Google OR-Tools v9.9
a fast and portable software suite for combinatorial optimization
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Classes | |
class | Builder |
enum | CostScalingAlgorithm |
enum | InitialBasisHeuristic |
enum | PricingRule |
enum | ScalingAlgorithm |
enum | SolverBehavior |
Static Public Member Functions | |
static final com.google.protobuf.Descriptors.Descriptor | getDescriptor () |
static com.google.ortools.glop.GlopParameters | parseFrom (java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException |
static com.google.ortools.glop.GlopParameters | parseFrom (java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException |
static com.google.ortools.glop.GlopParameters | parseFrom (com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException |
static com.google.ortools.glop.GlopParameters | parseFrom (com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException |
static com.google.ortools.glop.GlopParameters | parseFrom (byte[] data) throws com.google.protobuf.InvalidProtocolBufferException |
static com.google.ortools.glop.GlopParameters | parseFrom (byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException |
static com.google.ortools.glop.GlopParameters | parseFrom (java.io.InputStream input) throws java.io.IOException |
static com.google.ortools.glop.GlopParameters | parseFrom (java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException |
static com.google.ortools.glop.GlopParameters | parseDelimitedFrom (java.io.InputStream input) throws java.io.IOException |
static com.google.ortools.glop.GlopParameters | parseDelimitedFrom (java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException |
static com.google.ortools.glop.GlopParameters | parseFrom (com.google.protobuf.CodedInputStream input) throws java.io.IOException |
static com.google.ortools.glop.GlopParameters | parseFrom (com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException |
static Builder | newBuilder () |
static Builder | newBuilder (com.google.ortools.glop.GlopParameters prototype) |
static com.google.ortools.glop.GlopParameters | getDefaultInstance () |
static com.google.protobuf.Parser< GlopParameters > | parser () |
Protected Member Functions | |
java.lang.Object | newInstance (UnusedPrivateParameter unused) |
com.google.protobuf.GeneratedMessageV3.FieldAccessorTable | internalGetFieldAccessorTable () |
Builder | newBuilderForType (com.google.protobuf.GeneratedMessageV3.BuilderParent parent) |
next id = 72
Protobuf type operations_research.glop.GlopParameters
Definition at line 14 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.equals | ( | final java.lang.Object | obj | ) |
Definition at line 3179 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getAllowSimplexAlgorithmChange | ( | ) |
During incremental solve, let the solver decide if it use the primal or dual simplex algorithm depending on the current solution and on the new problem. Note that even if this is true, the value of use_dual_simplex still indicates the default algorithm that the solver will use.
optional bool allow_simplex_algorithm_change = 32 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1881 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.getBasisRefactorizationPeriod | ( | ) |
Number of iterations between two basis refactorizations. Note that various conditions in the algorithm may trigger a refactorization before this period is reached. Set this to 0 if you want to refactorize at each step.
optional int32 basis_refactorization_period = 19 [default = 64];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1375 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getChangeStatusToImprecise | ( | ) |
If true, the internal API will change the return status to imprecise if the solution does not respect the internal tolerances.
optional bool change_status_to_imprecise = 58 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1602 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters.CostScalingAlgorithm com.google.ortools.glop.GlopParameters.getCostScaling | ( | ) |
optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1284 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getCrossoverBoundSnappingDistance | ( | ) |
If the starting basis contains FREE variable with bounds, we will move any such variable to their closer bounds if the distance is smaller than this parameter. The starting statuses can contains FREE variables with bounds, if a user set it like this externally. Also, any variable with an initial BASIC status that was not kept in the initial basis is marked as FREE before this step is applied. Note that by default a FREE variable is assumed to be zero unless a starting value was specified via SetStartingVariableValuesForNextSolve(). Note that, at the end of the solve, some of these FREE variable with bounds and an interior point value might still be left in the final solution. Enable push_to_vertex to clean these up.
optional double crossover_bound_snapping_distance = 64 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2577 of file GlopParameters.java.
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static |
Definition at line 8895 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters com.google.ortools.glop.GlopParameters.getDefaultInstanceForType | ( | ) |
Definition at line 8931 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getDegenerateMinistepFactor | ( | ) |
During a degenerate iteration, the more conservative approach is to do a step of length zero (while shifting the bound of the leaving variable). That is, the variable values are unchanged for the primal simplex or the reduced cost are unchanged for the dual simplex. However, instead of doing a step of length zero, it seems to be better on degenerate problems to do a small positive step. This is what is recommended in the EXPAND procedure described in: P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright. "A practical anti- cycling procedure for linearly constrained optimization". Mathematical Programming, 45:437\u2013474, 1989. Here, during a degenerate iteration we do a small positive step of this factor times the primal (resp. dual) tolerance. In the primal simplex, this may effectively push variable values (very slightly) further out of their bounds (resp. reduced costs for the dual simplex). Setting this to zero reverts to the more conservative approach of a zero step during degenerate iterations.
optional double degenerate_ministep_factor = 42 [default = 0.01];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2223 of file GlopParameters.java.
