb_cleanup_increment = 46 [default = 200];

Returns:

Whether the pbCleanupIncrement field is set.

getPbCleanupIncrement

int getPbCleanupIncrement()

Same as for the clauses, but for the learned pseudo-Boolean constraints.

optional int32 pb_cleanup_increment = 46 [default = 200];

Returns:

The pbCleanupIncrement.

hasPbCleanupRatio

boolean hasPbCleanupRatio()

optional double pb_cleanup_ratio = 47 [default = 0.5];

Returns:

Whether the pbCleanupRatio field is set.

getPbCleanupRatio

double getPbCleanupRatio()

optional double pb_cleanup_ratio = 47 [default = 0.5];

Returns:

The pbCleanupRatio.

hasVariableActivityDecay

boolean hasVariableActivityDecay()

Each time a conflict is found, the activities of some variables are
increased by one. Then, the activity of all variables are multiplied by
variable_activity_decay.

To implement this efficiently, the activity of all the variables is not
decayed at each conflict. Instead, the activity increment is multiplied by
1 / decay. When an activity reach max_variable_activity_value, all the
activity are multiplied by 1 / max_variable_activity_value.

optional double variable_activity_decay = 15 [default = 0.8];

Returns:

Whether the variableActivityDecay field is set.

getVariableActivityDecay

double getVariableActivityDecay()

Each time a conflict is found, the activities of some variables are
increased by one. Then, the activity of all variables are multiplied by
variable_activity_decay.

To implement this efficiently, the activity of all the variables is not
decayed at each conflict. Instead, the activity increment is multiplied by
1 / decay. When an activity reach max_variable_activity_value, all the
activity are multiplied by 1 / max_variable_activity_value.

optional double variable_activity_decay = 15 [default = 0.8];

Returns:

The variableActivityDecay.

hasMaxVariableActivityValue

boolean hasMaxVariableActivityValue()

optional double max_variable_activity_value = 16 [default = 1e+100];

Returns:

Whether the maxVariableActivityValue field is set.

getMaxVariableActivityValue

double getMaxVariableActivityValue()

optional double max_variable_activity_value = 16 [default = 1e+100];

Returns:

The maxVariableActivityValue.

hasGlucoseMaxDecay

boolean hasGlucoseMaxDecay()

The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until
0.95. This "hack" seems to work well and comes from:

Glucose 2.3 in the SAT 2013 Competition - SAT Competition 2013
http://edacc4.informatik.uni-ulm.de/SC13/solver-description-download/136

optional double glucose_max_decay = 22 [default = 0.95];

Returns:

Whether the glucoseMaxDecay field is set.

getGlucoseMaxDecay

double getGlucoseMaxDecay()

The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until
0.95. This "hack" seems to work well and comes from:

Glucose 2.3 in the SAT 2013 Competition - SAT Competition 2013
http://edacc4.informatik.uni-ulm.de/SC13/solver-description-download/136

optional double glucose_max_decay = 22 [default = 0.95];

Returns:

The glucoseMaxDecay.

hasGlucoseDecayIncrement

boolean hasGlucoseDecayIncrement()

optional double glucose_decay_increment = 23 [default = 0.01];

Returns:

Whether the glucoseDecayIncrement field is set.

getGlucoseDecayIncrement

double getGlucoseDecayIncrement()

optional double glucose_decay_increment = 23 [default = 0.01];

Returns:

The glucoseDecayIncrement.

hasGlucoseDecayIncrementPeriod

boolean hasGlucoseDecayIncrementPeriod()

optional int32 glucose_decay_increment_period = 24 [default = 5000];

Returns:

Whether the glucoseDecayIncrementPeriod field is set.

getGlucoseDecayIncrementPeriod

int getGlucoseDecayIncrementPeriod()

optional int32 glucose_decay_increment_period = 24 [default = 5000];

Returns:

The glucoseDecayIncrementPeriod.

hasClauseActivityDecay

boolean hasClauseActivityDecay()

Clause activity parameters (same effect as the one on the variables).

optional double clause_activity_decay = 17 [default = 0.999];

Returns:

Whether the clauseActivityDecay field is set.

getClauseActivityDecay

double getClauseActivityDecay()

Clause activity parameters (same effect as the one on the variables).

optional double clause_activity_decay = 17 [default = 0.999];

Returns:

The clauseActivityDecay.

hasMaxClauseActivityValue

boolean hasMaxClauseActivityValue()

optional double max_clause_activity_value = 18 [default = 1e+20];

Returns:

Whether the maxClauseActivityValue field is set.

getMaxClauseActivityValue

double getMaxClauseActivityValue()

optional double max_clause_activity_value = 18 [default = 1e+20];

Returns:

The maxClauseActivityValue.

getRestartAlgorithmsList

List<SatParameters.RestartAlgorithm> getRestartAlgorithmsList()

The restart strategies will change each time the strategy_counter is
increased. The current strategy will simply be the one at index
strategy_counter modulo the number of strategy. Note that if this list
includes a NO_RESTART, nothing will change when it is reached because the
strategy_counter will only increment after a restart.

The idea of switching of search strategy tailored for SAT/UNSAT comes from
Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/.
But more generally, it seems REALLY beneficial to try different strategy.

repeated .operations_research.sat.SatParameters.RestartAlgorithm restart_algorithms = 61;

Returns:

A list containing the restartAlgorithms.

getRestartAlgorithmsCount

int getRestartAlgorithmsCount()

The restart strategies will change each time the strategy_counter is
increased. The current strategy will simply be the one at index
strategy_counter modulo the number of strategy. Note that if this list
includes a NO_RESTART, nothing will change when it is reached because the
strategy_counter will only increment after a restart.

The idea of switching of search strategy tailored for SAT/UNSAT comes from
Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/.
But more generally, it seems REALLY beneficial to try different strategy.

repeated .operations_research.sat.SatParameters.RestartAlgorithm restart_algorithms = 61;

Returns:

The count of restartAlgorithms.

getRestartAlgorithms

SatParameters.RestartAlgorithm getRestartAlgorithms(int index)

The restart strategies will change each time the strategy_counter is
increased. The current strategy will simply be the one at index
strategy_counter modulo the number of strategy. Note that if this list
includes a NO_RESTART, nothing will change when it is reached because the
strategy_counter will only increment after a restart.

The idea of switching of search strategy tailored for SAT/UNSAT comes from
Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/.
But more generally, it seems REALLY beneficial to try different strategy.

repeated .operations_research.sat.SatParameters.RestartAlgorithm restart_algorithms = 61;

Parameters:

index - The index of the element to return.

Returns:

The restartAlgorithms at the given index.

hasDefaultRestartAlgorithms

boolean hasDefaultRestartAlgorithms()

optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];

Returns:

Whether the defaultRestartAlgorithms field is set.

getDefaultRestartAlgorithms

String getDefaultRestartAlgorithms()

optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];

Returns:

The defaultRestartAlgorithms.

getDefaultRestartAlgorithmsBytes

com.google.protobuf.ByteString getDefaultRestartAlgorithmsBytes()

optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];

Returns:

The bytes for defaultRestartAlgorithms.

hasRestartPeriod

boolean hasRestartPeriod()

Restart period for the FIXED_RESTART strategy. This is also the multiplier
used by the LUBY_RESTART strategy.

optional int32 restart_period = 30 [default = 50];

Returns:

Whether the restartPeriod field is set.

getRestartPeriod

int getRestartPeriod()

Restart period for the FIXED_RESTART strategy. This is also the multiplier
used by the LUBY_RESTART strategy.

optional int32 restart_period = 30 [default = 50];

Returns:

The restartPeriod.

hasRestartRunningWindowSize

boolean hasRestartRunningWindowSize()

Size of the window for the moving average restarts.

optional int32 restart_running_window_size = 62 [default = 50];

Returns:

Whether the restartRunningWindowSize field is set.

getRestartRunningWindowSize

int getRestartRunningWindowSize()

Size of the window for the moving average restarts.

optional int32 restart_running_window_size = 62 [default = 50];

Returns:

The restartRunningWindowSize.

hasRestartDlAverageRatio

boolean hasRestartDlAverageRatio()

In the moving average restart algorithms, a restart is triggered if the
window average times this ratio is greater that the global average.

optional double restart_dl_average_ratio = 63 [default = 1];

Returns:

Whether the restartDlAverageRatio field is set.

getRestartDlAverageRatio

double getRestartDlAverageRatio()

In the moving average restart algorithms, a restart is triggered if the
window average times this ratio is greater that the global average.

optional double restart_dl_average_ratio = 63 [default = 1];

Returns:

The restartDlAverageRatio.

hasRestartLbdAverageRatio

boolean hasRestartLbdAverageRatio()

optional double restart_lbd_average_ratio = 71 [default = 1];

Returns:

Whether the restartLbdAverageRatio field is set.

getRestartLbdAverageRatio

double getRestartLbdAverageRatio()

optional double restart_lbd_average_ratio = 71 [default = 1];

Returns:

The restartLbdAverageRatio.

hasUseBlockingRestart

boolean hasUseBlockingRestart()

Block a moving restart algorithm if the trail size of the current conflict
is greater than the multiplier times the moving average of the trail size
at the previous conflicts.

optional bool use_blocking_restart = 64 [default = false];

Returns:

Whether the useBlockingRestart field is set.

getUseBlockingRestart

boolean getUseBlockingRestart()

Block a moving restart algorithm if the trail size of the current conflict
is greater than the multiplier times the moving average of the trail size
at the previous conflicts.

optional bool use_blocking_restart = 64 [default = false];

Returns:

The useBlockingRestart.

hasBlockingRestartWindowSize

boolean hasBlockingRestartWindowSize()

optional int32 blocking_restart_window_size = 65 [default = 5000];

Returns:

Whether the blockingRestartWindowSize field is set.

getBlockingRestartWindowSize

int getBlockingRestartWindowSize()

optional int32 blocking_restart_window_size = 65 [default = 5000];

Returns:

The blockingRestartWindowSize.

hasBlockingRestartMultiplier

boolean hasBlockingRestartMultiplier()

optional double blocking_restart_multiplier = 66 [default = 1.4];

Returns:

Whether the blockingRestartMultiplier field is set.

getBlockingRestartMultiplier

double getBlockingRestartMultiplier()

optional double blocking_restart_multiplier = 66 [default = 1.4];

Returns:

The blockingRestartMultiplier.

hasNumConflictsBeforeStrategyChanges

boolean hasNumConflictsBeforeStrategyChanges()

After each restart, if the number of conflict since the last strategy
change is greater that this, then we increment a "strategy_counter" that
can be use to change the search strategy used by the following restarts.

optional int32 num_conflicts_before_strategy_changes = 68 [default = 0];

Returns:

Whether the numConflictsBeforeStrategyChanges field is set.

getNumConflictsBeforeStrategyChanges

int getNumConflictsBeforeStrategyChanges()

After each restart, if the number of conflict since the last strategy
change is greater that this, then we increment a "strategy_counter" that
can be use to change the search strategy used by the following restarts.

optional int32 num_conflicts_before_strategy_changes = 68 [default = 0];

Returns:

The numConflictsBeforeStrategyChanges.

hasStrategyChangeIncreaseRatio

boolean hasStrategyChangeIncreaseRatio()

The parameter num_conflicts_before_strategy_changes is increased by that
much after each strategy change.

optional double strategy_change_increase_ratio = 69 [default = 0];

Returns:

Whether the strategyChangeIncreaseRatio field is set.

getStrategyChangeIncreaseRatio

double getStrategyChangeIncreaseRatio()

The parameter num_conflicts_before_strategy_changes is increased by that
much after each strategy change.

optional double strategy_change_increase_ratio = 69 [default = 0];

Returns:

The strategyChangeIncreaseRatio.

hasMaxTimeInSeconds

boolean hasMaxTimeInSeconds()

Maximum time allowed in seconds to solve a problem.
The counter will starts at the beginning of the Solve() call.

optional double max_time_in_seconds = 36 [default = inf];

Returns:

Whether the maxTimeInSeconds field is set.

getMaxTimeInSeconds

double getMaxTimeInSeconds()

Maximum time allowed in seconds to solve a problem.
The counter will starts at the beginning of the Solve() call.

optional double max_time_in_seconds = 36 [default = inf];

Returns:

The maxTimeInSeconds.

hasMaxDeterministicTime

boolean hasMaxDeterministicTime()

Maximum time allowed in deterministic time to solve a problem.
The deterministic time should be correlated with the real time used by the
solver, the time unit being as close as possible to a second.

optional double max_deterministic_time = 67 [default = inf];

Returns:

Whether the maxDeterministicTime field is set.

getMaxDeterministicTime

double getMaxDeterministicTime()

Maximum time allowed in deterministic time to solve a problem.
The deterministic time should be correlated with the real time used by the
solver, the time unit being as close as possible to a second.

optional double max_deterministic_time = 67 [default = inf];

Returns:

The maxDeterministicTime.

hasMaxNumDeterministicBatches

boolean hasMaxNumDeterministicBatches()

Stops after that number of batches has been scheduled. This only make sense
when interleave_search is true.

optional int32 max_num_deterministic_batches = 291 [default = 0];

Returns:

Whether the maxNumDeterministicBatches field is set.

getMaxNumDeterministicBatches

int getMaxNumDeterministicBatches()

Stops after that number of batches has been scheduled. This only make sense
when interleave_search is true.

optional int32 max_num_deterministic_batches = 291 [default = 0];

Returns:

The maxNumDeterministicBatches.

hasMaxNumberOfConflicts

boolean hasMaxNumberOfConflicts()

Maximum number of conflicts allowed to solve a problem.

TODO(user): Maybe change the way the conflict limit is enforced?
currently it is enforced on each independent internal SAT solve, rather
than on the overall number of conflicts across all solves. So in the
context of an optimization problem, this is not really usable directly by a
client.

optional int64 max_number_of_conflicts = 37 [default = 9223372036854775807];

Returns:

Whether the maxNumberOfConflicts field is set.

getMaxNumberOfConflicts

long getMaxNumberOfConflicts()

Maximum number of conflicts allowed to solve a problem.

TODO(user): Maybe change the way the conflict limit is enforced?
currently it is enforced on each independent internal SAT solve, rather
than on the overall number of conflicts across all solves. So in the
context of an optimization problem, this is not really usable directly by a
client.

optional int64 max_number_of_conflicts = 37 [default = 9223372036854775807];

Returns:

The maxNumberOfConflicts.

hasMaxMemoryInMb

boolean hasMaxMemoryInMb()

Maximum memory allowed for the whole thread containing the solver. The
solver will abort as soon as it detects that this limit is crossed. As a
result, this limit is approximative, but usually the solver will not go too
much over.

TODO(user): This is only used by the pure SAT solver, generalize to CP-SAT.

optional int64 max_memory_in_mb = 40 [default = 10000];

Returns:

Whether the maxMemoryInMb field is set.

getMaxMemoryInMb

long getMaxMemoryInMb()

Maximum memory allowed for the whole thread containing the solver. The
solver will abort as soon as it detects that this limit is crossed. As a
result, this limit is approximative, but usually the solver will not go too
much over.

TODO(user): This is only used by the pure SAT solver, generalize to CP-SAT.

optional int64 max_memory_in_mb = 40 [default = 10000];

Returns:

The maxMemoryInMb.

hasAbsoluteGapLimit

boolean hasAbsoluteGapLimit()

Stop the search when the gap between the best feasible objective (O) and
our best objective bound (B) is smaller than a limit.
The exact definition is:
- Absolute: abs(O - B)
- Relative: abs(O - B) / max(1, abs(O)).

Important: The relative gap depends on the objective offset! If you
artificially shift the objective, you will get widely different value of
the relative gap.

Note that if the gap is reached, the search status will be OPTIMAL. But
one can check the best objective bound to see the actual gap.

If the objective is integer, then any absolute gap < 1 will lead to a true
optimal. If the objective is floating point, a gap of zero make little
sense so is is why we use a non-zero default value. At the end of the
search, we will display a warning if OPTIMAL is reported yet the gap is
greater than this absolute gap.

optional double absolute_gap_limit = 159 [default = 0.0001];

Returns:

Whether the absoluteGapLimit field is set.

getAbsoluteGapLimit

double getAbsoluteGapLimit()

Stop the search when the gap between the best feasible objective (O) and
our best objective bound (B) is smaller than a limit.
The exact definition is:
- Absolute: abs(O - B)
- Relative: abs(O - B) / max(1, abs(O)).

Important: The relative gap depends on the objective offset! If you
artificially shift the objective, you will get widely different value of
the relative gap.

Note that if the gap is reached, the search status will be OPTIMAL. But
one can check the best objective bound to see the actual gap.

If the objective is integer, then any absolute gap < 1 will lead to a true
optimal. If the objective is floating point, a gap of zero make little
sense so is is why we use a non-zero default value. At the end of the
search, we will display a warning if OPTIMAL is reported yet the gap is
greater than this absolute gap.

optional double absolute_gap_limit = 159 [default = 0.0001];

Returns:

The absoluteGapLimit.

hasRelativeGapLimit

boolean hasRelativeGapLimit()

optional double relative_gap_limit = 160 [default = 0];

Returns:

Whether the relativeGapLimit field is set.

getRelativeGapLimit

double getRelativeGapLimit()

optional double relative_gap_limit = 160 [default = 0];

Returns:

The relativeGapLimit.

hasRandomSeed

boolean hasRandomSeed()

At the beginning of each solve, the random number generator used in some
part of the solver is reinitialized to this seed. If you change the random
seed, the solver may make different choices during the solving process.

For some problems, the running time may vary a lot depending on small
change in the solving algorithm. Running the solver with different seeds
enables to have more robust benchmarks when evaluating new features.

optional int32 random_seed = 31 [default = 1];

Returns:

Whether the randomSeed field is set.

getRandomSeed

int getRandomSeed()

At the beginning of each solve, the random number generator used in some
part of the solver is reinitialized to this seed. If you change the random
seed, the solver may make different choices during the solving process.

For some problems, the running time may vary a lot depending on small
change in the solving algorithm. Running the solver with different seeds
enables to have more robust benchmarks when evaluating new features.

optional int32 random_seed = 31 [default = 1];

Returns:

The randomSeed.

hasPermuteVariableRandomly

boolean hasPermuteVariableRandomly()

This is mainly here to test the solver variability. Note that in tests, if
not explicitly set to false, all 3 options will be set to true so that
clients do not rely on the solver returning a specific solution if they are
many equivalent optimal solutions.

optional bool permute_variable_randomly = 178 [default = false];

Returns:

Whether the permuteVariableRandomly field is set.

getPermuteVariableRandomly

boolean getPermuteVariableRandomly()

This is mainly here to test the solver variability. Note that in tests, if
not explicitly set to false, all 3 options will be set to true so that
clients do not rely on the solver returning a specific solution if they are
many equivalent optimal solutions.

optional bool permute_variable_randomly = 178 [default = false];

Returns:

The permuteVariableRandomly.

hasPermutePresolveConstraintOrder

boolean hasPermutePresolveConstraintOrder()

optional bool permute_presolve_constraint_order = 179 [default = false];

Returns:

Whether the permutePresolveConstraintOrder field is set.

getPermutePresolveConstraintOrder

boolean getPermutePresolveConstraintOrder()

optional bool permute_presolve_constraint_order = 179 [default = false];

Returns:

The permutePresolveConstraintOrder.

hasUseAbslRandom

boolean hasUseAbslRandom()

optional bool use_absl_random = 180 [default = false];

Returns:

Whether the useAbslRandom field is set.

getUseAbslRandom

boolean getUseAbslRandom()

optional bool use_absl_random = 180 [default = false];

Returns:

The useAbslRandom.

hasLogSearchProgress

boolean hasLogSearchProgress()

Whether the solver should log the search progress. This is the maing
logging parameter and if this is false, none of the logging (callbacks,
log_to_stdout, log_to_response, ...) will do anything.

optional bool log_search_progress = 41 [default = false];

Returns:

Whether the logSearchProgress field is set.

getLogSearchProgress

boolean getLogSearchProgress()

Whether the solver should log the search progress. This is the maing
logging parameter and if this is false, none of the logging (callbacks,
log_to_stdout, log_to_response, ...) will do anything.

optional bool log_search_progress = 41 [default = false];

Returns:

The logSearchProgress.

hasLogSubsolverStatistics

boolean hasLogSubsolverStatistics()

Whether the solver should display per sub-solver search statistics.
This is only useful is log_search_progress is set to true, and if the
number of search workers is > 1. Note that in all case we display a bit
of stats with one line per subsolver.

optional bool log_subsolver_statistics = 189 [default = false];

Returns:

Whether the logSubsolverStatistics field is set.

getLogSubsolverStatistics

boolean getLogSubsolverStatistics()

Whether the solver should display per sub-solver search statistics.
This is only useful is log_search_progress is set to true, and if the
number of search workers is > 1. Note that in all case we display a bit
of stats with one line per subsolver.

optional bool log_subsolver_statistics = 189 [default = false];

Returns:

The logSubsolverStatistics.

hasLogPrefix

boolean hasLogPrefix()

Add a prefix to all logs.

optional string log_prefix = 185 [default = ""];

Returns:

Whether the logPrefix field is set.

getLogPrefix

String getLogPrefix()

Add a prefix to all logs.

optional string log_prefix = 185 [default = ""];

Returns:

The logPrefix.

getLogPrefixBytes

com.google.protobuf.ByteString getLogPrefixBytes()

Add a prefix to all logs.

optional string log_prefix = 185 [default = ""];

Returns:

The bytes for logPrefix.

hasLogToStdout

boolean hasLogToStdout()

Log to stdout.

optional bool log_to_stdout = 186 [default = true];

Returns:

Whether the logToStdout field is set.

getLogToStdout

boolean getLogToStdout()

Log to stdout.

optional bool log_to_stdout = 186 [default = true];

Returns:

The logToStdout.

hasLogToResponse

boolean hasLogToResponse()

Log to response proto.

optional bool log_to_response = 187 [default = false];

Returns:

Whether the logToResponse field is set.

getLogToResponse

boolean getLogToResponse()

Log to response proto.

optional bool log_to_response = 187 [default = false];

Returns:

The logToResponse.

hasUsePbResolution

boolean hasUsePbResolution()

Experimental.

This is an old experiment, it might cause crashes in multi-thread and you
should double check the solver result. It can still be used if you only
care about feasible solutions (these are checked) and it gives good result
on your problem. We might revive it at some point.

Whether to use pseudo-Boolean resolution to analyze a conflict. Note that
this option only make sense if your problem is modelized using
pseudo-Boolean constraints. If you only have clauses, this shouldn't change
anything (except slow the solver down).

optional bool use_pb_resolution = 43 [default = false];

Returns:

Whether the usePbResolution field is set.

getUsePbResolution

boolean getUsePbResolution()

Experimental.

This is an old experiment, it might cause crashes in multi-thread and you
should double check the solver result. It can still be used if you only
care about feasible solutions (these are checked) and it gives good result
on your problem. We might revive it at some point.

Whether to use pseudo-Boolean resolution to analyze a conflict. Note that
this option only make sense if your problem is modelized using
pseudo-Boolean constraints. If you only have clauses, this shouldn't change
anything (except slow the solver down).

optional bool use_pb_resolution = 43 [default = false];

Returns:

The usePbResolution.

hasMinimizeReductionDuringPbResolution

boolean hasMinimizeReductionDuringPbResolution()

A different algorithm during PB resolution. It minimizes the number of
calls to ReduceCoefficients() which can be time consuming. However, the
search space will be different and if the coefficients are large, this may
lead to integer overflows that could otherwise be prevented.

optional bool minimize_reduction_during_pb_resolution = 48 [default = false];

Returns:

Whether the minimizeReductionDuringPbResolution field is set.

getMinimizeReductionDuringPbResolution

boolean getMinimizeReductionDuringPbResolution()

A different algorithm during PB resolution. It minimizes the number of
calls to ReduceCoefficients() which can be time consuming. However, the
search space will be different and if the coefficients are large, this may
lead to integer overflows that could otherwise be prevented.

optional bool minimize_reduction_during_pb_resolution = 48 [default = false];

Returns:

The minimizeReductionDuringPbResolution.

hasCountAssumptionLevelsInLbd

boolean hasCountAssumptionLevelsInLbd()

Whether or not the assumption levels are taken into account during the LBD
computation. According to the reference below, not counting them improves
the solver in some situation. Note that this only impact solves under
assumptions.

Gilles Audemard, Jean-Marie Lagniez, Laurent Simon, "Improving Glucose for
Incremental SAT Solving with Assumptions: Application to MUS Extraction"
Theory and Applications of Satisfiability Testing - SAT 2013, Lecture Notes
in Computer Science Volume 7962, 2013, pp 309-317.

optional bool count_assumption_levels_in_lbd = 49 [default = true];

Returns:

Whether the countAssumptionLevelsInLbd field is set.

getCountAssumptionLevelsInLbd

boolean getCountAssumptionLevelsInLbd()

Whether or not the assumption levels are taken into account during the LBD
computation. According to the reference below, not counting them improves
the solver in some situation. Note that this only impact solves under
assumptions.

