public final class SatParameters extends com.google.protobuf.GeneratedMessage implements SatParametersOrBuilder
Contains the definitions for all the sat algorithm parameters and their default values. NEXT TAG: 316Protobuf type
operations_research.sat.SatParameters| Modifier and Type | Class and Description |
|---|---|
static class |
SatParameters.BinaryMinizationAlgorithm
Whether to expoit the binary clause to minimize learned clauses further.
|
static class |
SatParameters.Builder
Contains the definitions for all the sat algorithm parameters and their
default values.
|
static class |
SatParameters.ClauseOrdering
The clauses that will be kept during a cleanup are the ones that come
first under this order.
|
static class |
SatParameters.ClauseProtection
Each time a clause activity is bumped, the clause has a chance to be
protected during the next cleanup phase.
|
static class |
SatParameters.ConflictMinimizationAlgorithm
Do we try to minimize conflicts (greedily) when creating them.
|
static class |
SatParameters.FPRoundingMethod
Rounding method to use for feasibility pump.
|
static class |
SatParameters.MaxSatAssumptionOrder
In what order do we add the assumptions in a core-based max-sat algorithm
Protobuf enum
operations_research.sat.SatParameters.MaxSatAssumptionOrder |
static class |
SatParameters.MaxSatStratificationAlgorithm
What stratification algorithm we use in the presence of weight.
|
static class |
SatParameters.Polarity
Specifies the initial polarity (true/false) when the solver branches on a
variable.
|
static class |
SatParameters.RestartAlgorithm
Restart algorithms.
|
static class |
SatParameters.SearchBranching
The search branching will be used to decide how to branch on unfixed nodes.
|
static class |
SatParameters.SharedTreeSplitStrategy
Protobuf enum
operations_research.sat.SatParameters.SharedTreeSplitStrategy |
static class |
SatParameters.VariableOrder
Variables without activity (i.e. at the beginning of the search) will be
tried in this preferred order.
|
com.google.protobuf.GeneratedMessage.ExtendableBuilder<MessageT extends com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT>,BuilderT extends com.google.protobuf.GeneratedMessage.ExtendableBuilder<MessageT,BuilderT>>, com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT extends com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessage.ExtendableMessageOrBuilder<MessageT extends com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessage.FieldAccessorTable, com.google.protobuf.GeneratedMessage.GeneratedExtension<ContainingT extends com.google.protobuf.Message,T>, com.google.protobuf.GeneratedMessage.UnusedPrivateParameter| Modifier and Type | Method and Description |
|---|---|
boolean |
equals(java.lang.Object obj) |
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 |
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_FIRST]; |
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.
|
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.
|
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.
|
SatParameters.ClauseProtection |
getClauseCleanupProtection()
optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE]; |
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
|
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.
|
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 |
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.
|
static SatParameters |
getDefaultInstance() |
SatParameters |
getDefaultInstanceForType() |
java.lang.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"]; |
static com.google.protobuf.Descriptors.Descriptor |
getDescriptor() |
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.
|
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.
|
java.lang.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.
|
com.google.protobuf.ProtocolStringList |
getExtraSubsolversList()
A convenient way to add more workers types.
|
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.
|
java.lang.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; |
com.google.protobuf.ProtocolStringList |
getFilterSubsolversList()
repeated string filter_subsolvers = 293; |
boolean |
getFindBigLinearOverlap()
Try to find large "rectangle" in the linear constraint matrix with
identical lines.
|
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.
|
java.lang.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.
|
com.google.protobuf.ProtocolStringList |
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 |
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.
|
java.lang.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.
|
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 |
getMaxDomainSizeWhenEncodingEqNeqConstraints()
When loading a*x + b*y ==/!
|
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.
|
java.lang.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 |
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.
|
com.google.protobuf.Parser<SatParameters> |
getParserForType() |
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.
|
java.util.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 |
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 |
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.
|
int |
getSerializedSize() |
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.
|
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.
|
boolean |
getShareLevelZeroBounds()
Allows sharing of the bounds of modified variables at level 0.
|
boolean |
getShareObjectiveBounds()
Allows objective sharing between workers.
|
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 |
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.
|
java.util.List<SatParameters> |
getSubsolverParamsList()
It is possible to specify additional subsolver configuration.
|
SatParametersOrBuilder |
getSubsolverParamsOrBuilder(int index)
It is possible to specify additional subsolver configuration.
|
java.util.List<? extends SatParametersOrBuilder> |
getSubsolverParamsOrBuilderList()
It is possible to specify additional subsolver configuration.
|
java.lang.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.
|
com.google.protobuf.ProtocolStringList |
getSubsolversList()
In multi-thread, the solver can be mainly seen as a portfolio of solvers
with different parameters.
|
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.
|
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 |
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 |
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 |
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()
Whether to use pseudo-Boolean resolution to analyze a conflict.
|
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 |
getUseShavingInProbingSearch()
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 |
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]; |
boolean |
getUseVariablesShavingSearch()
This search takes all Boolean or integer variables, and maximize or
minimize them in order to reduce their domain.
|
double |
getVariableActivityDecay()
Each time a conflict is found, the activities of some variables are
increased by one.
|
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 |
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_FIRST]; |
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 |
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 |
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 |
hasClauseCleanupProtection()
optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE]; |
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
|
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 |
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 |
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 |
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 |
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 |
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 |
hasFindBigLinearOverlap()
Try to find large "rectangle" in the linear constraint matrix with
identical lines.
|
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.
|
int |
hashCode() |
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 |
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 |
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 |
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 |
hasMaxDomainSizeWhenEncodingEqNeqConstraints()
When loading a*x + b*y ==/!
|
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 |
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 |
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 |
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 |
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 |
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 |
hasShareLevelZeroBounds()
Allows sharing of the bounds of modified variables at level 0.
|
boolean |
hasShareObjectiveBounds()
Allows objective sharing between workers.
|
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 |
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 |
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 |
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 |
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 |
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 |
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()
Whether to use pseudo-Boolean resolution to analyze a conflict.
|
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 |
hasUseShavingInProbingSearch()
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 |
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 |
hasUseVariablesShavingSearch()
This search takes all Boolean or integer variables, and maximize or
minimize them in order to reduce their domain.
|
boolean |
hasVariableActivityDecay()
Each time a conflict is found, the activities of some variables are
increased by one.
|
boolean |
hasViolationLsCompoundMoveProbability()
Probability of using compound move search each restart.
|
boolean |
hasViolationLsPerturbationPeriod()
How long violation_ls should wait before perturbating a solution.
|
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable |
internalGetFieldAccessorTable() |
boolean |
isInitialized() |
static SatParameters.Builder |
newBuilder() |
static SatParameters.Builder |
newBuilder(SatParameters prototype) |
SatParameters.Builder |
newBuilderForType() |
protected SatParameters.Builder |
newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent) |
static SatParameters |
parseDelimitedFrom(java.io.InputStream input) |
static SatParameters |
parseDelimitedFrom(java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
static SatParameters |
parseFrom(byte[] data) |
static SatParameters |
parseFrom(byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
static SatParameters |
parseFrom(java.nio.ByteBuffer data) |
static SatParameters |
parseFrom(java.nio.ByteBuffer data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
static SatParameters |
parseFrom(com.google.protobuf.ByteString data) |
static SatParameters |
parseFrom(com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
static SatParameters |
parseFrom(com.google.protobuf.CodedInputStream input) |
static SatParameters |
parseFrom(com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
static SatParameters |
parseFrom(java.io.InputStream input) |
static SatParameters |
parseFrom(java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
static com.google.protobuf.Parser<SatParameters> |
parser() |
SatParameters.Builder |
toBuilder() |
void |
writeTo(com.google.protobuf.CodedOutputStream output) |