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static |
Definition at line 87 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.getDevexWeightsResetPeriod | ( | ) |
Devex weights will be reset to 1.0 after that number of updates.
optional int32 devex_weights_reset_period = 33 [default = 150];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1908 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getDropMagnitude | ( | ) |
Value in the input LP lower than this will be ignored. This is similar to drop_tolerance but more aggressive as this is used before scaling. This is mainly here to avoid underflow and have simpler invariant in the code, like a * b == 0 iff a or b is zero and things like this.
optional double drop_magnitude = 71 [default = 1e-30];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2711 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getDropTolerance | ( | ) |
In order to increase the sparsity of the manipulated vectors, floating point values with a magnitude smaller than this parameter are set to zero (only in some places). This parameter should be positive or zero.
optional double drop_tolerance = 52 [default = 1e-14];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1238 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getDualFeasibilityTolerance | ( | ) |
Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates. That is they do not take part in deciding whether a solution is dual-feasible or not. Note that this value can temporarily increase during the execution of the algorithm if the estimated precision of the reduced costs is higher than this tolerance. Note also that we scale the costs (in the presolve step) so that the cost magnitude range contains one. This is also known as the optimality tolerance in other solvers.
optional double dual_feasibility_tolerance = 11 [default = 1e-08];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1061 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getDualizerThreshold | ( | ) |
When solve_dual_problem is LET_SOLVER_DECIDE, take the dual if the number of constraints of the problem is more than this threshold times the number of variables.
optional double dualizer_threshold = 21 [default = 1.5];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1471 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getDualPricePrioritizeNorm | ( | ) |
On some problem like stp3d or pds-100 this makes a huge difference in speed and number of iterations of the dual simplex.
optional bool dual_price_prioritize_norm = 69 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2740 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getDualSmallPivotThreshold | ( | ) |
Like small_pivot_threshold but for the dual simplex. This is needed because the dual algorithm does not interpret this value in the same way. TODO(user): Clean this up and use the same small pivot detection.
optional double dual_small_pivot_threshold = 38 [default = 0.0001];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2063 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getDynamicallyAdjustRefactorizationPeriod | ( | ) |
If this is true, then basis_refactorization_period becomes a lower bound on the number of iterations between two refactorization (provided there is no numerical accuracy issues). Depending on the estimated time to refactorize vs the extra time spend in each solves because of the LU update, we try to balance the two times.
optional bool dynamically_adjust_refactorization_period = 63 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1410 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getExploitSingletonColumnInInitialBasis | ( | ) |
Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
optional bool exploit_singleton_column_in_initial_basis = 37 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2032 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters.PricingRule com.google.ortools.glop.GlopParameters.getFeasibilityRule | ( | ) |
PricingRule to use during the feasibility phase.
optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 843 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getHarrisToleranceRatio | ( | ) |
This impacts the ratio test and indicates by how much we allow a basic variable value that we move to go out of bounds. The value should be in [0.0, 1.0) and should be interpreted as a ratio of the primal_feasibility_tolerance. Setting this to 0.0 basically disables the Harris ratio test while setting this too close to 1.0 will make it difficult to keep the variable values inside their bounds modulo the primal_feasibility_tolerance. Note that the same comment applies to the dual simplex ratio test. There, we allow the reduced costs to be of an infeasible sign by as much as this ratio times the dual_feasibility_tolerance.
optional double harris_tolerance_ratio = 13 [default = 0.5];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1147 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters.InitialBasisHeuristic com.google.ortools.glop.GlopParameters.getInitialBasis | ( | ) |
What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1312 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getInitialConditionNumberThreshold | ( | ) |
If our upper bound on the condition number of the initial basis (from our heurisitic or a warm start) is above this threshold, we revert to an all slack basis.
optional double initial_condition_number_threshold = 59 [default = 1e+50];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2464 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getInitializeDevexWithColumnNorms | ( | ) |
Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
optional bool initialize_devex_with_column_norms = 36 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2003 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getLogSearchProgress | ( | ) |
If true, logs the progress of a solve to LOG(INFO). Note that the same messages can also be turned on by displaying logs at level 1 for the relevant files.
optional bool log_search_progress = 61 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2495 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getLogToStdout | ( | ) |
If true, logs will be displayed to stdout instead of using Google log info.
optional bool log_to_stdout = 66 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2522 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getLuFactorizationPivotThreshold | ( | ) |
Threshold for LU-factorization: for stability reasons, the magnitude of the chosen pivot at a given step is guaranteed to be greater than this threshold times the maximum magnitude of all the possible pivot choices in the same column. The value must be in [0,1].
optional double lu_factorization_pivot_threshold = 25 [default = 0.01];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1668 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getMarkowitzSingularityThreshold | ( | ) |
If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular. Note that this is an absolute threshold and is not relative to the other possible pivots on the same column (see lu_factorization_pivot_threshold).