Gilles Audemard, Jean-Marie Lagniez, Laurent Simon, "Improving Glucose for
Incremental SAT Solving with Assumptions: Application to MUS Extraction"
Theory and Applications of Satisfiability Testing - SAT 2013, Lecture Notes
in Computer Science Volume 7962, 2013, pp 309-317.

optional bool count_assumption_levels_in_lbd = 49 [default = true];

Returns:

The countAssumptionLevelsInLbd.

hasPresolveBveThreshold

boolean hasPresolveBveThreshold()

During presolve, only try to perform the bounded variable elimination (BVE)
of a variable x if the number of occurrences of x times the number of
occurrences of not(x) is not greater than this parameter.

optional int32 presolve_bve_threshold = 54 [default = 500];

Returns:

Whether the presolveBveThreshold field is set.

getPresolveBveThreshold

int getPresolveBveThreshold()

During presolve, only try to perform the bounded variable elimination (BVE)
of a variable x if the number of occurrences of x times the number of
occurrences of not(x) is not greater than this parameter.

optional int32 presolve_bve_threshold = 54 [default = 500];

Returns:

The presolveBveThreshold.

hasFilterSatPostsolveClauses

boolean hasFilterSatPostsolveClauses()

Internal parameter. During BVE, if we eliminate a variable x, by default we
will push all clauses containing x and all clauses containing not(x) to the
postsolve. However, it is possible to write the postsolve code so that only
one such set is needed. The idea is that, if we push the set containing a
literal l, is to set l to false except if it is needed to satisfy one of
the clause in the set. This is always beneficial, but for historical
reason, not all our postsolve algorithm support this.

optional bool filter_sat_postsolve_clauses = 324 [default = false];

Returns:

Whether the filterSatPostsolveClauses field is set.

getFilterSatPostsolveClauses

boolean getFilterSatPostsolveClauses()

Internal parameter. During BVE, if we eliminate a variable x, by default we
will push all clauses containing x and all clauses containing not(x) to the
postsolve. However, it is possible to write the postsolve code so that only
one such set is needed. The idea is that, if we push the set containing a
literal l, is to set l to false except if it is needed to satisfy one of
the clause in the set. This is always beneficial, but for historical
reason, not all our postsolve algorithm support this.

optional bool filter_sat_postsolve_clauses = 324 [default = false];

Returns:

The filterSatPostsolveClauses.

hasPresolveBveClauseWeight

boolean hasPresolveBveClauseWeight()

During presolve, we apply BVE only if this weight times the number of
clauses plus the number of clause literals is not increased.

optional int32 presolve_bve_clause_weight = 55 [default = 3];

Returns:

Whether the presolveBveClauseWeight field is set.

getPresolveBveClauseWeight

int getPresolveBveClauseWeight()

During presolve, we apply BVE only if this weight times the number of
clauses plus the number of clause literals is not increased.

optional int32 presolve_bve_clause_weight = 55 [default = 3];

Returns:

The presolveBveClauseWeight.

hasProbingDeterministicTimeLimit

boolean hasProbingDeterministicTimeLimit()

The maximum "deterministic" time limit to spend in probing. A value of
zero will disable the probing.

TODO(user): Clean up. The first one is used in CP-SAT, the other in pure
SAT presolve.

optional double probing_deterministic_time_limit = 226 [default = 1];

Returns:

Whether the probingDeterministicTimeLimit field is set.

getProbingDeterministicTimeLimit

double getProbingDeterministicTimeLimit()

The maximum "deterministic" time limit to spend in probing. A value of
zero will disable the probing.

TODO(user): Clean up. The first one is used in CP-SAT, the other in pure
SAT presolve.

optional double probing_deterministic_time_limit = 226 [default = 1];

Returns:

The probingDeterministicTimeLimit.

hasPresolveProbingDeterministicTimeLimit

boolean hasPresolveProbingDeterministicTimeLimit()

optional double presolve_probing_deterministic_time_limit = 57 [default = 30];

Returns:

Whether the presolveProbingDeterministicTimeLimit field is set.

getPresolveProbingDeterministicTimeLimit

double getPresolveProbingDeterministicTimeLimit()

optional double presolve_probing_deterministic_time_limit = 57 [default = 30];

Returns:

The presolveProbingDeterministicTimeLimit.

hasPresolveBlockedClause

boolean hasPresolveBlockedClause()

Whether we use an heuristic to detect some basic case of blocked clause
in the SAT presolve.

optional bool presolve_blocked_clause = 88 [default = true];

Returns:

Whether the presolveBlockedClause field is set.

getPresolveBlockedClause

boolean getPresolveBlockedClause()

Whether we use an heuristic to detect some basic case of blocked clause
in the SAT presolve.

optional bool presolve_blocked_clause = 88 [default = true];

Returns:

The presolveBlockedClause.

hasPresolveUseBva

boolean hasPresolveUseBva()

Whether or not we use Bounded Variable Addition (BVA) in the presolve.

optional bool presolve_use_bva = 72 [default = true];

Returns:

Whether the presolveUseBva field is set.

getPresolveUseBva

boolean getPresolveUseBva()

Whether or not we use Bounded Variable Addition (BVA) in the presolve.

optional bool presolve_use_bva = 72 [default = true];

Returns:

The presolveUseBva.

hasPresolveBvaThreshold

boolean hasPresolveBvaThreshold()

Apply Bounded Variable Addition (BVA) if the number of clauses is reduced
by stricly more than this threshold. The algorithm described in the paper
uses 0, but quick experiments showed that 1 is a good value. It may not be
worth it to add a new variable just to remove one clause.

optional int32 presolve_bva_threshold = 73 [default = 1];

Returns:

Whether the presolveBvaThreshold field is set.

getPresolveBvaThreshold

int getPresolveBvaThreshold()

Apply Bounded Variable Addition (BVA) if the number of clauses is reduced
by stricly more than this threshold. The algorithm described in the paper
uses 0, but quick experiments showed that 1 is a good value. It may not be
worth it to add a new variable just to remove one clause.

optional int32 presolve_bva_threshold = 73 [default = 1];

Returns:

The presolveBvaThreshold.

hasMaxPresolveIterations

boolean hasMaxPresolveIterations()

In case of large reduction in a presolve iteration, we perform multiple
presolve iterations. This parameter controls the maximum number of such
presolve iterations.

optional int32 max_presolve_iterations = 138 [default = 3];

Returns:

Whether the maxPresolveIterations field is set.

getMaxPresolveIterations

int getMaxPresolveIterations()

In case of large reduction in a presolve iteration, we perform multiple
presolve iterations. This parameter controls the maximum number of such
presolve iterations.

optional int32 max_presolve_iterations = 138 [default = 3];

Returns:

The maxPresolveIterations.

hasCpModelPresolve

boolean hasCpModelPresolve()

Whether we presolve the cp_model before solving it.

optional bool cp_model_presolve = 86 [default = true];

Returns:

Whether the cpModelPresolve field is set.

getCpModelPresolve

boolean getCpModelPresolve()

Whether we presolve the cp_model before solving it.

optional bool cp_model_presolve = 86 [default = true];

Returns:

The cpModelPresolve.

hasCpModelProbingLevel

boolean hasCpModelProbingLevel()

How much effort do we spend on probing. 0 disables it completely.

optional int32 cp_model_probing_level = 110 [default = 2];

Returns:

Whether the cpModelProbingLevel field is set.

getCpModelProbingLevel

int getCpModelProbingLevel()

How much effort do we spend on probing. 0 disables it completely.

optional int32 cp_model_probing_level = 110 [default = 2];

Returns:

The cpModelProbingLevel.

hasCpModelUseSatPresolve

boolean hasCpModelUseSatPresolve()

Whether we also use the sat presolve when cp_model_presolve is true.

optional bool cp_model_use_sat_presolve = 93 [default = true];

Returns:

Whether the cpModelUseSatPresolve field is set.

getCpModelUseSatPresolve

boolean getCpModelUseSatPresolve()

Whether we also use the sat presolve when cp_model_presolve is true.

optional bool cp_model_use_sat_presolve = 93 [default = true];

Returns:

The cpModelUseSatPresolve.

hasLoadAtMostOnesInSatPresolve

boolean hasLoadAtMostOnesInSatPresolve()

If we try to load at most ones and exactly ones constraints when running
the pure SAT presolve. Or if we just ignore them.

If one detects at_most_one via merge_at_most_one_work_limit or exactly one
with find_clauses_that_are_exactly_one, it might be good to also set this
to true.

optional bool load_at_most_ones_in_sat_presolve = 335 [default = false];

Returns:

Whether the loadAtMostOnesInSatPresolve field is set.

getLoadAtMostOnesInSatPresolve

boolean getLoadAtMostOnesInSatPresolve()

If we try to load at most ones and exactly ones constraints when running
the pure SAT presolve. Or if we just ignore them.

If one detects at_most_one via merge_at_most_one_work_limit or exactly one
with find_clauses_that_are_exactly_one, it might be good to also set this
to true.

optional bool load_at_most_ones_in_sat_presolve = 335 [default = false];

Returns:

The loadAtMostOnesInSatPresolve.

hasRemoveFixedVariablesEarly

boolean hasRemoveFixedVariablesEarly()

If cp_model_presolve is true and there is a large proportion of fixed
variable after the first model copy, remap all the model to a dense set of
variable before the full presolve even starts. This should help for LNS on
large models.

optional bool remove_fixed_variables_early = 310 [default = true];

Returns:

Whether the removeFixedVariablesEarly field is set.

getRemoveFixedVariablesEarly

boolean getRemoveFixedVariablesEarly()

If cp_model_presolve is true and there is a large proportion of fixed
variable after the first model copy, remap all the model to a dense set of
variable before the full presolve even starts. This should help for LNS on
large models.

optional bool remove_fixed_variables_early = 310 [default = true];

Returns:

The removeFixedVariablesEarly.

hasDetectTableWithCost

boolean hasDetectTableWithCost()

If true, we detect variable that are unique to a table constraint and only
there to encode a cost on each tuple. This is usually the case when a WCSP
(weighted constraint program) is encoded into CP-SAT format.

This can lead to a dramatic speed-up for such problems but is still
experimental at this point.

optional bool detect_table_with_cost = 216 [default = false];

Returns:

Whether the detectTableWithCost field is set.

getDetectTableWithCost

boolean getDetectTableWithCost()

If true, we detect variable that are unique to a table constraint and only
there to encode a cost on each tuple. This is usually the case when a WCSP
(weighted constraint program) is encoded into CP-SAT format.

This can lead to a dramatic speed-up for such problems but is still
experimental at this point.

optional bool detect_table_with_cost = 216 [default = false];

Returns:

The detectTableWithCost.

hasTableCompressionLevel

boolean hasTableCompressionLevel()

How much we try to "compress" a table constraint. Compressing more leads to
less Booleans and faster propagation but can reduced the quality of the lp
relaxation. Values goes from 0 to 3 where we always try to fully compress a
table. At 2, we try to automatically decide if it is worth it.

optional int32 table_compression_level = 217 [default = 2];

Returns:

Whether the tableCompressionLevel field is set.

getTableCompressionLevel

int getTableCompressionLevel()

How much we try to "compress" a table constraint. Compressing more leads to
less Booleans and faster propagation but can reduced the quality of the lp
relaxation. Values goes from 0 to 3 where we always try to fully compress a
table. At 2, we try to automatically decide if it is worth it.

optional int32 table_compression_level = 217 [default = 2];

Returns:

The tableCompressionLevel.

hasExpandAlldiffConstraints

boolean hasExpandAlldiffConstraints()

If true, expand all_different constraints that are not permutations.
Permutations (#Variables = #Values) are always expanded.

optional bool expand_alldiff_constraints = 170 [default = false];

Returns:

Whether the expandAlldiffConstraints field is set.

getExpandAlldiffConstraints

boolean getExpandAlldiffConstraints()

If true, expand all_different constraints that are not permutations.
Permutations (#Variables = #Values) are always expanded.

optional bool expand_alldiff_constraints = 170 [default = false];

Returns:

The expandAlldiffConstraints.

hasMaxAlldiffDomainSize

boolean hasMaxAlldiffDomainSize()

Max domain size for all_different constraints to be expanded.

optional int32 max_alldiff_domain_size = 320 [default = 256];

Returns:

Whether the maxAlldiffDomainSize field is set.

getMaxAlldiffDomainSize

int getMaxAlldiffDomainSize()

Max domain size for all_different constraints to be expanded.

optional int32 max_alldiff_domain_size = 320 [default = 256];

Returns:

The maxAlldiffDomainSize.

hasExpandReservoirConstraints

boolean hasExpandReservoirConstraints()

If true, expand the reservoir constraints by creating booleans for all
possible precedences between event and encoding the constraint.

optional bool expand_reservoir_constraints = 182 [default = true];

Returns:

Whether the expandReservoirConstraints field is set.

getExpandReservoirConstraints

boolean getExpandReservoirConstraints()

If true, expand the reservoir constraints by creating booleans for all
possible precedences between event and encoding the constraint.

optional bool expand_reservoir_constraints = 182 [default = true];

Returns:

The expandReservoirConstraints.

hasMaxDomainSizeForLinear2Expansion

boolean hasMaxDomainSizeForLinear2Expansion()

Max domain size for expanding linear2 constraints (ax + by ==/!= c).

optional int32 max_domain_size_for_linear2_expansion = 340 [default = 8];

Returns:

Whether the maxDomainSizeForLinear2Expansion field is set.

getMaxDomainSizeForLinear2Expansion

int getMaxDomainSizeForLinear2Expansion()

Max domain size for expanding linear2 constraints (ax + by ==/!= c).

optional int32 max_domain_size_for_linear2_expansion = 340 [default = 8];

Returns:

The maxDomainSizeForLinear2Expansion.

hasExpandReservoirUsingCircuit

boolean hasExpandReservoirUsingCircuit()

Mainly useful for testing.

If this and expand_reservoir_constraints is true, we use a different
encoding of the reservoir constraint using circuit instead of precedences.
Note that this is usually slower, but can exercise different part of the
solver. Note that contrary to the precedence encoding, this easily support
variable demands.

WARNING: with this encoding, the constraint takes a slightly different
meaning. There must exist a permutation of the events occurring at the same
time such that the level is within the reservoir after each of these events
(in this permuted order). So we cannot have +100 and -100 at the same time
if the level must be between 0 and 10 (as authorized by the reservoir
constraint).

optional bool expand_reservoir_using_circuit = 288 [default = false];

Returns:

Whether the expandReservoirUsingCircuit field is set.

getExpandReservoirUsingCircuit

boolean getExpandReservoirUsingCircuit()

Mainly useful for testing.

If this and expand_reservoir_constraints is true, we use a different
encoding of the reservoir constraint using circuit instead of precedences.
Note that this is usually slower, but can exercise different part of the
solver. Note that contrary to the precedence encoding, this easily support
variable demands.

WARNING: with this encoding, the constraint takes a slightly different
meaning. There must exist a permutation of the events occurring at the same
time such that the level is within the reservoir after each of these events
(in this permuted order). So we cannot have +100 and -100 at the same time
if the level must be between 0 and 10 (as authorized by the reservoir
constraint).

optional bool expand_reservoir_using_circuit = 288 [default = false];

Returns:

The expandReservoirUsingCircuit.

hasEncodeCumulativeAsReservoir

boolean hasEncodeCumulativeAsReservoir()

Encore cumulative with fixed demands and capacity as a reservoir
constraint. The only reason you might want to do that is to test the
reservoir propagation code!

optional bool encode_cumulative_as_reservoir = 287 [default = false];

Returns:

Whether the encodeCumulativeAsReservoir field is set.

getEncodeCumulativeAsReservoir

boolean getEncodeCumulativeAsReservoir()

Encore cumulative with fixed demands and capacity as a reservoir
constraint. The only reason you might want to do that is to test the
reservoir propagation code!

optional bool encode_cumulative_as_reservoir = 287 [default = false];

Returns:

The encodeCumulativeAsReservoir.

hasMaxLinMaxSizeForExpansion

boolean hasMaxLinMaxSizeForExpansion()

If the number of expressions in the lin_max is less that the max size
parameter, model expansion replaces target = max(xi) by linear constraint
with the introduction of new booleans bi such that bi => target == xi.

This is mainly for experimenting compared to a custom lin_max propagator.

optional int32 max_lin_max_size_for_expansion = 280 [default = 0];

Returns:

Whether the maxLinMaxSizeForExpansion field is set.

getMaxLinMaxSizeForExpansion

int getMaxLinMaxSizeForExpansion()

If the number of expressions in the lin_max is less that the max size
parameter, model expansion replaces target = max(xi) by linear constraint
with the introduction of new booleans bi such that bi => target == xi.

This is mainly for experimenting compared to a custom lin_max propagator.

optional int32 max_lin_max_size_for_expansion = 280 [default = 0];

Returns:

The maxLinMaxSizeForExpansion.

hasDisableConstraintExpansion

boolean hasDisableConstraintExpansion()

If true, it disable all constraint expansion.
This should only be used to test the presolve of expanded constraints.

optional bool disable_constraint_expansion = 181 [default = false];

Returns:

Whether the disableConstraintExpansion field is set.

getDisableConstraintExpansion

boolean getDisableConstraintExpansion()

If true, it disable all constraint expansion.
This should only be used to test the presolve of expanded constraints.

optional bool disable_constraint_expansion = 181 [default = false];

Returns:

The disableConstraintExpansion.

hasEncodeComplexLinearConstraintWithInteger

boolean hasEncodeComplexLinearConstraintWithInteger()

Linear constraint with a complex right hand side (more than a single
interval) need to be expanded, there is a couple of way to do that.

optional bool encode_complex_linear_constraint_with_integer = 223 [default = false];

Returns:

Whether the encodeComplexLinearConstraintWithInteger field is set.

getEncodeComplexLinearConstraintWithInteger

boolean getEncodeComplexLinearConstraintWithInteger()

Linear constraint with a complex right hand side (more than a single
interval) need to be expanded, there is a couple of way to do that.

optional bool encode_complex_linear_constraint_with_integer = 223 [default = false];

Returns:

The encodeComplexLinearConstraintWithInteger.

hasMergeNoOverlapWorkLimit

boolean hasMergeNoOverlapWorkLimit()

During presolve, we use a maximum clique heuristic to merge together
no-overlap constraints or at most one constraints. This code can be slow,
so we have a limit in place on the number of explored nodes in the
underlying graph. The internal limit is an int64, but we use double here to
simplify manual input.

optional double merge_no_overlap_work_limit = 145 [default = 1000000000000];

Returns:

Whether the mergeNoOverlapWorkLimit field is set.

getMergeNoOverlapWorkLimit

double getMergeNoOverlapWorkLimit()

During presolve, we use a maximum clique heuristic to merge together
no-overlap constraints or at most one constraints. This code can be slow,
so we have a limit in place on the number of explored nodes in the
underlying graph. The internal limit is an int64, but we use double here to
simplify manual input.

optional double merge_no_overlap_work_limit = 145 [default = 1000000000000];

Returns:

The mergeNoOverlapWorkLimit.

hasMergeAtMostOneWorkLimit

boolean hasMergeAtMostOneWorkLimit()

optional double merge_at_most_one_work_limit = 146 [default = 100000000];

Returns:

Whether the mergeAtMostOneWorkLimit field is set.

getMergeAtMostOneWorkLimit

double getMergeAtMostOneWorkLimit()

optional double merge_at_most_one_work_limit = 146 [default = 100000000];

Returns:

The mergeAtMostOneWorkLimit.

hasPresolveSubstitutionLevel

boolean hasPresolveSubstitutionLevel()

How much substitution (also called free variable aggregation in MIP
litterature) should we perform at presolve. This currently only concerns
variable appearing only in linear constraints. For now the value 0 turns it
off and any positive value performs substitution.

optional int32 presolve_substitution_level = 147 [default = 1];

Returns:

Whether the presolveSubstitutionLevel field is set.

getPresolveSubstitutionLevel

int getPresolveSubstitutionLevel()

How much substitution (also called free variable aggregation in MIP
litterature) should we perform at presolve. This currently only concerns
variable appearing only in linear constraints. For now the value 0 turns it
off and any positive value performs substitution.

optional int32 presolve_substitution_level = 147 [default = 1];

Returns:

The presolveSubstitutionLevel.

hasPresolveExtractIntegerEnforcement

boolean hasPresolveExtractIntegerEnforcement()

If true, we will extract from linear constraints, enforcement literals of
the form "integer variable at bound => simplified constraint". This should
always be beneficial except that we don't always handle them as efficiently
as we could for now. This causes problem on manna81.mps (LP relaxation not
as tight it seems) and on neos-3354841-apure.mps.gz (too many literals
created this way).

optional bool presolve_extract_integer_enforcement = 174 [default = false];

Returns:

Whether the presolveExtractIntegerEnforcement field is set.

getPresolveExtractIntegerEnforcement

boolean getPresolveExtractIntegerEnforcement()

If true, we will extract from linear constraints, enforcement literals of
the form "integer variable at bound => simplified constraint". This should
always be beneficial except that we don't always handle them as efficiently
as we could for now. This causes problem on manna81.mps (LP relaxation not
as tight it seems) and on neos-3354841-apure.mps.gz (too many literals
created this way).

optional bool presolve_extract_integer_enforcement = 174 [default = false];

Returns:

The presolveExtractIntegerEnforcement.

hasPresolveInclusionWorkLimit

boolean hasPresolveInclusionWorkLimit()

A few presolve operations involve detecting constraints included in other
constraint. Since there can be a quadratic number of such pairs, and
processing them usually involve scanning them, the complexity of these
operations can be big. This enforce a local deterministic limit on the
number of entries scanned. Default is 1e8.

A value of zero will disable these presolve rules completely.

optional int64 presolve_inclusion_work_limit = 201 [default = 100000000];

Returns:

Whether the presolveInclusionWorkLimit field is set.

getPresolveInclusionWorkLimit

long getPresolveInclusionWorkLimit()

A few presolve operations involve detecting constraints included in other
constraint. Since there can be a quadratic number of such pairs, and
processing them usually involve scanning them, the complexity of these
operations can be big. This enforce a local deterministic limit on the
number of entries scanned. Default is 1e8.

A value of zero will disable these presolve rules completely.

optional int64 presolve_inclusion_work_limit = 201 [default = 100000000];

Returns:

The presolveInclusionWorkLimit.

hasIgnoreNames

boolean hasIgnoreNames()

If true, we don't keep names in our internal copy of the user given model.

optional bool ignore_names = 202 [default = true];

Returns:

Whether the ignoreNames field is set.

getIgnoreNames

boolean getIgnoreNames()

If true, we don't keep names in our internal copy of the user given model.

optional bool ignore_names = 202 [default = true];

Returns:

The ignoreNames.

hasInferAllDiffs

boolean hasInferAllDiffs()

Run a max-clique code amongst all the x != y we can find and try to infer
set of variables that are all different. This allows to close neos16.mps
for instance. Note that we only run this code if there is no all_diff
already in the model so that if a user want to add some all_diff, we assume
it is well done and do not try to add more.

This will also detect and add no_overlap constraints, if all the relations
x != y have "offsets" between them. I.e. x > y + offset.

optional bool infer_all_diffs = 233 [default = true];

Returns:

Whether the inferAllDiffs field is set.

getInferAllDiffs

boolean getInferAllDiffs()

Run a max-clique code amongst all the x != y we can find and try to infer
set of variables that are all different. This allows to close neos16.mps
for instance. Note that we only run this code if there is no all_diff
already in the model so that if a user want to add some all_diff, we assume
it is well done and do not try to add more.