canUseUnsafe, computeStringSize, computeStringSizeNoTag, emptyBooleanList, emptyDoubleList, emptyFloatList, emptyIntList, emptyList, emptyLongList, getAllFields, getDescriptorForType, getField, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, isStringEmpty, makeMutableCopy, makeMutableCopy, mergeFromAndMakeImmutableInternal, newFileScopedGeneratedExtension, newInstance, newMessageScopedGeneratedExtension, parseDelimitedWithIOException, parseDelimitedWithIOException, parseUnknownField, parseUnknownFieldProto3, parseWithIOException, parseWithIOException, parseWithIOException, parseWithIOException, serializeBooleanMapTo, serializeIntegerMapTo, serializeLongMapTo, serializeStringMapTo, writeReplace, writeString, writeStringNoTagfindInitializationErrors, getInitializationErrorString, hashFields, toStringaddAll, checkByteStringIsUtf8, toByteArray, toByteString, writeDelimitedTo, writeToclone, finalize, getClass, notify, notifyAll, wait, wait, waitpublic static final int NAME_FIELD_NUMBER
public static final int PREFERRED_VARIABLE_ORDER_FIELD_NUMBER
public static final int INITIAL_POLARITY_FIELD_NUMBER
public static final int USE_PHASE_SAVING_FIELD_NUMBER
public static final int POLARITY_REPHASE_INCREMENT_FIELD_NUMBER
public static final int POLARITY_EXPLOIT_LS_HINTS_FIELD_NUMBER
public static final int RANDOM_POLARITY_RATIO_FIELD_NUMBER
public static final int RANDOM_BRANCHES_RATIO_FIELD_NUMBER
public static final int USE_ERWA_HEURISTIC_FIELD_NUMBER
public static final int INITIAL_VARIABLES_ACTIVITY_FIELD_NUMBER
public static final int ALSO_BUMP_VARIABLES_IN_CONFLICT_REASONS_FIELD_NUMBER
public static final int MINIMIZATION_ALGORITHM_FIELD_NUMBER
public static final int BINARY_MINIMIZATION_ALGORITHM_FIELD_NUMBER
public static final int SUBSUMPTION_DURING_CONFLICT_ANALYSIS_FIELD_NUMBER
public static final int CLAUSE_CLEANUP_PERIOD_FIELD_NUMBER
public static final int CLAUSE_CLEANUP_TARGET_FIELD_NUMBER
public static final int CLAUSE_CLEANUP_RATIO_FIELD_NUMBER
public static final int CLAUSE_CLEANUP_PROTECTION_FIELD_NUMBER
public static final int CLAUSE_CLEANUP_LBD_BOUND_FIELD_NUMBER
public static final int CLAUSE_CLEANUP_ORDERING_FIELD_NUMBER
public static final int PB_CLEANUP_INCREMENT_FIELD_NUMBER
public static final int PB_CLEANUP_RATIO_FIELD_NUMBER
public static final int VARIABLE_ACTIVITY_DECAY_FIELD_NUMBER
public static final int MAX_VARIABLE_ACTIVITY_VALUE_FIELD_NUMBER
public static final int GLUCOSE_MAX_DECAY_FIELD_NUMBER
public static final int GLUCOSE_DECAY_INCREMENT_FIELD_NUMBER
public static final int GLUCOSE_DECAY_INCREMENT_PERIOD_FIELD_NUMBER
public static final int CLAUSE_ACTIVITY_DECAY_FIELD_NUMBER
public static final int MAX_CLAUSE_ACTIVITY_VALUE_FIELD_NUMBER
public static final int RESTART_ALGORITHMS_FIELD_NUMBER
public static final int DEFAULT_RESTART_ALGORITHMS_FIELD_NUMBER
public static final int RESTART_PERIOD_FIELD_NUMBER
public static final int RESTART_RUNNING_WINDOW_SIZE_FIELD_NUMBER
public static final int RESTART_DL_AVERAGE_RATIO_FIELD_NUMBER
public static final int RESTART_LBD_AVERAGE_RATIO_FIELD_NUMBER
public static final int USE_BLOCKING_RESTART_FIELD_NUMBER
public static final int BLOCKING_RESTART_WINDOW_SIZE_FIELD_NUMBER
public static final int BLOCKING_RESTART_MULTIPLIER_FIELD_NUMBER
public static final int NUM_CONFLICTS_BEFORE_STRATEGY_CHANGES_FIELD_NUMBER
public static final int STRATEGY_CHANGE_INCREASE_RATIO_FIELD_NUMBER
public static final int MAX_TIME_IN_SECONDS_FIELD_NUMBER
public static final int MAX_DETERMINISTIC_TIME_FIELD_NUMBER
public static final int MAX_NUM_DETERMINISTIC_BATCHES_FIELD_NUMBER
public static final int MAX_NUMBER_OF_CONFLICTS_FIELD_NUMBER
public static final int MAX_MEMORY_IN_MB_FIELD_NUMBER
public static final int ABSOLUTE_GAP_LIMIT_FIELD_NUMBER
public static final int RELATIVE_GAP_LIMIT_FIELD_NUMBER
public static final int RANDOM_SEED_FIELD_NUMBER
public static final int PERMUTE_VARIABLE_RANDOMLY_FIELD_NUMBER
public static final int PERMUTE_PRESOLVE_CONSTRAINT_ORDER_FIELD_NUMBER
public static final int USE_ABSL_RANDOM_FIELD_NUMBER
public static final int LOG_SEARCH_PROGRESS_FIELD_NUMBER
public static final int LOG_SUBSOLVER_STATISTICS_FIELD_NUMBER
public static final int LOG_PREFIX_FIELD_NUMBER
public static final int LOG_TO_STDOUT_FIELD_NUMBER
public static final int LOG_TO_RESPONSE_FIELD_NUMBER
public static final int USE_PB_RESOLUTION_FIELD_NUMBER
public static final int MINIMIZE_REDUCTION_DURING_PB_RESOLUTION_FIELD_NUMBER
public static final int COUNT_ASSUMPTION_LEVELS_IN_LBD_FIELD_NUMBER
public static final int PRESOLVE_BVE_THRESHOLD_FIELD_NUMBER
public static final int PRESOLVE_BVE_CLAUSE_WEIGHT_FIELD_NUMBER
public static final int PROBING_DETERMINISTIC_TIME_LIMIT_FIELD_NUMBER
public static final int PRESOLVE_PROBING_DETERMINISTIC_TIME_LIMIT_FIELD_NUMBER
public static final int PRESOLVE_BLOCKED_CLAUSE_FIELD_NUMBER
public static final int PRESOLVE_USE_BVA_FIELD_NUMBER
public static final int PRESOLVE_BVA_THRESHOLD_FIELD_NUMBER
public static final int MAX_PRESOLVE_ITERATIONS_FIELD_NUMBER
public static final int CP_MODEL_PRESOLVE_FIELD_NUMBER
public static final int CP_MODEL_PROBING_LEVEL_FIELD_NUMBER
public static final int CP_MODEL_USE_SAT_PRESOLVE_FIELD_NUMBER
public static final int REMOVE_FIXED_VARIABLES_EARLY_FIELD_NUMBER
public static final int DETECT_TABLE_WITH_COST_FIELD_NUMBER
public static final int TABLE_COMPRESSION_LEVEL_FIELD_NUMBER
public static final int EXPAND_ALLDIFF_CONSTRAINTS_FIELD_NUMBER
public static final int EXPAND_RESERVOIR_CONSTRAINTS_FIELD_NUMBER
public static final int EXPAND_RESERVOIR_USING_CIRCUIT_FIELD_NUMBER
public static final int ENCODE_CUMULATIVE_AS_RESERVOIR_FIELD_NUMBER
public static final int MAX_LIN_MAX_SIZE_FOR_EXPANSION_FIELD_NUMBER
public static final int DISABLE_CONSTRAINT_EXPANSION_FIELD_NUMBER
public static final int ENCODE_COMPLEX_LINEAR_CONSTRAINT_WITH_INTEGER_FIELD_NUMBER
public static final int MERGE_NO_OVERLAP_WORK_LIMIT_FIELD_NUMBER
public static final int MERGE_AT_MOST_ONE_WORK_LIMIT_FIELD_NUMBER
public static final int PRESOLVE_SUBSTITUTION_LEVEL_FIELD_NUMBER
public static final int PRESOLVE_EXTRACT_INTEGER_ENFORCEMENT_FIELD_NUMBER
public static final int PRESOLVE_INCLUSION_WORK_LIMIT_FIELD_NUMBER
public static final int IGNORE_NAMES_FIELD_NUMBER
public static final int INFER_ALL_DIFFS_FIELD_NUMBER
public static final int FIND_BIG_LINEAR_OVERLAP_FIELD_NUMBER
public static final int USE_SAT_INPROCESSING_FIELD_NUMBER
public static final int INPROCESSING_DTIME_RATIO_FIELD_NUMBER
public static final int INPROCESSING_PROBING_DTIME_FIELD_NUMBER
public static final int INPROCESSING_MINIMIZATION_DTIME_FIELD_NUMBER
public static final int INPROCESSING_MINIMIZATION_USE_CONFLICT_ANALYSIS_FIELD_NUMBER
public static final int INPROCESSING_MINIMIZATION_USE_ALL_ORDERINGS_FIELD_NUMBER
public static final int NUM_WORKERS_FIELD_NUMBER
public static final int NUM_SEARCH_WORKERS_FIELD_NUMBER
public static final int NUM_FULL_SUBSOLVERS_FIELD_NUMBER
public static final int SUBSOLVERS_FIELD_NUMBER
public static final int EXTRA_SUBSOLVERS_FIELD_NUMBER
public static final int IGNORE_SUBSOLVERS_FIELD_NUMBER
public static final int FILTER_SUBSOLVERS_FIELD_NUMBER
public static final int SUBSOLVER_PARAMS_FIELD_NUMBER
public static final int INTERLEAVE_SEARCH_FIELD_NUMBER
public static final int INTERLEAVE_BATCH_SIZE_FIELD_NUMBER
public static final int SHARE_OBJECTIVE_BOUNDS_FIELD_NUMBER
public static final int SHARE_LEVEL_ZERO_BOUNDS_FIELD_NUMBER
public static final int SHARE_BINARY_CLAUSES_FIELD_NUMBER
public static final int SHARE_GLUE_CLAUSES_FIELD_NUMBER
public static final int MINIMIZE_SHARED_CLAUSES_FIELD_NUMBER
public static final int DEBUG_POSTSOLVE_WITH_FULL_SOLVER_FIELD_NUMBER
public static final int DEBUG_MAX_NUM_PRESOLVE_OPERATIONS_FIELD_NUMBER
public static final int DEBUG_CRASH_ON_BAD_HINT_FIELD_NUMBER
public static final int DEBUG_CRASH_IF_PRESOLVE_BREAKS_HINT_FIELD_NUMBER
public static final int USE_OPTIMIZATION_HINTS_FIELD_NUMBER
public static final int CORE_MINIMIZATION_LEVEL_FIELD_NUMBER
public static final int FIND_MULTIPLE_CORES_FIELD_NUMBER
public static final int COVER_OPTIMIZATION_FIELD_NUMBER
public static final int MAX_SAT_ASSUMPTION_ORDER_FIELD_NUMBER
public static final int MAX_SAT_REVERSE_ASSUMPTION_ORDER_FIELD_NUMBER
public static final int MAX_SAT_STRATIFICATION_FIELD_NUMBER
public static final int PROPAGATION_LOOP_DETECTION_FACTOR_FIELD_NUMBER
public static final int USE_PRECEDENCES_IN_DISJUNCTIVE_CONSTRAINT_FIELD_NUMBER
public static final int MAX_SIZE_TO_CREATE_PRECEDENCE_LITERALS_IN_DISJUNCTIVE_FIELD_NUMBER
public static final int USE_STRONG_PROPAGATION_IN_DISJUNCTIVE_FIELD_NUMBER
public static final int USE_DYNAMIC_PRECEDENCE_IN_DISJUNCTIVE_FIELD_NUMBER
public static final int USE_DYNAMIC_PRECEDENCE_IN_CUMULATIVE_FIELD_NUMBER
public static final int USE_OVERLOAD_CHECKER_IN_CUMULATIVE_FIELD_NUMBER
public static final int USE_CONSERVATIVE_SCALE_OVERLOAD_CHECKER_FIELD_NUMBER
public static final int USE_TIMETABLE_EDGE_FINDING_IN_CUMULATIVE_FIELD_NUMBER
public static final int MAX_NUM_INTERVALS_FOR_TIMETABLE_EDGE_FINDING_FIELD_NUMBER
public static final int USE_HARD_PRECEDENCES_IN_CUMULATIVE_FIELD_NUMBER
public static final int EXPLOIT_ALL_PRECEDENCES_FIELD_NUMBER
public static final int USE_DISJUNCTIVE_CONSTRAINT_IN_CUMULATIVE_FIELD_NUMBER
public static final int USE_TIMETABLING_IN_NO_OVERLAP_2D_FIELD_NUMBER
public static final int USE_ENERGETIC_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER
public static final int USE_AREA_ENERGETIC_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER
public static final int USE_TRY_EDGE_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER
public static final int MAX_PAIRS_PAIRWISE_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER
public static final int MAXIMUM_REGIONS_TO_SPLIT_IN_DISCONNECTED_NO_OVERLAP_2D_FIELD_NUMBER
public static final int USE_DUAL_SCHEDULING_HEURISTICS_FIELD_NUMBER
public static final int USE_ALL_DIFFERENT_FOR_CIRCUIT_FIELD_NUMBER
public static final int ROUTING_CUT_SUBSET_SIZE_FOR_BINARY_RELATION_BOUND_FIELD_NUMBER
public static final int ROUTING_CUT_SUBSET_SIZE_FOR_TIGHT_BINARY_RELATION_BOUND_FIELD_NUMBER
public static final int ROUTING_CUT_DP_EFFORT_FIELD_NUMBER
public static final int SEARCH_BRANCHING_FIELD_NUMBER
public static final int HINT_CONFLICT_LIMIT_FIELD_NUMBER
public static final int REPAIR_HINT_FIELD_NUMBER
public static final int FIX_VARIABLES_TO_THEIR_HINTED_VALUE_FIELD_NUMBER
public static final int USE_PROBING_SEARCH_FIELD_NUMBER
public static final int USE_EXTENDED_PROBING_FIELD_NUMBER
public static final int PROBING_NUM_COMBINATIONS_LIMIT_FIELD_NUMBER
public static final int USE_SHAVING_IN_PROBING_SEARCH_FIELD_NUMBER
public static final int SHAVING_SEARCH_DETERMINISTIC_TIME_FIELD_NUMBER
public static final int SHAVING_SEARCH_THRESHOLD_FIELD_NUMBER
public static final int USE_OBJECTIVE_LB_SEARCH_FIELD_NUMBER
public static final int USE_OBJECTIVE_SHAVING_SEARCH_FIELD_NUMBER
public static final int USE_VARIABLES_SHAVING_SEARCH_FIELD_NUMBER
public static final int PSEUDO_COST_RELIABILITY_THRESHOLD_FIELD_NUMBER
public static final int OPTIMIZE_WITH_CORE_FIELD_NUMBER
public static final int OPTIMIZE_WITH_LB_TREE_SEARCH_FIELD_NUMBER
public static final int SAVE_LP_BASIS_IN_LB_TREE_SEARCH_FIELD_NUMBER
public static final int BINARY_SEARCH_NUM_CONFLICTS_FIELD_NUMBER
public static final int OPTIMIZE_WITH_MAX_HS_FIELD_NUMBER
public static final int USE_FEASIBILITY_JUMP_FIELD_NUMBER
public static final int USE_LS_ONLY_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_DECAY_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_LINEARIZATION_LEVEL_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_RESTART_FACTOR_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_BATCH_DTIME_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_VAR_RANDOMIZATION_PROBABILITY_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_VAR_PERBURBATION_RANGE_RATIO_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_ENABLE_RESTARTS_FIELD_NUMBER
public static final int FEASIBILITY_JUMP_MAX_EXPANDED_CONSTRAINT_SIZE_FIELD_NUMBER
public static final int NUM_VIOLATION_LS_FIELD_NUMBER
public static final int VIOLATION_LS_PERTURBATION_PERIOD_FIELD_NUMBER
public static final int VIOLATION_LS_COMPOUND_MOVE_PROBABILITY_FIELD_NUMBER
public static final int SHARED_TREE_NUM_WORKERS_FIELD_NUMBER