optional double markowitz_singularity_threshold = 30 [default = 1e-15];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1821 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.getMarkowitzZlatevParameter | ( | ) |
How many columns do we look at in the Markowitz pivoting rule to find a good pivot. See markowitz.h.
optional int32 markowitz_zlatev_parameter = 29 [default = 3];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1788 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getMaxDeterministicTime | ( | ) |
Maximum deterministic time allowed to solve a problem. The deterministic time is more or less correlated to the running time, and its unit should be around the second (at least on a Xeon(R) CPU E5-1650 v2 @ 3.50GHz). TODO(user): Improve the correlation.
optional double max_deterministic_time = 45 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1730 of file GlopParameters.java.
long com.google.ortools.glop.GlopParameters.getMaxNumberOfIterations | ( | ) |
Maximum number of simplex iterations to solve a problem. A value of -1 means no limit.
optional int64 max_number_of_iterations = 27 [default = -1];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1759 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getMaxNumberOfReoptimizations | ( | ) |
When the solution of phase II is imprecise, we re-run the phase II with the opposite algorithm from that imprecise solution (i.e., if primal or dual simplex was used, we use dual or primal simplex, respectively). We repeat such re-optimization until the solution is precise, or we hit this limit.
optional double max_number_of_reoptimizations = 56 [default = 40];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1635 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getMaxTimeInSeconds | ( | ) |
Maximum time allowed in seconds to solve a problem.
optional double max_time_in_seconds = 26 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1695 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getMaxValidMagnitude | ( | ) |
Any finite values in the input LP 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. In practice, users shouldn't use super large values in an LP. With the default threshold, even evaluating large constraint with variables at their bound shouldn't cause any overflow.
optional double max_valid_magnitude = 70 [default = 1e+30];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2678 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getMinimumAcceptablePivot | ( | ) |
We never follow a basis change with a pivot under this threshold.
optional double minimum_acceptable_pivot = 15 [default = 1e-06];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1207 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.getNumOmpThreads | ( | ) |
Number of threads in the OMP parallel sections. If left to 1, the code will not create any OMP threads and will remain single-threaded.
optional int32 num_omp_threads = 44 [default = 1];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2307 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getObjectiveLowerLimit | ( | ) |
The solver will stop as soon as it has proven that the objective is smaller than objective_lower_limit or greater than objective_upper_limit. Depending on the simplex algorithm (primal or dual) and the optimization direction, note that only one bound will be used at the time. Important: The solver does not add any tolerances to these values, and as soon as the objective (as computed by the solver, so with some imprecision) crosses one of these bounds (strictly), the search will stop. It is up to the client to add any tolerance if needed.
optional double objective_lower_limit = 40 [default = -inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2143 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getObjectiveUpperLimit | ( | ) |
optional double objective_upper_limit = 41 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2162 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters.PricingRule com.google.ortools.glop.GlopParameters.getOptimizationRule | ( | ) |
PricingRule to use during the optimization phase.
optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 869 of file GlopParameters.java.
com.google.protobuf.Parser< GlopParameters > com.google.ortools.glop.GlopParameters.getParserForType | ( | ) |
Definition at line 8926 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getPerturbCostsInDualSimplex | ( | ) |
When this is true, then the costs are randomly perturbed before the dual simplex is even started. This has been shown to improve the dual simplex performance. For a good reference, see Huangfu Q (2013) "High performance simplex solver", Ph.D, dissertation, University of Edinburgh.
optional bool perturb_costs_in_dual_simplex = 53 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2340 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getPreprocessorZeroTolerance | ( | ) |
A floating point tolerance used by the preprocessors. This is used for things like detecting if two columns/rows are proportional or if an interval is empty. Note that the preprocessors also use solution_feasibility_tolerance() to detect if a problem is infeasible.
optional double preprocessor_zero_tolerance = 39 [default = 1e-09];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2100 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getPrimalFeasibilityTolerance | ( | ) |
This tolerance indicates by how much we allow the variable values to go out of bounds and still consider the current solution primal-feasible. We also use the same tolerance for the error A.x - b. Note that the two errors are closely related if A is scaled in such a way that the greatest coefficient magnitude on each column is 1.0. This is also simply called feasibility tolerance in other solvers.
optional double primal_feasibility_tolerance = 10 [default = 1e-08];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1016 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getProvideStrongOptimalGuarantee | ( | ) |
If true, then when the solver returns a solution with an OPTIMAL status, we can guarantee that: - The primal variable are in their bounds. - The dual variable are in their bounds. - If we modify each component of the right-hand side a bit and each component of the objective function a bit, then the pair (primal values, dual values) is an EXACT optimal solution of the perturbed problem. - The modifications above are smaller than the associated tolerances as defined in the comment for solution_feasibility_tolerance (*). (*): This is the only place where the guarantee is not tight since we compute the upper bounds with scalar product of the primal/dual solution and the initial problem coefficients with only double precision. Note that whether or not this option is true, we still check the primal/dual infeasibility and objective gap. However if it is false, we don't move the primal/dual values within their bounds and leave them untouched.