This will also detect and add no_overlap constraints, if all the relations
x != y have "offsets" between them. I.e. x > y + offset.

optional bool infer_all_diffs = 233 [default = true];

Returns:

The inferAllDiffs.

hasFindBigLinearOverlap

boolean hasFindBigLinearOverlap()

Try to find large "rectangle" in the linear constraint matrix with
identical lines. If such rectangle is big enough, we can introduce a new
integer variable corresponding to the common expression and greatly reduce
the number of non-zero.

optional bool find_big_linear_overlap = 234 [default = true];

Returns:

Whether the findBigLinearOverlap field is set.

getFindBigLinearOverlap

boolean getFindBigLinearOverlap()

Try to find large "rectangle" in the linear constraint matrix with
identical lines. If such rectangle is big enough, we can introduce a new
integer variable corresponding to the common expression and greatly reduce
the number of non-zero.

optional bool find_big_linear_overlap = 234 [default = true];

Returns:

The findBigLinearOverlap.

hasFindClausesThatAreExactlyOne

boolean hasFindClausesThatAreExactlyOne()

By propagating (or just using binary clauses), one can detect that all
literal of a clause are actually in at most one relationship. Thus this
constraint can be promoted to an exactly one constraints. This should help
as it convey more structure. Note that this is expensive, so we have a
deterministic limit in place.

optional bool find_clauses_that_are_exactly_one = 333 [default = true];

Returns:

Whether the findClausesThatAreExactlyOne field is set.

getFindClausesThatAreExactlyOne

boolean getFindClausesThatAreExactlyOne()

By propagating (or just using binary clauses), one can detect that all
literal of a clause are actually in at most one relationship. Thus this
constraint can be promoted to an exactly one constraints. This should help
as it convey more structure. Note that this is expensive, so we have a
deterministic limit in place.

optional bool find_clauses_that_are_exactly_one = 333 [default = true];

Returns:

The findClausesThatAreExactlyOne.

hasUseSatInprocessing

boolean hasUseSatInprocessing()

Enable or disable "inprocessing" which is some SAT presolving done at
each restart to the root level.

optional bool use_sat_inprocessing = 163 [default = true];

Returns:

Whether the useSatInprocessing field is set.

getUseSatInprocessing

boolean getUseSatInprocessing()

Enable or disable "inprocessing" which is some SAT presolving done at
each restart to the root level.

optional bool use_sat_inprocessing = 163 [default = true];

Returns:

The useSatInprocessing.

hasInprocessingDtimeRatio

boolean hasInprocessingDtimeRatio()

Proportion of deterministic time we should spend on inprocessing.
At each "restart", if the proportion is below this ratio, we will do some
inprocessing, otherwise, we skip it for this restart.

optional double inprocessing_dtime_ratio = 273 [default = 0.2];

Returns:

Whether the inprocessingDtimeRatio field is set.

getInprocessingDtimeRatio

double getInprocessingDtimeRatio()

Proportion of deterministic time we should spend on inprocessing.
At each "restart", if the proportion is below this ratio, we will do some
inprocessing, otherwise, we skip it for this restart.

optional double inprocessing_dtime_ratio = 273 [default = 0.2];

Returns:

The inprocessingDtimeRatio.

hasInprocessingProbingDtime

boolean hasInprocessingProbingDtime()

The amount of dtime we should spend on probing for each inprocessing round.

optional double inprocessing_probing_dtime = 274 [default = 1];

Returns:

Whether the inprocessingProbingDtime field is set.

getInprocessingProbingDtime

double getInprocessingProbingDtime()

The amount of dtime we should spend on probing for each inprocessing round.

optional double inprocessing_probing_dtime = 274 [default = 1];

Returns:

The inprocessingProbingDtime.

hasInprocessingMinimizationDtime

boolean hasInprocessingMinimizationDtime()

Parameters for an heuristic similar to the one described in "An effective
learnt clause minimization approach for CDCL Sat Solvers",
https://www.ijcai.org/proceedings/2017/0098.pdf

This is the amount of dtime we should spend on this technique during each
inprocessing phase.

The minimization technique is the same as the one used to minimize core in
max-sat. We also minimize problem clauses and not just the learned clause
that we keep forever like in the paper.

optional double inprocessing_minimization_dtime = 275 [default = 1];

Returns:

Whether the inprocessingMinimizationDtime field is set.

getInprocessingMinimizationDtime

double getInprocessingMinimizationDtime()

Parameters for an heuristic similar to the one described in "An effective
learnt clause minimization approach for CDCL Sat Solvers",
https://www.ijcai.org/proceedings/2017/0098.pdf

This is the amount of dtime we should spend on this technique during each
inprocessing phase.

The minimization technique is the same as the one used to minimize core in
max-sat. We also minimize problem clauses and not just the learned clause
that we keep forever like in the paper.

optional double inprocessing_minimization_dtime = 275 [default = 1];

Returns:

The inprocessingMinimizationDtime.

hasInprocessingMinimizationUseConflictAnalysis

boolean hasInprocessingMinimizationUseConflictAnalysis()

optional bool inprocessing_minimization_use_conflict_analysis = 297 [default = true];

Returns:

Whether the inprocessingMinimizationUseConflictAnalysis field is set.

getInprocessingMinimizationUseConflictAnalysis

boolean getInprocessingMinimizationUseConflictAnalysis()

optional bool inprocessing_minimization_use_conflict_analysis = 297 [default = true];

Returns:

The inprocessingMinimizationUseConflictAnalysis.

hasInprocessingMinimizationUseAllOrderings

boolean hasInprocessingMinimizationUseAllOrderings()

optional bool inprocessing_minimization_use_all_orderings = 298 [default = false];

Returns:

Whether the inprocessingMinimizationUseAllOrderings field is set.

getInprocessingMinimizationUseAllOrderings

boolean getInprocessingMinimizationUseAllOrderings()

optional bool inprocessing_minimization_use_all_orderings = 298 [default = false];

Returns:

The inprocessingMinimizationUseAllOrderings.

hasInprocessingUseCongruenceClosure

boolean hasInprocessingUseCongruenceClosure()

Whether we use the algorithm described in "Clausal Congruence closure",
Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2024.

Note that we only have a basic version currently.

optional bool inprocessing_use_congruence_closure = 342 [default = true];

Returns:

Whether the inprocessingUseCongruenceClosure field is set.

getInprocessingUseCongruenceClosure

boolean getInprocessingUseCongruenceClosure()

Whether we use the algorithm described in "Clausal Congruence closure",
Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2024.

Note that we only have a basic version currently.

optional bool inprocessing_use_congruence_closure = 342 [default = true];

Returns:

The inprocessingUseCongruenceClosure.

hasInprocessingUseSatSweeping

boolean hasInprocessingUseSatSweeping()

Whether we use the SAT sweeping algorithm described in "Clausal Equivalence
Sweeping", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks,
2025.

optional bool inprocessing_use_sat_sweeping = 354 [default = false];

Returns:

Whether the inprocessingUseSatSweeping field is set.

getInprocessingUseSatSweeping

boolean getInprocessingUseSatSweeping()

Whether we use the SAT sweeping algorithm described in "Clausal Equivalence
Sweeping", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks,
2025.

optional bool inprocessing_use_sat_sweeping = 354 [default = false];

Returns:

The inprocessingUseSatSweeping.

hasNumWorkers

boolean hasNumWorkers()

Specify the number of parallel workers (i.e. threads) to use during search.
This should usually be lower than your number of available cpus +
hyperthread in your machine.

A value of 0 means the solver will try to use all cores on the machine.
A number of 1 means no parallelism.

Note that 'num_workers' is the preferred name, but if it is set to zero,
we will still read the deprecated 'num_search_workers'.

As of 2020-04-10, if you're using SAT via MPSolver (to solve integer
programs) this field is overridden with a value of 8, if the field is not
set *explicitly*. Thus, always set this field explicitly or via
MPSolver::SetNumThreads().

optional int32 num_workers = 206 [default = 0];

Returns:

Whether the numWorkers field is set.

getNumWorkers

int getNumWorkers()

Specify the number of parallel workers (i.e. threads) to use during search.
This should usually be lower than your number of available cpus +
hyperthread in your machine.

A value of 0 means the solver will try to use all cores on the machine.
A number of 1 means no parallelism.

Note that 'num_workers' is the preferred name, but if it is set to zero,
we will still read the deprecated 'num_search_workers'.

As of 2020-04-10, if you're using SAT via MPSolver (to solve integer
programs) this field is overridden with a value of 8, if the field is not
set *explicitly*. Thus, always set this field explicitly or via
MPSolver::SetNumThreads().

optional int32 num_workers = 206 [default = 0];

Returns:

The numWorkers.

hasNumSearchWorkers

boolean hasNumSearchWorkers()

optional int32 num_search_workers = 100 [default = 0];

Returns:

Whether the numSearchWorkers field is set.

getNumSearchWorkers

int getNumSearchWorkers()

optional int32 num_search_workers = 100 [default = 0];

Returns:

The numSearchWorkers.

hasNumFullSubsolvers

boolean hasNumFullSubsolvers()

We distinguish subsolvers that consume a full thread, and the ones that are
always interleaved. If left at zero, we will fix this with a default
formula that depends on num_workers. But if you start modifying what runs,
you might want to fix that to a given value depending on the num_workers
you use.

optional int32 num_full_subsolvers = 294 [default = 0];

Returns:

Whether the numFullSubsolvers field is set.

getNumFullSubsolvers

int getNumFullSubsolvers()

We distinguish subsolvers that consume a full thread, and the ones that are
always interleaved. If left at zero, we will fix this with a default
formula that depends on num_workers. But if you start modifying what runs,
you might want to fix that to a given value depending on the num_workers
you use.

optional int32 num_full_subsolvers = 294 [default = 0];

Returns:

The numFullSubsolvers.

getSubsolversList

List<String> getSubsolversList()

In multi-thread, the solver can be mainly seen as a portfolio of solvers
with different parameters. This field indicates the names of the parameters
that are used in multithread. This only applies to "full" subsolvers.

See cp_model_search.cc to see a list of the names and the default value (if
left empty) that looks like:
- default_lp           (linearization_level:1)
- fixed                (only if fixed search specified or scheduling)
- no_lp                (linearization_level:0)
- max_lp               (linearization_level:2)
- pseudo_costs         (only if objective, change search heuristic)
- reduced_costs        (only if objective, change search heuristic)
- quick_restart        (kind of probing)
- quick_restart_no_lp  (kind of probing with linearization_level:0)
- lb_tree_search       (to improve lower bound, MIP like tree search)
- probing              (continuous probing and shaving)

Also, note that some set of parameters will be ignored if they do not make
sense. For instance if there is no objective, pseudo_cost or reduced_cost
search will be ignored. Core based search will only work if the objective
has many terms. If there is no fixed strategy fixed will be ignored. And so
on.

The order is important, as only the first num_full_subsolvers will be
scheduled. You can see in the log which one are selected for a given run.

repeated string subsolvers = 207;

Returns:

A list containing the subsolvers.

getSubsolversCount

int getSubsolversCount()

In multi-thread, the solver can be mainly seen as a portfolio of solvers
with different parameters. This field indicates the names of the parameters
that are used in multithread. This only applies to "full" subsolvers.

See cp_model_search.cc to see a list of the names and the default value (if
left empty) that looks like:
- default_lp           (linearization_level:1)
- fixed                (only if fixed search specified or scheduling)
- no_lp                (linearization_level:0)
- max_lp               (linearization_level:2)
- pseudo_costs         (only if objective, change search heuristic)
- reduced_costs        (only if objective, change search heuristic)
- quick_restart        (kind of probing)
- quick_restart_no_lp  (kind of probing with linearization_level:0)
- lb_tree_search       (to improve lower bound, MIP like tree search)
- probing              (continuous probing and shaving)

Also, note that some set of parameters will be ignored if they do not make
sense. For instance if there is no objective, pseudo_cost or reduced_cost
search will be ignored. Core based search will only work if the objective
has many terms. If there is no fixed strategy fixed will be ignored. And so
on.

The order is important, as only the first num_full_subsolvers will be
scheduled. You can see in the log which one are selected for a given run.

repeated string subsolvers = 207;

Returns:

The count of subsolvers.

getSubsolvers

String getSubsolvers(int index)

In multi-thread, the solver can be mainly seen as a portfolio of solvers
with different parameters. This field indicates the names of the parameters
that are used in multithread. This only applies to "full" subsolvers.

See cp_model_search.cc to see a list of the names and the default value (if
left empty) that looks like:
- default_lp           (linearization_level:1)
- fixed                (only if fixed search specified or scheduling)
- no_lp                (linearization_level:0)
- max_lp               (linearization_level:2)
- pseudo_costs         (only if objective, change search heuristic)
- reduced_costs        (only if objective, change search heuristic)
- quick_restart        (kind of probing)
- quick_restart_no_lp  (kind of probing with linearization_level:0)
- lb_tree_search       (to improve lower bound, MIP like tree search)
- probing              (continuous probing and shaving)

Also, note that some set of parameters will be ignored if they do not make
sense. For instance if there is no objective, pseudo_cost or reduced_cost
search will be ignored. Core based search will only work if the objective
has many terms. If there is no fixed strategy fixed will be ignored. And so
on.

The order is important, as only the first num_full_subsolvers will be
scheduled. You can see in the log which one are selected for a given run.

repeated string subsolvers = 207;

Parameters:

index - The index of the element to return.

Returns:

The subsolvers at the given index.

getSubsolversBytes

com.google.protobuf.ByteString getSubsolversBytes(int index)

In multi-thread, the solver can be mainly seen as a portfolio of solvers
with different parameters. This field indicates the names of the parameters
that are used in multithread. This only applies to "full" subsolvers.

See cp_model_search.cc to see a list of the names and the default value (if
left empty) that looks like:
- default_lp           (linearization_level:1)
- fixed                (only if fixed search specified or scheduling)
- no_lp                (linearization_level:0)
- max_lp               (linearization_level:2)
- pseudo_costs         (only if objective, change search heuristic)
- reduced_costs        (only if objective, change search heuristic)
- quick_restart        (kind of probing)
- quick_restart_no_lp  (kind of probing with linearization_level:0)
- lb_tree_search       (to improve lower bound, MIP like tree search)
- probing              (continuous probing and shaving)

Also, note that some set of parameters will be ignored if they do not make
sense. For instance if there is no objective, pseudo_cost or reduced_cost
search will be ignored. Core based search will only work if the objective
has many terms. If there is no fixed strategy fixed will be ignored. And so
on.

The order is important, as only the first num_full_subsolvers will be
scheduled. You can see in the log which one are selected for a given run.

repeated string subsolvers = 207;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the subsolvers at the given index.

getExtraSubsolversList

List<String> getExtraSubsolversList()

A convenient way to add more workers types.
These will be added at the beginning of the list.

repeated string extra_subsolvers = 219;

Returns:

A list containing the extraSubsolvers.

getExtraSubsolversCount

int getExtraSubsolversCount()

A convenient way to add more workers types.
These will be added at the beginning of the list.

repeated string extra_subsolvers = 219;

Returns:

The count of extraSubsolvers.

getExtraSubsolvers

String getExtraSubsolvers(int index)

A convenient way to add more workers types.
These will be added at the beginning of the list.

repeated string extra_subsolvers = 219;

Parameters:

index - The index of the element to return.

Returns:

The extraSubsolvers at the given index.

getExtraSubsolversBytes

com.google.protobuf.ByteString getExtraSubsolversBytes(int index)

A convenient way to add more workers types.
These will be added at the beginning of the list.

repeated string extra_subsolvers = 219;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the extraSubsolvers at the given index.

getIgnoreSubsolversList

List<String> getIgnoreSubsolversList()

Rather than fully specifying subsolvers, it is often convenient to just
remove the ones that are not useful on a given problem or only keep
specific ones for testing. Each string is interpreted as a "glob", so we
support '*' and '?'.

The way this work is that we will only accept a name that match a filter
pattern (if non-empty) and do not match an ignore pattern. Note also that
these fields work on LNS or LS names even if these are currently not
specified via the subsolvers field.

repeated string ignore_subsolvers = 209;

Returns:

A list containing the ignoreSubsolvers.

getIgnoreSubsolversCount

int getIgnoreSubsolversCount()

Rather than fully specifying subsolvers, it is often convenient to just
remove the ones that are not useful on a given problem or only keep
specific ones for testing. Each string is interpreted as a "glob", so we
support '*' and '?'.

The way this work is that we will only accept a name that match a filter
pattern (if non-empty) and do not match an ignore pattern. Note also that
these fields work on LNS or LS names even if these are currently not
specified via the subsolvers field.

repeated string ignore_subsolvers = 209;

Returns:

The count of ignoreSubsolvers.

getIgnoreSubsolvers

String getIgnoreSubsolvers(int index)

Rather than fully specifying subsolvers, it is often convenient to just
remove the ones that are not useful on a given problem or only keep
specific ones for testing. Each string is interpreted as a "glob", so we
support '*' and '?'.

The way this work is that we will only accept a name that match a filter
pattern (if non-empty) and do not match an ignore pattern. Note also that
these fields work on LNS or LS names even if these are currently not
specified via the subsolvers field.

repeated string ignore_subsolvers = 209;

Parameters:

index - The index of the element to return.

Returns:

The ignoreSubsolvers at the given index.

getIgnoreSubsolversBytes

com.google.protobuf.ByteString getIgnoreSubsolversBytes(int index)

Rather than fully specifying subsolvers, it is often convenient to just
remove the ones that are not useful on a given problem or only keep
specific ones for testing. Each string is interpreted as a "glob", so we
support '*' and '?'.

The way this work is that we will only accept a name that match a filter
pattern (if non-empty) and do not match an ignore pattern. Note also that
these fields work on LNS or LS names even if these are currently not
specified via the subsolvers field.

repeated string ignore_subsolvers = 209;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the ignoreSubsolvers at the given index.

getFilterSubsolversList

List<String> getFilterSubsolversList()

repeated string filter_subsolvers = 293;

Returns:

A list containing the filterSubsolvers.

getFilterSubsolversCount

int getFilterSubsolversCount()

repeated string filter_subsolvers = 293;

Returns:

The count of filterSubsolvers.

getFilterSubsolvers

String getFilterSubsolvers(int index)

repeated string filter_subsolvers = 293;

Parameters:

index - The index of the element to return.

Returns:

The filterSubsolvers at the given index.

getFilterSubsolversBytes

com.google.protobuf.ByteString getFilterSubsolversBytes(int index)

repeated string filter_subsolvers = 293;

Parameters:

index - The index of the value to return.

Returns:

The bytes of the filterSubsolvers at the given index.

getSubsolverParamsList

List<SatParameters> getSubsolverParamsList()

It is possible to specify additional subsolver configuration. These can be
referred by their params.name() in the fields above. Note that only the
specified field will "overwrite" the ones of the base parameter. If a
subsolver_params has the name of an existing subsolver configuration, the
named parameters will be merged into the subsolver configuration.

repeated .operations_research.sat.SatParameters subsolver_params = 210;

getSubsolverParams

SatParameters getSubsolverParams(int index)

It is possible to specify additional subsolver configuration. These can be
referred by their params.name() in the fields above. Note that only the
specified field will "overwrite" the ones of the base parameter. If a
subsolver_params has the name of an existing subsolver configuration, the
named parameters will be merged into the subsolver configuration.

repeated .operations_research.sat.SatParameters subsolver_params = 210;

getSubsolverParamsCount

int getSubsolverParamsCount()

It is possible to specify additional subsolver configuration. These can be
referred by their params.name() in the fields above. Note that only the
specified field will "overwrite" the ones of the base parameter. If a
subsolver_params has the name of an existing subsolver configuration, the
named parameters will be merged into the subsolver configuration.

repeated .operations_research.sat.SatParameters subsolver_params = 210;

getSubsolverParamsOrBuilderList

List<? extends SatParametersOrBuilder> getSubsolverParamsOrBuilderList()

It is possible to specify additional subsolver configuration. These can be
referred by their params.name() in the fields above. Note that only the
specified field will "overwrite" the ones of the base parameter. If a
subsolver_params has the name of an existing subsolver configuration, the
named parameters will be merged into the subsolver configuration.

repeated .operations_research.sat.SatParameters subsolver_params = 210;

getSubsolverParamsOrBuilder

SatParametersOrBuilder getSubsolverParamsOrBuilder(int index)

It is possible to specify additional subsolver configuration. These can be
referred by their params.name() in the fields above. Note that only the
specified field will "overwrite" the ones of the base parameter. If a
subsolver_params has the name of an existing subsolver configuration, the
named parameters will be merged into the subsolver configuration.

repeated .operations_research.sat.SatParameters subsolver_params = 210;

hasInterleaveSearch

boolean hasInterleaveSearch()

Experimental. If this is true, then we interleave all our major search
strategy and distribute the work amongst num_workers.

The search is deterministic (independently of num_workers!), and we
schedule and wait for interleave_batch_size task to be completed before
synchronizing and scheduling the next batch of tasks.

optional bool interleave_search = 136 [default = false];

Returns:

Whether the interleaveSearch field is set.

getInterleaveSearch

boolean getInterleaveSearch()

Experimental. If this is true, then we interleave all our major search
strategy and distribute the work amongst num_workers.

The search is deterministic (independently of num_workers!), and we
schedule and wait for interleave_batch_size task to be completed before
synchronizing and scheduling the next batch of tasks.

optional bool interleave_search = 136 [default = false];

Returns:

The interleaveSearch.

hasInterleaveBatchSize

boolean hasInterleaveBatchSize()

optional int32 interleave_batch_size = 134 [default = 0];

Returns:

Whether the interleaveBatchSize field is set.

getInterleaveBatchSize

int getInterleaveBatchSize()

optional int32 interleave_batch_size = 134 [default = 0];

Returns:

The interleaveBatchSize.

hasShareObjectiveBounds

boolean hasShareObjectiveBounds()

Allows objective sharing between workers.

optional bool share_objective_bounds = 113 [default = true];

Returns:

Whether the shareObjectiveBounds field is set.

getShareObjectiveBounds

boolean getShareObjectiveBounds()

Allows objective sharing between workers.

optional bool share_objective_bounds = 113 [default = true];

Returns:

The shareObjectiveBounds.

hasShareLevelZeroBounds

boolean hasShareLevelZeroBounds()

Allows sharing of the bounds of modified variables at level 0.

optional bool share_level_zero_bounds = 114 [default = true];

Returns:

Whether the shareLevelZeroBounds field is set.

getShareLevelZeroBounds

boolean getShareLevelZeroBounds()

Allows sharing of the bounds of modified variables at level 0.

optional bool share_level_zero_bounds = 114 [default = true];

Returns:

The shareLevelZeroBounds.

hasShareLinear2Bounds

boolean hasShareLinear2Bounds()

Allows sharing of the bounds on linear2 discovered at level 0. This is
mainly interesting on scheduling type of problems when we branch on
precedences.

Warning: This currently non-deterministic.

optional bool share_linear2_bounds = 326 [default = false];

Returns:

Whether the shareLinear2Bounds field is set.

getShareLinear2Bounds

boolean getShareLinear2Bounds()

Allows sharing of the bounds on linear2 discovered at level 0. This is
mainly interesting on scheduling type of problems when we branch on
precedences.