public static final int USE_SHARED_TREE_SEARCH_FIELD_NUMBER
public static final int SHARED_TREE_WORKER_MIN_RESTARTS_PER_SUBTREE_FIELD_NUMBER
public static final int SHARED_TREE_WORKER_ENABLE_TRAIL_SHARING_FIELD_NUMBER
public static final int SHARED_TREE_WORKER_ENABLE_PHASE_SHARING_FIELD_NUMBER
public static final int SHARED_TREE_OPEN_LEAVES_PER_WORKER_FIELD_NUMBER
public static final int SHARED_TREE_MAX_NODES_PER_WORKER_FIELD_NUMBER
public static final int SHARED_TREE_SPLIT_STRATEGY_FIELD_NUMBER
public static final int SHARED_TREE_BALANCE_TOLERANCE_FIELD_NUMBER
public static final int ENUMERATE_ALL_SOLUTIONS_FIELD_NUMBER
public static final int KEEP_ALL_FEASIBLE_SOLUTIONS_IN_PRESOLVE_FIELD_NUMBER
public static final int FILL_TIGHTENED_DOMAINS_IN_RESPONSE_FIELD_NUMBER
public static final int FILL_ADDITIONAL_SOLUTIONS_IN_RESPONSE_FIELD_NUMBER
public static final int INSTANTIATE_ALL_VARIABLES_FIELD_NUMBER
public static final int AUTO_DETECT_GREATER_THAN_AT_LEAST_ONE_OF_FIELD_NUMBER
public static final int STOP_AFTER_FIRST_SOLUTION_FIELD_NUMBER
public static final int STOP_AFTER_PRESOLVE_FIELD_NUMBER
public static final int STOP_AFTER_ROOT_PROPAGATION_FIELD_NUMBER
public static final int LNS_INITIAL_DIFFICULTY_FIELD_NUMBER
public static final int LNS_INITIAL_DETERMINISTIC_LIMIT_FIELD_NUMBER
public static final int USE_LNS_FIELD_NUMBER
public static final int USE_LNS_ONLY_FIELD_NUMBER
public static final int SOLUTION_POOL_SIZE_FIELD_NUMBER
public static final int USE_RINS_LNS_FIELD_NUMBER
public static final int USE_FEASIBILITY_PUMP_FIELD_NUMBER
public static final int USE_LB_RELAX_LNS_FIELD_NUMBER
public static final int LB_RELAX_NUM_WORKERS_THRESHOLD_FIELD_NUMBER
public static final int FP_ROUNDING_FIELD_NUMBER
public static final int DIVERSIFY_LNS_PARAMS_FIELD_NUMBER
public static final int RANDOMIZE_SEARCH_FIELD_NUMBER
public static final int SEARCH_RANDOM_VARIABLE_POOL_SIZE_FIELD_NUMBER
public static final int PUSH_ALL_TASKS_TOWARD_START_FIELD_NUMBER
public static final int USE_OPTIONAL_VARIABLES_FIELD_NUMBER
public static final int USE_EXACT_LP_REASON_FIELD_NUMBER
public static final int USE_COMBINED_NO_OVERLAP_FIELD_NUMBER
public static final int AT_MOST_ONE_MAX_EXPANSION_SIZE_FIELD_NUMBER
public static final int CATCH_SIGINT_SIGNAL_FIELD_NUMBER
public static final int USE_IMPLIED_BOUNDS_FIELD_NUMBER
public static final int POLISH_LP_SOLUTION_FIELD_NUMBER
public static final int LP_PRIMAL_TOLERANCE_FIELD_NUMBER
public static final int LP_DUAL_TOLERANCE_FIELD_NUMBER
public static final int CONVERT_INTERVALS_FIELD_NUMBER
public static final int SYMMETRY_LEVEL_FIELD_NUMBER
public static final int USE_SYMMETRY_IN_LP_FIELD_NUMBER
public static final int KEEP_SYMMETRY_IN_PRESOLVE_FIELD_NUMBER
public static final int SYMMETRY_DETECTION_DETERMINISTIC_TIME_LIMIT_FIELD_NUMBER
public static final int NEW_LINEAR_PROPAGATION_FIELD_NUMBER
public static final int LINEAR_SPLIT_SIZE_FIELD_NUMBER
public static final int LINEARIZATION_LEVEL_FIELD_NUMBER
public static final int BOOLEAN_ENCODING_LEVEL_FIELD_NUMBER
public static final int MAX_DOMAIN_SIZE_WHEN_ENCODING_EQ_NEQ_CONSTRAINTS_FIELD_NUMBER
public static final int MAX_NUM_CUTS_FIELD_NUMBER
public static final int CUT_LEVEL_FIELD_NUMBER
public static final int ONLY_ADD_CUTS_AT_LEVEL_ZERO_FIELD_NUMBER
public static final int ADD_OBJECTIVE_CUT_FIELD_NUMBER
public static final int ADD_CG_CUTS_FIELD_NUMBER
public static final int ADD_MIR_CUTS_FIELD_NUMBER
public static final int ADD_ZERO_HALF_CUTS_FIELD_NUMBER
public static final int ADD_CLIQUE_CUTS_FIELD_NUMBER
public static final int ADD_RLT_CUTS_FIELD_NUMBER
public static final int MAX_ALL_DIFF_CUT_SIZE_FIELD_NUMBER
public static final int ADD_LIN_MAX_CUTS_FIELD_NUMBER
public static final int MAX_INTEGER_ROUNDING_SCALING_FIELD_NUMBER
public static final int ADD_LP_CONSTRAINTS_LAZILY_FIELD_NUMBER
public static final int ROOT_LP_ITERATIONS_FIELD_NUMBER
public static final int MIN_ORTHOGONALITY_FOR_LP_CONSTRAINTS_FIELD_NUMBER
public static final int MAX_CUT_ROUNDS_AT_LEVEL_ZERO_FIELD_NUMBER
public static final int MAX_CONSECUTIVE_INACTIVE_COUNT_FIELD_NUMBER
public static final int CUT_MAX_ACTIVE_COUNT_VALUE_FIELD_NUMBER
public static final int CUT_ACTIVE_COUNT_DECAY_FIELD_NUMBER
public static final int CUT_CLEANUP_TARGET_FIELD_NUMBER
public static final int NEW_CONSTRAINTS_BATCH_SIZE_FIELD_NUMBER
public static final int EXPLOIT_INTEGER_LP_SOLUTION_FIELD_NUMBER
public static final int EXPLOIT_ALL_LP_SOLUTION_FIELD_NUMBER
public static final int EXPLOIT_BEST_SOLUTION_FIELD_NUMBER
public static final int EXPLOIT_RELAXATION_SOLUTION_FIELD_NUMBER
public static final int EXPLOIT_OBJECTIVE_FIELD_NUMBER
public static final int DETECT_LINEARIZED_PRODUCT_FIELD_NUMBER
public static final int MIP_MAX_BOUND_FIELD_NUMBER
public static final int MIP_VAR_SCALING_FIELD_NUMBER
public static final int MIP_SCALE_LARGE_DOMAIN_FIELD_NUMBER
public static final int MIP_AUTOMATICALLY_SCALE_VARIABLES_FIELD_NUMBER
public static final int ONLY_SOLVE_IP_FIELD_NUMBER
public static final int MIP_WANTED_PRECISION_FIELD_NUMBER
public static final int MIP_MAX_ACTIVITY_EXPONENT_FIELD_NUMBER
public static final int MIP_CHECK_PRECISION_FIELD_NUMBER
public static final int MIP_COMPUTE_TRUE_OBJECTIVE_BOUND_FIELD_NUMBER
public static final int MIP_MAX_VALID_MAGNITUDE_FIELD_NUMBER
public static final int MIP_TREAT_HIGH_MAGNITUDE_BOUNDS_AS_INFINITY_FIELD_NUMBER
public static final int MIP_DROP_TOLERANCE_FIELD_NUMBER
public static final int MIP_PRESOLVE_LEVEL_FIELD_NUMBER
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessagepublic 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 = ""];hasName in interface SatParametersOrBuilderpublic java.lang.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 = ""];getName in interface SatParametersOrBuilderpublic 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 = ""];getNameBytes in interface SatParametersOrBuilderpublic boolean hasPreferredVariableOrder()
optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];hasPreferredVariableOrder in interface SatParametersOrBuilderpublic SatParameters.VariableOrder getPreferredVariableOrder()
optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];getPreferredVariableOrder in interface SatParametersOrBuilderpublic boolean hasInitialPolarity()
optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];hasInitialPolarity in interface SatParametersOrBuilderpublic SatParameters.Polarity getInitialPolarity()
optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];getInitialPolarity in interface SatParametersOrBuilderpublic 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];hasUsePhaseSaving in interface SatParametersOrBuilderpublic 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];getUsePhaseSaving in interface SatParametersOrBuilderpublic 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];hasPolarityRephaseIncrement in interface SatParametersOrBuilderpublic 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];getPolarityRephaseIncrement in interface SatParametersOrBuilderpublic 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];hasPolarityExploitLsHints in interface SatParametersOrBuilderpublic 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];getPolarityExploitLsHints in interface SatParametersOrBuilderpublic 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];hasRandomPolarityRatio in interface SatParametersOrBuilderpublic 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];getRandomPolarityRatio in interface SatParametersOrBuilderpublic 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];hasRandomBranchesRatio in interface SatParametersOrBuilderpublic 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];getRandomBranchesRatio in interface SatParametersOrBuilderpublic 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];hasUseErwaHeuristic in interface SatParametersOrBuilderpublic 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];getUseErwaHeuristic in interface SatParametersOrBuilderpublic 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];hasInitialVariablesActivity in interface SatParametersOrBuilderpublic 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];getInitialVariablesActivity in interface SatParametersOrBuilderpublic 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];hasAlsoBumpVariablesInConflictReasons in interface SatParametersOrBuilderpublic 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];getAlsoBumpVariablesInConflictReasons in interface SatParametersOrBuilderpublic boolean hasMinimizationAlgorithm()
optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];hasMinimizationAlgorithm in interface SatParametersOrBuilderpublic SatParameters.ConflictMinimizationAlgorithm getMinimizationAlgorithm()
optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];getMinimizationAlgorithm in interface SatParametersOrBuilderpublic boolean hasBinaryMinimizationAlgorithm()
optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FIRST];hasBinaryMinimizationAlgorithm in interface SatParametersOrBuilderpublic SatParameters.BinaryMinizationAlgorithm getBinaryMinimizationAlgorithm()
optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FIRST];getBinaryMinimizationAlgorithm in interface SatParametersOrBuilderpublic 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];hasSubsumptionDuringConflictAnalysis in interface SatParametersOrBuilderpublic 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];getSubsumptionDuringConflictAnalysis in interface SatParametersOrBuilderpublic boolean hasClauseCleanupPeriod()
Trigger a cleanup when this number of "deletable" clauses is learned.
optional int32 clause_cleanup_period = 11 [default = 10000];hasClauseCleanupPeriod in interface SatParametersOrBuilderpublic int getClauseCleanupPeriod()
Trigger a cleanup when this number of "deletable" clauses is learned.
optional int32 clause_cleanup_period = 11 [default = 10000];getClauseCleanupPeriod in interface SatParametersOrBuilderpublic 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];hasClauseCleanupTarget in interface SatParametersOrBuilderpublic 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];getClauseCleanupTarget in interface SatParametersOrBuilderpublic 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];hasClauseCleanupRatio in interface SatParametersOrBuilderpublic 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];getClauseCleanupRatio in interface SatParametersOrBuilderpublic boolean hasClauseCleanupProtection()
optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE];hasClauseCleanupProtection in interface SatParametersOrBuilderpublic SatParameters.ClauseProtection getClauseCleanupProtection()
optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE];getClauseCleanupProtection in interface SatParametersOrBuilderpublic boolean hasClauseCleanupLbdBound()
All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept.
optional int32 clause_cleanup_lbd_bound = 59 [default = 5];hasClauseCleanupLbdBound in interface SatParametersOrBuilderpublic int getClauseCleanupLbdBound()
All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept.
optional int32 clause_cleanup_lbd_bound = 59 [default = 5];getClauseCleanupLbdBound in interface SatParametersOrBuilderpublic boolean hasClauseCleanupOrdering()
optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];hasClauseCleanupOrdering in interface SatParametersOrBuilderpublic SatParameters.ClauseOrdering getClauseCleanupOrdering()
optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];getClauseCleanupOrdering in interface SatParametersOrBuilderpublic boolean hasPbCleanupIncrement()
Same as for the clauses, but for the learned pseudo-Boolean constraints.
optional int32 pb_cleanup_increment = 46 [default = 200];hasPbCleanupIncrement in interface SatParametersOrBuilderpublic int getPbCleanupIncrement()
Same as for the clauses, but for the learned pseudo-Boolean constraints.