optional bool provide_strong_optimal_guarantee = 24 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1573 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getPushToVertex | ( | ) |
If the optimization phases finishes with super-basic variables (i.e., variables that either 1) have bounds but are FREE in the basis, or 2) have no bounds and are FREE in the basis at a nonzero value), then run a "push" phase to push these variables to bounds, obtaining a vertex solution. Note this situation can happen only if a starting value was specified via SetStartingVariableValuesForNextSolve().
optional bool push_to_vertex = 65 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2614 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.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. Note that this may lead to a different solution, for example a different optimal basis. 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. Also note that the solver is fully deterministic: two runs of the same binary, on the same machine, on the exact same data and with the same parameters will go through the exact same iterations. If they hit a time limit, they might of course yield different results because one will have advanced farther than the other.
optional int32 random_seed = 43 [default = 1];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2278 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getRatioTestZeroThreshold | ( | ) |
During the primal simplex (resp. dual simplex), the coefficients of the direction (resp. update row) with a magnitude lower than this threshold are not considered during the ratio test. This tolerance is related to the precision at which a Solve() involving the basis matrix can be performed. TODO(user): Automatically increase it when we detect that the precision of the Solve() is worse than this.
optional double ratio_test_zero_threshold = 12 [default = 1e-09];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1100 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getRecomputeEdgesNormThreshold | ( | ) |
Note that the threshold is a relative error on the actual norm (not the squared one) and that edge norms are always greater than 1. Recomputing norms is a really expensive operation and a large threshold is ok since this doesn't impact directly the solution but just the entering variable choice.
optional double recompute_edges_norm_threshold = 9 [default = 100];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 977 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getRecomputeReducedCostsThreshold | ( | ) |
We estimate the accuracy of the iteratively computed reduced costs. If it falls below this threshold, we reinitialize them from scratch. Note that such an operation is pretty fast, so we can use a low threshold. It is important to have a good accuracy here (better than the dual_feasibility_tolerance below) to be sure of the sign of such a cost.
optional double recompute_reduced_costs_threshold = 8 [default = 1e-08];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 942 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getRefactorizationThreshold | ( | ) |
We estimate the factorization accuracy of B during each pivot by using the fact that we can compute the pivot coefficient in two ways: - From direction[leaving_row]. - From update_row[entering_column]. If the two values have a relative difference above this threshold, we trigger a refactorization.
optional double refactorization_threshold = 6 [default = 1e-09];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 907 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getRelativeCostPerturbation | ( | ) |
The magnitude of the cost perturbation is given by RandomIn(1.0, 2.0) * ( relative_cost_perturbation * cost + relative_max_cost_perturbation * max_cost);
optional double relative_cost_perturbation = 54 [default = 1e-05];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2414 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getRelativeMaxCostPerturbation | ( | ) |
optional double relative_max_cost_perturbation = 55 [default = 1e-07];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2433 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters.ScalingAlgorithm com.google.ortools.glop.GlopParameters.getScalingMethod | ( | ) |
optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 817 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.getSerializedSize | ( | ) |
Definition at line 2936 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getSmallPivotThreshold | ( | ) |
When we choose the leaving variable, we want to avoid small pivot because they are the less precise and may cause numerical instabilities. For a pivot under this threshold times the infinity norm of the direction, we try various countermeasures in order to avoid using it.
optional double small_pivot_threshold = 14 [default = 1e-06];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1180 of file GlopParameters.java.
double com.google.ortools.glop.GlopParameters.getSolutionFeasibilityTolerance | ( | ) |
When the problem status is OPTIMAL, we check the optimality using this relative tolerance and change the status to IMPRECISE if an issue is detected. The tolerance is "relative" in the sense that our thresholds are: - tolerance * max(1.0, abs(bound)) for crossing a given bound. - tolerance * max(1.0, abs(cost)) for an infeasible reduced cost. - tolerance for an infeasible dual value.
optional double solution_feasibility_tolerance = 22 [default = 1e-06];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1512 of file GlopParameters.java.
com.google.ortools.glop.GlopParameters.SolverBehavior com.google.ortools.glop.GlopParameters.getSolveDualProblem | ( | ) |
Whether or not we solve the dual of the given problem. With a value of auto, the algorithm decide which approach is probably the fastest depending on the problem dimensions (see dualizer_threshold).
optional .operations_research.glop.GlopParameters.SolverBehavior solve_dual_problem = 20 [default = LET_SOLVER_DECIDE];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1439 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUseDedicatedDualFeasibilityAlgorithm | ( | ) |
We have two possible dual phase I algorithms. Both work on an LP that minimize the sum of dual infeasiblities. One use dedicated code (when this param is true), the other one use exactly the same code as the dual phase II but on an auxiliary problem where the variable bounds of the original problem are changed. TODO(user): For now we have both, but ideally the non-dedicated version will win since it is a lot less code to maintain.
optional bool use_dedicated_dual_feasibility_algorithm = 62 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2381 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUseDualSimplex | ( | ) |
Whether or not we use the dual simplex algorithm instead of the primal.