Warning: This currently non-deterministic.

optional bool share_linear2_bounds = 326 [default = false];

Returns:

The shareLinear2Bounds.

hasShareBinaryClauses

boolean hasShareBinaryClauses()

Allows sharing of new learned binary clause between workers.

optional bool share_binary_clauses = 203 [default = true];

Returns:

Whether the shareBinaryClauses field is set.

getShareBinaryClauses

boolean getShareBinaryClauses()

Allows sharing of new learned binary clause between workers.

optional bool share_binary_clauses = 203 [default = true];

Returns:

The shareBinaryClauses.

hasShareGlueClauses

boolean hasShareGlueClauses()

Allows sharing of short glue clauses between workers.
Implicitly disabled if share_binary_clauses is false.

optional bool share_glue_clauses = 285 [default = true];

Returns:

Whether the shareGlueClauses field is set.

getShareGlueClauses

boolean getShareGlueClauses()

Allows sharing of short glue clauses between workers.
Implicitly disabled if share_binary_clauses is false.

optional bool share_glue_clauses = 285 [default = true];

Returns:

The shareGlueClauses.

hasMinimizeSharedClauses

boolean hasMinimizeSharedClauses()

Minimize and detect subsumption of shared clauses immediately after they
are imported.

optional bool minimize_shared_clauses = 300 [default = true];

Returns:

Whether the minimizeSharedClauses field is set.

getMinimizeSharedClauses

boolean getMinimizeSharedClauses()

Minimize and detect subsumption of shared clauses immediately after they
are imported.

optional bool minimize_shared_clauses = 300 [default = true];

Returns:

The minimizeSharedClauses.

hasShareGlueClausesDtime

boolean hasShareGlueClausesDtime()

The amount of dtime between each export of shared glue clauses.

optional double share_glue_clauses_dtime = 322 [default = 1];

Returns:

Whether the shareGlueClausesDtime field is set.

getShareGlueClausesDtime

double getShareGlueClausesDtime()

The amount of dtime between each export of shared glue clauses.

optional double share_glue_clauses_dtime = 322 [default = 1];

Returns:

The shareGlueClausesDtime.

hasCheckLratProof

boolean hasCheckLratProof()

If true, inferred clauses are checked with an LRAT checker as they are
learned, in presolve (reduced to trivial simplifications if
cp_model_presolve is false), and in each worker. As of December 2025, this
only works with pure SAT problems, with
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool check_lrat_proof = 344 [default = false];

Returns:

Whether the checkLratProof field is set.

getCheckLratProof

boolean getCheckLratProof()

If true, inferred clauses are checked with an LRAT checker as they are
learned, in presolve (reduced to trivial simplifications if
cp_model_presolve is false), and in each worker. As of December 2025, this
only works with pure SAT problems, with
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool check_lrat_proof = 344 [default = false];

Returns:

The checkLratProof.

hasCheckMergedLratProof

boolean hasCheckMergedLratProof()

If true, and if output_lrat_proof is true and the problem is UNSAT, check
that the merged proof file is valid, i.e., that clause sharing between
workers is correct. This checks each inferred clause, so you might want to
disable check_lrat_proof to avoid redundant work. As of November 2025, this
only works for pure SAT problems, with num_workers = 1.

optional bool check_merged_lrat_proof = 352 [default = false];

Returns:

Whether the checkMergedLratProof field is set.

getCheckMergedLratProof

boolean getCheckMergedLratProof()

If true, and if output_lrat_proof is true and the problem is UNSAT, check
that the merged proof file is valid, i.e., that clause sharing between
workers is correct. This checks each inferred clause, so you might want to
disable check_lrat_proof to avoid redundant work. As of November 2025, this
only works for pure SAT problems, with num_workers = 1.

optional bool check_merged_lrat_proof = 352 [default = false];

Returns:

The checkMergedLratProof.

hasOutputLratProof

boolean hasOutputLratProof()

If true, an LRAT proof that all the clauses inferred by the solver are
valid is output to several files (one for presolve -- reduced to trivial
simplifications if cp_model_presolve is false, one per worker, and one for
the merged proof). As of December 2025, this only works for pure SAT
problems, with
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool output_lrat_proof = 345 [default = false];

Returns:

Whether the outputLratProof field is set.

getOutputLratProof

boolean getOutputLratProof()

If true, an LRAT proof that all the clauses inferred by the solver are
valid is output to several files (one for presolve -- reduced to trivial
simplifications if cp_model_presolve is false, one per worker, and one for
the merged proof). As of December 2025, this only works for pure SAT
problems, with
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool output_lrat_proof = 345 [default = false];

Returns:

The outputLratProof.

hasCheckDratProof

boolean hasCheckDratProof()

If true, and if the problem is UNSAT, a DRAT proof of this UNSAT property
is checked after the solver has finished. As of November 2025, this only
works for pure SAT problems, with
- num_workers = 1,
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool check_drat_proof = 346 [default = false];

Returns:

Whether the checkDratProof field is set.

getCheckDratProof

boolean getCheckDratProof()

If true, and if the problem is UNSAT, a DRAT proof of this UNSAT property
is checked after the solver has finished. As of November 2025, this only
works for pure SAT problems, with
- num_workers = 1,
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool check_drat_proof = 346 [default = false];

Returns:

The checkDratProof.

hasOutputDratProof

boolean hasOutputDratProof()

If true, a DRAT proof that all the clauses inferred by the solver are valid
is output to a file. As of December 2025, this only works for pure SAT
problems, with
- num_workers = 1,
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool output_drat_proof = 347 [default = false];

Returns:

Whether the outputDratProof field is set.

getOutputDratProof

boolean getOutputDratProof()

If true, a DRAT proof that all the clauses inferred by the solver are valid
is output to a file. As of December 2025, this only works for pure SAT
problems, with
- num_workers = 1,
- cp_model_presolve = false,
- linearization_level <= 1,
- symmetry_level <= 1.

optional bool output_drat_proof = 347 [default = false];

Returns:

The outputDratProof.

hasMaxDratTimeInSeconds

boolean hasMaxDratTimeInSeconds()

The maximum time allowed to check the DRAT proof (this can take more time
than the solve itself). Only used if check_drat_proof is true.

optional double max_drat_time_in_seconds = 348 [default = inf];

Returns:

Whether the maxDratTimeInSeconds field is set.

getMaxDratTimeInSeconds

double getMaxDratTimeInSeconds()

The maximum time allowed to check the DRAT proof (this can take more time
than the solve itself). Only used if check_drat_proof is true.

optional double max_drat_time_in_seconds = 348 [default = inf];

Returns:

The maxDratTimeInSeconds.

hasDebugPostsolveWithFullSolver

boolean hasDebugPostsolveWithFullSolver()

We have two different postsolve code. The default one should be better and
it allows for a more powerful presolve, but it can be useful to postsolve
using the full solver instead.

optional bool debug_postsolve_with_full_solver = 162 [default = false];

Returns:

Whether the debugPostsolveWithFullSolver field is set.

getDebugPostsolveWithFullSolver

boolean getDebugPostsolveWithFullSolver()

We have two different postsolve code. The default one should be better and
it allows for a more powerful presolve, but it can be useful to postsolve
using the full solver instead.

optional bool debug_postsolve_with_full_solver = 162 [default = false];

Returns:

The debugPostsolveWithFullSolver.

hasDebugMaxNumPresolveOperations

boolean hasDebugMaxNumPresolveOperations()

If positive, try to stop just after that many presolve rules have been
applied. This is mainly useful for debugging presolve.

optional int32 debug_max_num_presolve_operations = 151 [default = 0];

Returns:

Whether the debugMaxNumPresolveOperations field is set.

getDebugMaxNumPresolveOperations

int getDebugMaxNumPresolveOperations()

If positive, try to stop just after that many presolve rules have been
applied. This is mainly useful for debugging presolve.

optional int32 debug_max_num_presolve_operations = 151 [default = 0];

Returns:

The debugMaxNumPresolveOperations.

hasDebugCrashOnBadHint

boolean hasDebugCrashOnBadHint()

Crash if we do not manage to complete the hint into a full solution.

optional bool debug_crash_on_bad_hint = 195 [default = false];

Returns:

Whether the debugCrashOnBadHint field is set.

getDebugCrashOnBadHint

boolean getDebugCrashOnBadHint()

Crash if we do not manage to complete the hint into a full solution.

optional bool debug_crash_on_bad_hint = 195 [default = false];

Returns:

The debugCrashOnBadHint.

hasDebugCrashIfPresolveBreaksHint

boolean hasDebugCrashIfPresolveBreaksHint()

Crash if presolve breaks a feasible hint.

optional bool debug_crash_if_presolve_breaks_hint = 306 [default = false];

Returns:

Whether the debugCrashIfPresolveBreaksHint field is set.

getDebugCrashIfPresolveBreaksHint

boolean getDebugCrashIfPresolveBreaksHint()

Crash if presolve breaks a feasible hint.

optional bool debug_crash_if_presolve_breaks_hint = 306 [default = false];

Returns:

The debugCrashIfPresolveBreaksHint.

hasDebugCrashIfLratCheckFails

boolean hasDebugCrashIfLratCheckFails()

Crash if the LRAT UNSAT proof is invalid.

optional bool debug_crash_if_lrat_check_fails = 339 [default = false];

Returns:

Whether the debugCrashIfLratCheckFails field is set.

getDebugCrashIfLratCheckFails

boolean getDebugCrashIfLratCheckFails()

Crash if the LRAT UNSAT proof is invalid.

optional bool debug_crash_if_lrat_check_fails = 339 [default = false];

Returns:

The debugCrashIfLratCheckFails.

hasUseOptimizationHints

boolean hasUseOptimizationHints()

For an optimization problem, whether we follow some hints in order to find
a better first solution. For a variable with hint, the solver will always
try to follow the hint. It will revert to the variable_branching default
otherwise.

optional bool use_optimization_hints = 35 [default = true];

Returns:

Whether the useOptimizationHints field is set.

getUseOptimizationHints

boolean getUseOptimizationHints()

For an optimization problem, whether we follow some hints in order to find
a better first solution. For a variable with hint, the solver will always
try to follow the hint. It will revert to the variable_branching default
otherwise.

optional bool use_optimization_hints = 35 [default = true];

Returns:

The useOptimizationHints.

hasCoreMinimizationLevel

boolean hasCoreMinimizationLevel()

If positive, we spend some effort on each core:
- At level 1, we use a simple heuristic to try to minimize an UNSAT core.
- At level 2, we use propagation to minimize the core but also identify
literal in at most one relationship in this core.

optional int32 core_minimization_level = 50 [default = 2];

Returns:

Whether the coreMinimizationLevel field is set.

getCoreMinimizationLevel

int getCoreMinimizationLevel()

If positive, we spend some effort on each core:
- At level 1, we use a simple heuristic to try to minimize an UNSAT core.
- At level 2, we use propagation to minimize the core but also identify
literal in at most one relationship in this core.

optional int32 core_minimization_level = 50 [default = 2];

Returns:

The coreMinimizationLevel.

hasFindMultipleCores

boolean hasFindMultipleCores()

Whether we try to find more independent cores for a given set of
assumptions in the core based max-SAT algorithms.

optional bool find_multiple_cores = 84 [default = true];

Returns:

Whether the findMultipleCores field is set.

getFindMultipleCores

boolean getFindMultipleCores()

Whether we try to find more independent cores for a given set of
assumptions in the core based max-SAT algorithms.

optional bool find_multiple_cores = 84 [default = true];

Returns:

The findMultipleCores.

hasCoverOptimization

boolean hasCoverOptimization()

If true, when the max-sat algo find a core, we compute the minimal number
of literals in the core that needs to be true to have a feasible solution.
This is also called core exhaustion in more recent max-SAT papers.

optional bool cover_optimization = 89 [default = true];

Returns:

Whether the coverOptimization field is set.

getCoverOptimization

boolean getCoverOptimization()

If true, when the max-sat algo find a core, we compute the minimal number
of literals in the core that needs to be true to have a feasible solution.
This is also called core exhaustion in more recent max-SAT papers.

optional bool cover_optimization = 89 [default = true];

Returns:

The coverOptimization.

hasMaxSatAssumptionOrder

boolean hasMaxSatAssumptionOrder()

optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];

Returns:

Whether the maxSatAssumptionOrder field is set.

getMaxSatAssumptionOrder

SatParameters.MaxSatAssumptionOrder getMaxSatAssumptionOrder()

optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];

Returns:

The maxSatAssumptionOrder.

hasMaxSatReverseAssumptionOrder

boolean hasMaxSatReverseAssumptionOrder()

If true, adds the assumption in the reverse order of the one defined by
max_sat_assumption_order.

optional bool max_sat_reverse_assumption_order = 52 [default = false];

Returns:

Whether the maxSatReverseAssumptionOrder field is set.

getMaxSatReverseAssumptionOrder

boolean getMaxSatReverseAssumptionOrder()

If true, adds the assumption in the reverse order of the one defined by
max_sat_assumption_order.

optional bool max_sat_reverse_assumption_order = 52 [default = false];

Returns:

The maxSatReverseAssumptionOrder.

hasMaxSatStratification

boolean hasMaxSatStratification()

optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];

Returns:

Whether the maxSatStratification field is set.

getMaxSatStratification

SatParameters.MaxSatStratificationAlgorithm getMaxSatStratification()

optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];

Returns:

The maxSatStratification.

hasPropagationLoopDetectionFactor

boolean hasPropagationLoopDetectionFactor()

Some search decisions might cause a really large number of propagations to
happen when integer variables with large domains are only reduced by 1 at
each step. If we propagate more than the number of variable times this
parameters we try to take counter-measure. Setting this to 0.0 disable this
feature.

TODO(user): Setting this to something like 10 helps in most cases, but the
code is currently buggy and can cause the solve to enter a bad state where
no progress is made.

optional double propagation_loop_detection_factor = 221 [default = 10];

Returns:

Whether the propagationLoopDetectionFactor field is set.

getPropagationLoopDetectionFactor

double getPropagationLoopDetectionFactor()

Some search decisions might cause a really large number of propagations to
happen when integer variables with large domains are only reduced by 1 at
each step. If we propagate more than the number of variable times this
parameters we try to take counter-measure. Setting this to 0.0 disable this
feature.

TODO(user): Setting this to something like 10 helps in most cases, but the
code is currently buggy and can cause the solve to enter a bad state where
no progress is made.

optional double propagation_loop_detection_factor = 221 [default = 10];

Returns:

The propagationLoopDetectionFactor.

hasUsePrecedencesInDisjunctiveConstraint

boolean hasUsePrecedencesInDisjunctiveConstraint()

When this is true, then a disjunctive constraint will try to use the
precedence relations between time intervals to propagate their bounds
further. For instance if task A and B are both before C and task A and B
are in disjunction, then we can deduce that task C must start after
duration(A) + duration(B) instead of simply max(duration(A), duration(B)),
provided that the start time for all task was currently zero.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_precedences_in_disjunctive_constraint = 74 [default = true];

Returns:

Whether the usePrecedencesInDisjunctiveConstraint field is set.

getUsePrecedencesInDisjunctiveConstraint

boolean getUsePrecedencesInDisjunctiveConstraint()

When this is true, then a disjunctive constraint will try to use the
precedence relations between time intervals to propagate their bounds
further. For instance if task A and B are both before C and task A and B
are in disjunction, then we can deduce that task C must start after
duration(A) + duration(B) instead of simply max(duration(A), duration(B)),
provided that the start time for all task was currently zero.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_precedences_in_disjunctive_constraint = 74 [default = true];

Returns:

The usePrecedencesInDisjunctiveConstraint.

hasTransitivePrecedencesWorkLimit

boolean hasTransitivePrecedencesWorkLimit()

At root level, we might compute the transitive closure of "precedences"
relations so that we can exploit that in scheduling problems. Setting this
to zero disable the feature.

optional int32 transitive_precedences_work_limit = 327 [default = 1000000];

Returns:

Whether the transitivePrecedencesWorkLimit field is set.

getTransitivePrecedencesWorkLimit

int getTransitivePrecedencesWorkLimit()

At root level, we might compute the transitive closure of "precedences"
relations so that we can exploit that in scheduling problems. Setting this
to zero disable the feature.

optional int32 transitive_precedences_work_limit = 327 [default = 1000000];

Returns:

The transitivePrecedencesWorkLimit.

hasMaxSizeToCreatePrecedenceLiteralsInDisjunctive

boolean hasMaxSizeToCreatePrecedenceLiteralsInDisjunctive()

Create one literal for each disjunction of two pairs of tasks. This slows
down the solve time, but improves the lower bound of the objective in the
makespan case. This will be triggered if the number of intervals is less or
equal than the parameter and if use_strong_propagation_in_disjunctive is
true.

optional int32 max_size_to_create_precedence_literals_in_disjunctive = 229 [default = 60];

Returns:

Whether the maxSizeToCreatePrecedenceLiteralsInDisjunctive field is set.

getMaxSizeToCreatePrecedenceLiteralsInDisjunctive

int getMaxSizeToCreatePrecedenceLiteralsInDisjunctive()

Create one literal for each disjunction of two pairs of tasks. This slows
down the solve time, but improves the lower bound of the objective in the
makespan case. This will be triggered if the number of intervals is less or
equal than the parameter and if use_strong_propagation_in_disjunctive is
true.

optional int32 max_size_to_create_precedence_literals_in_disjunctive = 229 [default = 60];

Returns:

The maxSizeToCreatePrecedenceLiteralsInDisjunctive.

hasUseStrongPropagationInDisjunctive

boolean hasUseStrongPropagationInDisjunctive()

Enable stronger and more expensive propagation on no_overlap constraint.

optional bool use_strong_propagation_in_disjunctive = 230 [default = false];

Returns:

Whether the useStrongPropagationInDisjunctive field is set.

getUseStrongPropagationInDisjunctive

boolean getUseStrongPropagationInDisjunctive()

Enable stronger and more expensive propagation on no_overlap constraint.

optional bool use_strong_propagation_in_disjunctive = 230 [default = false];

Returns:

The useStrongPropagationInDisjunctive.

hasUseDynamicPrecedenceInDisjunctive

boolean hasUseDynamicPrecedenceInDisjunctive()

Whether we try to branch on decision "interval A before interval B" rather
than on intervals bounds. This usually works better, but slow down a bit
the time to find the first solution.

These parameters are still EXPERIMENTAL, the result should be correct, but
it some corner cases, they can cause some failing CHECK in the solver.

optional bool use_dynamic_precedence_in_disjunctive = 263 [default = false];

Returns:

Whether the useDynamicPrecedenceInDisjunctive field is set.

getUseDynamicPrecedenceInDisjunctive

boolean getUseDynamicPrecedenceInDisjunctive()

Whether we try to branch on decision "interval A before interval B" rather
than on intervals bounds. This usually works better, but slow down a bit
the time to find the first solution.

These parameters are still EXPERIMENTAL, the result should be correct, but
it some corner cases, they can cause some failing CHECK in the solver.

optional bool use_dynamic_precedence_in_disjunctive = 263 [default = false];

Returns:

The useDynamicPrecedenceInDisjunctive.

hasUseDynamicPrecedenceInCumulative

boolean hasUseDynamicPrecedenceInCumulative()

optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];

Returns:

Whether the useDynamicPrecedenceInCumulative field is set.

getUseDynamicPrecedenceInCumulative

boolean getUseDynamicPrecedenceInCumulative()

optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];

Returns:

The useDynamicPrecedenceInCumulative.

hasUseOverloadCheckerInCumulative

boolean hasUseOverloadCheckerInCumulative()

When this is true, the cumulative constraint is reinforced with overload
checking, i.e., an additional level of reasoning based on energy. This
additional level supplements the default level of reasoning as well as
timetable edge finding.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_overload_checker_in_cumulative = 78 [default = false];

Returns:

Whether the useOverloadCheckerInCumulative field is set.

getUseOverloadCheckerInCumulative

boolean getUseOverloadCheckerInCumulative()

When this is true, the cumulative constraint is reinforced with overload
checking, i.e., an additional level of reasoning based on energy. This
additional level supplements the default level of reasoning as well as
timetable edge finding.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_overload_checker_in_cumulative = 78 [default = false];

Returns:

The useOverloadCheckerInCumulative.

hasUseConservativeScaleOverloadChecker

boolean hasUseConservativeScaleOverloadChecker()

Enable a heuristic to solve cumulative constraints using a modified energy
constraint. We modify the usual energy definition by applying a
super-additive function (also called "conservative scale" or "dual-feasible
function") to the demand and the durations of the tasks.

This heuristic is fast but for most problems it does not help much to find
a solution.

optional bool use_conservative_scale_overload_checker = 286 [default = false];

Returns:

Whether the useConservativeScaleOverloadChecker field is set.

getUseConservativeScaleOverloadChecker

boolean getUseConservativeScaleOverloadChecker()

Enable a heuristic to solve cumulative constraints using a modified energy
constraint. We modify the usual energy definition by applying a
super-additive function (also called "conservative scale" or "dual-feasible
function") to the demand and the durations of the tasks.

This heuristic is fast but for most problems it does not help much to find
a solution.

optional bool use_conservative_scale_overload_checker = 286 [default = false];

Returns:

The useConservativeScaleOverloadChecker.

hasUseTimetableEdgeFindingInCumulative

boolean hasUseTimetableEdgeFindingInCumulative()

When this is true, the cumulative constraint is reinforced with timetable
edge finding, i.e., an additional level of reasoning based on the
conjunction of energy and mandatory parts. This additional level
supplements the default level of reasoning as well as overload_checker.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_timetable_edge_finding_in_cumulative = 79 [default = false];

Returns:

Whether the useTimetableEdgeFindingInCumulative field is set.

getUseTimetableEdgeFindingInCumulative

boolean getUseTimetableEdgeFindingInCumulative()

When this is true, the cumulative constraint is reinforced with timetable
edge finding, i.e., an additional level of reasoning based on the
conjunction of energy and mandatory parts. This additional level
supplements the default level of reasoning as well as overload_checker.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_timetable_edge_finding_in_cumulative = 79 [default = false];

Returns:

The useTimetableEdgeFindingInCumulative.

hasMaxNumIntervalsForTimetableEdgeFinding

boolean hasMaxNumIntervalsForTimetableEdgeFinding()

Max number of intervals for the timetable_edge_finding algorithm to
propagate. A value of 0 disables the constraint.

optional int32 max_num_intervals_for_timetable_edge_finding = 260 [default = 100];

Returns:

Whether the maxNumIntervalsForTimetableEdgeFinding field is set.

getMaxNumIntervalsForTimetableEdgeFinding

int getMaxNumIntervalsForTimetableEdgeFinding()

Max number of intervals for the timetable_edge_finding algorithm to
propagate. A value of 0 disables the constraint.

optional int32 max_num_intervals_for_timetable_edge_finding = 260 [default = 100];

Returns:

The maxNumIntervalsForTimetableEdgeFinding.

hasUseHardPrecedencesInCumulative

boolean hasUseHardPrecedencesInCumulative()

If true, detect and create constraint for integer variable that are "after"
a set of intervals in the same cumulative constraint.

Experimental: by default we just use "direct" precedences. If
exploit_all_precedences is true, we explore the full precedence graph. This
assumes we have a DAG otherwise it fails.

optional bool use_hard_precedences_in_cumulative = 215 [default = false];

Returns:

Whether the useHardPrecedencesInCumulative field is set.

getUseHardPrecedencesInCumulative

boolean getUseHardPrecedencesInCumulative()

If true, detect and create constraint for integer variable that are "after"
a set of intervals in the same cumulative constraint.

Experimental: by default we just use "direct" precedences. If
exploit_all_precedences is true, we explore the full precedence graph. This
assumes we have a DAG otherwise it fails.

optional bool use_hard_precedences_in_cumulative = 215 [default = false];

Returns:

The useHardPrecedencesInCumulative.

hasExploitAllPrecedences

boolean hasExploitAllPrecedences()

optional bool exploit_all_precedences = 220 [default = false];

Returns:

Whether the exploitAllPrecedences field is set.

getExploitAllPrecedences

boolean getExploitAllPrecedences()

optional bool exploit_all_precedences = 220 [default = false];

Returns:

The exploitAllPrecedences.

hasUseDisjunctiveConstraintInCumulative

boolean hasUseDisjunctiveConstraintInCumulative()

When this is true, the cumulative constraint is reinforced with propagators
from the disjunctive constraint to improve the inference on a set of tasks
that are disjunctive at the root of the problem. This additional level
supplements the default level of reasoning.

Propagators of the cumulative constraint will not be used at all if all the
tasks are disjunctive at root node.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_disjunctive_constraint_in_cumulative = 80 [default = true];

Returns:

Whether the useDisjunctiveConstraintInCumulative field is set.

getUseDisjunctiveConstraintInCumulative

boolean getUseDisjunctiveConstraintInCumulative()

When this is true, the cumulative constraint is reinforced with propagators
from the disjunctive constraint to improve the inference on a set of tasks
that are disjunctive at the root of the problem. This additional level
supplements the default level of reasoning.

Propagators of the cumulative constraint will not be used at all if all the
tasks are disjunctive at root node.

This always result in better propagation, but it is usually slow, so
depending on the problem, turning this off may lead to a faster solution.

optional bool use_disjunctive_constraint_in_cumulative = 80 [default = true];

Returns:

The useDisjunctiveConstraintInCumulative.

hasNoOverlap2DBooleanRelationsLimit

boolean hasNoOverlap2DBooleanRelationsLimit()

If less than this number of boxes are present in a no-overlap 2d, we
create 4 Booleans per pair of boxes:
- Box 2 is after Box 1 on x.
- Box 1 is after Box 2 on x.
- Box 2 is after Box 1 on y.
- Box 1 is after Box 2 on y.

Note that at least one of them must be true, and at most one on x and one
on y can be true.

This can significantly help in closing small problem. The SAT reasoning
can be a lot more powerful when we take decision on such positional
relations.

optional int32 no_overlap_2d_boolean_relations_limit = 321 [default = 10];

Returns:

Whether the noOverlap2dBooleanRelationsLimit field is set.

getNoOverlap2DBooleanRelationsLimit

int getNoOverlap2DBooleanRelationsLimit()

If less than this number of boxes are present in a no-overlap 2d, we
create 4 Booleans per pair of boxes:
- Box 2 is after Box 1 on x.
- Box 1 is after Box 2 on x.
- Box 2 is after Box 1 on y.
- Box 1 is after Box 2 on y.

Note that at least one of them must be true, and at most one on x and one
on y can be true.

