Interface GlopParametersOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
GlopParameters, GlopParameters.Builder
@Generated public interface GlopParametersOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Summary

    Modifier and Type
    Method
    Description
    boolean
    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.
    int
    getBasisRefactorizationPeriod()
    Number of iterations between two basis refactorizations.
    boolean
    getChangeStatusToImprecise()
    If true, the internal API will change the return status to imprecise if the solution does not respect the internal tolerances.
    GlopParameters.CostScalingAlgorithm
    getCostScaling()
    optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
    double
    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.
    double
    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).
    int
    getDevexWeightsResetPeriod()
    Devex weights will be reset to 1.0 after that number of updates.
    double
    getDropMagnitude()
    Value in the input LP lower than this will be ignored.
    double
    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).
    double
    getDualFeasibilityTolerance()
    Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates.
    double
    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.
    boolean
    getDualPricePrioritizeNorm()
    On some problem like stp3d or pds-100 this makes a huge difference in speed and number of iterations of the dual simplex.
    double
    getDualSmallPivotThreshold()
    Like small_pivot_threshold but for the dual simplex.
    boolean
    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).
    boolean
    getExploitSingletonColumnInInitialBasis()
    Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
    GlopParameters.PricingRule
    getFeasibilityRule()
    PricingRule to use during the feasibility phase.
    double
    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.
    GlopParameters.InitialBasisHeuristic
    getInitialBasis()
    What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
    double
    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.
    boolean
    getInitializeDevexWithColumnNorms()
    Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
    boolean
    getLogSearchProgress()
    If true, logs the progress of a solve to LOG(INFO).
    boolean
    getLogToStdout()
    If true, logs will be displayed to stdout instead of using Google log info.
    double
    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.
    double
    getMarkowitzSingularityThreshold()
    If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular.
    int
    getMarkowitzZlatevParameter()
    How many columns do we look at in the Markowitz pivoting rule to find a good pivot.
    double
    getMaxDeterministicTime()
    Maximum deterministic time allowed to solve a problem.
    long
    getMaxNumberOfIterations()
    Maximum number of simplex iterations to solve a problem.
    double
    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).
    double
    getMaxTimeInSeconds()
    Maximum time allowed in seconds to solve a problem.
    double
    getMaxValidMagnitude()
    Any finite values in the input LP must be below this threshold, otherwise the model will be reported invalid.
    double
    getMinimumAcceptablePivot()
    We never follow a basis change with a pivot under this threshold.
    int
    getNumOmpThreads()
    Number of threads in the OMP parallel sections.
    double
    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.
    double
    getObjectiveUpperLimit()
    optional double objective_upper_limit = 41 [default = inf];
    GlopParameters.PricingRule
    getOptimizationRule()
    PricingRule to use during the optimization phase.
    boolean
    getPerturbCostsInDualSimplex()
    When this is true, then the costs are randomly perturbed before the dual simplex is even started.
    double
    getPreprocessorZeroTolerance()
    A floating point tolerance used by the preprocessors.
    double
    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.
    boolean
    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
    boolean
    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.
    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
    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.
    double
    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.
    double
    getRecomputeReducedCostsThreshold()
    We estimate the accuracy of the iteratively computed reduced costs.
    double
    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]
    double
    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);
    double
    getRelativeMaxCostPerturbation()
    optional double relative_max_cost_perturbation = 55 [default = 1e-07];
    GlopParameters.ScalingAlgorithm
    getScalingMethod()
    optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
    double
    getSmallPivotThreshold()
    When we choose the leaving variable, we want to avoid small pivot because they are the less precise and may cause numerical instabilities.
    double
    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.
    GlopParameters.SolverBehavior
    getSolveDualProblem()
    Whether or not we solve the dual of the given problem.
    boolean
    getUseAbslRandom()
    Whether to use absl::BitGen instead of MTRandom.
    boolean
    getUseDedicatedDualFeasibilityAlgorithm()
    We have two possible dual phase I algorithms.
    boolean
    getUseDualSimplex()
    Whether or not we use the dual simplex algorithm instead of the primal.
    boolean
    getUseImpliedFreePreprocessor()
    If presolve runs, include the pass that detects implied free variables.
    boolean
    getUseMiddleProductFormUpdate()
    Whether or not to use the middle product form update rather than the standard eta LU update.
    boolean
    getUsePreprocessing()
    Whether or not we use advanced preprocessing techniques.
    boolean
    getUseScaling()