optional int32 pb_cleanup_increment = 46 [default = 200];getPbCleanupIncrement in interface SatParametersOrBuilderpublic boolean hasPbCleanupRatio()
optional double pb_cleanup_ratio = 47 [default = 0.5];hasPbCleanupRatio in interface SatParametersOrBuilderpublic double getPbCleanupRatio()
optional double pb_cleanup_ratio = 47 [default = 0.5];getPbCleanupRatio in interface SatParametersOrBuilderpublic 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];hasVariableActivityDecay in interface SatParametersOrBuilderpublic 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];getVariableActivityDecay in interface SatParametersOrBuilderpublic boolean hasMaxVariableActivityValue()
optional double max_variable_activity_value = 16 [default = 1e+100];hasMaxVariableActivityValue in interface SatParametersOrBuilderpublic double getMaxVariableActivityValue()
optional double max_variable_activity_value = 16 [default = 1e+100];getMaxVariableActivityValue in interface SatParametersOrBuilderpublic 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];hasGlucoseMaxDecay in interface SatParametersOrBuilderpublic 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];getGlucoseMaxDecay in interface SatParametersOrBuilderpublic boolean hasGlucoseDecayIncrement()
optional double glucose_decay_increment = 23 [default = 0.01];hasGlucoseDecayIncrement in interface SatParametersOrBuilderpublic double getGlucoseDecayIncrement()
optional double glucose_decay_increment = 23 [default = 0.01];getGlucoseDecayIncrement in interface SatParametersOrBuilderpublic boolean hasGlucoseDecayIncrementPeriod()
optional int32 glucose_decay_increment_period = 24 [default = 5000];hasGlucoseDecayIncrementPeriod in interface SatParametersOrBuilderpublic int getGlucoseDecayIncrementPeriod()
optional int32 glucose_decay_increment_period = 24 [default = 5000];getGlucoseDecayIncrementPeriod in interface SatParametersOrBuilderpublic boolean hasClauseActivityDecay()
Clause activity parameters (same effect as the one on the variables).
optional double clause_activity_decay = 17 [default = 0.999];hasClauseActivityDecay in interface SatParametersOrBuilderpublic double getClauseActivityDecay()
Clause activity parameters (same effect as the one on the variables).
optional double clause_activity_decay = 17 [default = 0.999];getClauseActivityDecay in interface SatParametersOrBuilderpublic boolean hasMaxClauseActivityValue()
optional double max_clause_activity_value = 18 [default = 1e+20];hasMaxClauseActivityValue in interface SatParametersOrBuilderpublic double getMaxClauseActivityValue()
optional double max_clause_activity_value = 18 [default = 1e+20];getMaxClauseActivityValue in interface SatParametersOrBuilderpublic java.util.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;getRestartAlgorithmsList in interface SatParametersOrBuilderpublic 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;getRestartAlgorithmsCount in interface SatParametersOrBuilderpublic 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;getRestartAlgorithms in interface SatParametersOrBuilderindex - The index of the element to return.public boolean hasDefaultRestartAlgorithms()
optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];hasDefaultRestartAlgorithms in interface SatParametersOrBuilderpublic java.lang.String getDefaultRestartAlgorithms()
optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];getDefaultRestartAlgorithms in interface SatParametersOrBuilderpublic com.google.protobuf.ByteString getDefaultRestartAlgorithmsBytes()
optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];getDefaultRestartAlgorithmsBytes in interface SatParametersOrBuilderpublic 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];hasRestartPeriod in interface SatParametersOrBuilderpublic 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];getRestartPeriod in interface SatParametersOrBuilderpublic boolean hasRestartRunningWindowSize()
Size of the window for the moving average restarts.
optional int32 restart_running_window_size = 62 [default = 50];hasRestartRunningWindowSize in interface SatParametersOrBuilderpublic int getRestartRunningWindowSize()
Size of the window for the moving average restarts.
optional int32 restart_running_window_size = 62 [default = 50];getRestartRunningWindowSize in interface SatParametersOrBuilderpublic 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];hasRestartDlAverageRatio in interface SatParametersOrBuilderpublic 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];getRestartDlAverageRatio in interface SatParametersOrBuilderpublic boolean hasRestartLbdAverageRatio()
optional double restart_lbd_average_ratio = 71 [default = 1];hasRestartLbdAverageRatio in interface SatParametersOrBuilderpublic double getRestartLbdAverageRatio()
optional double restart_lbd_average_ratio = 71 [default = 1];getRestartLbdAverageRatio in interface SatParametersOrBuilderpublic 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];hasUseBlockingRestart in interface SatParametersOrBuilderpublic 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];getUseBlockingRestart in interface SatParametersOrBuilderpublic boolean hasBlockingRestartWindowSize()
optional int32 blocking_restart_window_size = 65 [default = 5000];hasBlockingRestartWindowSize in interface SatParametersOrBuilderpublic int getBlockingRestartWindowSize()
optional int32 blocking_restart_window_size = 65 [default = 5000];getBlockingRestartWindowSize in interface SatParametersOrBuilderpublic boolean hasBlockingRestartMultiplier()
optional double blocking_restart_multiplier = 66 [default = 1.4];hasBlockingRestartMultiplier in interface SatParametersOrBuilderpublic double getBlockingRestartMultiplier()
optional double blocking_restart_multiplier = 66 [default = 1.4];getBlockingRestartMultiplier in interface SatParametersOrBuilderpublic 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];hasNumConflictsBeforeStrategyChanges in interface SatParametersOrBuilderpublic 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];getNumConflictsBeforeStrategyChanges in interface SatParametersOrBuilderpublic 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];hasStrategyChangeIncreaseRatio in interface SatParametersOrBuilderpublic 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];getStrategyChangeIncreaseRatio in interface SatParametersOrBuilderpublic 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];hasMaxTimeInSeconds in interface SatParametersOrBuilderpublic 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];getMaxTimeInSeconds in interface SatParametersOrBuilderpublic 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];hasMaxDeterministicTime in interface SatParametersOrBuilderpublic 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];getMaxDeterministicTime in interface SatParametersOrBuilderpublic 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];hasMaxNumDeterministicBatches in interface SatParametersOrBuilderpublic 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];getMaxNumDeterministicBatches in interface SatParametersOrBuilderpublic 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];hasMaxNumberOfConflicts in interface SatParametersOrBuilderpublic 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];getMaxNumberOfConflicts in interface SatParametersOrBuilderpublic 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];hasMaxMemoryInMb in interface SatParametersOrBuilderpublic 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];getMaxMemoryInMb in interface SatParametersOrBuilderpublic 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];hasAbsoluteGapLimit in interface SatParametersOrBuilderpublic 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];getAbsoluteGapLimit in interface SatParametersOrBuilderpublic boolean hasRelativeGapLimit()
optional double relative_gap_limit = 160 [default = 0];hasRelativeGapLimit in interface SatParametersOrBuilderpublic double getRelativeGapLimit()
optional double relative_gap_limit = 160 [default = 0];getRelativeGapLimit in interface SatParametersOrBuilderpublic 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];hasRandomSeed in interface SatParametersOrBuilderpublic 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];getRandomSeed in interface SatParametersOrBuilderpublic 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];hasPermuteVariableRandomly in interface SatParametersOrBuilderpublic 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];getPermuteVariableRandomly in interface SatParametersOrBuilderpublic boolean hasPermutePresolveConstraintOrder()
optional bool permute_presolve_constraint_order = 179 [default = false];hasPermutePresolveConstraintOrder in interface SatParametersOrBuilderpublic boolean getPermutePresolveConstraintOrder()
optional bool permute_presolve_constraint_order = 179 [default = false];getPermutePresolveConstraintOrder in interface SatParametersOrBuilderpublic boolean hasUseAbslRandom()
optional bool use_absl_random = 180 [default = false];hasUseAbslRandom in interface SatParametersOrBuilderpublic boolean getUseAbslRandom()
optional bool use_absl_random = 180 [default = false];getUseAbslRandom in interface SatParametersOrBuilderpublic 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];hasLogSearchProgress in interface SatParametersOrBuilderpublic 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];getLogSearchProgress in interface SatParametersOrBuilderpublic 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];hasLogSubsolverStatistics in interface SatParametersOrBuilderpublic 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];getLogSubsolverStatistics in interface SatParametersOrBuilderpublic boolean hasLogPrefix()
Add a prefix to all logs.
optional string log_prefix = 185 [default = ""];hasLogPrefix in interface SatParametersOrBuilderpublic java.lang.String getLogPrefix()
Add a prefix to all logs.
optional string log_prefix = 185 [default = ""];getLogPrefix in interface SatParametersOrBuilderpublic com.google.protobuf.ByteString getLogPrefixBytes()
Add a prefix to all logs.
optional string log_prefix = 185 [default = ""];getLogPrefixBytes in interface SatParametersOrBuilderpublic boolean hasLogToStdout()
Log to stdout.
optional bool log_to_stdout = 186 [default = true];hasLogToStdout in interface SatParametersOrBuilderpublic boolean getLogToStdout()
Log to stdout.
optional bool log_to_stdout = 186 [default = true];getLogToStdout in interface SatParametersOrBuilderpublic boolean hasLogToResponse()
Log to response proto.
optional bool log_to_response = 187 [default = false];hasLogToResponse in interface SatParametersOrBuilderpublic boolean getLogToResponse()
Log to response proto.
optional bool log_to_response = 187 [default = false];getLogToResponse in interface SatParametersOrBuilderpublic boolean hasUsePbResolution()
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];hasUsePbResolution in interface SatParametersOrBuilderpublic boolean getUsePbResolution()
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];getUsePbResolution in interface SatParametersOrBuilderpublic 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];hasMinimizeReductionDuringPbResolution in interface SatParametersOrBuilderpublic 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];getMinimizeReductionDuringPbResolution in interface SatParametersOrBuilderpublic 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];hasCountAssumptionLevelsInLbd in interface SatParametersOrBuilderpublic 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];getCountAssumptionLevelsInLbd in interface SatParametersOrBuilderpublic 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];hasPresolveBveThreshold in interface SatParametersOrBuilderpublic 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];getPresolveBveThreshold in interface SatParametersOrBuilderpublic 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];hasPresolveBveClauseWeight in interface SatParametersOrBuilderpublic 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];getPresolveBveClauseWeight in interface SatParametersOrBuilderpublic 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];hasProbingDeterministicTimeLimit in interface SatParametersOrBuilderpublic 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];getProbingDeterministicTimeLimit in interface SatParametersOrBuilderpublic boolean hasPresolveProbingDeterministicTimeLimit()
optional double presolve_probing_deterministic_time_limit = 57 [default = 30];hasPresolveProbingDeterministicTimeLimit in interface SatParametersOrBuilderpublic double getPresolveProbingDeterministicTimeLimit()
optional double presolve_probing_deterministic_time_limit = 57 [default = 30];getPresolveProbingDeterministicTimeLimit in interface SatParametersOrBuilderpublic 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];hasPresolveBlockedClause in interface SatParametersOrBuilderpublic 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];getPresolveBlockedClause in interface SatParametersOrBuilderpublic boolean hasPresolveUseBva()
Whether or not we use Bounded Variable Addition (BVA) in the presolve.
optional bool presolve_use_bva = 72 [default = true];hasPresolveUseBva in interface SatParametersOrBuilderpublic boolean getPresolveUseBva()
Whether or not we use Bounded Variable Addition (BVA) in the presolve.
optional bool presolve_use_bva = 72 [default = true];getPresolveUseBva in interface SatParametersOrBuilderpublic 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];hasPresolveBvaThreshold in interface SatParametersOrBuilderpublic 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];getPresolveBvaThreshold in interface SatParametersOrBuilderpublic 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];hasMaxPresolveIterations in interface SatParametersOrBuilderpublic 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];getMaxPresolveIterations in interface SatParametersOrBuilderpublic boolean hasCpModelPresolve()
Whether we presolve the cp_model before solving it.
optional bool cp_model_presolve = 86 [default = true];hasCpModelPresolve in interface SatParametersOrBuilderpublic boolean getCpModelPresolve()
Whether we presolve the cp_model before solving it.
optional bool cp_model_presolve = 86 [default = true];getCpModelPresolve in interface SatParametersOrBuilderpublic boolean hasCpModelProbingLevel()
How much effort do we spend on probing. 0 disables it completely.
