Uses of Class
com.google.ortools.glop.GlopParameters.Builder

Packages that use GlopParameters.Builder

Package	Description
com.google.ortools.glop
operations_research.pdlp

Uses of GlopParameters.Builder in com.google.ortools.glop

Methods in com.google.ortools.glop that return GlopParameters.Builder

Modifier and Type	Method	Description
GlopParameters.Builder	GlopParameters.Builder.addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
GlopParameters.Builder	GlopParameters.Builder.clear()
GlopParameters.Builder	GlopParameters.Builder.clearAllowSimplexAlgorithmChange()	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.
GlopParameters.Builder	GlopParameters.Builder.clearBasisRefactorizationPeriod()	Number of iterations between two basis refactorizations.
GlopParameters.Builder	GlopParameters.Builder.clearChangeStatusToImprecise()	If true, the internal API will change the return status to imprecise if the solution does not respect the internal tolerances.
GlopParameters.Builder	GlopParameters.Builder.clearCostScaling()	optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
GlopParameters.Builder	GlopParameters.Builder.clearCrossoverBoundSnappingDistance()	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.
GlopParameters.Builder	GlopParameters.Builder.clearDegenerateMinistepFactor()	During a degenerate iteration, the more conservative approach is to do a step of length zero (while shifting the bound of the leaving variable).
GlopParameters.Builder	GlopParameters.Builder.clearDevexWeightsResetPeriod()	Devex weights will be reset to 1.0 after that number of updates.
GlopParameters.Builder	GlopParameters.Builder.clearDropTolerance()	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).
GlopParameters.Builder	GlopParameters.Builder.clearDualFeasibilityTolerance()	Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates.
GlopParameters.Builder	GlopParameters.Builder.clearDualizerThreshold()	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.
GlopParameters.Builder	GlopParameters.Builder.clearDualPricePrioritizeNorm()	On some problem like stp3d or pds-100 this makes a huge difference in speed and number of iterations of the dual simplex.
GlopParameters.Builder	GlopParameters.Builder.clearDualSmallPivotThreshold()	Like small_pivot_threshold but for the dual simplex.
GlopParameters.Builder	GlopParameters.Builder.clearDynamicallyAdjustRefactorizationPeriod()	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).
GlopParameters.Builder	GlopParameters.Builder.clearExploitSingletonColumnInInitialBasis()	Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
GlopParameters.Builder	GlopParameters.Builder.clearFeasibilityRule()	PricingRule to use during the feasibility phase.
GlopParameters.Builder	GlopParameters.Builder.clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
GlopParameters.Builder	GlopParameters.Builder.clearHarrisToleranceRatio()	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.Builder	GlopParameters.Builder.clearInitialBasis()	What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
GlopParameters.Builder	GlopParameters.Builder.clearInitialConditionNumberThreshold()	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.
GlopParameters.Builder	GlopParameters.Builder.clearInitializeDevexWithColumnNorms()	Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
GlopParameters.Builder	GlopParameters.Builder.clearLogSearchProgress()	If true, logs the progress of a solve to LOG(INFO).
GlopParameters.Builder	GlopParameters.Builder.clearLogToStdout()	If true, logs will be displayed to stdout instead of using Google log info.
GlopParameters.Builder	GlopParameters.Builder.clearLuFactorizationPivotThreshold()	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.
GlopParameters.Builder	GlopParameters.Builder.clearMarkowitzSingularityThreshold()	If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular.
GlopParameters.Builder	GlopParameters.Builder.clearMarkowitzZlatevParameter()	How many columns do we look at in the Markowitz pivoting rule to find a good pivot.
GlopParameters.Builder	GlopParameters.Builder.clearMaxDeterministicTime()	Maximum deterministic time allowed to solve a problem.
GlopParameters.Builder	GlopParameters.Builder.clearMaxNumberOfIterations()	Maximum number of simplex iterations to solve a problem.
GlopParameters.Builder	GlopParameters.Builder.clearMaxNumberOfReoptimizations()	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).
GlopParameters.Builder	GlopParameters.Builder.clearMaxTimeInSeconds()	Maximum time allowed in seconds to solve a problem.
GlopParameters.Builder	GlopParameters.Builder.clearMaxValidMagnitude()	Any finite values in the input LP must be below this threshold, otherwise the model will be reported invalid.
GlopParameters.Builder	GlopParameters.Builder.clearMinimumAcceptablePivot()	We never follow a basis change with a pivot under this threshold.