    Whether or not we scale the matrix A so that the maximum coefficient on each line and each column is 1.0.
    boolean
    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.
    boolean
    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.
    boolean
    hasBasisRefactorizationPeriod()
    Number of iterations between two basis refactorizations.
    boolean
    hasChangeStatusToImprecise()
    If true, the internal API will change the return status to imprecise if the solution does not respect the internal tolerances.
    boolean
    hasCostScaling()
    optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
    boolean
    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.
    boolean
    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).
    boolean
    hasDevexWeightsResetPeriod()
    Devex weights will be reset to 1.0 after that number of updates.
    boolean
    hasDropMagnitude()
    Value in the input LP lower than this will be ignored.
    boolean
    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).
    boolean
    hasDualFeasibilityTolerance()
    Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates.
    boolean
    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.
    boolean
    hasDualPricePrioritizeNorm()
    On some problem like stp3d or pds-100 this makes a huge difference in speed and number of iterations of the dual simplex.
    boolean
    hasDualSmallPivotThreshold()
    Like small_pivot_threshold but for the dual simplex.
    boolean
    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).
    boolean
    hasExploitSingletonColumnInInitialBasis()
    Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
    boolean
    hasFeasibilityRule()
    PricingRule to use during the feasibility phase.
    boolean
    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.
    boolean
    hasInitialBasis()
    What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
    boolean
    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.
    boolean
    hasInitializeDevexWithColumnNorms()
    Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
    boolean
    hasLogSearchProgress()
    If true, logs the progress of a solve to LOG(INFO).
    boolean
    hasLogToStdout()
    If true, logs will be displayed to stdout instead of using Google log info.
    boolean
    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.
    boolean
    hasMarkowitzSingularityThreshold()
    If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular.
    boolean
    hasMarkowitzZlatevParameter()
    How many columns do we look at in the Markowitz pivoting rule to find a good pivot.
    boolean
    hasMaxDeterministicTime()
    Maximum deterministic time allowed to solve a problem.
    boolean
    hasMaxNumberOfIterations()
    Maximum number of simplex iterations to solve a problem.
    boolean
    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).
    boolean
    hasMaxTimeInSeconds()
    Maximum time allowed in seconds to solve a problem.
    boolean
    hasMaxValidMagnitude()
    Any finite values in the input LP must be below this threshold, otherwise the model will be reported invalid.
    boolean
    hasMinimumAcceptablePivot()
    We never follow a basis change with a pivot under this threshold.
    boolean
    hasNumOmpThreads()
    Number of threads in the OMP parallel sections.
    boolean
    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.
    boolean
    hasObjectiveUpperLimit()
    optional double objective_upper_limit = 41 [default = inf];
    boolean
    hasOptimizationRule()
    PricingRule to use during the optimization phase.
    boolean
    hasPerturbCostsInDualSimplex()
    When this is true, then the costs are randomly perturbed before the dual simplex is even started.
    boolean
    hasPreprocessorZeroTolerance()
    A floating point tolerance used by the preprocessors.
    boolean
    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.
    boolean
    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
    boolean
    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.
    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
    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.
    boolean
    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.
    boolean
    hasRecomputeReducedCostsThreshold()
    We estimate the accuracy of the iteratively computed reduced costs.
    boolean
    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]
    boolean
    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);
    boolean
    hasRelativeMaxCostPerturbation()
    optional double relative_max_cost_perturbation = 55 [default = 1e-07];
    boolean
    hasScalingMethod()
    optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
    boolean
    hasSmallPivotThreshold()
    When we choose the leaving variable, we want to avoid small pivot because they are the less precise and may cause numerical instabilities.
    boolean
    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.
    boolean
    hasSolveDualProblem()
    Whether or not we solve the dual of the given problem.
    boolean
    hasUseAbslRandom()
    Whether to use absl::BitGen instead of MTRandom.
    boolean
    hasUseDedicatedDualFeasibilityAlgorithm()
    We have two possible dual phase I algorithms.
    boolean
    hasUseDualSimplex()
    Whether or not we use the dual simplex algorithm instead of the primal.
    boolean
    hasUseImpliedFreePreprocessor()
    If presolve runs, include the pass that detects implied free variables.
    boolean
    hasUseMiddleProductFormUpdate()
    Whether or not to use the middle product form update rather than the standard eta LU update.
    boolean
    hasUsePreprocessing()
    Whether or not we use advanced preprocessing techniques.
    boolean
    hasUseScaling()
    Whether or not we scale the matrix A so that the maximum coefficient on each line and each column is 1.0.
    boolean
    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.

    Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

    isInitialized

    Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

  • Method Details

    • hasScalingMethod

      boolean hasScalingMethod()
      optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
      Returns:
      Whether the scalingMethod field is set.

    • getScalingMethod

      GlopParameters.ScalingAlgorithm getScalingMethod()
      optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
      Returns:
      The scalingMethod.

    • hasFeasibilityRule

      boolean hasFeasibilityRule()
       PricingRule to use during the feasibility phase.
      optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
      Returns:
      Whether the feasibilityRule field is set.

    • getFeasibilityRule

      GlopParameters.PricingRule getFeasibilityRule()
       PricingRule to use during the feasibility phase.
      optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
      Returns:
      The feasibilityRule.

    • hasOptimizationRule

      boolean hasOptimizationRule()
       PricingRule to use during the optimization phase.
      optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
      Returns:
      Whether the optimizationRule field is set.

    • getOptimizationRule

      GlopParameters.PricingRule getOptimizationRule()
       PricingRule to use during the optimization phase.
      optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
      Returns:
      The optimizationRule.

    • hasRefactorizationThreshold

      boolean 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];
      Returns:
      Whether the refactorizationThreshold field is set.

    • getRefactorizationThreshold

      double 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];
      Returns:
      The refactorizationThreshold.

    • hasRecomputeReducedCostsThreshold

      boolean 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];
      Returns:
      Whether the recomputeReducedCostsThreshold field is set.

    • getRecomputeReducedCostsThreshold

      double 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];
      Returns:
      The recomputeReducedCostsThreshold.

    • hasRecomputeEdgesNormThreshold

      boolean 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];
      Returns:
      Whether the recomputeEdgesNormThreshold field is set.

    • getRecomputeEdgesNormThreshold

      double 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];
      Returns:
      The recomputeEdgesNormThreshold.

    • hasPrimalFeasibilityTolerance

      boolean 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];
      Returns:
      Whether the primalFeasibilityTolerance field is set.

    • getPrimalFeasibilityTolerance

      double 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];
      Returns:
      The primalFeasibilityTolerance.

    • hasDualFeasibilityTolerance

      boolean 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];
      Returns:
      Whether the dualFeasibilityTolerance field is set.

    • getDualFeasibilityTolerance

      double 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];
      Returns:
      The dualFeasibilityTolerance.

    • hasRatioTestZeroThreshold

      boolean 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];
      Returns:
      Whether the ratioTestZeroThreshold field is set.

    • getRatioTestZeroThreshold

      double 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];
      Returns:
      The ratioTestZeroThreshold.

    • hasHarrisToleranceRatio

      boolean 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];
      Returns:
      Whether the harrisToleranceRatio field is set.

    • getHarrisToleranceRatio

      double 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];
      Returns:
      The harrisToleranceRatio.

    • hasSmallPivotThreshold

      boolean 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];
      Returns:
      Whether the smallPivotThreshold field is set.

    • getSmallPivotThreshold

      double 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];
      Returns:
      The smallPivotThreshold.

    • hasMinimumAcceptablePivot

      boolean hasMinimumAcceptablePivot()
       We never follow a basis change with a pivot under this threshold.
      optional double minimum_acceptable_pivot = 15 [default = 1e-06];
      Returns:
      Whether the minimumAcceptablePivot field is set.

    • getMinimumAcceptablePivot

      double getMinimumAcceptablePivot()
       We never follow a basis change with a pivot under this threshold.
      optional double minimum_acceptable_pivot = 15 [default = 1e-06];
      Returns:
      The minimumAcceptablePivot.

    • hasDropTolerance

      boolean 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];
      Returns:
      Whether the dropTolerance field is set.

    • getDropTolerance

      double 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];
      Returns:
      The dropTolerance.

    • hasUseScaling

      boolean 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];
      Returns:
      Whether the useScaling field is set.

    • getUseScaling

      boolean 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];
      Returns:
      The useScaling.

    • hasCostScaling

      boolean hasCostScaling()
      optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
      Returns:
      Whether the costScaling field is set.

    • getCostScaling

      GlopParameters.CostScalingAlgorithm getCostScaling()
      optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
      Returns:
      The costScaling.

    • hasInitialBasis

      boolean 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];
      Returns:
      Whether the initialBasis field is set.