optional int32 cp_model_probing_level = 110 [default = 2];hasCpModelProbingLevel in interface SatParametersOrBuilderpublic int getCpModelProbingLevel()
How much effort do we spend on probing. 0 disables it completely.
optional int32 cp_model_probing_level = 110 [default = 2];getCpModelProbingLevel in interface SatParametersOrBuilderpublic 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];hasCpModelUseSatPresolve in interface SatParametersOrBuilderpublic 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];getCpModelUseSatPresolve in interface SatParametersOrBuilderpublic 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];hasRemoveFixedVariablesEarly in interface SatParametersOrBuilderpublic 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];getRemoveFixedVariablesEarly in interface SatParametersOrBuilderpublic 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];hasDetectTableWithCost in interface SatParametersOrBuilderpublic 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];getDetectTableWithCost in interface SatParametersOrBuilderpublic 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];hasTableCompressionLevel in interface SatParametersOrBuilderpublic 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];getTableCompressionLevel in interface SatParametersOrBuilderpublic 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];hasExpandAlldiffConstraints in interface SatParametersOrBuilderpublic 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];getExpandAlldiffConstraints in interface SatParametersOrBuilderpublic 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];hasExpandReservoirConstraints in interface SatParametersOrBuilderpublic 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];getExpandReservoirConstraints in interface SatParametersOrBuilderpublic 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];hasExpandReservoirUsingCircuit in interface SatParametersOrBuilderpublic 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];getExpandReservoirUsingCircuit in interface SatParametersOrBuilderpublic 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];hasEncodeCumulativeAsReservoir in interface SatParametersOrBuilderpublic 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];getEncodeCumulativeAsReservoir in interface SatParametersOrBuilderpublic 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];hasMaxLinMaxSizeForExpansion in interface SatParametersOrBuilderpublic 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];getMaxLinMaxSizeForExpansion in interface SatParametersOrBuilderpublic 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];hasDisableConstraintExpansion in interface SatParametersOrBuilderpublic 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];getDisableConstraintExpansion in interface SatParametersOrBuilderpublic 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];hasEncodeComplexLinearConstraintWithInteger in interface SatParametersOrBuilderpublic 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];getEncodeComplexLinearConstraintWithInteger in interface SatParametersOrBuilderpublic 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];hasMergeNoOverlapWorkLimit in interface SatParametersOrBuilderpublic 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];getMergeNoOverlapWorkLimit in interface SatParametersOrBuilderpublic boolean hasMergeAtMostOneWorkLimit()
optional double merge_at_most_one_work_limit = 146 [default = 100000000];hasMergeAtMostOneWorkLimit in interface SatParametersOrBuilderpublic double getMergeAtMostOneWorkLimit()
optional double merge_at_most_one_work_limit = 146 [default = 100000000];getMergeAtMostOneWorkLimit in interface SatParametersOrBuilderpublic 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];hasPresolveSubstitutionLevel in interface SatParametersOrBuilderpublic 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];getPresolveSubstitutionLevel in interface SatParametersOrBuilderpublic 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];hasPresolveExtractIntegerEnforcement in interface SatParametersOrBuilderpublic 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];getPresolveExtractIntegerEnforcement in interface SatParametersOrBuilderpublic 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];hasPresolveInclusionWorkLimit in interface SatParametersOrBuilderpublic 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];getPresolveInclusionWorkLimit in interface SatParametersOrBuilderpublic 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];hasIgnoreNames in interface SatParametersOrBuilderpublic 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];getIgnoreNames in interface SatParametersOrBuilderpublic 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];hasInferAllDiffs in interface SatParametersOrBuilderpublic 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];getInferAllDiffs in interface SatParametersOrBuilderpublic 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];hasFindBigLinearOverlap in interface SatParametersOrBuilderpublic 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];getFindBigLinearOverlap in interface SatParametersOrBuilderpublic 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];hasUseSatInprocessing in interface SatParametersOrBuilderpublic 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];getUseSatInprocessing in interface SatParametersOrBuilderpublic 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];hasInprocessingDtimeRatio in interface SatParametersOrBuilderpublic 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];getInprocessingDtimeRatio in interface SatParametersOrBuilderpublic boolean hasInprocessingProbingDtime()
The amount of dtime we should spend on probing for each inprocessing round.
optional double inprocessing_probing_dtime = 274 [default = 1];hasInprocessingProbingDtime in interface SatParametersOrBuilderpublic double getInprocessingProbingDtime()
The amount of dtime we should spend on probing for each inprocessing round.
optional double inprocessing_probing_dtime = 274 [default = 1];getInprocessingProbingDtime in interface SatParametersOrBuilderpublic 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];hasInprocessingMinimizationDtime in interface SatParametersOrBuilderpublic 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];getInprocessingMinimizationDtime in interface SatParametersOrBuilderpublic boolean hasInprocessingMinimizationUseConflictAnalysis()
optional bool inprocessing_minimization_use_conflict_analysis = 297 [default = true];hasInprocessingMinimizationUseConflictAnalysis in interface SatParametersOrBuilderpublic boolean getInprocessingMinimizationUseConflictAnalysis()
optional bool inprocessing_minimization_use_conflict_analysis = 297 [default = true];getInprocessingMinimizationUseConflictAnalysis in interface SatParametersOrBuilderpublic boolean hasInprocessingMinimizationUseAllOrderings()
optional bool inprocessing_minimization_use_all_orderings = 298 [default = false];hasInprocessingMinimizationUseAllOrderings in interface SatParametersOrBuilderpublic boolean getInprocessingMinimizationUseAllOrderings()
optional bool inprocessing_minimization_use_all_orderings = 298 [default = false];getInprocessingMinimizationUseAllOrderings in interface SatParametersOrBuilderpublic 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];hasNumWorkers in interface SatParametersOrBuilderpublic 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];getNumWorkers in interface SatParametersOrBuilderpublic boolean hasNumSearchWorkers()
optional int32 num_search_workers = 100 [default = 0];hasNumSearchWorkers in interface SatParametersOrBuilderpublic int getNumSearchWorkers()
optional int32 num_search_workers = 100 [default = 0];getNumSearchWorkers in interface SatParametersOrBuilderpublic 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];hasNumFullSubsolvers in interface SatParametersOrBuilderpublic 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];getNumFullSubsolvers in interface SatParametersOrBuilderpublic com.google.protobuf.ProtocolStringList 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;getSubsolversList in interface SatParametersOrBuilderpublic 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;getSubsolversCount in interface SatParametersOrBuilderpublic java.lang.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;getSubsolvers in interface SatParametersOrBuilderindex - The index of the element to return.public 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;getSubsolversBytes in interface SatParametersOrBuilderindex - The index of the value to return.public com.google.protobuf.ProtocolStringList getExtraSubsolversList()
A convenient way to add more workers types. These will be added at the beginning of the list.
repeated string extra_subsolvers = 219;getExtraSubsolversList in interface SatParametersOrBuilderpublic 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;getExtraSubsolversCount in interface SatParametersOrBuilderpublic java.lang.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;getExtraSubsolvers in interface SatParametersOrBuilderindex - The index of the element to return.public 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;getExtraSubsolversBytes in interface SatParametersOrBuilderindex - The index of the value to return.public com.google.protobuf.ProtocolStringList 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;getIgnoreSubsolversList in interface SatParametersOrBuilderpublic 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;getIgnoreSubsolversCount in interface SatParametersOrBuilderpublic java.lang.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;getIgnoreSubsolvers in interface SatParametersOrBuilderindex - The index of the element to return.public 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;getIgnoreSubsolversBytes in interface SatParametersOrBuilderindex - The index of the value to return.public com.google.protobuf.ProtocolStringList getFilterSubsolversList()
repeated string filter_subsolvers = 293;getFilterSubsolversList in interface SatParametersOrBuilderpublic int getFilterSubsolversCount()
repeated string filter_subsolvers = 293;getFilterSubsolversCount in interface SatParametersOrBuilderpublic java.lang.String getFilterSubsolvers(int index)
repeated string filter_subsolvers = 293;getFilterSubsolvers in interface SatParametersOrBuilderindex - The index of the element to return.public com.google.protobuf.ByteString getFilterSubsolversBytes(int index)
repeated string filter_subsolvers = 293;getFilterSubsolversBytes in interface SatParametersOrBuilderindex - The index of the value to return.public java.util.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;getSubsolverParamsList in interface SatParametersOrBuilderpublic java.util.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;getSubsolverParamsOrBuilderList in interface SatParametersOrBuilderpublic 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;getSubsolverParamsCount in interface SatParametersOrBuilderpublic 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;getSubsolverParams in interface SatParametersOrBuilderpublic 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;getSubsolverParamsOrBuilder in interface SatParametersOrBuilderpublic 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];hasInterleaveSearch in interface SatParametersOrBuilderpublic 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];getInterleaveSearch in interface SatParametersOrBuilderpublic boolean hasInterleaveBatchSize()
optional int32 interleave_batch_size = 134 [default = 0];hasInterleaveBatchSize in interface SatParametersOrBuilderpublic int getInterleaveBatchSize()
optional int32 interleave_batch_size = 134 [default = 0];getInterleaveBatchSize in interface SatParametersOrBuilderpublic boolean hasShareObjectiveBounds()
Allows objective sharing between workers.
optional bool share_objective_bounds = 113 [default = true];hasShareObjectiveBounds in interface SatParametersOrBuilderpublic boolean getShareObjectiveBounds()
Allows objective sharing between workers.
optional bool share_objective_bounds = 113 [default = true];getShareObjectiveBounds in interface SatParametersOrBuilderpublic boolean hasShareLevelZeroBounds()
Allows sharing of the bounds of modified variables at level 0.
optional bool share_level_zero_bounds = 114 [default = true];hasShareLevelZeroBounds in interface SatParametersOrBuilderpublic boolean getShareLevelZeroBounds()
Allows sharing of the bounds of modified variables at level 0.
optional bool share_level_zero_bounds = 114 [default = true];getShareLevelZeroBounds in interface SatParametersOrBuilderpublic boolean hasShareBinaryClauses()
Allows sharing of new learned binary clause between workers.
optional bool share_binary_clauses = 203 [default = true];hasShareBinaryClauses in interface SatParametersOrBuilderpublic boolean getShareBinaryClauses()
Allows sharing of new learned binary clause between workers.
optional bool share_binary_clauses = 203 [default = true];getShareBinaryClauses in interface SatParametersOrBuilderpublic 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 = false];hasShareGlueClauses in interface SatParametersOrBuilderpublic 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 = false];getShareGlueClauses in interface SatParametersOrBuilderpublic boolean hasMinimizeSharedClauses()
Minimize and detect subsumption of shared clauses immediately after they are imported.
optional bool minimize_shared_clauses = 300 [default = true];hasMinimizeSharedClauses in interface SatParametersOrBuilderpublic boolean getMinimizeSharedClauses()
Minimize and detect subsumption of shared clauses immediately after they are imported.
optional bool minimize_shared_clauses = 300 [default = true];getMinimizeSharedClauses in interface SatParametersOrBuilderpublic 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];hasDebugPostsolveWithFullSolver in interface SatParametersOrBuilderpublic 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];getDebugPostsolveWithFullSolver in interface SatParametersOrBuilderpublic 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];hasDebugMaxNumPresolveOperations in interface SatParametersOrBuilderpublic 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];getDebugMaxNumPresolveOperations in interface SatParametersOrBuilderpublic 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];hasDebugCrashOnBadHint in interface SatParametersOrBuilderpublic 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];getDebugCrashOnBadHint in interface SatParametersOrBuilderpublic boolean hasDebugCrashIfPresolveBreaksHint()
Crash if presolve breaks a feasible hint.
optional bool debug_crash_if_presolve_breaks_hint = 306 [default = false];hasDebugCrashIfPresolveBreaksHint in interface SatParametersOrBuilderpublic boolean getDebugCrashIfPresolveBreaksHint()
Crash if presolve breaks a feasible hint.