optional bool use_dual_simplex = 31 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1848 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUseImpliedFreePreprocessor | ( | ) |
If presolve runs, include the pass that detects implied free variables.
optional bool use_implied_free_preprocessor = 67 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2641 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUseMiddleProductFormUpdate | ( | ) |
Whether or not to use the middle product form update rather than the standard eta LU update. The middle form product update should be a lot more efficient (close to the Forrest-Tomlin update, a bit slower but easier to implement). See for more details: Qi Huangfu, J. A. Julian Hall, "Novel update techniques for the revised simplex method", 28 january 2013, Technical Report ERGO-13-0001 http://www.maths.ed.ac.uk/hall/HuHa12/ERGO-13-001.pdf
optional bool use_middle_product_form_update = 35 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1974 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUsePreprocessing | ( | ) |
Whether or not we use advanced preprocessing techniques.
optional bool use_preprocessing = 34 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1935 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUseScaling | ( | ) |
Whether or not we scale the matrix A so that the maximum coefficient on each line and each column is 1.0.
optional bool use_scaling = 16 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1267 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.getUseTransposedMatrix | ( | ) |
Whether or not we keep a transposed version of the matrix A to speed-up the pricing at the cost of extra memory and the initial tranposition computation.
optional bool use_transposed_matrix = 18 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1344 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasAllowSimplexAlgorithmChange | ( | ) |
During incremental solve, let the solver decide if it use the primal or dual simplex algorithm depending on the current solution and on the new problem. Note that even if this is true, the value of use_dual_simplex still indicates the default algorithm that the solver will use.
optional bool allow_simplex_algorithm_change = 32 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1866 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasBasisRefactorizationPeriod | ( | ) |
Number of iterations between two basis refactorizations. Note that various conditions in the algorithm may trigger a refactorization before this period is reached. Set this to 0 if you want to refactorize at each step.
optional int32 basis_refactorization_period = 19 [default = 64];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1361 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasChangeStatusToImprecise | ( | ) |
If true, the internal API will change the return status to imprecise if the solution does not respect the internal tolerances.
optional bool change_status_to_imprecise = 58 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1589 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasCostScaling | ( | ) |
optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1277 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasCrossoverBoundSnappingDistance | ( | ) |
If the starting basis contains FREE variable with bounds, we will move any such variable to their closer bounds if the distance is smaller than this parameter. The starting statuses can contains FREE variables with bounds, if a user set it like this externally. Also, any variable with an initial BASIC status that was not kept in the initial basis is marked as FREE before this step is applied. Note that by default a FREE variable is assumed to be zero unless a starting value was specified via SetStartingVariableValuesForNextSolve(). Note that, at the end of the solve, some of these FREE variable with bounds and an interior point value might still be left in the final solution. Enable push_to_vertex to clean these up.
optional double crossover_bound_snapping_distance = 64 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2551 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDegenerateMinistepFactor | ( | ) |
During a degenerate iteration, the more conservative approach is to do a step of length zero (while shifting the bound of the leaving variable). That is, the variable values are unchanged for the primal simplex or the reduced cost are unchanged for the dual simplex. However, instead of doing a step of length zero, it seems to be better on degenerate problems to do a small positive step. This is what is recommended in the EXPAND procedure described in: P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright. "A practical anti- cycling procedure for linearly constrained optimization". Mathematical Programming, 45:437\u2013474, 1989. Here, during a degenerate iteration we do a small positive step of this factor times the primal (resp. dual) tolerance. In the primal simplex, this may effectively push variable values (very slightly) further out of their bounds (resp. reduced costs for the dual simplex). Setting this to zero reverts to the more conservative approach of a zero step during degenerate iterations.
optional double degenerate_ministep_factor = 42 [default = 0.01];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2194 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDevexWeightsResetPeriod | ( | ) |
Devex weights will be reset to 1.0 after that number of updates.
optional int32 devex_weights_reset_period = 33 [default = 150];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1896 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDropMagnitude | ( | ) |
Value in the input LP lower than this will be ignored. This is similar to drop_tolerance but more aggressive as this is used before scaling. This is mainly here to avoid underflow and have simpler invariant in the code, like a * b == 0 iff a or b is zero and things like this.
optional double drop_magnitude = 71 [default = 1e-30];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2696 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDropTolerance | ( | ) |
In order to increase the sparsity of the manipulated vectors, floating point values with a magnitude smaller than this parameter are set to zero (only in some places). This parameter should be positive or zero.
optional double drop_tolerance = 52 [default = 1e-14];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1224 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDualFeasibilityTolerance | ( | ) |
Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates. That is they do not take part in deciding whether a solution is dual-feasible or not. Note that this value can temporarily increase during the execution of the algorithm if the estimated precision of the reduced costs is higher than this tolerance. Note also that we scale the costs (in the presolve step) so that the cost magnitude range contains one. This is also known as the optimality tolerance in other solvers.