    • getInitialBasis

      GlopParameters.InitialBasisHeuristic 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];
      Returns:
      The initialBasis.

    • hasUseTransposedMatrix

      boolean 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];
      Returns:
      Whether the useTransposedMatrix field is set.

    • getUseTransposedMatrix

      boolean 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];
      Returns:
      The useTransposedMatrix.

    • hasBasisRefactorizationPeriod

      boolean 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];
      Returns:
      Whether the basisRefactorizationPeriod field is set.

    • getBasisRefactorizationPeriod

      int 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];
      Returns:
      The basisRefactorizationPeriod.

    • hasDynamicallyAdjustRefactorizationPeriod

      boolean 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];
      Returns:
      Whether the dynamicallyAdjustRefactorizationPeriod field is set.

    • getDynamicallyAdjustRefactorizationPeriod

      boolean 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];
      Returns:
      The dynamicallyAdjustRefactorizationPeriod.

    • hasSolveDualProblem

      boolean 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];
      Returns:
      Whether the solveDualProblem field is set.

    • getSolveDualProblem

      GlopParameters.SolverBehavior 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];
      Returns:
      The solveDualProblem.

    • hasDualizerThreshold

      boolean 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];
      Returns:
      Whether the dualizerThreshold field is set.

    • getDualizerThreshold

      double 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];
      Returns:
      The dualizerThreshold.

    • hasSolutionFeasibilityTolerance

      boolean 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];
      Returns:
      Whether the solutionFeasibilityTolerance field is set.

    • getSolutionFeasibilityTolerance

      double 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];
      Returns:
      The solutionFeasibilityTolerance.

    • hasProvideStrongOptimalGuarantee

      boolean 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];
      Returns:
      Whether the provideStrongOptimalGuarantee field is set.

    • getProvideStrongOptimalGuarantee

      boolean 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];
      Returns:
      The provideStrongOptimalGuarantee.

    • hasChangeStatusToImprecise

      boolean 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];
      Returns:
      Whether the changeStatusToImprecise field is set.

    • getChangeStatusToImprecise

      boolean 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];
      Returns:
      The changeStatusToImprecise.

    • hasMaxNumberOfReoptimizations

      boolean 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];
      Returns:
      Whether the maxNumberOfReoptimizations field is set.

    • getMaxNumberOfReoptimizations

      double 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];
      Returns:
      The maxNumberOfReoptimizations.

    • hasLuFactorizationPivotThreshold

      boolean 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];
      Returns:
      Whether the luFactorizationPivotThreshold field is set.

    • getLuFactorizationPivotThreshold

      double 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];
      Returns:
      The luFactorizationPivotThreshold.

    • hasMaxTimeInSeconds

      boolean hasMaxTimeInSeconds()
       Maximum time allowed in seconds to solve a problem.
      optional double max_time_in_seconds = 26 [default = inf];
      Returns:
      Whether the maxTimeInSeconds field is set.

    • getMaxTimeInSeconds

      double getMaxTimeInSeconds()
       Maximum time allowed in seconds to solve a problem.
      optional double max_time_in_seconds = 26 [default = inf];
      Returns:
      The maxTimeInSeconds.

    • hasMaxDeterministicTime

      boolean 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];
      Returns:
      Whether the maxDeterministicTime field is set.

    • getMaxDeterministicTime

      double 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];
      Returns:
      The maxDeterministicTime.

    • hasMaxNumberOfIterations

      boolean 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];
      Returns:
      Whether the maxNumberOfIterations field is set.

    • getMaxNumberOfIterations

      long 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];
      Returns:
      The maxNumberOfIterations.

    • hasMarkowitzZlatevParameter

      boolean 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];
      Returns:
      Whether the markowitzZlatevParameter field is set.

    • getMarkowitzZlatevParameter

      int 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];
      Returns:
      The markowitzZlatevParameter.

    • hasMarkowitzSingularityThreshold

      boolean 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];
      Returns:
      Whether the markowitzSingularityThreshold field is set.

    • getMarkowitzSingularityThreshold

      double 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];
      Returns:
      The markowitzSingularityThreshold.

    • hasUseDualSimplex

      boolean hasUseDualSimplex()
       Whether or not we use the dual simplex algorithm instead of the primal.
      optional bool use_dual_simplex = 31 [default = false];
      Returns:
      Whether the useDualSimplex field is set.