GlopParameters.Builder	GlopParameters.Builder.clearNumOmpThreads()	Number of threads in the OMP parallel sections.
GlopParameters.Builder	GlopParameters.Builder.clearObjectiveLowerLimit()	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.
GlopParameters.Builder	GlopParameters.Builder.clearObjectiveUpperLimit()	optional double objective_upper_limit = 41 [default = inf];
GlopParameters.Builder	GlopParameters.Builder.clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
GlopParameters.Builder	GlopParameters.Builder.clearOptimizationRule()	PricingRule to use during the optimization phase.
GlopParameters.Builder	GlopParameters.Builder.clearPerturbCostsInDualSimplex()	When this is true, then the costs are randomly perturbed before the dual simplex is even started.
GlopParameters.Builder	GlopParameters.Builder.clearPreprocessorZeroTolerance()	A floating point tolerance used by the preprocessors.
GlopParameters.Builder	GlopParameters.Builder.clearPrimalFeasibilityTolerance()	This tolerance indicates by how much we allow the variable values to go out of bounds and still consider the current solution primal-feasible.
GlopParameters.Builder	GlopParameters.Builder.clearProvideStrongOptimalGuarantee()	If true, then when the solver returns a solution with an OPTIMAL status, we can guarantee that: - The primal variable are in their bounds.
GlopParameters.Builder	GlopParameters.Builder.clearPushToVertex()	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.
GlopParameters.Builder	GlopParameters.Builder.clearRandomSeed()	At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed.
GlopParameters.Builder	GlopParameters.Builder.clearRatioTestZeroThreshold()	During the primal simplex (resp.
GlopParameters.Builder	GlopParameters.Builder.clearRecomputeEdgesNormThreshold()	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.
GlopParameters.Builder	GlopParameters.Builder.clearRecomputeReducedCostsThreshold()	We estimate the accuracy of the iteratively computed reduced costs.
GlopParameters.Builder	GlopParameters.Builder.clearRefactorizationThreshold()	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].
GlopParameters.Builder	GlopParameters.Builder.clearRelativeCostPerturbation()	The magnitude of the cost perturbation is given by RandomIn(1.0, 2.0) * ( relative_cost_perturbation * cost + relative_max_cost_perturbation * max_cost);
GlopParameters.Builder	GlopParameters.Builder.clearRelativeMaxCostPerturbation()	optional double relative_max_cost_perturbation = 55 [default = 1e-07];
GlopParameters.Builder	GlopParameters.Builder.clearScalingMethod()	optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
GlopParameters.Builder	GlopParameters.Builder.clearSmallPivotThreshold()	When we choose the leaving variable, we want to avoid small pivot because they are the less precise and may cause numerical instabilities.
GlopParameters.Builder	GlopParameters.Builder.clearSolutionFeasibilityTolerance()	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.Builder	GlopParameters.Builder.clearSolveDualProblem()	Whether or not we solve the dual of the given problem.
GlopParameters.Builder	GlopParameters.Builder.clearUseDedicatedDualFeasibilityAlgorithm()	We have two possible dual phase I algorithms.
GlopParameters.Builder	GlopParameters.Builder.clearUseDualSimplex()	Whether or not we use the dual simplex algorithm instead of the primal.
GlopParameters.Builder	GlopParameters.Builder.clearUseImpliedFreePreprocessor()	If presolve runs, include the pass that detects implied free variables.
GlopParameters.Builder	GlopParameters.Builder.clearUseMiddleProductFormUpdate()	Whether or not to use the middle product form update rather than the standard eta LU update.
GlopParameters.Builder	GlopParameters.Builder.clearUsePreprocessing()	Whether or not we use advanced preprocessing techniques.
GlopParameters.Builder	GlopParameters.Builder.clearUseScaling()	Whether or not we scale the matrix A so that the maximum coefficient on each line and each column is 1.0.
GlopParameters.Builder	GlopParameters.Builder.clearUseTransposedMatrix()	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.
GlopParameters.Builder	GlopParameters.Builder.clone()
GlopParameters.Builder	GlopParameters.Builder.mergeFrom(GlopParameters other)
GlopParameters.Builder	GlopParameters.Builder.mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
GlopParameters.Builder	GlopParameters.Builder.mergeFrom(com.google.protobuf.Message other)
GlopParameters.Builder	GlopParameters.Builder.mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
static GlopParameters.Builder	GlopParameters.newBuilder()
static GlopParameters.Builder	GlopParameters.newBuilder(GlopParameters prototype)
GlopParameters.Builder	GlopParameters.newBuilderForType()
protected GlopParameters.Builder	GlopParameters.newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)