optional double dual_feasibility_tolerance = 11 [default = 1e-08];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1040 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDualizerThreshold | ( | ) |
When solve_dual_problem is LET_SOLVER_DECIDE, take the dual if the number of constraints of the problem is more than this threshold times the number of variables.
optional double dualizer_threshold = 21 [default = 1.5];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1457 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDualPricePrioritizeNorm | ( | ) |
On some problem like stp3d or pds-100 this makes a huge difference in speed and number of iterations of the dual simplex.
optional bool dual_price_prioritize_norm = 69 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2727 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDualSmallPivotThreshold | ( | ) |
Like small_pivot_threshold but for the dual simplex. This is needed because the dual algorithm does not interpret this value in the same way. TODO(user): Clean this up and use the same small pivot detection.
optional double dual_small_pivot_threshold = 38 [default = 0.0001];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2049 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasDynamicallyAdjustRefactorizationPeriod | ( | ) |
If this is true, then basis_refactorization_period becomes a lower bound on the number of iterations between two refactorization (provided there is no numerical accuracy issues). Depending on the estimated time to refactorize vs the extra time spend in each solves because of the LU update, we try to balance the two times.
optional bool dynamically_adjust_refactorization_period = 63 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1394 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasExploitSingletonColumnInInitialBasis | ( | ) |
Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
optional bool exploit_singleton_column_in_initial_basis = 37 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2019 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasFeasibilityRule | ( | ) |
PricingRule to use during the feasibility phase.
optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 832 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasHarrisToleranceRatio | ( | ) |
This impacts the ratio test and indicates by how much we allow a basic variable value that we move to go out of bounds. The value should be in [0.0, 1.0) and should be interpreted as a ratio of the primal_feasibility_tolerance. Setting this to 0.0 basically disables the Harris ratio test while setting this too close to 1.0 will make it difficult to keep the variable values inside their bounds modulo the primal_feasibility_tolerance. Note that the same comment applies to the dual simplex ratio test. There, we allow the reduced costs to be of an infeasible sign by as much as this ratio times the dual_feasibility_tolerance.
optional double harris_tolerance_ratio = 13 [default = 0.5];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1125 of file GlopParameters.java.
int com.google.ortools.glop.GlopParameters.hashCode | ( | ) |
Definition at line 3505 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasInitialBasis | ( | ) |
What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1300 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasInitialConditionNumberThreshold | ( | ) |
If our upper bound on the condition number of the initial basis (from our heurisitic or a warm start) is above this threshold, we revert to an all slack basis.
optional double initial_condition_number_threshold = 59 [default = 1e+50];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2450 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasInitializeDevexWithColumnNorms | ( | ) |
Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
optional bool initialize_devex_with_column_norms = 36 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1990 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasLogSearchProgress | ( | ) |
If true, logs the progress of a solve to LOG(INFO). Note that the same messages can also be turned on by displaying logs at level 1 for the relevant files.
optional bool log_search_progress = 61 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2481 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasLogToStdout | ( | ) |
If true, logs will be displayed to stdout instead of using Google log info.
optional bool log_to_stdout = 66 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2510 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasLuFactorizationPivotThreshold | ( | ) |
Threshold for LU-factorization: for stability reasons, the magnitude of the chosen pivot at a given step is guaranteed to be greater than this threshold times the maximum magnitude of all the possible pivot choices in the same column. The value must be in [0,1].
optional double lu_factorization_pivot_threshold = 25 [default = 0.01];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1653 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMarkowitzSingularityThreshold | ( | ) |
If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular. Note that this is an absolute threshold and is not relative to the other possible pivots on the same column (see lu_factorization_pivot_threshold).
optional double markowitz_singularity_threshold = 30 [default = 1e-15];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1806 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMarkowitzZlatevParameter | ( | ) |
How many columns do we look at in the Markowitz pivoting rule to find a good pivot. See markowitz.h.
optional int32 markowitz_zlatev_parameter = 29 [default = 3];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1775 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMaxDeterministicTime | ( | ) |
Maximum deterministic time allowed to solve a problem. The deterministic time is more or less correlated to the running time, and its unit should be around the second (at least on a Xeon(R) CPU E5-1650 v2 @ 3.50GHz). TODO(user): Improve the correlation.
optional double max_deterministic_time = 45 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1714 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMaxNumberOfIterations | ( | ) |
Maximum number of simplex iterations to solve a problem. A value of -1 means no limit.
optional int64 max_number_of_iterations = 27 [default = -1];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1746 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMaxNumberOfReoptimizations | ( | ) |
When the solution of phase II is imprecise, we re-run the phase II with the opposite algorithm from that imprecise solution (i.e., if primal or dual simplex was used, we use dual or primal simplex, respectively). We repeat such re-optimization until the solution is precise, or we hit this limit.
optional double max_number_of_reoptimizations = 56 [default = 40];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1620 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMaxTimeInSeconds | ( | ) |
Maximum time allowed in seconds to solve a problem.