optional bool debug_crash_if_presolve_breaks_hint = 306 [default = false];getDebugCrashIfPresolveBreaksHint in interface SatParametersOrBuilderpublic 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];hasUseOptimizationHints in interface SatParametersOrBuilderpublic 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];getUseOptimizationHints in interface SatParametersOrBuilderpublic 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];hasCoreMinimizationLevel in interface SatParametersOrBuilderpublic 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];getCoreMinimizationLevel in interface SatParametersOrBuilderpublic 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];hasFindMultipleCores in interface SatParametersOrBuilderpublic 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];getFindMultipleCores in interface SatParametersOrBuilderpublic 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];hasCoverOptimization in interface SatParametersOrBuilderpublic 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];getCoverOptimization in interface SatParametersOrBuilderpublic boolean hasMaxSatAssumptionOrder()
optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];hasMaxSatAssumptionOrder in interface SatParametersOrBuilderpublic SatParameters.MaxSatAssumptionOrder getMaxSatAssumptionOrder()
optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];getMaxSatAssumptionOrder in interface SatParametersOrBuilderpublic 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];hasMaxSatReverseAssumptionOrder in interface SatParametersOrBuilderpublic 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];getMaxSatReverseAssumptionOrder in interface SatParametersOrBuilderpublic boolean hasMaxSatStratification()
optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];hasMaxSatStratification in interface SatParametersOrBuilderpublic SatParameters.MaxSatStratificationAlgorithm getMaxSatStratification()
optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];getMaxSatStratification in interface SatParametersOrBuilderpublic 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];hasPropagationLoopDetectionFactor in interface SatParametersOrBuilderpublic 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];getPropagationLoopDetectionFactor in interface SatParametersOrBuilderpublic 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];hasUsePrecedencesInDisjunctiveConstraint in interface SatParametersOrBuilderpublic 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];getUsePrecedencesInDisjunctiveConstraint in interface SatParametersOrBuilderpublic 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];hasMaxSizeToCreatePrecedenceLiteralsInDisjunctive in interface SatParametersOrBuilderpublic 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];getMaxSizeToCreatePrecedenceLiteralsInDisjunctive in interface SatParametersOrBuilderpublic boolean hasUseStrongPropagationInDisjunctive()
Enable stronger and more expensive propagation on no_overlap constraint.
optional bool use_strong_propagation_in_disjunctive = 230 [default = false];hasUseStrongPropagationInDisjunctive in interface SatParametersOrBuilderpublic boolean getUseStrongPropagationInDisjunctive()
Enable stronger and more expensive propagation on no_overlap constraint.
optional bool use_strong_propagation_in_disjunctive = 230 [default = false];getUseStrongPropagationInDisjunctive in interface SatParametersOrBuilderpublic 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];hasUseDynamicPrecedenceInDisjunctive in interface SatParametersOrBuilderpublic 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];getUseDynamicPrecedenceInDisjunctive in interface SatParametersOrBuilderpublic boolean hasUseDynamicPrecedenceInCumulative()
optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];hasUseDynamicPrecedenceInCumulative in interface SatParametersOrBuilderpublic boolean getUseDynamicPrecedenceInCumulative()
optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];getUseDynamicPrecedenceInCumulative in interface SatParametersOrBuilderpublic 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];hasUseOverloadCheckerInCumulative in interface SatParametersOrBuilderpublic 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];getUseOverloadCheckerInCumulative in interface SatParametersOrBuilderpublic 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];hasUseConservativeScaleOverloadChecker in interface SatParametersOrBuilderpublic 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];getUseConservativeScaleOverloadChecker in interface SatParametersOrBuilderpublic 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];hasUseTimetableEdgeFindingInCumulative in interface SatParametersOrBuilderpublic 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];getUseTimetableEdgeFindingInCumulative in interface SatParametersOrBuilderpublic 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];hasMaxNumIntervalsForTimetableEdgeFinding in interface SatParametersOrBuilderpublic 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];getMaxNumIntervalsForTimetableEdgeFinding in interface SatParametersOrBuilderpublic 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];hasUseHardPrecedencesInCumulative in interface SatParametersOrBuilderpublic 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];getUseHardPrecedencesInCumulative in interface SatParametersOrBuilderpublic boolean hasExploitAllPrecedences()
optional bool exploit_all_precedences = 220 [default = false];hasExploitAllPrecedences in interface SatParametersOrBuilderpublic boolean getExploitAllPrecedences()
optional bool exploit_all_precedences = 220 [default = false];getExploitAllPrecedences in interface SatParametersOrBuilderpublic 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];hasUseDisjunctiveConstraintInCumulative in interface SatParametersOrBuilderpublic 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];getUseDisjunctiveConstraintInCumulative in interface SatParametersOrBuilderpublic 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];hasUseTimetablingInNoOverlap2D in interface SatParametersOrBuilderpublic 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];getUseTimetablingInNoOverlap2D in interface SatParametersOrBuilderpublic 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];hasUseEnergeticReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic 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];getUseEnergeticReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic 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];hasUseAreaEnergeticReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic 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];getUseAreaEnergeticReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic boolean hasUseTryEdgeReasoningInNoOverlap2D()
optional bool use_try_edge_reasoning_in_no_overlap_2d = 299 [default = false];hasUseTryEdgeReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic boolean getUseTryEdgeReasoningInNoOverlap2D()
optional bool use_try_edge_reasoning_in_no_overlap_2d = 299 [default = false];getUseTryEdgeReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic 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];hasMaxPairsPairwiseReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic 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];getMaxPairsPairwiseReasoningInNoOverlap2D in interface SatParametersOrBuilderpublic 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];hasMaximumRegionsToSplitInDisconnectedNoOverlap2D in interface SatParametersOrBuilderpublic 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];getMaximumRegionsToSplitInDisconnectedNoOverlap2D in interface SatParametersOrBuilderpublic 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];hasUseDualSchedulingHeuristics in interface SatParametersOrBuilderpublic 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];getUseDualSchedulingHeuristics in interface SatParametersOrBuilderpublic 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];hasUseAllDifferentForCircuit in interface SatParametersOrBuilderpublic 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];getUseAllDifferentForCircuit in interface SatParametersOrBuilderpublic 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];hasRoutingCutSubsetSizeForBinaryRelationBound in interface SatParametersOrBuilderpublic 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];getRoutingCutSubsetSizeForBinaryRelationBound in interface SatParametersOrBuilderpublic 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];hasRoutingCutSubsetSizeForTightBinaryRelationBound in interface SatParametersOrBuilderpublic 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];getRoutingCutSubsetSizeForTightBinaryRelationBound in interface SatParametersOrBuilderpublic 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];hasRoutingCutDpEffort in interface SatParametersOrBuilderpublic 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];getRoutingCutDpEffort in interface SatParametersOrBuilderpublic boolean hasSearchBranching()
optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];hasSearchBranching in interface SatParametersOrBuilderpublic SatParameters.SearchBranching getSearchBranching()
optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];getSearchBranching in interface SatParametersOrBuilderpublic boolean hasHintConflictLimit()
Conflict limit used in the phase that exploit the solution hint.
optional int32 hint_conflict_limit = 153 [default = 10];hasHintConflictLimit in interface SatParametersOrBuilderpublic int getHintConflictLimit()
Conflict limit used in the phase that exploit the solution hint.
optional int32 hint_conflict_limit = 153 [default = 10];getHintConflictLimit in interface SatParametersOrBuilderpublic 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];hasRepairHint in interface SatParametersOrBuilderpublic 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];getRepairHint in interface SatParametersOrBuilderpublic 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];hasFixVariablesToTheirHintedValue in interface SatParametersOrBuilderpublic 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];getFixVariablesToTheirHintedValue in interface SatParametersOrBuilderpublic 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];hasUseProbingSearch in interface SatParametersOrBuilderpublic 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];getUseProbingSearch in interface SatParametersOrBuilderpublic boolean hasUseExtendedProbing()
Use extended probing (probe bool_or, at_most_one, exactly_one).
optional bool use_extended_probing = 269 [default = true];hasUseExtendedProbing in interface SatParametersOrBuilderpublic boolean getUseExtendedProbing()
Use extended probing (probe bool_or, at_most_one, exactly_one).
optional bool use_extended_probing = 269 [default = true];getUseExtendedProbing in interface SatParametersOrBuilderpublic boolean hasProbingNumCombinationsLimit()
How many combinations of pairs or triplets of variables we want to scan.
optional int32 probing_num_combinations_limit = 272 [default = 20000];hasProbingNumCombinationsLimit in interface SatParametersOrBuilderpublic int getProbingNumCombinationsLimit()
How many combinations of pairs or triplets of variables we want to scan.
optional int32 probing_num_combinations_limit = 272 [default = 20000];getProbingNumCombinationsLimit in interface SatParametersOrBuilderpublic boolean hasUseShavingInProbingSearch()
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.
optional bool use_shaving_in_probing_search = 204 [default = true];hasUseShavingInProbingSearch in interface SatParametersOrBuilderpublic boolean getUseShavingInProbingSearch()
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.
optional bool use_shaving_in_probing_search = 204 [default = true];getUseShavingInProbingSearch in interface SatParametersOrBuilderpublic 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 = 0.001];hasShavingSearchDeterministicTime in interface SatParametersOrBuilderpublic 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 = 0.001];getShavingSearchDeterministicTime in interface SatParametersOrBuilderpublic 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];hasShavingSearchThreshold in interface SatParametersOrBuilderpublic 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];getShavingSearchThreshold in interface SatParametersOrBuilderpublic 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];hasUseObjectiveLbSearch in interface SatParametersOrBuilderpublic 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];getUseObjectiveLbSearch in interface SatParametersOrBuilderpublic 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];hasUseObjectiveShavingSearch in interface SatParametersOrBuilderpublic 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];getUseObjectiveShavingSearch in interface SatParametersOrBuilderpublic boolean hasUseVariablesShavingSearch()
This search takes all Boolean or integer variables, and maximize or minimize them in order to reduce their domain.
optional bool use_variables_shaving_search = 289 [default = false];hasUseVariablesShavingSearch in interface SatParametersOrBuilderpublic boolean getUseVariablesShavingSearch()
This search takes all Boolean or integer variables, and maximize or minimize them in order to reduce their domain.
optional bool use_variables_shaving_search = 289 [default = false];getUseVariablesShavingSearch in interface SatParametersOrBuilderpublic 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];hasPseudoCostReliabilityThreshold in interface SatParametersOrBuilderpublic 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];getPseudoCostReliabilityThreshold in interface SatParametersOrBuilderpublic 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];hasOptimizeWithCore in interface SatParametersOrBuilderpublic 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];getOptimizeWithCore in interface SatParametersOrBuilderpublic 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];hasOptimizeWithLbTreeSearch in interface SatParametersOrBuilderpublic 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];getOptimizeWithLbTreeSearch in interface SatParametersOrBuilderpublic 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];hasSaveLpBasisInLbTreeSearch in interface SatParametersOrBuilderpublic 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];getSaveLpBasisInLbTreeSearch in interface SatParametersOrBuilderpublic 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];hasBinarySearchNumConflicts in interface SatParametersOrBuilderpublic 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];getBinarySearchNumConflicts in interface SatParametersOrBuilderpublic 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];hasOptimizeWithMaxHs in interface SatParametersOrBuilderpublic 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];getOptimizeWithMaxHs in interface SatParametersOrBuilderpublic 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];hasUseFeasibilityJump in interface SatParametersOrBuilderpublic 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];getUseFeasibilityJump in interface SatParametersOrBuilderpublic 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];hasUseLsOnly in interface SatParametersOrBuilderpublic 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];getUseLsOnly in interface SatParametersOrBuilderpublic 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];hasFeasibilityJumpDecay in interface SatParametersOrBuilderpublic 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];getFeasibilityJumpDecay in interface SatParametersOrBuilderpublic boolean hasFeasibilityJumpLinearizationLevel()
How much do we linearize the problem in the local search code.
optional int32 feasibility_jump_linearization_level = 257 [default = 2];hasFeasibilityJumpLinearizationLevel in interface SatParametersOrBuilderpublic int getFeasibilityJumpLinearizationLevel()
How much do we linearize the problem in the local search code.
optional int32 feasibility_jump_linearization_level = 257 [default = 2];getFeasibilityJumpLinearizationLevel in interface SatParametersOrBuilderpublic 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];hasFeasibilityJumpRestartFactor in interface SatParametersOrBuilderpublic 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];getFeasibilityJumpRestartFactor in interface SatParametersOrBuilderpublic boolean hasFeasibilityJumpBatchDtime()
How much dtime for each LS batch.
optional double feasibility_jump_batch_dtime = 292 [default = 0.1];hasFeasibilityJumpBatchDtime in interface SatParametersOrBuilderpublic double getFeasibilityJumpBatchDtime()
How much dtime for each LS batch.