This can significantly help in closing small problem. The SAT reasoning
can be a lot more powerful when we take decision on such positional
relations.

optional int32 no_overlap_2d_boolean_relations_limit = 321 [default = 10];

Returns:

The noOverlap2dBooleanRelationsLimit.

hasUseTimetablingInNoOverlap2D

boolean hasUseTimetablingInNoOverlap2D()

When this is true, the no_overlap_2d constraint is reinforced with
propagators from the cumulative constraints. It consists of ignoring the
position of rectangles in one position and projecting the no_overlap_2d on
the other dimension to create a cumulative constraint. This is done on both
axis. This additional level supplements the default level of reasoning.

optional bool use_timetabling_in_no_overlap_2d = 200 [default = false];

Returns:

Whether the useTimetablingInNoOverlap2d field is set.

getUseTimetablingInNoOverlap2D

boolean getUseTimetablingInNoOverlap2D()

When this is true, the no_overlap_2d constraint is reinforced with
propagators from the cumulative constraints. It consists of ignoring the
position of rectangles in one position and projecting the no_overlap_2d on
the other dimension to create a cumulative constraint. This is done on both
axis. This additional level supplements the default level of reasoning.

optional bool use_timetabling_in_no_overlap_2d = 200 [default = false];

Returns:

The useTimetablingInNoOverlap2d.

hasUseEnergeticReasoningInNoOverlap2D

boolean hasUseEnergeticReasoningInNoOverlap2D()

When this is true, the no_overlap_2d constraint is reinforced with
energetic reasoning. This additional level supplements the default level of
reasoning.

optional bool use_energetic_reasoning_in_no_overlap_2d = 213 [default = false];

Returns:

Whether the useEnergeticReasoningInNoOverlap2d field is set.

getUseEnergeticReasoningInNoOverlap2D

boolean getUseEnergeticReasoningInNoOverlap2D()

When this is true, the no_overlap_2d constraint is reinforced with
energetic reasoning. This additional level supplements the default level of
reasoning.

optional bool use_energetic_reasoning_in_no_overlap_2d = 213 [default = false];

Returns:

The useEnergeticReasoningInNoOverlap2d.

hasUseAreaEnergeticReasoningInNoOverlap2D

boolean hasUseAreaEnergeticReasoningInNoOverlap2D()

When this is true, the no_overlap_2d constraint is reinforced with
an energetic reasoning that uses an area-based energy. This can be combined
with the two other overlap heuristics above.

optional bool use_area_energetic_reasoning_in_no_overlap_2d = 271 [default = false];

Returns:

Whether the useAreaEnergeticReasoningInNoOverlap2d field is set.

getUseAreaEnergeticReasoningInNoOverlap2D

boolean getUseAreaEnergeticReasoningInNoOverlap2D()

When this is true, the no_overlap_2d constraint is reinforced with
an energetic reasoning that uses an area-based energy. This can be combined
with the two other overlap heuristics above.

optional bool use_area_energetic_reasoning_in_no_overlap_2d = 271 [default = false];

Returns:

The useAreaEnergeticReasoningInNoOverlap2d.

hasUseTryEdgeReasoningInNoOverlap2D

boolean hasUseTryEdgeReasoningInNoOverlap2D()

optional bool use_try_edge_reasoning_in_no_overlap_2d = 299 [default = false];

Returns:

Whether the useTryEdgeReasoningInNoOverlap2d field is set.

getUseTryEdgeReasoningInNoOverlap2D

boolean getUseTryEdgeReasoningInNoOverlap2D()

optional bool use_try_edge_reasoning_in_no_overlap_2d = 299 [default = false];

Returns:

The useTryEdgeReasoningInNoOverlap2d.

hasMaxPairsPairwiseReasoningInNoOverlap2D

boolean hasMaxPairsPairwiseReasoningInNoOverlap2D()

If the number of pairs to look is below this threshold, do an extra step of
propagation in the no_overlap_2d constraint by looking at all pairs of
intervals.

optional int32 max_pairs_pairwise_reasoning_in_no_overlap_2d = 276 [default = 1250];

Returns:

Whether the maxPairsPairwiseReasoningInNoOverlap2d field is set.

getMaxPairsPairwiseReasoningInNoOverlap2D

int getMaxPairsPairwiseReasoningInNoOverlap2D()

If the number of pairs to look is below this threshold, do an extra step of
propagation in the no_overlap_2d constraint by looking at all pairs of
intervals.

optional int32 max_pairs_pairwise_reasoning_in_no_overlap_2d = 276 [default = 1250];

Returns:

The maxPairsPairwiseReasoningInNoOverlap2d.

hasMaximumRegionsToSplitInDisconnectedNoOverlap2D

boolean hasMaximumRegionsToSplitInDisconnectedNoOverlap2D()

Detects when the space where items of a no_overlap_2d constraint can placed
is disjoint (ie., fixed boxes split the domain). When it is the case, we
can introduce a boolean for each pair <item, component> encoding whether
the item is in the component or not. Then we replace the original
no_overlap_2d constraint by one no_overlap_2d constraint for each
component, with the new booleans as the enforcement_literal of the
intervals. This is equivalent to expanding the original no_overlap_2d
constraint into a bin packing problem with each connected component being a
bin. This heuristic is only done when the number of regions to split
is less than this parameter and <= 1 disables it.

optional int32 maximum_regions_to_split_in_disconnected_no_overlap_2d = 315 [default = 0];

Returns:

Whether the maximumRegionsToSplitInDisconnectedNoOverlap2d field is set.

getMaximumRegionsToSplitInDisconnectedNoOverlap2D

int getMaximumRegionsToSplitInDisconnectedNoOverlap2D()

Detects when the space where items of a no_overlap_2d constraint can placed
is disjoint (ie., fixed boxes split the domain). When it is the case, we
can introduce a boolean for each pair <item, component> encoding whether
the item is in the component or not. Then we replace the original
no_overlap_2d constraint by one no_overlap_2d constraint for each
component, with the new booleans as the enforcement_literal of the
intervals. This is equivalent to expanding the original no_overlap_2d
constraint into a bin packing problem with each connected component being a
bin. This heuristic is only done when the number of regions to split
is less than this parameter and <= 1 disables it.

optional int32 maximum_regions_to_split_in_disconnected_no_overlap_2d = 315 [default = 0];

Returns:

The maximumRegionsToSplitInDisconnectedNoOverlap2d.

hasUseLinear3ForNoOverlap2DPrecedences

boolean hasUseLinear3ForNoOverlap2DPrecedences()

When set, this activates a propagator for the no_overlap_2d constraint that
uses any eventual linear constraints of the model in the form
`{start interval 1} - {end interval 2} + c*w <= ub` to detect that two
intervals must overlap in one dimension for some values of `w`. This is
particularly useful for problems where the distance between two boxes is
part of the model.

optional bool use_linear3_for_no_overlap_2d_precedences = 323 [default = true];

Returns:

Whether the useLinear3ForNoOverlap2dPrecedences field is set.

getUseLinear3ForNoOverlap2DPrecedences

boolean getUseLinear3ForNoOverlap2DPrecedences()

When set, this activates a propagator for the no_overlap_2d constraint that
uses any eventual linear constraints of the model in the form
`{start interval 1} - {end interval 2} + c*w <= ub` to detect that two
intervals must overlap in one dimension for some values of `w`. This is
particularly useful for problems where the distance between two boxes is
part of the model.

optional bool use_linear3_for_no_overlap_2d_precedences = 323 [default = true];

Returns:

The useLinear3ForNoOverlap2dPrecedences.

hasUseDualSchedulingHeuristics

boolean hasUseDualSchedulingHeuristics()

When set, it activates a few scheduling parameters to improve the lower
bound of scheduling problems. This is only effective with multiple workers
as it modifies the reduced_cost, lb_tree_search, and probing workers.

optional bool use_dual_scheduling_heuristics = 214 [default = true];

Returns:

Whether the useDualSchedulingHeuristics field is set.

getUseDualSchedulingHeuristics

boolean getUseDualSchedulingHeuristics()

When set, it activates a few scheduling parameters to improve the lower
bound of scheduling problems. This is only effective with multiple workers
as it modifies the reduced_cost, lb_tree_search, and probing workers.

optional bool use_dual_scheduling_heuristics = 214 [default = true];

Returns:

The useDualSchedulingHeuristics.

hasUseAllDifferentForCircuit

boolean hasUseAllDifferentForCircuit()

Turn on extra propagation for the circuit constraint.
This can be quite slow.

optional bool use_all_different_for_circuit = 311 [default = false];

Returns:

Whether the useAllDifferentForCircuit field is set.

getUseAllDifferentForCircuit

boolean getUseAllDifferentForCircuit()

Turn on extra propagation for the circuit constraint.
This can be quite slow.

optional bool use_all_different_for_circuit = 311 [default = false];

Returns:

The useAllDifferentForCircuit.

hasRoutingCutSubsetSizeForBinaryRelationBound

boolean hasRoutingCutSubsetSizeForBinaryRelationBound()

If the size of a subset of nodes of a RoutesConstraint is less than this
value, use linear constraints of size 1 and 2 (such as capacity and time
window constraints) enforced by the arc literals to compute cuts for this
subset (unless the subset size is less than
routing_cut_subset_size_for_tight_binary_relation_bound, in which case the
corresponding algorithm is used instead). The algorithm for these cuts has
a O(n^3) complexity, where n is the subset size. Hence the value of this
parameter should not be too large (e.g. 10 or 20).

optional int32 routing_cut_subset_size_for_binary_relation_bound = 312 [default = 0];

Returns:

Whether the routingCutSubsetSizeForBinaryRelationBound field is set.

getRoutingCutSubsetSizeForBinaryRelationBound

int getRoutingCutSubsetSizeForBinaryRelationBound()

If the size of a subset of nodes of a RoutesConstraint is less than this
value, use linear constraints of size 1 and 2 (such as capacity and time
window constraints) enforced by the arc literals to compute cuts for this
subset (unless the subset size is less than
routing_cut_subset_size_for_tight_binary_relation_bound, in which case the
corresponding algorithm is used instead). The algorithm for these cuts has
a O(n^3) complexity, where n is the subset size. Hence the value of this
parameter should not be too large (e.g. 10 or 20).

optional int32 routing_cut_subset_size_for_binary_relation_bound = 312 [default = 0];

Returns:

The routingCutSubsetSizeForBinaryRelationBound.

hasRoutingCutSubsetSizeForTightBinaryRelationBound

boolean hasRoutingCutSubsetSizeForTightBinaryRelationBound()

Similar to above, but with a different algorithm producing better cuts, at
the price of a higher O(2^n) complexity, where n is the subset size. Hence
the value of this parameter should be small (e.g. less than 10).

optional int32 routing_cut_subset_size_for_tight_binary_relation_bound = 313 [default = 0];

Returns:

Whether the routingCutSubsetSizeForTightBinaryRelationBound field is set.

getRoutingCutSubsetSizeForTightBinaryRelationBound

int getRoutingCutSubsetSizeForTightBinaryRelationBound()

Similar to above, but with a different algorithm producing better cuts, at
the price of a higher O(2^n) complexity, where n is the subset size. Hence
the value of this parameter should be small (e.g. less than 10).

optional int32 routing_cut_subset_size_for_tight_binary_relation_bound = 313 [default = 0];

Returns:

The routingCutSubsetSizeForTightBinaryRelationBound.

hasRoutingCutSubsetSizeForExactBinaryRelationBound

boolean hasRoutingCutSubsetSizeForExactBinaryRelationBound()

Similar to above, but with an even stronger algorithm in O(n!). We try to
be defensive and abort early or not run that often. Still the value of
that parameter shouldn't really be much more than 10.

optional int32 routing_cut_subset_size_for_exact_binary_relation_bound = 316 [default = 8];

Returns:

Whether the routingCutSubsetSizeForExactBinaryRelationBound field is set.

getRoutingCutSubsetSizeForExactBinaryRelationBound

int getRoutingCutSubsetSizeForExactBinaryRelationBound()

Similar to above, but with an even stronger algorithm in O(n!). We try to
be defensive and abort early or not run that often. Still the value of
that parameter shouldn't really be much more than 10.

optional int32 routing_cut_subset_size_for_exact_binary_relation_bound = 316 [default = 8];

Returns:

The routingCutSubsetSizeForExactBinaryRelationBound.

hasRoutingCutSubsetSizeForShortestPathsBound

boolean hasRoutingCutSubsetSizeForShortestPathsBound()

Similar to routing_cut_subset_size_for_exact_binary_relation_bound but
use a bound based on shortest path distances (which respect triangular
inequality). This allows to derive bounds that are valid for any superset
of a given subset. This is slow, so it shouldn't really be larger than 10.

optional int32 routing_cut_subset_size_for_shortest_paths_bound = 318 [default = 8];

Returns:

Whether the routingCutSubsetSizeForShortestPathsBound field is set.

getRoutingCutSubsetSizeForShortestPathsBound

int getRoutingCutSubsetSizeForShortestPathsBound()

Similar to routing_cut_subset_size_for_exact_binary_relation_bound but
use a bound based on shortest path distances (which respect triangular
inequality). This allows to derive bounds that are valid for any superset
of a given subset. This is slow, so it shouldn't really be larger than 10.

optional int32 routing_cut_subset_size_for_shortest_paths_bound = 318 [default = 8];

Returns:

The routingCutSubsetSizeForShortestPathsBound.

hasRoutingCutDpEffort

boolean hasRoutingCutDpEffort()

The amount of "effort" to spend in dynamic programming for computing
routing cuts. This is in term of basic operations needed by the algorithm
in the worst case, so a value like 1e8 should take less than a second to
compute.

optional double routing_cut_dp_effort = 314 [default = 10000000];

Returns:

Whether the routingCutDpEffort field is set.

getRoutingCutDpEffort

double getRoutingCutDpEffort()

The amount of "effort" to spend in dynamic programming for computing
routing cuts. This is in term of basic operations needed by the algorithm
in the worst case, so a value like 1e8 should take less than a second to
compute.

optional double routing_cut_dp_effort = 314 [default = 10000000];

Returns:

The routingCutDpEffort.

hasRoutingCutMaxInfeasiblePathLength

boolean hasRoutingCutMaxInfeasiblePathLength()

If the length of an infeasible path is less than this value, a cut will be
added to exclude it.

optional int32 routing_cut_max_infeasible_path_length = 317 [default = 6];

Returns:

Whether the routingCutMaxInfeasiblePathLength field is set.

getRoutingCutMaxInfeasiblePathLength

int getRoutingCutMaxInfeasiblePathLength()

If the length of an infeasible path is less than this value, a cut will be
added to exclude it.

optional int32 routing_cut_max_infeasible_path_length = 317 [default = 6];

Returns:

The routingCutMaxInfeasiblePathLength.

hasSearchBranching

boolean hasSearchBranching()

optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];

Returns:

Whether the searchBranching field is set.

getSearchBranching

SatParameters.SearchBranching getSearchBranching()

optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];

Returns:

The searchBranching.

hasHintConflictLimit

boolean hasHintConflictLimit()

Conflict limit used in the phase that exploit the solution hint.

optional int32 hint_conflict_limit = 153 [default = 10];

Returns:

Whether the hintConflictLimit field is set.

getHintConflictLimit

int getHintConflictLimit()

Conflict limit used in the phase that exploit the solution hint.

optional int32 hint_conflict_limit = 153 [default = 10];

Returns:

The hintConflictLimit.

hasRepairHint

boolean hasRepairHint()

If true, the solver tries to repair the solution given in the hint. This
search terminates after the 'hint_conflict_limit' is reached and the solver
switches to regular search. If false, then  we do a FIXED_SEARCH using the
hint until the hint_conflict_limit is reached.

optional bool repair_hint = 167 [default = false];

Returns:

Whether the repairHint field is set.

getRepairHint

boolean getRepairHint()

If true, the solver tries to repair the solution given in the hint. This
search terminates after the 'hint_conflict_limit' is reached and the solver
switches to regular search. If false, then  we do a FIXED_SEARCH using the
hint until the hint_conflict_limit is reached.

optional bool repair_hint = 167 [default = false];

Returns:

The repairHint.

hasFixVariablesToTheirHintedValue

boolean hasFixVariablesToTheirHintedValue()

If true, variables appearing in the solution hints will be fixed to their
hinted value.

optional bool fix_variables_to_their_hinted_value = 192 [default = false];

Returns:

Whether the fixVariablesToTheirHintedValue field is set.

getFixVariablesToTheirHintedValue

boolean getFixVariablesToTheirHintedValue()

If true, variables appearing in the solution hints will be fixed to their
hinted value.

optional bool fix_variables_to_their_hinted_value = 192 [default = false];

Returns:

The fixVariablesToTheirHintedValue.

hasUseProbingSearch

boolean hasUseProbingSearch()

If true, search will continuously probe Boolean variables, and integer
variable bounds. This parameter is set to true in parallel on the probing
worker.

optional bool use_probing_search = 176 [default = false];

Returns:

Whether the useProbingSearch field is set.

getUseProbingSearch

boolean getUseProbingSearch()

If true, search will continuously probe Boolean variables, and integer
variable bounds. This parameter is set to true in parallel on the probing
worker.

optional bool use_probing_search = 176 [default = false];

Returns:

The useProbingSearch.

hasUseExtendedProbing

boolean hasUseExtendedProbing()

Use extended probing (probe bool_or, at_most_one, exactly_one).

optional bool use_extended_probing = 269 [default = true];

Returns:

Whether the useExtendedProbing field is set.

getUseExtendedProbing

boolean getUseExtendedProbing()

Use extended probing (probe bool_or, at_most_one, exactly_one).

optional bool use_extended_probing = 269 [default = true];

Returns:

The useExtendedProbing.

hasProbingNumCombinationsLimit

boolean hasProbingNumCombinationsLimit()

How many combinations of pairs or triplets of variables we want to scan.

optional int32 probing_num_combinations_limit = 272 [default = 20000];

Returns:

Whether the probingNumCombinationsLimit field is set.

getProbingNumCombinationsLimit

int getProbingNumCombinationsLimit()

How many combinations of pairs or triplets of variables we want to scan.

optional int32 probing_num_combinations_limit = 272 [default = 20000];

Returns:

The probingNumCombinationsLimit.

hasShavingDeterministicTimeInProbingSearch

boolean hasShavingDeterministicTimeInProbingSearch()

Add a shaving phase (where the solver tries to prove that the lower or
upper bound of a variable are infeasible) to the probing search. (<= 0
disables it).

optional double shaving_deterministic_time_in_probing_search = 204 [default = 0.001];

Returns:

Whether the shavingDeterministicTimeInProbingSearch field is set.

getShavingDeterministicTimeInProbingSearch

double getShavingDeterministicTimeInProbingSearch()

Add a shaving phase (where the solver tries to prove that the lower or
upper bound of a variable are infeasible) to the probing search. (<= 0
disables it).

optional double shaving_deterministic_time_in_probing_search = 204 [default = 0.001];

Returns:

The shavingDeterministicTimeInProbingSearch.

hasShavingSearchDeterministicTime

boolean hasShavingSearchDeterministicTime()

Specifies the amount of deterministic time spent of each try at shaving a
bound in the shaving search.

optional double shaving_search_deterministic_time = 205 [default = 0.1];

Returns:

Whether the shavingSearchDeterministicTime field is set.

getShavingSearchDeterministicTime

double getShavingSearchDeterministicTime()

Specifies the amount of deterministic time spent of each try at shaving a
bound in the shaving search.

optional double shaving_search_deterministic_time = 205 [default = 0.1];

Returns:

The shavingSearchDeterministicTime.

hasShavingSearchThreshold

boolean hasShavingSearchThreshold()

Specifies the threshold between two modes in the shaving procedure.
If the range of the variable/objective is less than this threshold, then
the shaving procedure will try to remove values one by one. Otherwise, it
will try to remove one range at a time.

optional int64 shaving_search_threshold = 290 [default = 64];

Returns:

Whether the shavingSearchThreshold field is set.

getShavingSearchThreshold

long getShavingSearchThreshold()

Specifies the threshold between two modes in the shaving procedure.
If the range of the variable/objective is less than this threshold, then
the shaving procedure will try to remove values one by one. Otherwise, it
will try to remove one range at a time.

optional int64 shaving_search_threshold = 290 [default = 64];

Returns:

The shavingSearchThreshold.

hasUseObjectiveLbSearch

boolean hasUseObjectiveLbSearch()

If true, search will search in ascending max objective value (when
minimizing) starting from the lower bound of the objective.

optional bool use_objective_lb_search = 228 [default = false];

Returns:

Whether the useObjectiveLbSearch field is set.

getUseObjectiveLbSearch

boolean getUseObjectiveLbSearch()

If true, search will search in ascending max objective value (when
minimizing) starting from the lower bound of the objective.

optional bool use_objective_lb_search = 228 [default = false];

Returns:

The useObjectiveLbSearch.

hasUseObjectiveShavingSearch

boolean hasUseObjectiveShavingSearch()

This search differs from the previous search as it will not use assumptions
to bound the objective, and it will recreate a full model with the
hardcoded objective value.

optional bool use_objective_shaving_search = 253 [default = false];

Returns:

Whether the useObjectiveShavingSearch field is set.

getUseObjectiveShavingSearch

boolean getUseObjectiveShavingSearch()

This search differs from the previous search as it will not use assumptions
to bound the objective, and it will recreate a full model with the
hardcoded objective value.

optional bool use_objective_shaving_search = 253 [default = false];

Returns:

The useObjectiveShavingSearch.

hasVariablesShavingLevel

boolean hasVariablesShavingLevel()

This search takes all Boolean or integer variables, and maximize or
minimize them in order to reduce their domain. -1 is automatic, otherwise
value 0 disables it, and 1, 2, or 3 changes something.

optional int32 variables_shaving_level = 289 [default = -1];

Returns:

Whether the variablesShavingLevel field is set.

getVariablesShavingLevel

int getVariablesShavingLevel()

This search takes all Boolean or integer variables, and maximize or
minimize them in order to reduce their domain. -1 is automatic, otherwise
value 0 disables it, and 1, 2, or 3 changes something.

optional int32 variables_shaving_level = 289 [default = -1];

Returns:

The variablesShavingLevel.

hasPseudoCostReliabilityThreshold

boolean hasPseudoCostReliabilityThreshold()

The solver ignores the pseudo costs of variables with number of recordings
less than this threshold.

optional int64 pseudo_cost_reliability_threshold = 123 [default = 100];

Returns:

Whether the pseudoCostReliabilityThreshold field is set.

getPseudoCostReliabilityThreshold

long getPseudoCostReliabilityThreshold()

The solver ignores the pseudo costs of variables with number of recordings
less than this threshold.

optional int64 pseudo_cost_reliability_threshold = 123 [default = 100];

Returns:

The pseudoCostReliabilityThreshold.

hasOptimizeWithCore

boolean hasOptimizeWithCore()

The default optimization method is a simple "linear scan", each time trying
to find a better solution than the previous one. If this is true, then we
use a core-based approach (like in max-SAT) when we try to increase the
lower bound instead.

optional bool optimize_with_core = 83 [default = false];

Returns:

Whether the optimizeWithCore field is set.

getOptimizeWithCore

boolean getOptimizeWithCore()

The default optimization method is a simple "linear scan", each time trying
to find a better solution than the previous one. If this is true, then we
use a core-based approach (like in max-SAT) when we try to increase the
lower bound instead.

optional bool optimize_with_core = 83 [default = false];

Returns:

The optimizeWithCore.

hasOptimizeWithLbTreeSearch

boolean hasOptimizeWithLbTreeSearch()

Do a more conventional tree search (by opposition to SAT based one) where
we keep all the explored node in a tree. This is meant to be used in a
portfolio and focus on improving the objective lower bound. Keeping the
whole tree allow us to report a better objective lower bound coming from
the worst open node in the tree.

optional bool optimize_with_lb_tree_search = 188 [default = false];

Returns:

Whether the optimizeWithLbTreeSearch field is set.

getOptimizeWithLbTreeSearch

boolean getOptimizeWithLbTreeSearch()

Do a more conventional tree search (by opposition to SAT based one) where
we keep all the explored node in a tree. This is meant to be used in a
portfolio and focus on improving the objective lower bound. Keeping the
whole tree allow us to report a better objective lower bound coming from
the worst open node in the tree.

optional bool optimize_with_lb_tree_search = 188 [default = false];

Returns:

The optimizeWithLbTreeSearch.

hasSaveLpBasisInLbTreeSearch

boolean hasSaveLpBasisInLbTreeSearch()

Experimental. Save the current LP basis at each node of the search tree so
that when we jump around, we can load it and reduce the number of LP
iterations needed.

It currently works okay if we do not change the lp with cuts or
simplification... More work is needed to make it robust in all cases.

optional bool save_lp_basis_in_lb_tree_search = 284 [default = false];

Returns:

Whether the saveLpBasisInLbTreeSearch field is set.

getSaveLpBasisInLbTreeSearch

boolean getSaveLpBasisInLbTreeSearch()

Experimental. Save the current LP basis at each node of the search tree so
that when we jump around, we can load it and reduce the number of LP
iterations needed.