optional double max_time_in_seconds = 26 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1683 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMaxValidMagnitude | ( | ) |
Any finite values in the input LP 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. In practice, users shouldn't use super large values in an LP. With the default threshold, even evaluating large constraint with variables at their bound shouldn't cause any overflow.
optional double max_valid_magnitude = 70 [default = 1e+30];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2661 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasMinimumAcceptablePivot | ( | ) |
We never follow a basis change with a pivot under this threshold.
optional double minimum_acceptable_pivot = 15 [default = 1e-06];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1195 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasNumOmpThreads | ( | ) |
Number of threads in the OMP parallel sections. If left to 1, the code will not create any OMP threads and will remain single-threaded.
optional int32 num_omp_threads = 44 [default = 1];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2294 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasObjectiveLowerLimit | ( | ) |
The solver will stop as soon as it has proven that the objective is smaller than objective_lower_limit or greater than objective_upper_limit. Depending on the simplex algorithm (primal or dual) and the optimization direction, note that only one bound will be used at the time. Important: The solver does not add any tolerances to these values, and as soon as the objective (as computed by the solver, so with some imprecision) crosses one of these bounds (strictly), the search will stop. It is up to the client to add any tolerance if needed.
optional double objective_lower_limit = 40 [default = -inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2123 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasObjectiveUpperLimit | ( | ) |
optional double objective_upper_limit = 41 [default = inf];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2154 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasOptimizationRule | ( | ) |
PricingRule to use during the optimization phase.
optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 858 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasPerturbCostsInDualSimplex | ( | ) |
When this is true, then the costs are randomly perturbed before the dual simplex is even started. This has been shown to improve the dual simplex performance. For a good reference, see Huangfu Q (2013) "High performance simplex solver", Ph.D, dissertation, University of Edinburgh.
optional bool perturb_costs_in_dual_simplex = 53 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2325 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasPreprocessorZeroTolerance | ( | ) |
A floating point tolerance used by the preprocessors. This is used for things like detecting if two columns/rows are proportional or if an interval is empty. Note that the preprocessors also use solution_feasibility_tolerance() to detect if a problem is infeasible.
optional double preprocessor_zero_tolerance = 39 [default = 1e-09];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2083 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasPrimalFeasibilityTolerance | ( | ) |
This tolerance indicates by how much we allow the variable values to go out of bounds and still consider the current solution primal-feasible. We also use the same tolerance for the error A.x - b. Note that the two errors are closely related if A is scaled in such a way that the greatest coefficient magnitude on each column is 1.0. This is also simply called feasibility tolerance in other solvers.
optional double primal_feasibility_tolerance = 10 [default = 1e-08];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 998 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasProvideStrongOptimalGuarantee | ( | ) |
If true, then when the solver returns a solution with an OPTIMAL status, we can guarantee that: - The primal variable are in their bounds. - The dual variable are in their bounds. - If we modify each component of the right-hand side a bit and each component of the objective function a bit, then the pair (primal values, dual values) is an EXACT optimal solution of the perturbed problem. - The modifications above are smaller than the associated tolerances as defined in the comment for solution_feasibility_tolerance (*). (*): This is the only place where the guarantee is not tight since we compute the upper bounds with scalar product of the primal/dual solution and the initial problem coefficients with only double precision. Note that whether or not this option is true, we still check the primal/dual infeasibility and objective gap. However if it is false, we don't move the primal/dual values within their bounds and leave them untouched.
optional bool provide_strong_optimal_guarantee = 24 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1544 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasPushToVertex | ( | ) |
If the optimization phases finishes with super-basic variables (i.e., variables that either 1) have bounds but are FREE in the basis, or 2) have no bounds and are FREE in the basis at a nonzero value), then run a "push" phase to push these variables to bounds, obtaining a vertex solution. Note this situation can happen only if a starting value was specified via SetStartingVariableValuesForNextSolve().
optional bool push_to_vertex = 65 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2597 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.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. Note that this may lead to a different solution, for example a different optimal basis. 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. Also note that the solver is fully deterministic: two runs of the same binary, on the same machine, on the exact same data and with the same parameters will go through the exact same iterations. If they hit a time limit, they might of course yield different results because one will have advanced farther than the other.
optional int32 random_seed = 43 [default = 1];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2252 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasRatioTestZeroThreshold | ( | ) |
During the primal simplex (resp. dual simplex), the coefficients of the direction (resp. update row) with a magnitude lower than this threshold are not considered during the ratio test. This tolerance is related to the precision at which a Solve() involving the basis matrix can be performed. TODO(user): Automatically increase it when we detect that the precision of the Solve() is worse than this.
optional double ratio_test_zero_threshold = 12 [default = 1e-09];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1082 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasRecomputeEdgesNormThreshold | ( | ) |
Note that the threshold is a relative error on the actual norm (not the squared one) and that edge norms are always greater than 1. Recomputing norms is a really expensive operation and a large threshold is ok since this doesn't impact directly the solution but just the entering variable choice.