optional double feasibility_jump_batch_dtime = 292 [default = 0.1];getFeasibilityJumpBatchDtime in interface SatParametersOrBuilderpublic 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];hasFeasibilityJumpVarRandomizationProbability in interface SatParametersOrBuilderpublic 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];getFeasibilityJumpVarRandomizationProbability in interface SatParametersOrBuilderpublic 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];hasFeasibilityJumpVarPerburbationRangeRatio in interface SatParametersOrBuilderpublic 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];getFeasibilityJumpVarPerburbationRangeRatio in interface SatParametersOrBuilderpublic 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];hasFeasibilityJumpEnableRestarts in interface SatParametersOrBuilderpublic 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];getFeasibilityJumpEnableRestarts in interface SatParametersOrBuilderpublic 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];hasFeasibilityJumpMaxExpandedConstraintSize in interface SatParametersOrBuilderpublic 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];getFeasibilityJumpMaxExpandedConstraintSize in interface SatParametersOrBuilderpublic 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];hasNumViolationLs in interface SatParametersOrBuilderpublic 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];getNumViolationLs in interface SatParametersOrBuilderpublic boolean hasViolationLsPerturbationPeriod()
How long violation_ls should wait before perturbating a solution.
optional int32 violation_ls_perturbation_period = 249 [default = 100];hasViolationLsPerturbationPeriod in interface SatParametersOrBuilderpublic int getViolationLsPerturbationPeriod()
How long violation_ls should wait before perturbating a solution.
optional int32 violation_ls_perturbation_period = 249 [default = 100];getViolationLsPerturbationPeriod in interface SatParametersOrBuilderpublic 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];hasViolationLsCompoundMoveProbability in interface SatParametersOrBuilderpublic 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];getViolationLsCompoundMoveProbability in interface SatParametersOrBuilderpublic 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 = 0];hasSharedTreeNumWorkers in interface SatParametersOrBuilderpublic 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 = 0];getSharedTreeNumWorkers in interface SatParametersOrBuilderpublic boolean hasUseSharedTreeSearch()
Set on shared subtree workers. Users should not set this directly.
optional bool use_shared_tree_search = 236 [default = false];hasUseSharedTreeSearch in interface SatParametersOrBuilderpublic boolean getUseSharedTreeSearch()
Set on shared subtree workers. Users should not set this directly.
optional bool use_shared_tree_search = 236 [default = false];getUseSharedTreeSearch in interface SatParametersOrBuilderpublic 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];hasSharedTreeWorkerMinRestartsPerSubtree in interface SatParametersOrBuilderpublic 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];getSharedTreeWorkerMinRestartsPerSubtree in interface SatParametersOrBuilderpublic 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];hasSharedTreeWorkerEnableTrailSharing in interface SatParametersOrBuilderpublic 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];getSharedTreeWorkerEnableTrailSharing in interface SatParametersOrBuilderpublic 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];hasSharedTreeWorkerEnablePhaseSharing in interface SatParametersOrBuilderpublic 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];getSharedTreeWorkerEnablePhaseSharing in interface SatParametersOrBuilderpublic 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];hasSharedTreeOpenLeavesPerWorker in interface SatParametersOrBuilderpublic 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];getSharedTreeOpenLeavesPerWorker in interface SatParametersOrBuilderpublic 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];hasSharedTreeMaxNodesPerWorker in interface SatParametersOrBuilderpublic 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];getSharedTreeMaxNodesPerWorker in interface SatParametersOrBuilderpublic boolean hasSharedTreeSplitStrategy()
optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];hasSharedTreeSplitStrategy in interface SatParametersOrBuilderpublic SatParameters.SharedTreeSplitStrategy getSharedTreeSplitStrategy()
optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];getSharedTreeSplitStrategy in interface SatParametersOrBuilderpublic 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];hasSharedTreeBalanceTolerance in interface SatParametersOrBuilderpublic 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];getSharedTreeBalanceTolerance in interface SatParametersOrBuilderpublic boolean hasEnumerateAllSolutions()
Whether we enumerate all solutions of a problem without objective. Note that setting this to true automatically disable some presolve reduction that can remove feasible solution. That is it has the same effect as setting keep_all_feasible_solutions_in_presolve. TODO(user): Do not do that and let the user choose what behavior is best by setting keep_all_feasible_solutions_in_presolve ?
optional bool enumerate_all_solutions = 87 [default = false];hasEnumerateAllSolutions in interface SatParametersOrBuilderpublic boolean getEnumerateAllSolutions()
Whether we enumerate all solutions of a problem without objective. Note that setting this to true automatically disable some presolve reduction that can remove feasible solution. That is it has the same effect as setting keep_all_feasible_solutions_in_presolve. TODO(user): Do not do that and let the user choose what behavior is best by setting keep_all_feasible_solutions_in_presolve ?
optional bool enumerate_all_solutions = 87 [default = false];getEnumerateAllSolutions in interface SatParametersOrBuilderpublic 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];hasKeepAllFeasibleSolutionsInPresolve in interface SatParametersOrBuilderpublic 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];getKeepAllFeasibleSolutionsInPresolve in interface SatParametersOrBuilderpublic 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];hasFillTightenedDomainsInResponse in interface SatParametersOrBuilderpublic 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];getFillTightenedDomainsInResponse in interface SatParametersOrBuilderpublic 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];hasFillAdditionalSolutionsInResponse in interface SatParametersOrBuilderpublic 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];getFillAdditionalSolutionsInResponse in interface SatParametersOrBuilderpublic 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];hasInstantiateAllVariables in interface SatParametersOrBuilderpublic 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];getInstantiateAllVariables in interface SatParametersOrBuilderpublic 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];hasAutoDetectGreaterThanAtLeastOneOf in interface SatParametersOrBuilderpublic 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];getAutoDetectGreaterThanAtLeastOneOf in interface SatParametersOrBuilderpublic 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];hasStopAfterFirstSolution in interface SatParametersOrBuilderpublic 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];getStopAfterFirstSolution in interface SatParametersOrBuilderpublic 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];hasStopAfterPresolve in interface SatParametersOrBuilderpublic 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];getStopAfterPresolve in interface SatParametersOrBuilderpublic boolean hasStopAfterRootPropagation()
optional bool stop_after_root_propagation = 252 [default = false];hasStopAfterRootPropagation in interface SatParametersOrBuilderpublic boolean getStopAfterRootPropagation()
optional bool stop_after_root_propagation = 252 [default = false];getStopAfterRootPropagation in interface SatParametersOrBuilderpublic boolean hasLnsInitialDifficulty()
Initial parameters for neighborhood generation.
optional double lns_initial_difficulty = 307 [default = 0.5];hasLnsInitialDifficulty in interface SatParametersOrBuilderpublic double getLnsInitialDifficulty()
Initial parameters for neighborhood generation.
optional double lns_initial_difficulty = 307 [default = 0.5];getLnsInitialDifficulty in interface SatParametersOrBuilderpublic boolean hasLnsInitialDeterministicLimit()
optional double lns_initial_deterministic_limit = 308 [default = 0.1];hasLnsInitialDeterministicLimit in interface SatParametersOrBuilderpublic double getLnsInitialDeterministicLimit()
optional double lns_initial_deterministic_limit = 308 [default = 0.1];getLnsInitialDeterministicLimit in interface SatParametersOrBuilderpublic boolean hasUseLns()
Testing parameters used to disable all lns workers.
optional bool use_lns = 283 [default = true];hasUseLns in interface SatParametersOrBuilderpublic boolean getUseLns()
Testing parameters used to disable all lns workers.
optional bool use_lns = 283 [default = true];getUseLns in interface SatParametersOrBuilderpublic boolean hasUseLnsOnly()
Experimental parameters to disable everything but lns.
optional bool use_lns_only = 101 [default = false];hasUseLnsOnly in interface SatParametersOrBuilderpublic boolean getUseLnsOnly()
Experimental parameters to disable everything but lns.
optional bool use_lns_only = 101 [default = false];getUseLnsOnly in interface SatParametersOrBuilderpublic boolean hasSolutionPoolSize()
Size of the top-n different solutions kept by the solver. This parameter must be > 0. Currently this only impact the "base" solution chosen for a LNS fragment.
optional int32 solution_pool_size = 193 [default = 3];hasSolutionPoolSize in interface SatParametersOrBuilderpublic int getSolutionPoolSize()
Size of the top-n different solutions kept by the solver. This parameter must be > 0. Currently this only impact the "base" solution chosen for a LNS fragment.
optional int32 solution_pool_size = 193 [default = 3];getSolutionPoolSize in interface SatParametersOrBuilderpublic boolean hasUseRinsLns()
Turns on relaxation induced neighborhood generator.
optional bool use_rins_lns = 129 [default = true];hasUseRinsLns in interface SatParametersOrBuilderpublic boolean getUseRinsLns()
Turns on relaxation induced neighborhood generator.
optional bool use_rins_lns = 129 [default = true];getUseRinsLns in interface SatParametersOrBuilderpublic boolean hasUseFeasibilityPump()
Adds a feasibility pump subsolver along with lns subsolvers.
optional bool use_feasibility_pump = 164 [default = true];hasUseFeasibilityPump in interface SatParametersOrBuilderpublic boolean getUseFeasibilityPump()
Adds a feasibility pump subsolver along with lns subsolvers.
optional bool use_feasibility_pump = 164 [default = true];getUseFeasibilityPump in interface SatParametersOrBuilderpublic 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];hasUseLbRelaxLns in interface SatParametersOrBuilderpublic 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];getUseLbRelaxLns in interface SatParametersOrBuilderpublic boolean hasLbRelaxNumWorkersThreshold()
Only use lb-relax if we have at least that many workers.
optional int32 lb_relax_num_workers_threshold = 296 [default = 16];hasLbRelaxNumWorkersThreshold in interface SatParametersOrBuilderpublic int getLbRelaxNumWorkersThreshold()
Only use lb-relax if we have at least that many workers.
optional int32 lb_relax_num_workers_threshold = 296 [default = 16];getLbRelaxNumWorkersThreshold in interface SatParametersOrBuilderpublic boolean hasFpRounding()
optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];hasFpRounding in interface SatParametersOrBuilderpublic SatParameters.FPRoundingMethod getFpRounding()
optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];getFpRounding in interface SatParametersOrBuilderpublic boolean hasDiversifyLnsParams()
If true, registers more lns subsolvers with different parameters.
optional bool diversify_lns_params = 137 [default = false];hasDiversifyLnsParams in interface SatParametersOrBuilderpublic boolean getDiversifyLnsParams()
If true, registers more lns subsolvers with different parameters.
optional bool diversify_lns_params = 137 [default = false];getDiversifyLnsParams in interface SatParametersOrBuilderpublic boolean hasRandomizeSearch()
Randomize fixed search.
optional bool randomize_search = 103 [default = false];hasRandomizeSearch in interface SatParametersOrBuilderpublic boolean getRandomizeSearch()
Randomize fixed search.
optional bool randomize_search = 103 [default = false];getRandomizeSearch in interface SatParametersOrBuilderpublic 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];hasSearchRandomVariablePoolSize in interface SatParametersOrBuilderpublic 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];getSearchRandomVariablePoolSize in interface SatParametersOrBuilderpublic 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];hasPushAllTasksTowardStart in interface SatParametersOrBuilderpublic 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];getPushAllTasksTowardStart in interface SatParametersOrBuilderpublic 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];hasUseOptionalVariables in interface SatParametersOrBuilderpublic 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];getUseOptionalVariables in interface SatParametersOrBuilderpublic 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];hasUseExactLpReason in interface SatParametersOrBuilderpublic 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];getUseExactLpReason in interface SatParametersOrBuilderpublic 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];hasUseCombinedNoOverlap in interface SatParametersOrBuilderpublic 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];getUseCombinedNoOverlap in interface SatParametersOrBuilderpublic 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];hasAtMostOneMaxExpansionSize in interface SatParametersOrBuilderpublic 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];getAtMostOneMaxExpansionSize in interface SatParametersOrBuilderpublic 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];hasCatchSigintSignal in interface SatParametersOrBuilderpublic 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];getCatchSigintSignal in interface SatParametersOrBuilderpublic 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];hasUseImpliedBounds in interface SatParametersOrBuilderpublic 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];getUseImpliedBounds in interface SatParametersOrBuilderpublic 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];hasPolishLpSolution in interface SatParametersOrBuilderpublic 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];getPolishLpSolution in interface SatParametersOrBuilderpublic 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];hasLpPrimalTolerance in interface SatParametersOrBuilderpublic 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];getLpPrimalTolerance in interface SatParametersOrBuilderpublic boolean hasLpDualTolerance()
optional double lp_dual_tolerance = 267 [default = 1e-07];hasLpDualTolerance in interface SatParametersOrBuilderpublic double getLpDualTolerance()
optional double lp_dual_tolerance = 267 [default = 1e-07];getLpDualTolerance in interface SatParametersOrBuilderpublic 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];hasConvertIntervals in interface SatParametersOrBuilderpublic 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];getConvertIntervals in interface SatParametersOrBuilderpublic 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];hasSymmetryLevel in interface SatParametersOrBuilderpublic 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];getSymmetryLevel in interface SatParametersOrBuilderpublic 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];hasUseSymmetryInLp in interface SatParametersOrBuilderpublic 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];getUseSymmetryInLp in interface SatParametersOrBuilderpublic 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];hasKeepSymmetryInPresolve in interface SatParametersOrBuilderpublic 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];getKeepSymmetryInPresolve in interface SatParametersOrBuilderpublic boolean hasSymmetryDetectionDeterministicTimeLimit()
Deterministic time limit for symmetry detection.