GlopParameters.Builder	GlopParameters.Builder.setAllowSimplexAlgorithmChange(boolean value)	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.
GlopParameters.Builder	GlopParameters.Builder.setBasisRefactorizationPeriod(int value)	Number of iterations between two basis refactorizations.
GlopParameters.Builder	GlopParameters.Builder.setChangeStatusToImprecise(boolean value)	If true, the internal API will change the return status to imprecise if the solution does not respect the internal tolerances.
GlopParameters.Builder	GlopParameters.Builder.setCostScaling(GlopParameters.CostScalingAlgorithm value)	optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];
GlopParameters.Builder	GlopParameters.Builder.setCrossoverBoundSnappingDistance(double value)	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.
GlopParameters.Builder	GlopParameters.Builder.setDegenerateMinistepFactor(double value)	During a degenerate iteration, the more conservative approach is to do a step of length zero (while shifting the bound of the leaving variable).
GlopParameters.Builder	GlopParameters.Builder.setDevexWeightsResetPeriod(int value)	Devex weights will be reset to 1.0 after that number of updates.
GlopParameters.Builder	GlopParameters.Builder.setDropTolerance(double value)	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).
GlopParameters.Builder	GlopParameters.Builder.setDualFeasibilityTolerance(double value)	Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates.
GlopParameters.Builder	GlopParameters.Builder.setDualizerThreshold(double value)	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.
GlopParameters.Builder	GlopParameters.Builder.setDualPricePrioritizeNorm(boolean value)	On some problem like stp3d or pds-100 this makes a huge difference in speed and number of iterations of the dual simplex.
GlopParameters.Builder	GlopParameters.Builder.setDualSmallPivotThreshold(double value)	Like small_pivot_threshold but for the dual simplex.
GlopParameters.Builder	GlopParameters.Builder.setDynamicallyAdjustRefactorizationPeriod(boolean value)	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).
GlopParameters.Builder	GlopParameters.Builder.setExploitSingletonColumnInInitialBasis(boolean value)	Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
GlopParameters.Builder	GlopParameters.Builder.setFeasibilityRule(GlopParameters.PricingRule value)	PricingRule to use during the feasibility phase.
GlopParameters.Builder	GlopParameters.Builder.setField(com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
GlopParameters.Builder	GlopParameters.Builder.setHarrisToleranceRatio(double value)	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.Builder	GlopParameters.Builder.setInitialBasis(GlopParameters.InitialBasisHeuristic value)	What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
GlopParameters.Builder	GlopParameters.Builder.setInitialConditionNumberThreshold(double value)	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.
GlopParameters.Builder	GlopParameters.Builder.setInitializeDevexWithColumnNorms(boolean value)	Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
GlopParameters.Builder	GlopParameters.Builder.setLogSearchProgress(boolean value)	If true, logs the progress of a solve to LOG(INFO).
GlopParameters.Builder	GlopParameters.Builder.setLogToStdout(boolean value)	If true, logs will be displayed to stdout instead of using Google log info.
GlopParameters.Builder	GlopParameters.Builder.setLuFactorizationPivotThreshold(double value)	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.
GlopParameters.Builder	GlopParameters.Builder.setMarkowitzSingularityThreshold(double value)	If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular.
GlopParameters.Builder	GlopParameters.Builder.setMarkowitzZlatevParameter(int value)	How many columns do we look at in the Markowitz pivoting rule to find a good pivot.
GlopParameters.Builder	GlopParameters.Builder.setMaxDeterministicTime(double value)	Maximum deterministic time allowed to solve a problem.
GlopParameters.Builder	GlopParameters.Builder.setMaxNumberOfIterations(long value)	Maximum number of simplex iterations to solve a problem.
GlopParameters.Builder	GlopParameters.Builder.setMaxNumberOfReoptimizations(double value)	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).
GlopParameters.Builder	GlopParameters.Builder.setMaxTimeInSeconds(double value)	Maximum time allowed in seconds to solve a problem.
GlopParameters.Builder	GlopParameters.Builder.setMaxValidMagnitude(double value)	Any finite values in the input LP must be below this threshold, otherwise the model will be reported invalid.
GlopParameters.Builder	GlopParameters.Builder.setMinimumAcceptablePivot(double value)	We never follow a basis change with a pivot under this threshold.
GlopParameters.Builder	GlopParameters.Builder.setNumOmpThreads(int value)	Number of threads in the OMP parallel sections.