    • getUseDualSimplex

      boolean getUseDualSimplex()
       Whether or not we use the dual simplex algorithm instead of the primal.
      optional bool use_dual_simplex = 31 [default = false];
      Returns:
      The useDualSimplex.

    • hasAllowSimplexAlgorithmChange

      boolean 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];
      Returns:
      Whether the allowSimplexAlgorithmChange field is set.

    • getAllowSimplexAlgorithmChange

      boolean 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];
      Returns:
      The allowSimplexAlgorithmChange.

    • hasDevexWeightsResetPeriod

      boolean hasDevexWeightsResetPeriod()
       Devex weights will be reset to 1.0 after that number of updates.
      optional int32 devex_weights_reset_period = 33 [default = 150];
      Returns:
      Whether the devexWeightsResetPeriod field is set.

    • getDevexWeightsResetPeriod

      int getDevexWeightsResetPeriod()
       Devex weights will be reset to 1.0 after that number of updates.
      optional int32 devex_weights_reset_period = 33 [default = 150];
      Returns:
      The devexWeightsResetPeriod.

    • hasUsePreprocessing

      boolean hasUsePreprocessing()
       Whether or not we use advanced preprocessing techniques.
      optional bool use_preprocessing = 34 [default = true];
      Returns:
      Whether the usePreprocessing field is set.

    • getUsePreprocessing

      boolean getUsePreprocessing()
       Whether or not we use advanced preprocessing techniques.
      optional bool use_preprocessing = 34 [default = true];
      Returns:
      The usePreprocessing.

    • hasUseMiddleProductFormUpdate

      boolean 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];
      Returns:
      Whether the useMiddleProductFormUpdate field is set.

    • getUseMiddleProductFormUpdate

      boolean 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];
      Returns:
      The useMiddleProductFormUpdate.

    • hasInitializeDevexWithColumnNorms

      boolean 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];
      Returns:
      Whether the initializeDevexWithColumnNorms field is set.

    • getInitializeDevexWithColumnNorms

      boolean 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];
      Returns:
      The initializeDevexWithColumnNorms.

    • hasExploitSingletonColumnInInitialBasis

      boolean 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];
      Returns:
      Whether the exploitSingletonColumnInInitialBasis field is set.

    • getExploitSingletonColumnInInitialBasis

      boolean 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];
      Returns:
      The exploitSingletonColumnInInitialBasis.

    • hasDualSmallPivotThreshold

      boolean 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];
      Returns:
      Whether the dualSmallPivotThreshold field is set.

    • getDualSmallPivotThreshold

      double 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];
      Returns:
      The dualSmallPivotThreshold.

    • hasPreprocessorZeroTolerance

      boolean 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];
      Returns:
      Whether the preprocessorZeroTolerance field is set.

    • getPreprocessorZeroTolerance

      double 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];
      Returns:
      The preprocessorZeroTolerance.

    • hasObjectiveLowerLimit

      boolean 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];
      Returns:
      Whether the objectiveLowerLimit field is set.

    • getObjectiveLowerLimit

      double 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];
      Returns:
      The objectiveLowerLimit.

    • hasObjectiveUpperLimit

      boolean hasObjectiveUpperLimit()
      optional double objective_upper_limit = 41 [default = inf];
      Returns:
      Whether the objectiveUpperLimit field is set.

    • getObjectiveUpperLimit

      double getObjectiveUpperLimit()
      optional double objective_upper_limit = 41 [default = inf];
      Returns:
      The objectiveUpperLimit.

    • hasDegenerateMinistepFactor

      boolean 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];
      Returns:
      Whether the degenerateMinistepFactor field is set.

    • getDegenerateMinistepFactor

      double 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];
      Returns:
      The degenerateMinistepFactor.

    • hasRandomSeed

      boolean hasRandomSeed()
       At the beginning of each solve, the random number generator used in some
 part of the solver is reinitialized to this seed. If you change the random
 seed, the solver may make different choices during the solving process.
 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];
      Returns:
      Whether the randomSeed field is set.

    • getRandomSeed

      int getRandomSeed()
       At the beginning of each solve, the random number generator used in some
 part of the solver is reinitialized to this seed. If you change the random
 seed, the solver may make different choices during the solving process.
 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];
      Returns:
      The randomSeed.

    • hasUseAbslRandom

      boolean hasUseAbslRandom()
       Whether to use absl::BitGen instead of MTRandom.
      optional bool use_absl_random = 72 [default = false];
      Returns:
      Whether the useAbslRandom field is set.