It currently works okay if we do not change the lp with cuts or
simplification... More work is needed to make it robust in all cases.

optional bool save_lp_basis_in_lb_tree_search = 284 [default = false];

Returns:

The saveLpBasisInLbTreeSearch.

hasBinarySearchNumConflicts

boolean hasBinarySearchNumConflicts()

If non-negative, perform a binary search on the objective variable in order
to find an [min, max] interval outside of which the solver proved unsat/sat
under this amount of conflict. This can quickly reduce the objective domain
on some problems.

optional int32 binary_search_num_conflicts = 99 [default = -1];

Returns:

Whether the binarySearchNumConflicts field is set.

getBinarySearchNumConflicts

int getBinarySearchNumConflicts()

If non-negative, perform a binary search on the objective variable in order
to find an [min, max] interval outside of which the solver proved unsat/sat
under this amount of conflict. This can quickly reduce the objective domain
on some problems.

optional int32 binary_search_num_conflicts = 99 [default = -1];

Returns:

The binarySearchNumConflicts.

hasOptimizeWithMaxHs

boolean hasOptimizeWithMaxHs()

This has no effect if optimize_with_core is false. If true, use a different
core-based algorithm similar to the max-HS algo for max-SAT. This is a
hybrid MIP/CP approach and it uses a MIP solver in addition to the CP/SAT
one. This is also related to the PhD work of tobyodavies@
"Automatic Logic-Based Benders Decomposition with MiniZinc"
http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14489

optional bool optimize_with_max_hs = 85 [default = false];

Returns:

Whether the optimizeWithMaxHs field is set.

getOptimizeWithMaxHs

boolean getOptimizeWithMaxHs()

This has no effect if optimize_with_core is false. If true, use a different
core-based algorithm similar to the max-HS algo for max-SAT. This is a
hybrid MIP/CP approach and it uses a MIP solver in addition to the CP/SAT
one. This is also related to the PhD work of tobyodavies@
"Automatic Logic-Based Benders Decomposition with MiniZinc"
http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14489

optional bool optimize_with_max_hs = 85 [default = false];

Returns:

The optimizeWithMaxHs.

hasUseFeasibilityJump

boolean hasUseFeasibilityJump()

Parameters for an heuristic similar to the one described in the paper:
"Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar
Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation.

optional bool use_feasibility_jump = 265 [default = true];

Returns:

Whether the useFeasibilityJump field is set.

getUseFeasibilityJump

boolean getUseFeasibilityJump()

Parameters for an heuristic similar to the one described in the paper:
"Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar
Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation.

optional bool use_feasibility_jump = 265 [default = true];

Returns:

The useFeasibilityJump.

hasUseLsOnly

boolean hasUseLsOnly()

Disable every other type of subsolver, setting this turns CP-SAT into a
pure local-search solver.

optional bool use_ls_only = 240 [default = false];

Returns:

Whether the useLsOnly field is set.

getUseLsOnly

boolean getUseLsOnly()

Disable every other type of subsolver, setting this turns CP-SAT into a
pure local-search solver.

optional bool use_ls_only = 240 [default = false];

Returns:

The useLsOnly.

hasFeasibilityJumpDecay

boolean hasFeasibilityJumpDecay()

On each restart, we randomly choose if we use decay (with this parameter)
or no decay.

optional double feasibility_jump_decay = 242 [default = 0.95];

Returns:

Whether the feasibilityJumpDecay field is set.

getFeasibilityJumpDecay

double getFeasibilityJumpDecay()

On each restart, we randomly choose if we use decay (with this parameter)
or no decay.

optional double feasibility_jump_decay = 242 [default = 0.95];

Returns:

The feasibilityJumpDecay.

hasFeasibilityJumpLinearizationLevel

boolean hasFeasibilityJumpLinearizationLevel()

How much do we linearize the problem in the local search code.

optional int32 feasibility_jump_linearization_level = 257 [default = 2];

Returns:

Whether the feasibilityJumpLinearizationLevel field is set.

getFeasibilityJumpLinearizationLevel

int getFeasibilityJumpLinearizationLevel()

How much do we linearize the problem in the local search code.

optional int32 feasibility_jump_linearization_level = 257 [default = 2];

Returns:

The feasibilityJumpLinearizationLevel.

hasFeasibilityJumpRestartFactor

boolean hasFeasibilityJumpRestartFactor()

This is a factor that directly influence the work before each restart.
Increasing it leads to longer restart.

optional int32 feasibility_jump_restart_factor = 258 [default = 1];

Returns:

Whether the feasibilityJumpRestartFactor field is set.

getFeasibilityJumpRestartFactor

int getFeasibilityJumpRestartFactor()

This is a factor that directly influence the work before each restart.
Increasing it leads to longer restart.

optional int32 feasibility_jump_restart_factor = 258 [default = 1];

Returns:

The feasibilityJumpRestartFactor.

hasFeasibilityJumpBatchDtime

boolean hasFeasibilityJumpBatchDtime()

How much dtime for each LS batch.

optional double feasibility_jump_batch_dtime = 292 [default = 0.1];

Returns:

Whether the feasibilityJumpBatchDtime field is set.

getFeasibilityJumpBatchDtime

double getFeasibilityJumpBatchDtime()

How much dtime for each LS batch.

optional double feasibility_jump_batch_dtime = 292 [default = 0.1];

Returns:

The feasibilityJumpBatchDtime.

hasFeasibilityJumpVarRandomizationProbability

boolean hasFeasibilityJumpVarRandomizationProbability()

Probability for a variable to have a non default value upon restarts or
perturbations.

optional double feasibility_jump_var_randomization_probability = 247 [default = 0.05];

Returns:

Whether the feasibilityJumpVarRandomizationProbability field is set.

getFeasibilityJumpVarRandomizationProbability

double getFeasibilityJumpVarRandomizationProbability()

Probability for a variable to have a non default value upon restarts or
perturbations.

optional double feasibility_jump_var_randomization_probability = 247 [default = 0.05];

Returns:

The feasibilityJumpVarRandomizationProbability.

hasFeasibilityJumpVarPerburbationRangeRatio

boolean hasFeasibilityJumpVarPerburbationRangeRatio()

Max distance between the default value and the pertubated value relative to
the range of the domain of the variable.

optional double feasibility_jump_var_perburbation_range_ratio = 248 [default = 0.2];

Returns:

Whether the feasibilityJumpVarPerburbationRangeRatio field is set.

getFeasibilityJumpVarPerburbationRangeRatio

double getFeasibilityJumpVarPerburbationRangeRatio()

Max distance between the default value and the pertubated value relative to
the range of the domain of the variable.

optional double feasibility_jump_var_perburbation_range_ratio = 248 [default = 0.2];

Returns:

The feasibilityJumpVarPerburbationRangeRatio.

hasFeasibilityJumpEnableRestarts

boolean hasFeasibilityJumpEnableRestarts()

When stagnating, feasibility jump will either restart from a default
solution (with some possible randomization), or randomly pertubate the
current solution. This parameter selects the first option.

optional bool feasibility_jump_enable_restarts = 250 [default = true];

Returns:

Whether the feasibilityJumpEnableRestarts field is set.

getFeasibilityJumpEnableRestarts

boolean getFeasibilityJumpEnableRestarts()

When stagnating, feasibility jump will either restart from a default
solution (with some possible randomization), or randomly pertubate the
current solution. This parameter selects the first option.

optional bool feasibility_jump_enable_restarts = 250 [default = true];

Returns:

The feasibilityJumpEnableRestarts.

hasFeasibilityJumpMaxExpandedConstraintSize

boolean hasFeasibilityJumpMaxExpandedConstraintSize()

Maximum size of no_overlap or no_overlap_2d constraint for a quadratic
expansion. This might look a lot, but by expanding such constraint, we get
a linear time evaluation per single variable moves instead of a slow O(n
log n) one.

optional int32 feasibility_jump_max_expanded_constraint_size = 264 [default = 500];

Returns:

Whether the feasibilityJumpMaxExpandedConstraintSize field is set.

getFeasibilityJumpMaxExpandedConstraintSize

int getFeasibilityJumpMaxExpandedConstraintSize()

Maximum size of no_overlap or no_overlap_2d constraint for a quadratic
expansion. This might look a lot, but by expanding such constraint, we get
a linear time evaluation per single variable moves instead of a slow O(n
log n) one.

optional int32 feasibility_jump_max_expanded_constraint_size = 264 [default = 500];

Returns:

The feasibilityJumpMaxExpandedConstraintSize.

hasNumViolationLs

boolean hasNumViolationLs()

This will create incomplete subsolvers (that are not LNS subsolvers)
that use the feasibility jump code to find improving solution, treating
the objective improvement as a hard constraint.

optional int32 num_violation_ls = 244 [default = 0];

Returns:

Whether the numViolationLs field is set.

getNumViolationLs

int getNumViolationLs()

This will create incomplete subsolvers (that are not LNS subsolvers)
that use the feasibility jump code to find improving solution, treating
the objective improvement as a hard constraint.

optional int32 num_violation_ls = 244 [default = 0];

Returns:

The numViolationLs.

hasViolationLsPerturbationPeriod

boolean hasViolationLsPerturbationPeriod()

How long violation_ls should wait before perturbating a solution.

optional int32 violation_ls_perturbation_period = 249 [default = 100];

Returns:

Whether the violationLsPerturbationPeriod field is set.

getViolationLsPerturbationPeriod

int getViolationLsPerturbationPeriod()

How long violation_ls should wait before perturbating a solution.

optional int32 violation_ls_perturbation_period = 249 [default = 100];

Returns:

The violationLsPerturbationPeriod.

hasViolationLsCompoundMoveProbability

boolean hasViolationLsCompoundMoveProbability()

Probability of using compound move search each restart.
TODO(user): Add reference to paper when published.

optional double violation_ls_compound_move_probability = 259 [default = 0.5];

Returns:

Whether the violationLsCompoundMoveProbability field is set.

getViolationLsCompoundMoveProbability

double getViolationLsCompoundMoveProbability()

Probability of using compound move search each restart.
TODO(user): Add reference to paper when published.

optional double violation_ls_compound_move_probability = 259 [default = 0.5];

Returns:

The violationLsCompoundMoveProbability.

hasSharedTreeNumWorkers

boolean hasSharedTreeNumWorkers()

Enables shared tree search.
If positive, start this many complete worker threads to explore a shared
search tree. These workers communicate objective bounds and simple decision
nogoods relating to the shared prefix of the tree, and will avoid exploring
the same subtrees as one another.
Specifying a negative number uses a heuristic to select an appropriate
number of shared tree workeres based on the total number of workers.

optional int32 shared_tree_num_workers = 235 [default = -1];

Returns:

Whether the sharedTreeNumWorkers field is set.

getSharedTreeNumWorkers

int getSharedTreeNumWorkers()

Enables shared tree search.
If positive, start this many complete worker threads to explore a shared
search tree. These workers communicate objective bounds and simple decision
nogoods relating to the shared prefix of the tree, and will avoid exploring
the same subtrees as one another.
Specifying a negative number uses a heuristic to select an appropriate
number of shared tree workeres based on the total number of workers.

optional int32 shared_tree_num_workers = 235 [default = -1];

Returns:

The sharedTreeNumWorkers.

hasUseSharedTreeSearch

boolean hasUseSharedTreeSearch()

Set on shared subtree workers. Users should not set this directly.

optional bool use_shared_tree_search = 236 [default = false];

Returns:

Whether the useSharedTreeSearch field is set.

getUseSharedTreeSearch

boolean getUseSharedTreeSearch()

Set on shared subtree workers. Users should not set this directly.

optional bool use_shared_tree_search = 236 [default = false];

Returns:

The useSharedTreeSearch.

hasSharedTreeWorkerMinRestartsPerSubtree

boolean hasSharedTreeWorkerMinRestartsPerSubtree()

Minimum restarts before a worker will replace a subtree
that looks "bad" based on the average LBD of learned clauses.

optional int32 shared_tree_worker_min_restarts_per_subtree = 282 [default = 1];

Returns:

Whether the sharedTreeWorkerMinRestartsPerSubtree field is set.

getSharedTreeWorkerMinRestartsPerSubtree

int getSharedTreeWorkerMinRestartsPerSubtree()

Minimum restarts before a worker will replace a subtree
that looks "bad" based on the average LBD of learned clauses.

optional int32 shared_tree_worker_min_restarts_per_subtree = 282 [default = 1];

Returns:

The sharedTreeWorkerMinRestartsPerSubtree.

hasSharedTreeWorkerEnableTrailSharing

boolean hasSharedTreeWorkerEnableTrailSharing()

If true, workers share more of the information from their local trail.
Specifically, literals implied by the shared tree decisions.

optional bool shared_tree_worker_enable_trail_sharing = 295 [default = true];

Returns:

Whether the sharedTreeWorkerEnableTrailSharing field is set.

getSharedTreeWorkerEnableTrailSharing

boolean getSharedTreeWorkerEnableTrailSharing()

If true, workers share more of the information from their local trail.
Specifically, literals implied by the shared tree decisions.

optional bool shared_tree_worker_enable_trail_sharing = 295 [default = true];

Returns:

The sharedTreeWorkerEnableTrailSharing.

hasSharedTreeWorkerEnablePhaseSharing

boolean hasSharedTreeWorkerEnablePhaseSharing()

If true, shared tree workers share their target phase when returning an
assigned subtree for the next worker to use.

optional bool shared_tree_worker_enable_phase_sharing = 304 [default = true];

Returns:

Whether the sharedTreeWorkerEnablePhaseSharing field is set.

getSharedTreeWorkerEnablePhaseSharing

boolean getSharedTreeWorkerEnablePhaseSharing()

If true, shared tree workers share their target phase when returning an
assigned subtree for the next worker to use.

optional bool shared_tree_worker_enable_phase_sharing = 304 [default = true];

Returns:

The sharedTreeWorkerEnablePhaseSharing.

hasSharedTreeOpenLeavesPerWorker

boolean hasSharedTreeOpenLeavesPerWorker()

How many open leaf nodes should the shared tree maintain per worker.

optional double shared_tree_open_leaves_per_worker = 281 [default = 2];

Returns:

Whether the sharedTreeOpenLeavesPerWorker field is set.

getSharedTreeOpenLeavesPerWorker

double getSharedTreeOpenLeavesPerWorker()

How many open leaf nodes should the shared tree maintain per worker.

optional double shared_tree_open_leaves_per_worker = 281 [default = 2];

Returns:

The sharedTreeOpenLeavesPerWorker.

hasSharedTreeMaxNodesPerWorker

boolean hasSharedTreeMaxNodesPerWorker()

In order to limit total shared memory and communication overhead, limit the
total number of nodes that may be generated in the shared tree. If the
shared tree runs out of unassigned leaves, workers act as portfolio
workers. Note: this limit includes interior nodes, not just leaves.

optional int32 shared_tree_max_nodes_per_worker = 238 [default = 10000];

Returns:

Whether the sharedTreeMaxNodesPerWorker field is set.

getSharedTreeMaxNodesPerWorker

int getSharedTreeMaxNodesPerWorker()

In order to limit total shared memory and communication overhead, limit the
total number of nodes that may be generated in the shared tree. If the
shared tree runs out of unassigned leaves, workers act as portfolio
workers. Note: this limit includes interior nodes, not just leaves.

optional int32 shared_tree_max_nodes_per_worker = 238 [default = 10000];

Returns:

The sharedTreeMaxNodesPerWorker.

hasSharedTreeSplitStrategy

boolean hasSharedTreeSplitStrategy()

optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];

Returns:

Whether the sharedTreeSplitStrategy field is set.

getSharedTreeSplitStrategy

SatParameters.SharedTreeSplitStrategy getSharedTreeSplitStrategy()

optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];

Returns:

The sharedTreeSplitStrategy.

hasSharedTreeBalanceTolerance

boolean hasSharedTreeBalanceTolerance()

How much deeper compared to the ideal max depth of the tree is considered
"balanced" enough to still accept a split. Without such a tolerance,
sometimes the tree can only be split by a single worker, and they may not
generate a split for some time. In contrast, with a tolerance of 1, at
least half of all workers should be able to split the tree as soon as a
split becomes required. This only has an effect on
SPLIT_STRATEGY_BALANCED_TREE and SPLIT_STRATEGY_DISCREPANCY.

optional int32 shared_tree_balance_tolerance = 305 [default = 1];

Returns:

Whether the sharedTreeBalanceTolerance field is set.

getSharedTreeBalanceTolerance

int getSharedTreeBalanceTolerance()

How much deeper compared to the ideal max depth of the tree is considered
"balanced" enough to still accept a split. Without such a tolerance,
sometimes the tree can only be split by a single worker, and they may not
generate a split for some time. In contrast, with a tolerance of 1, at
least half of all workers should be able to split the tree as soon as a
split becomes required. This only has an effect on
SPLIT_STRATEGY_BALANCED_TREE and SPLIT_STRATEGY_DISCREPANCY.

optional int32 shared_tree_balance_tolerance = 305 [default = 1];

Returns:

The sharedTreeBalanceTolerance.

hasSharedTreeSplitMinDtime

boolean hasSharedTreeSplitMinDtime()

How much dtime a worker will wait between proposing splits.
This limits the contention in splitting the shared tree, and also reduces
the number of too-easy subtrees that are generates.

optional double shared_tree_split_min_dtime = 328 [default = 0.1];

Returns:

Whether the sharedTreeSplitMinDtime field is set.

getSharedTreeSplitMinDtime

double getSharedTreeSplitMinDtime()

How much dtime a worker will wait between proposing splits.
This limits the contention in splitting the shared tree, and also reduces
the number of too-easy subtrees that are generates.

optional double shared_tree_split_min_dtime = 328 [default = 0.1];

Returns:

The sharedTreeSplitMinDtime.

hasEnumerateAllSolutions

boolean hasEnumerateAllSolutions()

Whether we enumerate all solutions of a problem without objective.

WARNING:
- This can be used with num_workers > 1 but then each solutions can be
found more than once, so it is up to the client to deduplicate them.
- If keep_all_feasible_solutions_in_presolve is unset, we will set it to
true as otherwise, many feasible solution can just be removed by the
presolve. It is still possible to manually set this to false if one only
wants to enumerate all solutions of the presolved model.

optional bool enumerate_all_solutions = 87 [default = false];

Returns:

Whether the enumerateAllSolutions field is set.

getEnumerateAllSolutions

boolean getEnumerateAllSolutions()

Whether we enumerate all solutions of a problem without objective.

WARNING:
- This can be used with num_workers > 1 but then each solutions can be
found more than once, so it is up to the client to deduplicate them.
- If keep_all_feasible_solutions_in_presolve is unset, we will set it to
true as otherwise, many feasible solution can just be removed by the
presolve. It is still possible to manually set this to false if one only
wants to enumerate all solutions of the presolved model.

optional bool enumerate_all_solutions = 87 [default = false];

Returns:

The enumerateAllSolutions.

hasKeepAllFeasibleSolutionsInPresolve

boolean hasKeepAllFeasibleSolutionsInPresolve()

If true, we disable the presolve reductions that remove feasible solutions
from the search space. Such solution are usually dominated by a "better"
solution that is kept, but depending on the situation, we might want to
keep all solutions.

A trivial example is when a variable is unused. If this is true, then the
presolve will not fix it to an arbitrary value and it will stay in the
search space.

optional bool keep_all_feasible_solutions_in_presolve = 173 [default = false];

Returns:

Whether the keepAllFeasibleSolutionsInPresolve field is set.

getKeepAllFeasibleSolutionsInPresolve

boolean getKeepAllFeasibleSolutionsInPresolve()

If true, we disable the presolve reductions that remove feasible solutions
from the search space. Such solution are usually dominated by a "better"
solution that is kept, but depending on the situation, we might want to
keep all solutions.

A trivial example is when a variable is unused. If this is true, then the
presolve will not fix it to an arbitrary value and it will stay in the
search space.

optional bool keep_all_feasible_solutions_in_presolve = 173 [default = false];

Returns:

The keepAllFeasibleSolutionsInPresolve.

hasFillTightenedDomainsInResponse

boolean hasFillTightenedDomainsInResponse()

If true, add information about the derived variable domains to the
CpSolverResponse. It is an option because it makes the response slighly
bigger and there is a bit more work involved during the postsolve to
construct it, but it should still have a low overhead. See the
tightened_variables field in CpSolverResponse for more details.

optional bool fill_tightened_domains_in_response = 132 [default = false];

Returns:

Whether the fillTightenedDomainsInResponse field is set.

getFillTightenedDomainsInResponse

boolean getFillTightenedDomainsInResponse()

If true, add information about the derived variable domains to the
CpSolverResponse. It is an option because it makes the response slighly
bigger and there is a bit more work involved during the postsolve to
construct it, but it should still have a low overhead. See the
tightened_variables field in CpSolverResponse for more details.

optional bool fill_tightened_domains_in_response = 132 [default = false];

Returns:

The fillTightenedDomainsInResponse.

hasFillAdditionalSolutionsInResponse

boolean hasFillAdditionalSolutionsInResponse()

If true, the final response addition_solutions field will be filled with
all solutions from our solutions pool.

Note that if both this field and enumerate_all_solutions is true, we will
copy to the pool all of the solution found. So if solution_pool_size is big
enough, you can get all solutions this way instead of using the solution
callback.

Note that this only affect the "final" solution, not the one passed to the
solution callbacks.

optional bool fill_additional_solutions_in_response = 194 [default = false];

Returns:

Whether the fillAdditionalSolutionsInResponse field is set.

getFillAdditionalSolutionsInResponse

boolean getFillAdditionalSolutionsInResponse()

If true, the final response addition_solutions field will be filled with
all solutions from our solutions pool.

Note that if both this field and enumerate_all_solutions is true, we will
copy to the pool all of the solution found. So if solution_pool_size is big
enough, you can get all solutions this way instead of using the solution
callback.

Note that this only affect the "final" solution, not the one passed to the
solution callbacks.

optional bool fill_additional_solutions_in_response = 194 [default = false];

Returns:

The fillAdditionalSolutionsInResponse.

hasInstantiateAllVariables

boolean hasInstantiateAllVariables()

If true, the solver will add a default integer branching strategy to the
already defined search strategy. If not, some variable might still not be
fixed at the end of the search. For now we assume these variable can just
be set to their lower bound.

optional bool instantiate_all_variables = 106 [default = true];

Returns:

Whether the instantiateAllVariables field is set.

getInstantiateAllVariables

boolean getInstantiateAllVariables()

If true, the solver will add a default integer branching strategy to the
already defined search strategy. If not, some variable might still not be
fixed at the end of the search. For now we assume these variable can just
be set to their lower bound.

optional bool instantiate_all_variables = 106 [default = true];

Returns:

The instantiateAllVariables.

hasAutoDetectGreaterThanAtLeastOneOf

boolean hasAutoDetectGreaterThanAtLeastOneOf()

If true, then the precedences propagator try to detect for each variable if
it has a set of "optional incoming arc" for which at least one of them is
present. This is usually useful to have but can be slow on model with a lot
of precedence.

optional bool auto_detect_greater_than_at_least_one_of = 95 [default = true];

Returns:

Whether the autoDetectGreaterThanAtLeastOneOf field is set.

getAutoDetectGreaterThanAtLeastOneOf

boolean getAutoDetectGreaterThanAtLeastOneOf()

If true, then the precedences propagator try to detect for each variable if
it has a set of "optional incoming arc" for which at least one of them is
present. This is usually useful to have but can be slow on model with a lot
of precedence.

optional bool auto_detect_greater_than_at_least_one_of = 95 [default = true];

Returns:

The autoDetectGreaterThanAtLeastOneOf.

hasStopAfterFirstSolution

boolean hasStopAfterFirstSolution()

For an optimization problem, stop the solver as soon as we have a solution.

optional bool stop_after_first_solution = 98 [default = false];

Returns:

Whether the stopAfterFirstSolution field is set.

getStopAfterFirstSolution

boolean getStopAfterFirstSolution()

For an optimization problem, stop the solver as soon as we have a solution.

optional bool stop_after_first_solution = 98 [default = false];

Returns:

The stopAfterFirstSolution.

hasStopAfterPresolve

boolean hasStopAfterPresolve()

Mainly used when improving the presolver. When true, stops the solver after
the presolve is complete (or after loading and root level propagation).

optional bool stop_after_presolve = 149 [default = false];

Returns:

Whether the stopAfterPresolve field is set.

getStopAfterPresolve

boolean getStopAfterPresolve()

Mainly used when improving the presolver. When true, stops the solver after
the presolve is complete (or after loading and root level propagation).

optional bool stop_after_presolve = 149 [default = false];

Returns:

The stopAfterPresolve.

hasStopAfterRootPropagation

boolean hasStopAfterRootPropagation()

optional bool stop_after_root_propagation = 252 [default = false];

Returns:

Whether the stopAfterRootPropagation field is set.

getStopAfterRootPropagation

boolean getStopAfterRootPropagation()

optional bool stop_after_root_propagation = 252 [default = false];

Returns:

The stopAfterRootPropagation.

hasLnsInitialDifficulty

boolean hasLnsInitialDifficulty()

Initial parameters for neighborhood generation.

optional double lns_initial_difficulty = 307 [default = 0.5];

Returns:

Whether the lnsInitialDifficulty field is set.

getLnsInitialDifficulty

double getLnsInitialDifficulty()

Initial parameters for neighborhood generation.

optional double lns_initial_difficulty = 307 [default = 0.5];

Returns:

The lnsInitialDifficulty.

hasLnsInitialDeterministicLimit

boolean hasLnsInitialDeterministicLimit()

optional double lns_initial_deterministic_limit = 308 [default = 0.1];

Returns:

Whether the lnsInitialDeterministicLimit field is set.

getLnsInitialDeterministicLimit

double getLnsInitialDeterministicLimit()

optional double lns_initial_deterministic_limit = 308 [default = 0.1];

Returns:

The lnsInitialDeterministicLimit.

hasUseLns

boolean hasUseLns()

Testing parameters used to disable all lns workers.

optional bool use_lns = 283 [default = true];

Returns:

Whether the useLns field is set.

getUseLns

boolean getUseLns()

Testing parameters used to disable all lns workers.

optional bool use_lns = 283 [default = true];

Returns:

The useLns.

hasUseLnsOnly

boolean hasUseLnsOnly()

Experimental parameters to disable everything but lns.

optional bool use_lns_only = 101 [default = false];

Returns:

Whether the useLnsOnly field is set.