optional double recompute_edges_norm_threshold = 9 [default = 100];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 961 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasRecomputeReducedCostsThreshold | ( | ) |
We estimate the accuracy of the iteratively computed reduced costs. If it falls below this threshold, we reinitialize them from scratch. Note that such an operation is pretty fast, so we can use a low threshold. It is important to have a good accuracy here (better than the dual_feasibility_tolerance below) to be sure of the sign of such a cost.
optional double recompute_reduced_costs_threshold = 8 [default = 1e-08];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 926 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasRefactorizationThreshold | ( | ) |
We estimate the factorization accuracy of B during each pivot by using the fact that we can compute the pivot coefficient in two ways: - From direction[leaving_row]. - From update_row[entering_column]. If the two values have a relative difference above this threshold, we trigger a refactorization.
optional double refactorization_threshold = 6 [default = 1e-09];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 890 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasRelativeCostPerturbation | ( | ) |
The magnitude of the cost perturbation is given by RandomIn(1.0, 2.0) * ( relative_cost_perturbation * cost + relative_max_cost_perturbation * max_cost);
optional double relative_cost_perturbation = 54 [default = 1e-05];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2399 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasRelativeMaxCostPerturbation | ( | ) |
optional double relative_max_cost_perturbation = 55 [default = 1e-07];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2425 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasScalingMethod | ( | ) |
optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 810 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasSmallPivotThreshold | ( | ) |
When we choose the leaving variable, we want to avoid small pivot because they are the less precise and may cause numerical instabilities. For a pivot under this threshold times the infinity norm of the direction, we try various countermeasures in order to avoid using it.
optional double small_pivot_threshold = 14 [default = 1e-06];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1165 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasSolutionFeasibilityTolerance | ( | ) |
When the problem status is OPTIMAL, we check the optimality using this relative tolerance and change the status to IMPRECISE if an issue is detected. The tolerance is "relative" in the sense that our thresholds are: - tolerance * max(1.0, abs(bound)) for crossing a given bound. - tolerance * max(1.0, abs(cost)) for an infeasible reduced cost. - tolerance for an infeasible dual value.
optional double solution_feasibility_tolerance = 22 [default = 1e-06];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1493 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasSolveDualProblem | ( | ) |
Whether or not we solve the dual of the given problem. With a value of auto, the algorithm decide which approach is probably the fastest depending on the problem dimensions (see dualizer_threshold).
optional .operations_research.glop.GlopParameters.SolverBehavior solve_dual_problem = 20 [default = LET_SOLVER_DECIDE];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1426 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasUseDedicatedDualFeasibilityAlgorithm | ( | ) |
We have two possible dual phase I algorithms. Both work on an LP that minimize the sum of dual infeasiblities. One use dedicated code (when this param is true), the other one use exactly the same code as the dual phase II but on an auxiliary problem where the variable bounds of the original problem are changed. TODO(user): For now we have both, but ideally the non-dedicated version will win since it is a lot less code to maintain.
optional bool use_dedicated_dual_feasibility_algorithm = 62 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2362 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasUseDualSimplex | ( | ) |
Whether or not we use the dual simplex algorithm instead of the primal.
optional bool use_dual_simplex = 31 [default = false];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1836 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasUseImpliedFreePreprocessor | ( | ) |
If presolve runs, include the pass that detects implied free variables.
optional bool use_implied_free_preprocessor = 67 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 2629 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasUseMiddleProductFormUpdate | ( | ) |
Whether or not to use the middle product form update rather than the standard eta LU update. The middle form product update should be a lot more efficient (close to the Forrest-Tomlin update, a bit slower but easier to implement). See for more details: Qi Huangfu, J. A. Julian Hall, "Novel update techniques for the revised simplex method", 28 january 2013, Technical Report ERGO-13-0001 http://www.maths.ed.ac.uk/hall/HuHa12/ERGO-13-001.pdf
optional bool use_middle_product_form_update = 35 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1956 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasUsePreprocessing | ( | ) |
Whether or not we use advanced preprocessing techniques.
optional bool use_preprocessing = 34 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
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boolean com.google.ortools.glop.GlopParameters.hasUseScaling | ( | ) |
Whether or not we scale the matrix A so that the maximum coefficient on each line and each column is 1.0.
optional bool use_scaling = 16 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
Definition at line 1254 of file GlopParameters.java.
boolean com.google.ortools.glop.GlopParameters.hasUseTransposedMatrix | ( | ) |
Whether or not we keep a transposed version of the matrix A to speed-up the pricing at the cost of extra memory and the initial tranposition computation.
optional bool use_transposed_matrix = 18 [default = true];
Implements com.google.ortools.glop.GlopParametersOrBuilder.
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final boolean com.google.ortools.glop.GlopParameters.isInitialized | ( | ) |
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Builder com.google.ortools.glop.GlopParameters.newBuilderForType | ( | ) |
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Builder com.google.ortools.glop.GlopParameters.toBuilder | ( | ) |
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void com.google.ortools.glop.GlopParameters.writeTo | ( | com.google.protobuf.CodedOutputStream | output | ) | throws java.io.IOException |
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