optional double symmetry_detection_deterministic_time_limit = 302 [default = 1];hasSymmetryDetectionDeterministicTimeLimit in interface SatParametersOrBuilderpublic double getSymmetryDetectionDeterministicTimeLimit()
Deterministic time limit for symmetry detection.
optional double symmetry_detection_deterministic_time_limit = 302 [default = 1];getSymmetryDetectionDeterministicTimeLimit in interface SatParametersOrBuilderpublic 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];hasNewLinearPropagation in interface SatParametersOrBuilderpublic 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];getNewLinearPropagation in interface SatParametersOrBuilderpublic 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];hasLinearSplitSize in interface SatParametersOrBuilderpublic 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];getLinearSplitSize in interface SatParametersOrBuilderpublic 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];hasLinearizationLevel in interface SatParametersOrBuilderpublic 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];getLinearizationLevel in interface SatParametersOrBuilderpublic 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];hasBooleanEncodingLevel in interface SatParametersOrBuilderpublic 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];getBooleanEncodingLevel in interface SatParametersOrBuilderpublic 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];hasMaxDomainSizeWhenEncodingEqNeqConstraints in interface SatParametersOrBuilderpublic 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];getMaxDomainSizeWhenEncodingEqNeqConstraints in interface SatParametersOrBuilderpublic 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];hasMaxNumCuts in interface SatParametersOrBuilderpublic 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];getMaxNumCuts in interface SatParametersOrBuilderpublic 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];hasCutLevel in interface SatParametersOrBuilderpublic 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];getCutLevel in interface SatParametersOrBuilderpublic 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];hasOnlyAddCutsAtLevelZero in interface SatParametersOrBuilderpublic 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];getOnlyAddCutsAtLevelZero in interface SatParametersOrBuilderpublic 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];hasAddObjectiveCut in interface SatParametersOrBuilderpublic 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];getAddObjectiveCut in interface SatParametersOrBuilderpublic 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];hasAddCgCuts in interface SatParametersOrBuilderpublic 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];getAddCgCuts in interface SatParametersOrBuilderpublic 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];hasAddMirCuts in interface SatParametersOrBuilderpublic 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];getAddMirCuts in interface SatParametersOrBuilderpublic 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];hasAddZeroHalfCuts in interface SatParametersOrBuilderpublic 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];getAddZeroHalfCuts in interface SatParametersOrBuilderpublic 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];hasAddCliqueCuts in interface SatParametersOrBuilderpublic 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];getAddCliqueCuts in interface SatParametersOrBuilderpublic 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];hasAddRltCuts in interface SatParametersOrBuilderpublic 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];getAddRltCuts in interface SatParametersOrBuilderpublic 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];hasMaxAllDiffCutSize in interface SatParametersOrBuilderpublic 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];getMaxAllDiffCutSize in interface SatParametersOrBuilderpublic 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];hasAddLinMaxCuts in interface SatParametersOrBuilderpublic 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];getAddLinMaxCuts in interface SatParametersOrBuilderpublic 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];hasMaxIntegerRoundingScaling in interface SatParametersOrBuilderpublic 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];getMaxIntegerRoundingScaling in interface SatParametersOrBuilderpublic 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];hasAddLpConstraintsLazily in interface SatParametersOrBuilderpublic 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];getAddLpConstraintsLazily in interface SatParametersOrBuilderpublic 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];hasRootLpIterations in interface SatParametersOrBuilderpublic 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];getRootLpIterations in interface SatParametersOrBuilderpublic 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];hasMinOrthogonalityForLpConstraints in interface SatParametersOrBuilderpublic 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];getMinOrthogonalityForLpConstraints in interface SatParametersOrBuilderpublic 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];hasMaxCutRoundsAtLevelZero in interface SatParametersOrBuilderpublic 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];getMaxCutRoundsAtLevelZero in interface SatParametersOrBuilderpublic 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];hasMaxConsecutiveInactiveCount in interface SatParametersOrBuilderpublic 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];getMaxConsecutiveInactiveCount in interface SatParametersOrBuilderpublic 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];hasCutMaxActiveCountValue in interface SatParametersOrBuilderpublic 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];getCutMaxActiveCountValue in interface SatParametersOrBuilderpublic boolean hasCutActiveCountDecay()
optional double cut_active_count_decay = 156 [default = 0.8];hasCutActiveCountDecay in interface SatParametersOrBuilderpublic double getCutActiveCountDecay()
optional double cut_active_count_decay = 156 [default = 0.8];getCutActiveCountDecay in interface SatParametersOrBuilderpublic boolean hasCutCleanupTarget()
Target number of constraints to remove during cleanup.
optional int32 cut_cleanup_target = 157 [default = 1000];hasCutCleanupTarget in interface SatParametersOrBuilderpublic int getCutCleanupTarget()
Target number of constraints to remove during cleanup.
optional int32 cut_cleanup_target = 157 [default = 1000];getCutCleanupTarget in interface SatParametersOrBuilderpublic 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];hasNewConstraintsBatchSize in interface SatParametersOrBuilderpublic 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];getNewConstraintsBatchSize in interface SatParametersOrBuilderpublic 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];hasExploitIntegerLpSolution in interface SatParametersOrBuilderpublic 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];getExploitIntegerLpSolution in interface SatParametersOrBuilderpublic 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];hasExploitAllLpSolution in interface SatParametersOrBuilderpublic 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];getExploitAllLpSolution in interface SatParametersOrBuilderpublic boolean hasExploitBestSolution()
When branching on a variable, follow the last best solution value.
optional bool exploit_best_solution = 130 [default = false];hasExploitBestSolution in interface SatParametersOrBuilderpublic boolean getExploitBestSolution()
When branching on a variable, follow the last best solution value.
optional bool exploit_best_solution = 130 [default = false];getExploitBestSolution in interface SatParametersOrBuilderpublic 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];hasExploitRelaxationSolution in interface SatParametersOrBuilderpublic 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];getExploitRelaxationSolution in interface SatParametersOrBuilderpublic 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];hasExploitObjective in interface SatParametersOrBuilderpublic 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];getExploitObjective in interface SatParametersOrBuilderpublic 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];hasDetectLinearizedProduct in interface SatParametersOrBuilderpublic 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];getDetectLinearizedProduct in interface SatParametersOrBuilderpublic 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];hasMipMaxBound in interface SatParametersOrBuilderpublic 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];getMipMaxBound in interface SatParametersOrBuilderpublic 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];hasMipVarScaling in interface SatParametersOrBuilderpublic 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];getMipVarScaling in interface SatParametersOrBuilderpublic 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];hasMipScaleLargeDomain in interface SatParametersOrBuilderpublic 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];getMipScaleLargeDomain in interface SatParametersOrBuilderpublic 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];hasMipAutomaticallyScaleVariables in interface SatParametersOrBuilderpublic 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];getMipAutomaticallyScaleVariables in interface SatParametersOrBuilderpublic 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];hasOnlySolveIp in interface SatParametersOrBuilderpublic 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];getOnlySolveIp in interface SatParametersOrBuilderpublic 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];hasMipWantedPrecision in interface SatParametersOrBuilderpublic 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];getMipWantedPrecision in interface SatParametersOrBuilderpublic 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];hasMipMaxActivityExponent in interface SatParametersOrBuilderpublic 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];getMipMaxActivityExponent in interface SatParametersOrBuilderpublic 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];hasMipCheckPrecision in interface SatParametersOrBuilderpublic 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];getMipCheckPrecision in interface SatParametersOrBuilderpublic 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];hasMipComputeTrueObjectiveBound in interface SatParametersOrBuilderpublic 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];getMipComputeTrueObjectiveBound in interface SatParametersOrBuilderpublic 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];hasMipMaxValidMagnitude in interface SatParametersOrBuilderpublic 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];getMipMaxValidMagnitude in interface SatParametersOrBuilderpublic 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];hasMipTreatHighMagnitudeBoundsAsInfinity in interface SatParametersOrBuilderpublic 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];getMipTreatHighMagnitudeBoundsAsInfinity in interface SatParametersOrBuilderpublic 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];hasMipDropTolerance in interface SatParametersOrBuilderpublic 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];getMipDropTolerance in interface SatParametersOrBuilderpublic 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];hasMipPresolveLevel in interface SatParametersOrBuilderpublic 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];getMipPresolveLevel in interface SatParametersOrBuilderpublic final boolean isInitialized()
isInitialized in interface com.google.protobuf.MessageLiteOrBuilderisInitialized in class com.google.protobuf.GeneratedMessagepublic void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException
writeTo in interface com.google.protobuf.MessageLitewriteTo in class com.google.protobuf.GeneratedMessagejava.io.IOExceptionpublic int getSerializedSize()
getSerializedSize in interface com.google.protobuf.MessageLitegetSerializedSize in class com.google.protobuf.GeneratedMessagepublic boolean equals(java.lang.Object obj)
equals in interface com.google.protobuf.Messageequals in class com.google.protobuf.AbstractMessagepublic int hashCode()
hashCode in interface com.google.protobuf.MessagehashCode in class com.google.protobuf.AbstractMessagepublic static SatParameters parseFrom(java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException
com.google.protobuf.InvalidProtocolBufferExceptionpublic static SatParameters parseFrom(java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
com.google.protobuf.InvalidProtocolBufferExceptionpublic static SatParameters parseFrom(com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException
com.google.protobuf.InvalidProtocolBufferExceptionpublic static SatParameters parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
com.google.protobuf.InvalidProtocolBufferExceptionpublic static SatParameters parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException
com.google.protobuf.InvalidProtocolBufferExceptionpublic static SatParameters parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
com.google.protobuf.InvalidProtocolBufferExceptionpublic static SatParameters parseFrom(java.io.InputStream input) throws java.io.IOException
java.io.IOExceptionpublic static SatParameters parseFrom(java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException
java.io.IOExceptionpublic static SatParameters parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException
java.io.IOExceptionpublic static SatParameters parseDelimitedFrom(java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException
java.io.IOExceptionpublic static SatParameters parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException
java.io.IOExceptionpublic static SatParameters parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException
java.io.IOExceptionpublic SatParameters.Builder newBuilderForType()
newBuilderForType in interface com.google.protobuf.MessagenewBuilderForType in interface com.google.protobuf.MessageLitepublic static SatParameters.Builder newBuilder()
public static SatParameters.Builder newBuilder(SatParameters prototype)
public SatParameters.Builder toBuilder()
toBuilder in interface com.google.protobuf.MessagetoBuilder in interface com.google.protobuf.MessageLiteprotected SatParameters.Builder newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent)
newBuilderForType in class com.google.protobuf.AbstractMessagepublic static SatParameters getDefaultInstance()
public static com.google.protobuf.Parser<SatParameters> parser()
public com.google.protobuf.Parser<SatParameters> getParserForType()
getParserForType in interface com.google.protobuf.MessagegetParserForType in interface com.google.protobuf.MessageLitegetParserForType in class com.google.protobuf.GeneratedMessagepublic SatParameters getDefaultInstanceForType()
getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuildergetDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilderCopyright © 2025. All rights reserved.