getUseLnsOnly

boolean getUseLnsOnly()

Experimental parameters to disable everything but lns.

optional bool use_lns_only = 101 [default = false];

Returns:

The useLnsOnly.

hasSolutionPoolSize

boolean hasSolutionPoolSize()

Size of the top-n different solutions kept by the solver.
This parameter must be > 0. Currently, having this larger than one mainly
impact the "base" solution chosen for a LNS/LS fragment.

optional int32 solution_pool_size = 193 [default = 3];

Returns:

Whether the solutionPoolSize field is set.

getSolutionPoolSize

int getSolutionPoolSize()

Size of the top-n different solutions kept by the solver.
This parameter must be > 0. Currently, having this larger than one mainly
impact the "base" solution chosen for a LNS/LS fragment.

optional int32 solution_pool_size = 193 [default = 3];

Returns:

The solutionPoolSize.

hasSolutionPoolDiversityLimit

boolean hasSolutionPoolDiversityLimit()

If solution_pool_size is <= this, we will use DP to keep a "diverse" set
of solutions (the one further apart via hamming distance) in the pool.
Setting this to large value might be slow, especially if your solution are
large.

optional int32 solution_pool_diversity_limit = 329 [default = 10];

Returns:

Whether the solutionPoolDiversityLimit field is set.

getSolutionPoolDiversityLimit

int getSolutionPoolDiversityLimit()

If solution_pool_size is <= this, we will use DP to keep a "diverse" set
of solutions (the one further apart via hamming distance) in the pool.
Setting this to large value might be slow, especially if your solution are
large.

optional int32 solution_pool_diversity_limit = 329 [default = 10];

Returns:

The solutionPoolDiversityLimit.

hasAlternativePoolSize

boolean hasAlternativePoolSize()

In order to not get stuck in local optima, when this is non-zero, we try to
also work on "older" solutions with a worse objective value so we get a
chance to follow a different LS/LNS trajectory.

optional int32 alternative_pool_size = 325 [default = 1];

Returns:

Whether the alternativePoolSize field is set.

getAlternativePoolSize

int getAlternativePoolSize()

In order to not get stuck in local optima, when this is non-zero, we try to
also work on "older" solutions with a worse objective value so we get a
chance to follow a different LS/LNS trajectory.

optional int32 alternative_pool_size = 325 [default = 1];

Returns:

The alternativePoolSize.

hasUseRinsLns

boolean hasUseRinsLns()

Turns on relaxation induced neighborhood generator.

optional bool use_rins_lns = 129 [default = true];

Returns:

Whether the useRinsLns field is set.

getUseRinsLns

boolean getUseRinsLns()

Turns on relaxation induced neighborhood generator.

optional bool use_rins_lns = 129 [default = true];

Returns:

The useRinsLns.

hasUseFeasibilityPump

boolean hasUseFeasibilityPump()

Adds a feasibility pump subsolver along with lns subsolvers.

optional bool use_feasibility_pump = 164 [default = true];

Returns:

Whether the useFeasibilityPump field is set.

getUseFeasibilityPump

boolean getUseFeasibilityPump()

Adds a feasibility pump subsolver along with lns subsolvers.

optional bool use_feasibility_pump = 164 [default = true];

Returns:

The useFeasibilityPump.

hasUseLbRelaxLns

boolean hasUseLbRelaxLns()

Turns on neighborhood generator based on local branching LP. Based on Huang
et al., "Local Branching Relaxation Heuristics for Integer Linear
Programs", 2023.

optional bool use_lb_relax_lns = 255 [default = true];

Returns:

Whether the useLbRelaxLns field is set.

getUseLbRelaxLns

boolean getUseLbRelaxLns()

Turns on neighborhood generator based on local branching LP. Based on Huang
et al., "Local Branching Relaxation Heuristics for Integer Linear
Programs", 2023.

optional bool use_lb_relax_lns = 255 [default = true];

Returns:

The useLbRelaxLns.

hasLbRelaxNumWorkersThreshold

boolean hasLbRelaxNumWorkersThreshold()

Only use lb-relax if we have at least that many workers.

optional int32 lb_relax_num_workers_threshold = 296 [default = 16];

Returns:

Whether the lbRelaxNumWorkersThreshold field is set.

getLbRelaxNumWorkersThreshold

int getLbRelaxNumWorkersThreshold()

Only use lb-relax if we have at least that many workers.

optional int32 lb_relax_num_workers_threshold = 296 [default = 16];

Returns:

The lbRelaxNumWorkersThreshold.

hasFpRounding

boolean hasFpRounding()

optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];

Returns:

Whether the fpRounding field is set.

getFpRounding

SatParameters.FPRoundingMethod getFpRounding()

optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];

Returns:

The fpRounding.

hasDiversifyLnsParams

boolean hasDiversifyLnsParams()

If true, registers more lns subsolvers with different parameters.

optional bool diversify_lns_params = 137 [default = false];

Returns:

Whether the diversifyLnsParams field is set.

getDiversifyLnsParams

boolean getDiversifyLnsParams()

If true, registers more lns subsolvers with different parameters.

optional bool diversify_lns_params = 137 [default = false];

Returns:

The diversifyLnsParams.

hasRandomizeSearch

boolean hasRandomizeSearch()

Randomize fixed search.

optional bool randomize_search = 103 [default = false];

Returns:

Whether the randomizeSearch field is set.

getRandomizeSearch

boolean getRandomizeSearch()

Randomize fixed search.

optional bool randomize_search = 103 [default = false];

Returns:

The randomizeSearch.

hasSearchRandomVariablePoolSize

boolean hasSearchRandomVariablePoolSize()

Search randomization will collect the top
'search_random_variable_pool_size' valued variables, and pick one randomly.
The value of the variable is specific to each strategy.

optional int64 search_random_variable_pool_size = 104 [default = 0];

Returns:

Whether the searchRandomVariablePoolSize field is set.

getSearchRandomVariablePoolSize

long getSearchRandomVariablePoolSize()

Search randomization will collect the top
'search_random_variable_pool_size' valued variables, and pick one randomly.
The value of the variable is specific to each strategy.

optional int64 search_random_variable_pool_size = 104 [default = 0];

Returns:

The searchRandomVariablePoolSize.

hasPushAllTasksTowardStart

boolean hasPushAllTasksTowardStart()

Experimental code: specify if the objective pushes all tasks toward the
start of the schedule.

optional bool push_all_tasks_toward_start = 262 [default = false];

Returns:

Whether the pushAllTasksTowardStart field is set.

getPushAllTasksTowardStart

boolean getPushAllTasksTowardStart()

Experimental code: specify if the objective pushes all tasks toward the
start of the schedule.

optional bool push_all_tasks_toward_start = 262 [default = false];

Returns:

The pushAllTasksTowardStart.

hasUseOptionalVariables

boolean hasUseOptionalVariables()

If true, we automatically detect variables whose constraint are always
enforced by the same literal and we mark them as optional. This allows
to propagate them as if they were present in some situation.

TODO(user): This is experimental and seems to lead to wrong optimal in
some situation. It should however gives correct solutions. Fix.

optional bool use_optional_variables = 108 [default = false];

Returns:

Whether the useOptionalVariables field is set.

getUseOptionalVariables

boolean getUseOptionalVariables()

If true, we automatically detect variables whose constraint are always
enforced by the same literal and we mark them as optional. This allows
to propagate them as if they were present in some situation.

TODO(user): This is experimental and seems to lead to wrong optimal in
some situation. It should however gives correct solutions. Fix.

optional bool use_optional_variables = 108 [default = false];

Returns:

The useOptionalVariables.

hasUseExactLpReason

boolean hasUseExactLpReason()

The solver usually exploit the LP relaxation of a model. If this option is
true, then whatever is infered by the LP will be used like an heuristic to
compute EXACT propagation on the IP. So with this option, there is no
numerical imprecision issues.

optional bool use_exact_lp_reason = 109 [default = true];

Returns:

Whether the useExactLpReason field is set.

getUseExactLpReason

boolean getUseExactLpReason()

The solver usually exploit the LP relaxation of a model. If this option is
true, then whatever is infered by the LP will be used like an heuristic to
compute EXACT propagation on the IP. So with this option, there is no
numerical imprecision issues.

optional bool use_exact_lp_reason = 109 [default = true];

Returns:

The useExactLpReason.

hasUseCombinedNoOverlap

boolean hasUseCombinedNoOverlap()

This can be beneficial if there is a lot of no-overlap constraints but a
relatively low number of different intervals in the problem. Like 1000
intervals, but 1M intervals in the no-overlap constraints covering them.

optional bool use_combined_no_overlap = 133 [default = false];

Returns:

Whether the useCombinedNoOverlap field is set.

getUseCombinedNoOverlap

boolean getUseCombinedNoOverlap()

This can be beneficial if there is a lot of no-overlap constraints but a
relatively low number of different intervals in the problem. Like 1000
intervals, but 1M intervals in the no-overlap constraints covering them.

optional bool use_combined_no_overlap = 133 [default = false];

Returns:

The useCombinedNoOverlap.

hasAtMostOneMaxExpansionSize

boolean hasAtMostOneMaxExpansionSize()

All at_most_one constraints with a size <= param will be replaced by a
quadratic number of binary implications.

optional int32 at_most_one_max_expansion_size = 270 [default = 3];

Returns:

Whether the atMostOneMaxExpansionSize field is set.

getAtMostOneMaxExpansionSize

int getAtMostOneMaxExpansionSize()

All at_most_one constraints with a size <= param will be replaced by a
quadratic number of binary implications.

optional int32 at_most_one_max_expansion_size = 270 [default = 3];

Returns:

The atMostOneMaxExpansionSize.

hasCatchSigintSignal

boolean hasCatchSigintSignal()

Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals
when calling solve. If set, catching the SIGINT signal will terminate the
search gracefully, as if a time limit was reached.

optional bool catch_sigint_signal = 135 [default = true];

Returns:

Whether the catchSigintSignal field is set.

getCatchSigintSignal

boolean getCatchSigintSignal()

Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals
when calling solve. If set, catching the SIGINT signal will terminate the
search gracefully, as if a time limit was reached.

optional bool catch_sigint_signal = 135 [default = true];

Returns:

The catchSigintSignal.

hasUseImpliedBounds

boolean hasUseImpliedBounds()

Stores and exploits "implied-bounds" in the solver. That is, relations of
the form literal => (var >= bound). This is currently used to derive
stronger cuts.

optional bool use_implied_bounds = 144 [default = true];

Returns:

Whether the useImpliedBounds field is set.

getUseImpliedBounds

boolean getUseImpliedBounds()

Stores and exploits "implied-bounds" in the solver. That is, relations of
the form literal => (var >= bound). This is currently used to derive
stronger cuts.

optional bool use_implied_bounds = 144 [default = true];

Returns:

The useImpliedBounds.

hasPolishLpSolution

boolean hasPolishLpSolution()

Whether we try to do a few degenerate iteration at the end of an LP solve
to minimize the fractionality of the integer variable in the basis. This
helps on some problems, but not so much on others. It also cost of bit of
time to do such polish step.

optional bool polish_lp_solution = 175 [default = false];

Returns:

Whether the polishLpSolution field is set.

getPolishLpSolution

boolean getPolishLpSolution()

Whether we try to do a few degenerate iteration at the end of an LP solve
to minimize the fractionality of the integer variable in the basis. This
helps on some problems, but not so much on others. It also cost of bit of
time to do such polish step.

optional bool polish_lp_solution = 175 [default = false];

Returns:

The polishLpSolution.

hasLpPrimalTolerance

boolean hasLpPrimalTolerance()

The internal LP tolerances used by CP-SAT. These applies to the internal
and scaled problem. If the domains of your variables are large it might be
good to use lower tolerances. If your problem is binary with low
coefficients, it might be good to use higher ones to speed-up the lp
solves.

optional double lp_primal_tolerance = 266 [default = 1e-07];

Returns:

Whether the lpPrimalTolerance field is set.

getLpPrimalTolerance

double getLpPrimalTolerance()

The internal LP tolerances used by CP-SAT. These applies to the internal
and scaled problem. If the domains of your variables are large it might be
good to use lower tolerances. If your problem is binary with low
coefficients, it might be good to use higher ones to speed-up the lp
solves.

optional double lp_primal_tolerance = 266 [default = 1e-07];

Returns:

The lpPrimalTolerance.

hasLpDualTolerance

boolean hasLpDualTolerance()

optional double lp_dual_tolerance = 267 [default = 1e-07];

Returns:

Whether the lpDualTolerance field is set.

getLpDualTolerance

double getLpDualTolerance()

optional double lp_dual_tolerance = 267 [default = 1e-07];

Returns:

The lpDualTolerance.

hasConvertIntervals

boolean hasConvertIntervals()

Temporary flag util the feature is more mature. This convert intervals to
the newer proto format that support affine start/var/end instead of just
variables.

optional bool convert_intervals = 177 [default = true];

Returns:

Whether the convertIntervals field is set.

getConvertIntervals

boolean getConvertIntervals()

Temporary flag util the feature is more mature. This convert intervals to
the newer proto format that support affine start/var/end instead of just
variables.

optional bool convert_intervals = 177 [default = true];

Returns:

The convertIntervals.

hasSymmetryLevel

boolean hasSymmetryLevel()

Whether we try to automatically detect the symmetries in a model and
exploit them. Currently, at level 1 we detect them in presolve and try
to fix Booleans. At level 2, we also do some form of dynamic symmetry
breaking during search. At level 3, we also detect symmetries for very
large models, which can be slow. At level 4, we try to break as much
symmetry as possible in presolve.

optional int32 symmetry_level = 183 [default = 2];

Returns:

Whether the symmetryLevel field is set.

getSymmetryLevel

int getSymmetryLevel()

Whether we try to automatically detect the symmetries in a model and
exploit them. Currently, at level 1 we detect them in presolve and try
to fix Booleans. At level 2, we also do some form of dynamic symmetry
breaking during search. At level 3, we also detect symmetries for very
large models, which can be slow. At level 4, we try to break as much
symmetry as possible in presolve.

optional int32 symmetry_level = 183 [default = 2];

Returns:

The symmetryLevel.

hasUseSymmetryInLp

boolean hasUseSymmetryInLp()

When we have symmetry, it is possible to "fold" all variables from the same
orbit into a single variable, while having the same power of LP relaxation.
This can help significantly on symmetric problem. However there is
currently a bit of overhead as the rest of the solver need to do some
translation between the folded LP and the rest of the problem.

optional bool use_symmetry_in_lp = 301 [default = false];

Returns:

Whether the useSymmetryInLp field is set.

getUseSymmetryInLp

boolean getUseSymmetryInLp()

When we have symmetry, it is possible to "fold" all variables from the same
orbit into a single variable, while having the same power of LP relaxation.
This can help significantly on symmetric problem. However there is
currently a bit of overhead as the rest of the solver need to do some
translation between the folded LP and the rest of the problem.

optional bool use_symmetry_in_lp = 301 [default = false];

Returns:

The useSymmetryInLp.

hasKeepSymmetryInPresolve

boolean hasKeepSymmetryInPresolve()

Experimental. This will compute the symmetry of the problem once and for
all. All presolve operations we do should keep the symmetry group intact
or modify it properly. For now we have really little support for this. We
will disable a bunch of presolve operations that could be supported.

optional bool keep_symmetry_in_presolve = 303 [default = false];

Returns:

Whether the keepSymmetryInPresolve field is set.

getKeepSymmetryInPresolve

boolean getKeepSymmetryInPresolve()

Experimental. This will compute the symmetry of the problem once and for
all. All presolve operations we do should keep the symmetry group intact
or modify it properly. For now we have really little support for this. We
will disable a bunch of presolve operations that could be supported.

optional bool keep_symmetry_in_presolve = 303 [default = false];

Returns:

The keepSymmetryInPresolve.

hasSymmetryDetectionDeterministicTimeLimit

boolean hasSymmetryDetectionDeterministicTimeLimit()

Deterministic time limit for symmetry detection.

optional double symmetry_detection_deterministic_time_limit = 302 [default = 1];

Returns:

Whether the symmetryDetectionDeterministicTimeLimit field is set.

getSymmetryDetectionDeterministicTimeLimit

double getSymmetryDetectionDeterministicTimeLimit()

Deterministic time limit for symmetry detection.

optional double symmetry_detection_deterministic_time_limit = 302 [default = 1];

Returns:

The symmetryDetectionDeterministicTimeLimit.

hasNewLinearPropagation

boolean hasNewLinearPropagation()

The new linear propagation code treat all constraints at once and use
an adaptation of Bellman-Ford-Tarjan to propagate constraint in a smarter
order and potentially detect propagation cycle earlier.

optional bool new_linear_propagation = 224 [default = true];

Returns:

Whether the newLinearPropagation field is set.

getNewLinearPropagation

boolean getNewLinearPropagation()

The new linear propagation code treat all constraints at once and use
an adaptation of Bellman-Ford-Tarjan to propagate constraint in a smarter
order and potentially detect propagation cycle earlier.

optional bool new_linear_propagation = 224 [default = true];

Returns:

The newLinearPropagation.

hasLinearSplitSize

boolean hasLinearSplitSize()

Linear constraints that are not pseudo-Boolean and that are longer than
this size will be split into sqrt(size) intermediate sums in order to have
faster propation in the CP engine.

optional int32 linear_split_size = 256 [default = 100];

Returns:

Whether the linearSplitSize field is set.

getLinearSplitSize

int getLinearSplitSize()

Linear constraints that are not pseudo-Boolean and that are longer than
this size will be split into sqrt(size) intermediate sums in order to have
faster propation in the CP engine.

optional int32 linear_split_size = 256 [default = 100];

Returns:

The linearSplitSize.

hasLinearizationLevel

boolean hasLinearizationLevel()

A non-negative level indicating the type of constraints we consider in the
LP relaxation. At level zero, no LP relaxation is used. At level 1, only
the linear constraint and full encoding are added. At level 2, we also add
all the Boolean constraints.

optional int32 linearization_level = 90 [default = 1];

Returns:

Whether the linearizationLevel field is set.

getLinearizationLevel

int getLinearizationLevel()

A non-negative level indicating the type of constraints we consider in the
LP relaxation. At level zero, no LP relaxation is used. At level 1, only
the linear constraint and full encoding are added. At level 2, we also add
all the Boolean constraints.

optional int32 linearization_level = 90 [default = 1];

Returns:

The linearizationLevel.

hasBooleanEncodingLevel

boolean hasBooleanEncodingLevel()

A non-negative level indicating how much we should try to fully encode
Integer variables as Boolean.

optional int32 boolean_encoding_level = 107 [default = 1];

Returns:

Whether the booleanEncodingLevel field is set.

getBooleanEncodingLevel

int getBooleanEncodingLevel()

A non-negative level indicating how much we should try to fully encode
Integer variables as Boolean.

optional int32 boolean_encoding_level = 107 [default = 1];

Returns:

The booleanEncodingLevel.

hasMaxDomainSizeWhenEncodingEqNeqConstraints

boolean hasMaxDomainSizeWhenEncodingEqNeqConstraints()

When loading a*x + b*y ==/!= c when x and y are both fully encoded.
The solver may decide to replace the linear equation by a set of clauses.
This is triggered if the sizes of the domains of x and y are below the
threshold.

optional int32 max_domain_size_when_encoding_eq_neq_constraints = 191 [default = 16];

Returns:

Whether the maxDomainSizeWhenEncodingEqNeqConstraints field is set.

getMaxDomainSizeWhenEncodingEqNeqConstraints

int getMaxDomainSizeWhenEncodingEqNeqConstraints()

When loading a*x + b*y ==/!= c when x and y are both fully encoded.
The solver may decide to replace the linear equation by a set of clauses.
This is triggered if the sizes of the domains of x and y are below the
threshold.

optional int32 max_domain_size_when_encoding_eq_neq_constraints = 191 [default = 16];

Returns:

The maxDomainSizeWhenEncodingEqNeqConstraints.

hasMaxNumCuts

boolean hasMaxNumCuts()

The limit on the number of cuts in our cut pool. When this is reached we do
not generate cuts anymore.

TODO(user): We should probably remove this parameters, and just always
generate cuts but only keep the best n or something.

optional int32 max_num_cuts = 91 [default = 10000];

Returns:

Whether the maxNumCuts field is set.

getMaxNumCuts

int getMaxNumCuts()

The limit on the number of cuts in our cut pool. When this is reached we do
not generate cuts anymore.

TODO(user): We should probably remove this parameters, and just always
generate cuts but only keep the best n or something.

optional int32 max_num_cuts = 91 [default = 10000];

Returns:

The maxNumCuts.

hasCutLevel

boolean hasCutLevel()

Control the global cut effort. Zero will turn off all cut. For now we just
have one level. Note also that most cuts are only used at linearization
level >= 2.

optional int32 cut_level = 196 [default = 1];

Returns:

Whether the cutLevel field is set.

getCutLevel

int getCutLevel()

Control the global cut effort. Zero will turn off all cut. For now we just
have one level. Note also that most cuts are only used at linearization
level >= 2.

optional int32 cut_level = 196 [default = 1];

Returns:

The cutLevel.

hasOnlyAddCutsAtLevelZero

boolean hasOnlyAddCutsAtLevelZero()

For the cut that can be generated at any level, this control if we only
try to generate them at the root node.

optional bool only_add_cuts_at_level_zero = 92 [default = false];

Returns:

Whether the onlyAddCutsAtLevelZero field is set.

getOnlyAddCutsAtLevelZero

boolean getOnlyAddCutsAtLevelZero()

For the cut that can be generated at any level, this control if we only
try to generate them at the root node.

optional bool only_add_cuts_at_level_zero = 92 [default = false];

Returns:

The onlyAddCutsAtLevelZero.

hasAddObjectiveCut

boolean hasAddObjectiveCut()

When the LP objective is fractional, do we add the cut that forces the
linear objective expression to be greater or equal to this fractional value
rounded up? We can always do that since our objective is integer, and
combined with MIR heuristic to reduce the coefficient of such cut, it can
help.

optional bool add_objective_cut = 197 [default = false];

Returns:

Whether the addObjectiveCut field is set.

getAddObjectiveCut

boolean getAddObjectiveCut()

When the LP objective is fractional, do we add the cut that forces the
linear objective expression to be greater or equal to this fractional value
rounded up? We can always do that since our objective is integer, and
combined with MIR heuristic to reduce the coefficient of such cut, it can
help.

optional bool add_objective_cut = 197 [default = false];

Returns:

The addObjectiveCut.

hasAddCgCuts

boolean hasAddCgCuts()

Whether we generate and add Chvatal-Gomory cuts to the LP at root node.
Note that for now, this is not heavily tuned.

optional bool add_cg_cuts = 117 [default = true];

Returns:

Whether the addCgCuts field is set.

getAddCgCuts

boolean getAddCgCuts()

Whether we generate and add Chvatal-Gomory cuts to the LP at root node.
Note that for now, this is not heavily tuned.

optional bool add_cg_cuts = 117 [default = true];

Returns:

The addCgCuts.

hasAddMirCuts

boolean hasAddMirCuts()

Whether we generate MIR cuts at root node.
Note that for now, this is not heavily tuned.

optional bool add_mir_cuts = 120 [default = true];

Returns:

Whether the addMirCuts field is set.

getAddMirCuts

boolean getAddMirCuts()

Whether we generate MIR cuts at root node.
Note that for now, this is not heavily tuned.

optional bool add_mir_cuts = 120 [default = true];

Returns:

The addMirCuts.

hasAddZeroHalfCuts

boolean hasAddZeroHalfCuts()

Whether we generate Zero-Half cuts at root node.
Note that for now, this is not heavily tuned.

optional bool add_zero_half_cuts = 169 [default = true];

Returns:

Whether the addZeroHalfCuts field is set.

getAddZeroHalfCuts

boolean getAddZeroHalfCuts()

Whether we generate Zero-Half cuts at root node.
Note that for now, this is not heavily tuned.

optional bool add_zero_half_cuts = 169 [default = true];

Returns:

The addZeroHalfCuts.

hasAddCliqueCuts

boolean hasAddCliqueCuts()

Whether we generate clique cuts from the binary implication graph. Note
that as the search goes on, this graph will contains new binary clauses
learned by the SAT engine.

optional bool add_clique_cuts = 172 [default = true];

Returns:

Whether the addCliqueCuts field is set.

getAddCliqueCuts

boolean getAddCliqueCuts()

Whether we generate clique cuts from the binary implication graph. Note
that as the search goes on, this graph will contains new binary clauses
learned by the SAT engine.

optional bool add_clique_cuts = 172 [default = true];

Returns:

The addCliqueCuts.

hasAddRltCuts

boolean hasAddRltCuts()

Whether we generate RLT cuts. This is still experimental but can help on
binary problem with a lot of clauses of size 3.

optional bool add_rlt_cuts = 279 [default = true];

Returns:

Whether the addRltCuts field is set.

getAddRltCuts

boolean getAddRltCuts()

Whether we generate RLT cuts. This is still experimental but can help on
binary problem with a lot of clauses of size 3.

optional bool add_rlt_cuts = 279 [default = true];

Returns:

The addRltCuts.

hasMaxAllDiffCutSize

boolean hasMaxAllDiffCutSize()

Cut generator for all diffs can add too many cuts for large all_diff
constraints. This parameter restricts the large all_diff constraints to
have a cut generator.

optional int32 max_all_diff_cut_size = 148 [default = 64];

Returns:

Whether the maxAllDiffCutSize field is set.

getMaxAllDiffCutSize

int getMaxAllDiffCutSize()

Cut generator for all diffs can add too many cuts for large all_diff
constraints. This parameter restricts the large all_diff constraints to
have a cut generator.

optional int32 max_all_diff_cut_size = 148 [default = 64];

Returns:

The maxAllDiffCutSize.

hasAddLinMaxCuts

boolean hasAddLinMaxCuts()

For the lin max constraints, generates the cuts described in "Strong
mixed-integer programming formulations for trained neural networks" by Ross
Anderson et. (https://arxiv.org/pdf/1811.01988.pdf)

optional bool add_lin_max_cuts = 152 [default = true];

Returns:

Whether the addLinMaxCuts field is set.

getAddLinMaxCuts

boolean getAddLinMaxCuts()

For the lin max constraints, generates the cuts described in "Strong
mixed-integer programming formulations for trained neural networks" by Ross
Anderson et. (https://arxiv.org/pdf/1811.01988.pdf)

optional bool add_lin_max_cuts = 152 [default = true];

Returns:

The addLinMaxCuts.

hasMaxIntegerRoundingScaling

boolean hasMaxIntegerRoundingScaling()

In the integer rounding procedure used for MIR and Gomory cut, the maximum
"scaling" we use (must be positive). The lower this is, the lower the
integer coefficients of the cut will be. Note that cut generated by lower
values are not necessarily worse than cut generated by larger value. There
is no strict dominance relationship.