    • getUseAbslRandom

      boolean getUseAbslRandom()
       Whether to use absl::BitGen instead of MTRandom.
      optional bool use_absl_random = 72 [default = false];
      Returns:
      The useAbslRandom.

    • hasNumOmpThreads

      boolean 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];
      Returns:
      Whether the numOmpThreads field is set.

    • getNumOmpThreads

      int 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];
      Returns:
      The numOmpThreads.

    • hasPerturbCostsInDualSimplex

      boolean 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];
      Returns:
      Whether the perturbCostsInDualSimplex field is set.

    • getPerturbCostsInDualSimplex

      boolean 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];
      Returns:
      The perturbCostsInDualSimplex.

    • hasUseDedicatedDualFeasibilityAlgorithm

      boolean 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];
      Returns:
      Whether the useDedicatedDualFeasibilityAlgorithm field is set.

    • getUseDedicatedDualFeasibilityAlgorithm

      boolean 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];
      Returns:
      The useDedicatedDualFeasibilityAlgorithm.

    • hasRelativeCostPerturbation

      boolean 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];
      Returns:
      Whether the relativeCostPerturbation field is set.

    • getRelativeCostPerturbation

      double 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];
      Returns:
      The relativeCostPerturbation.

    • hasRelativeMaxCostPerturbation

      boolean hasRelativeMaxCostPerturbation()
      optional double relative_max_cost_perturbation = 55 [default = 1e-07];
      Returns:
      Whether the relativeMaxCostPerturbation field is set.

    • getRelativeMaxCostPerturbation

      double getRelativeMaxCostPerturbation()
      optional double relative_max_cost_perturbation = 55 [default = 1e-07];
      Returns:
      The relativeMaxCostPerturbation.

    • hasInitialConditionNumberThreshold

      boolean 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];
      Returns:
      Whether the initialConditionNumberThreshold field is set.

    • getInitialConditionNumberThreshold

      double 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];
      Returns:
      The initialConditionNumberThreshold.

    • hasLogSearchProgress

      boolean 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];
      Returns:
      Whether the logSearchProgress field is set.

    • getLogSearchProgress

      boolean 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];
      Returns:
      The logSearchProgress.

    • hasLogToStdout

      boolean hasLogToStdout()
       If true, logs will be displayed to stdout instead of using Google log info.
      optional bool log_to_stdout = 66 [default = true];
      Returns:
      Whether the logToStdout field is set.

    • getLogToStdout

      boolean getLogToStdout()
       If true, logs will be displayed to stdout instead of using Google log info.
      optional bool log_to_stdout = 66 [default = true];
      Returns:
      The logToStdout.

    • hasCrossoverBoundSnappingDistance

      boolean 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];
      Returns:
      Whether the crossoverBoundSnappingDistance field is set.

    • getCrossoverBoundSnappingDistance

      double 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];
      Returns:
      The crossoverBoundSnappingDistance.

    • hasPushToVertex

      boolean 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];
      Returns:
      Whether the pushToVertex field is set.

    • getPushToVertex

      boolean 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];
      Returns:
      The pushToVertex.

    • hasUseImpliedFreePreprocessor

      boolean hasUseImpliedFreePreprocessor()
       If presolve runs, include the pass that detects implied free variables.
      optional bool use_implied_free_preprocessor = 67 [default = true];
      Returns:
      Whether the useImpliedFreePreprocessor field is set.

    • getUseImpliedFreePreprocessor

      boolean getUseImpliedFreePreprocessor()
       If presolve runs, include the pass that detects implied free variables.
      optional bool use_implied_free_preprocessor = 67 [default = true];
      Returns:
      The useImpliedFreePreprocessor.

    • hasMaxValidMagnitude

      boolean 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];
      Returns:
      Whether the maxValidMagnitude field is set.

    • getMaxValidMagnitude

      double 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];
      Returns:
      The maxValidMagnitude.

    • hasDropMagnitude

      boolean 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];
      Returns:
      Whether the dropMagnitude field is set.

    • getDropMagnitude

      double 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];
      Returns:
      The dropMagnitude.

    • hasDualPricePrioritizeNorm

      boolean 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];
      Returns:
      Whether the dualPricePrioritizeNorm field is set.

    • getDualPricePrioritizeNorm

      boolean 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];
      Returns:
      The dualPricePrioritizeNorm.