Setting this to 2 result in the "strong fractional rouding" of Letchford
and Lodi.

optional int32 max_integer_rounding_scaling = 119 [default = 600];

Returns:

Whether the maxIntegerRoundingScaling field is set.

getMaxIntegerRoundingScaling

int getMaxIntegerRoundingScaling()

In the integer rounding procedure used for MIR and Gomory cut, the maximum
"scaling" we use (must be positive). The lower this is, the lower the
integer coefficients of the cut will be. Note that cut generated by lower
values are not necessarily worse than cut generated by larger value. There
is no strict dominance relationship.

Setting this to 2 result in the "strong fractional rouding" of Letchford
and Lodi.

optional int32 max_integer_rounding_scaling = 119 [default = 600];

Returns:

The maxIntegerRoundingScaling.

hasAddLpConstraintsLazily

boolean hasAddLpConstraintsLazily()

If true, we start by an empty LP, and only add constraints not satisfied
by the current LP solution batch by batch. A constraint that is only added
like this is known as a "lazy" constraint in the literature, except that we
currently consider all constraints as lazy here.

optional bool add_lp_constraints_lazily = 112 [default = true];

Returns:

Whether the addLpConstraintsLazily field is set.

getAddLpConstraintsLazily

boolean getAddLpConstraintsLazily()

If true, we start by an empty LP, and only add constraints not satisfied
by the current LP solution batch by batch. A constraint that is only added
like this is known as a "lazy" constraint in the literature, except that we
currently consider all constraints as lazy here.

optional bool add_lp_constraints_lazily = 112 [default = true];

Returns:

The addLpConstraintsLazily.

hasRootLpIterations

boolean hasRootLpIterations()

Even at the root node, we do not want to spend too much time on the LP if
it is "difficult". So we solve it in "chunks" of that many iterations. The
solve will be continued down in the tree or the next time we go back to the
root node.

optional int32 root_lp_iterations = 227 [default = 2000];

Returns:

Whether the rootLpIterations field is set.

getRootLpIterations

int getRootLpIterations()

Even at the root node, we do not want to spend too much time on the LP if
it is "difficult". So we solve it in "chunks" of that many iterations. The
solve will be continued down in the tree or the next time we go back to the
root node.

optional int32 root_lp_iterations = 227 [default = 2000];

Returns:

The rootLpIterations.

hasMinOrthogonalityForLpConstraints

boolean hasMinOrthogonalityForLpConstraints()

While adding constraints, skip the constraints which have orthogonality
less than 'min_orthogonality_for_lp_constraints' with already added
constraints during current call. Orthogonality is defined as 1 -
cosine(vector angle between constraints). A value of zero disable this
feature.

optional double min_orthogonality_for_lp_constraints = 115 [default = 0.05];

Returns:

Whether the minOrthogonalityForLpConstraints field is set.

getMinOrthogonalityForLpConstraints

double getMinOrthogonalityForLpConstraints()

While adding constraints, skip the constraints which have orthogonality
less than 'min_orthogonality_for_lp_constraints' with already added
constraints during current call. Orthogonality is defined as 1 -
cosine(vector angle between constraints). A value of zero disable this
feature.

optional double min_orthogonality_for_lp_constraints = 115 [default = 0.05];

Returns:

The minOrthogonalityForLpConstraints.

hasMaxCutRoundsAtLevelZero

boolean hasMaxCutRoundsAtLevelZero()

Max number of time we perform cut generation and resolve the LP at level 0.

optional int32 max_cut_rounds_at_level_zero = 154 [default = 1];

Returns:

Whether the maxCutRoundsAtLevelZero field is set.

getMaxCutRoundsAtLevelZero

int getMaxCutRoundsAtLevelZero()

Max number of time we perform cut generation and resolve the LP at level 0.

optional int32 max_cut_rounds_at_level_zero = 154 [default = 1];

Returns:

The maxCutRoundsAtLevelZero.

hasMaxConsecutiveInactiveCount

boolean hasMaxConsecutiveInactiveCount()

If a constraint/cut in LP is not active for that many consecutive OPTIMAL
solves, remove it from the LP. Note that it might be added again later if
it become violated by the current LP solution.

optional int32 max_consecutive_inactive_count = 121 [default = 100];

Returns:

Whether the maxConsecutiveInactiveCount field is set.

getMaxConsecutiveInactiveCount

int getMaxConsecutiveInactiveCount()

If a constraint/cut in LP is not active for that many consecutive OPTIMAL
solves, remove it from the LP. Note that it might be added again later if
it become violated by the current LP solution.

optional int32 max_consecutive_inactive_count = 121 [default = 100];

Returns:

The maxConsecutiveInactiveCount.

hasCutMaxActiveCountValue

boolean hasCutMaxActiveCountValue()

These parameters are similar to sat clause management activity parameters.
They are effective only if the number of generated cuts exceed the storage
limit. Default values are based on a few experiments on miplib instances.

optional double cut_max_active_count_value = 155 [default = 10000000000];

Returns:

Whether the cutMaxActiveCountValue field is set.

getCutMaxActiveCountValue

double getCutMaxActiveCountValue()

These parameters are similar to sat clause management activity parameters.
They are effective only if the number of generated cuts exceed the storage
limit. Default values are based on a few experiments on miplib instances.

optional double cut_max_active_count_value = 155 [default = 10000000000];

Returns:

The cutMaxActiveCountValue.

hasCutActiveCountDecay

boolean hasCutActiveCountDecay()

optional double cut_active_count_decay = 156 [default = 0.8];

Returns:

Whether the cutActiveCountDecay field is set.

getCutActiveCountDecay

double getCutActiveCountDecay()

optional double cut_active_count_decay = 156 [default = 0.8];

Returns:

The cutActiveCountDecay.

hasCutCleanupTarget

boolean hasCutCleanupTarget()

Target number of constraints to remove during cleanup.

optional int32 cut_cleanup_target = 157 [default = 1000];

Returns:

Whether the cutCleanupTarget field is set.

getCutCleanupTarget

int getCutCleanupTarget()

Target number of constraints to remove during cleanup.

optional int32 cut_cleanup_target = 157 [default = 1000];

Returns:

The cutCleanupTarget.

hasNewConstraintsBatchSize

boolean hasNewConstraintsBatchSize()

Add that many lazy constraints (or cuts) at once in the LP. Note that at
the beginning of the solve, we do add more than this.

optional int32 new_constraints_batch_size = 122 [default = 50];

Returns:

Whether the newConstraintsBatchSize field is set.

getNewConstraintsBatchSize

int getNewConstraintsBatchSize()

Add that many lazy constraints (or cuts) at once in the LP. Note that at
the beginning of the solve, we do add more than this.

optional int32 new_constraints_batch_size = 122 [default = 50];

Returns:

The newConstraintsBatchSize.

hasExploitIntegerLpSolution

boolean hasExploitIntegerLpSolution()

If true and the Lp relaxation of the problem has an integer optimal
solution, try to exploit it. Note that since the LP relaxation may not
contain all the constraints, such a solution is not necessarily a solution
of the full problem.

optional bool exploit_integer_lp_solution = 94 [default = true];

Returns:

Whether the exploitIntegerLpSolution field is set.

getExploitIntegerLpSolution

boolean getExploitIntegerLpSolution()

If true and the Lp relaxation of the problem has an integer optimal
solution, try to exploit it. Note that since the LP relaxation may not
contain all the constraints, such a solution is not necessarily a solution
of the full problem.

optional bool exploit_integer_lp_solution = 94 [default = true];

Returns:

The exploitIntegerLpSolution.

hasExploitAllLpSolution

boolean hasExploitAllLpSolution()

If true and the Lp relaxation of the problem has a solution, try to exploit
it. This is same as above except in this case the lp solution might not be
an integer solution.

optional bool exploit_all_lp_solution = 116 [default = true];

Returns:

Whether the exploitAllLpSolution field is set.

getExploitAllLpSolution

boolean getExploitAllLpSolution()

If true and the Lp relaxation of the problem has a solution, try to exploit
it. This is same as above except in this case the lp solution might not be
an integer solution.

optional bool exploit_all_lp_solution = 116 [default = true];

Returns:

The exploitAllLpSolution.

hasExploitBestSolution

boolean hasExploitBestSolution()

When branching on a variable, follow the last best solution value.

optional bool exploit_best_solution = 130 [default = false];

Returns:

Whether the exploitBestSolution field is set.

getExploitBestSolution

boolean getExploitBestSolution()

When branching on a variable, follow the last best solution value.

optional bool exploit_best_solution = 130 [default = false];

Returns:

The exploitBestSolution.

hasExploitRelaxationSolution

boolean hasExploitRelaxationSolution()

When branching on a variable, follow the last best relaxation solution
value. We use the relaxation with the tightest bound on the objective as
the best relaxation solution.

optional bool exploit_relaxation_solution = 161 [default = false];

Returns:

Whether the exploitRelaxationSolution field is set.

getExploitRelaxationSolution

boolean getExploitRelaxationSolution()

When branching on a variable, follow the last best relaxation solution
value. We use the relaxation with the tightest bound on the objective as
the best relaxation solution.

optional bool exploit_relaxation_solution = 161 [default = false];

Returns:

The exploitRelaxationSolution.

hasExploitObjective

boolean hasExploitObjective()

When branching an a variable that directly affect the objective,
branch on the value that lead to the best objective first.

optional bool exploit_objective = 131 [default = true];

Returns:

Whether the exploitObjective field is set.

getExploitObjective

boolean getExploitObjective()

When branching an a variable that directly affect the objective,
branch on the value that lead to the best objective first.

optional bool exploit_objective = 131 [default = true];

Returns:

The exploitObjective.

hasDetectLinearizedProduct

boolean hasDetectLinearizedProduct()

Infer products of Boolean or of Boolean time IntegerVariable from the
linear constrainst in the problem. This can be used in some cuts, altough
for now we don't really exploit it.

optional bool detect_linearized_product = 277 [default = false];

Returns:

Whether the detectLinearizedProduct field is set.

getDetectLinearizedProduct

boolean getDetectLinearizedProduct()

Infer products of Boolean or of Boolean time IntegerVariable from the
linear constrainst in the problem. This can be used in some cuts, altough
for now we don't really exploit it.

optional bool detect_linearized_product = 277 [default = false];

Returns:

The detectLinearizedProduct.

hasUseNewIntegerConflictResolution

boolean hasUseNewIntegerConflictResolution()

This should be better on integer problems.
But it is still work in progress.

optional bool use_new_integer_conflict_resolution = 336 [default = false];

Returns:

Whether the useNewIntegerConflictResolution field is set.

getUseNewIntegerConflictResolution

boolean getUseNewIntegerConflictResolution()

This should be better on integer problems.
But it is still work in progress.

optional bool use_new_integer_conflict_resolution = 336 [default = false];

Returns:

The useNewIntegerConflictResolution.

hasCreate1UipBooleanDuringIcr

boolean hasCreate1UipBooleanDuringIcr()

If true, and during integer conflict resolution (icr) the 1-UIP is an
integer literal for which we do not have an associated Boolean. Create one.

optional bool create_1uip_boolean_during_icr = 341 [default = true];

Returns:

Whether the create1uipBooleanDuringIcr field is set.

getCreate1UipBooleanDuringIcr

boolean getCreate1UipBooleanDuringIcr()

If true, and during integer conflict resolution (icr) the 1-UIP is an
integer literal for which we do not have an associated Boolean. Create one.

optional bool create_1uip_boolean_during_icr = 341 [default = true];

Returns:

The create1uipBooleanDuringIcr.

hasMipMaxBound

boolean hasMipMaxBound()

We need to bound the maximum magnitude of the variables for CP-SAT, and
that is the bound we use. If the MIP model expect larger variable value in
the solution, then the converted model will likely not be relevant.

optional double mip_max_bound = 124 [default = 10000000];

Returns:

Whether the mipMaxBound field is set.

getMipMaxBound

double getMipMaxBound()

We need to bound the maximum magnitude of the variables for CP-SAT, and
that is the bound we use. If the MIP model expect larger variable value in
the solution, then the converted model will likely not be relevant.

optional double mip_max_bound = 124 [default = 10000000];

Returns:

The mipMaxBound.

hasMipVarScaling

boolean hasMipVarScaling()

All continuous variable of the problem will be multiplied by this factor.
By default, we don't do any variable scaling and rely on the MIP model to
specify continuous variable domain with the wanted precision.

optional double mip_var_scaling = 125 [default = 1];

Returns:

Whether the mipVarScaling field is set.

getMipVarScaling

double getMipVarScaling()

All continuous variable of the problem will be multiplied by this factor.
By default, we don't do any variable scaling and rely on the MIP model to
specify continuous variable domain with the wanted precision.

optional double mip_var_scaling = 125 [default = 1];

Returns:

The mipVarScaling.

hasMipScaleLargeDomain

boolean hasMipScaleLargeDomain()

If this is false, then mip_var_scaling is only applied to variables with
"small" domain. If it is true, we scale all floating point variable
independenlty of their domain.

optional bool mip_scale_large_domain = 225 [default = false];

Returns:

Whether the mipScaleLargeDomain field is set.

getMipScaleLargeDomain

boolean getMipScaleLargeDomain()

If this is false, then mip_var_scaling is only applied to variables with
"small" domain. If it is true, we scale all floating point variable
independenlty of their domain.

optional bool mip_scale_large_domain = 225 [default = false];

Returns:

The mipScaleLargeDomain.

hasMipAutomaticallyScaleVariables

boolean hasMipAutomaticallyScaleVariables()

If true, some continuous variable might be automatically scaled. For now,
this is only the case where we detect that a variable is actually an
integer multiple of a constant. For instance, variables of the form k * 0.5
are quite frequent, and if we detect this, we will scale such variable
domain by 2 to make it implied integer.

optional bool mip_automatically_scale_variables = 166 [default = true];

Returns:

Whether the mipAutomaticallyScaleVariables field is set.

getMipAutomaticallyScaleVariables

boolean getMipAutomaticallyScaleVariables()

If true, some continuous variable might be automatically scaled. For now,
this is only the case where we detect that a variable is actually an
integer multiple of a constant. For instance, variables of the form k * 0.5
are quite frequent, and if we detect this, we will scale such variable
domain by 2 to make it implied integer.

optional bool mip_automatically_scale_variables = 166 [default = true];

Returns:

The mipAutomaticallyScaleVariables.

hasOnlySolveIp

boolean hasOnlySolveIp()

If one try to solve a MIP model with CP-SAT, because we assume all variable
to be integer after scaling, we will not necessarily have the correct
optimal. Note however that all feasible solutions are valid since we will
just solve a more restricted version of the original problem.

This parameters is here to prevent user to think the solution is optimal
when it might not be. One will need to manually set this to false to solve
a MIP model where the optimal might be different.

Note that this is tested after some MIP presolve steps, so even if not
all original variable are integer, we might end up with a pure IP after
presolve and after implied integer detection.

optional bool only_solve_ip = 222 [default = false];

Returns:

Whether the onlySolveIp field is set.

getOnlySolveIp

boolean getOnlySolveIp()

If one try to solve a MIP model with CP-SAT, because we assume all variable
to be integer after scaling, we will not necessarily have the correct
optimal. Note however that all feasible solutions are valid since we will
just solve a more restricted version of the original problem.

This parameters is here to prevent user to think the solution is optimal
when it might not be. One will need to manually set this to false to solve
a MIP model where the optimal might be different.

Note that this is tested after some MIP presolve steps, so even if not
all original variable are integer, we might end up with a pure IP after
presolve and after implied integer detection.

optional bool only_solve_ip = 222 [default = false];

Returns:

The onlySolveIp.

hasMipWantedPrecision

boolean hasMipWantedPrecision()

When scaling constraint with double coefficients to integer coefficients,
we will multiply by a power of 2 and round the coefficients. We will choose
the lowest power such that we have no potential overflow (see
mip_max_activity_exponent) and the worst case constraint activity error
does not exceed this threshold.

Note that we also detect constraint with rational coefficients and scale
them accordingly when it seems better instead of using a power of 2.

We also relax all constraint bounds by this absolute value. For pure
integer constraint, if this value if lower than one, this will not change
anything. However it is needed when scaling MIP problems.

If we manage to scale a constraint correctly, the maximum error we can make
will be twice this value (once for the scaling error and once for the
relaxed bounds). If we are not able to scale that well, we will display
that fact but still scale as best as we can.

optional double mip_wanted_precision = 126 [default = 1e-06];

Returns:

Whether the mipWantedPrecision field is set.

getMipWantedPrecision

double getMipWantedPrecision()

When scaling constraint with double coefficients to integer coefficients,
we will multiply by a power of 2 and round the coefficients. We will choose
the lowest power such that we have no potential overflow (see
mip_max_activity_exponent) and the worst case constraint activity error
does not exceed this threshold.

Note that we also detect constraint with rational coefficients and scale
them accordingly when it seems better instead of using a power of 2.

We also relax all constraint bounds by this absolute value. For pure
integer constraint, if this value if lower than one, this will not change
anything. However it is needed when scaling MIP problems.

If we manage to scale a constraint correctly, the maximum error we can make
will be twice this value (once for the scaling error and once for the
relaxed bounds). If we are not able to scale that well, we will display
that fact but still scale as best as we can.

optional double mip_wanted_precision = 126 [default = 1e-06];

Returns:

The mipWantedPrecision.

hasMipMaxActivityExponent

boolean hasMipMaxActivityExponent()

To avoid integer overflow, we always force the maximum possible constraint
activity (and objective value) according to the initial variable domain to
be smaller than 2 to this given power. Because of this, we cannot always
reach the "mip_wanted_precision" parameter above.

This can go as high as 62, but some internal algo currently abort early if
they might run into integer overflow, so it is better to keep it a bit
lower than this.

optional int32 mip_max_activity_exponent = 127 [default = 53];

Returns:

Whether the mipMaxActivityExponent field is set.

getMipMaxActivityExponent

int getMipMaxActivityExponent()

To avoid integer overflow, we always force the maximum possible constraint
activity (and objective value) according to the initial variable domain to
be smaller than 2 to this given power. Because of this, we cannot always
reach the "mip_wanted_precision" parameter above.

This can go as high as 62, but some internal algo currently abort early if
they might run into integer overflow, so it is better to keep it a bit
lower than this.

optional int32 mip_max_activity_exponent = 127 [default = 53];

Returns:

The mipMaxActivityExponent.

hasMipCheckPrecision

boolean hasMipCheckPrecision()

As explained in mip_precision and mip_max_activity_exponent, we cannot
always reach the wanted precision during scaling. We use this threshold to
enphasize in the logs when the precision seems bad.

optional double mip_check_precision = 128 [default = 0.0001];

Returns:

Whether the mipCheckPrecision field is set.

getMipCheckPrecision

double getMipCheckPrecision()

As explained in mip_precision and mip_max_activity_exponent, we cannot
always reach the wanted precision during scaling. We use this threshold to
enphasize in the logs when the precision seems bad.

optional double mip_check_precision = 128 [default = 0.0001];

Returns:

The mipCheckPrecision.

hasMipComputeTrueObjectiveBound

boolean hasMipComputeTrueObjectiveBound()

Even if we make big error when scaling the objective, we can always derive
a correct lower bound on the original objective by using the exact lower
bound on the scaled integer version of the objective. This should be fast,
but if you don't care about having a precise lower bound, you can turn it
off.

optional bool mip_compute_true_objective_bound = 198 [default = true];

Returns:

Whether the mipComputeTrueObjectiveBound field is set.

getMipComputeTrueObjectiveBound

boolean getMipComputeTrueObjectiveBound()

Even if we make big error when scaling the objective, we can always derive
a correct lower bound on the original objective by using the exact lower
bound on the scaled integer version of the objective. This should be fast,
but if you don't care about having a precise lower bound, you can turn it
off.

optional bool mip_compute_true_objective_bound = 198 [default = true];

Returns:

The mipComputeTrueObjectiveBound.

hasMipMaxValidMagnitude

boolean hasMipMaxValidMagnitude()

Any finite values in the input MIP must be below this threshold, otherwise
the model will be reported invalid. This is needed to avoid floating point
overflow when evaluating bounds * coeff for instance. We are a bit more
defensive, but in practice, users shouldn't use super large values in a
MIP.

optional double mip_max_valid_magnitude = 199 [default = 1e+20];

Returns:

Whether the mipMaxValidMagnitude field is set.

getMipMaxValidMagnitude

double getMipMaxValidMagnitude()

Any finite values in the input MIP must be below this threshold, otherwise
the model will be reported invalid. This is needed to avoid floating point
overflow when evaluating bounds * coeff for instance. We are a bit more
defensive, but in practice, users shouldn't use super large values in a
MIP.

optional double mip_max_valid_magnitude = 199 [default = 1e+20];

Returns:

The mipMaxValidMagnitude.

hasMipTreatHighMagnitudeBoundsAsInfinity

boolean hasMipTreatHighMagnitudeBoundsAsInfinity()

By default, any variable/constraint bound with a finite value and a
magnitude greater than the mip_max_valid_magnitude will result with a
invalid model. This flags change the behavior such that such bounds are
silently transformed to +∞ or -∞.

It is recommended to keep it at false, and create valid bounds.

optional bool mip_treat_high_magnitude_bounds_as_infinity = 278 [default = false];

Returns:

Whether the mipTreatHighMagnitudeBoundsAsInfinity field is set.

getMipTreatHighMagnitudeBoundsAsInfinity

boolean getMipTreatHighMagnitudeBoundsAsInfinity()

By default, any variable/constraint bound with a finite value and a
magnitude greater than the mip_max_valid_magnitude will result with a
invalid model. This flags change the behavior such that such bounds are
silently transformed to +∞ or -∞.

It is recommended to keep it at false, and create valid bounds.

optional bool mip_treat_high_magnitude_bounds_as_infinity = 278 [default = false];

Returns:

The mipTreatHighMagnitudeBoundsAsInfinity.

hasMipDropTolerance

boolean hasMipDropTolerance()

Any value in the input mip with a magnitude lower than this will be set to
zero. This is to avoid some issue in LP presolving.

optional double mip_drop_tolerance = 232 [default = 1e-16];

Returns:

Whether the mipDropTolerance field is set.

getMipDropTolerance

double getMipDropTolerance()

Any value in the input mip with a magnitude lower than this will be set to
zero. This is to avoid some issue in LP presolving.

optional double mip_drop_tolerance = 232 [default = 1e-16];

Returns:

The mipDropTolerance.

hasMipPresolveLevel

boolean hasMipPresolveLevel()

When solving a MIP, we do some basic floating point presolving before
scaling the problem to integer to be handled by CP-SAT. This control how
much of that presolve we do. It can help to better scale floating point
model, but it is not always behaving nicely.

optional int32 mip_presolve_level = 261 [default = 2];

Returns:

Whether the mipPresolveLevel field is set.

getMipPresolveLevel

int getMipPresolveLevel()

When solving a MIP, we do some basic floating point presolving before
scaling the problem to integer to be handled by CP-SAT. This control how
much of that presolve we do. It can help to better scale floating point
model, but it is not always behaving nicely.

optional int32 mip_presolve_level = 261 [default = 2];

Returns:

The mipPresolveLevel.