GlopParameters.Builder	GlopParameters.Builder.setObjectiveLowerLimit(double value)	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.
GlopParameters.Builder	GlopParameters.Builder.setObjectiveUpperLimit(double value)	optional double objective_upper_limit = 41 [default = inf];
GlopParameters.Builder	GlopParameters.Builder.setOptimizationRule(GlopParameters.PricingRule value)	PricingRule to use during the optimization phase.
GlopParameters.Builder	GlopParameters.Builder.setPerturbCostsInDualSimplex(boolean value)	When this is true, then the costs are randomly perturbed before the dual simplex is even started.
GlopParameters.Builder	GlopParameters.Builder.setPreprocessorZeroTolerance(double value)	A floating point tolerance used by the preprocessors.
GlopParameters.Builder	GlopParameters.Builder.setPrimalFeasibilityTolerance(double value)	This tolerance indicates by how much we allow the variable values to go out of bounds and still consider the current solution primal-feasible.
GlopParameters.Builder	GlopParameters.Builder.setProvideStrongOptimalGuarantee(boolean value)	If true, then when the solver returns a solution with an OPTIMAL status, we can guarantee that: - The primal variable are in their bounds.
GlopParameters.Builder	GlopParameters.Builder.setPushToVertex(boolean value)	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.
GlopParameters.Builder	GlopParameters.Builder.setRandomSeed(int value)	At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed.
GlopParameters.Builder	GlopParameters.Builder.setRatioTestZeroThreshold(double value)	During the primal simplex (resp.
GlopParameters.Builder	GlopParameters.Builder.setRecomputeEdgesNormThreshold(double value)	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.
GlopParameters.Builder	GlopParameters.Builder.setRecomputeReducedCostsThreshold(double value)	We estimate the accuracy of the iteratively computed reduced costs.
GlopParameters.Builder	GlopParameters.Builder.setRefactorizationThreshold(double value)	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].
GlopParameters.Builder	GlopParameters.Builder.setRelativeCostPerturbation(double value)	The magnitude of the cost perturbation is given by RandomIn(1.0, 2.0) * ( relative_cost_perturbation * cost + relative_max_cost_perturbation * max_cost);
GlopParameters.Builder	GlopParameters.Builder.setRelativeMaxCostPerturbation(double value)	optional double relative_max_cost_perturbation = 55 [default = 1e-07];
GlopParameters.Builder	GlopParameters.Builder.setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value)
GlopParameters.Builder	GlopParameters.Builder.setScalingMethod(GlopParameters.ScalingAlgorithm value)	optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];
GlopParameters.Builder	GlopParameters.Builder.setSmallPivotThreshold(double value)	When we choose the leaving variable, we want to avoid small pivot because they are the less precise and may cause numerical instabilities.
GlopParameters.Builder	GlopParameters.Builder.setSolutionFeasibilityTolerance(double value)	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.Builder	GlopParameters.Builder.setSolveDualProblem(GlopParameters.SolverBehavior value)	Whether or not we solve the dual of the given problem.
GlopParameters.Builder	GlopParameters.Builder.setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
GlopParameters.Builder	GlopParameters.Builder.setUseDedicatedDualFeasibilityAlgorithm(boolean value)	We have two possible dual phase I algorithms.
GlopParameters.Builder	GlopParameters.Builder.setUseDualSimplex(boolean value)	Whether or not we use the dual simplex algorithm instead of the primal.
GlopParameters.Builder	GlopParameters.Builder.setUseImpliedFreePreprocessor(boolean value)	If presolve runs, include the pass that detects implied free variables.
GlopParameters.Builder	GlopParameters.Builder.setUseMiddleProductFormUpdate(boolean value)	Whether or not to use the middle product form update rather than the standard eta LU update.
GlopParameters.Builder	GlopParameters.Builder.setUsePreprocessing(boolean value)	Whether or not we use advanced preprocessing techniques.
GlopParameters.Builder	GlopParameters.Builder.setUseScaling(boolean value)	Whether or not we scale the matrix A so that the maximum coefficient on each line and each column is 1.0.
GlopParameters.Builder	GlopParameters.Builder.setUseTransposedMatrix(boolean value)	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.
GlopParameters.Builder	GlopParameters.toBuilder()

Uses of GlopParameters.Builder in operations_research.pdlp

Methods in operations_research.pdlp that return GlopParameters.Builder

Modifier and Type	Method	Description
GlopParameters.Builder	Solvers.PrimalDualHybridGradientParams.PresolveOptions.Builder.getGlopParametersBuilder()	Parameters to control glop's presolver.

Methods in operations_research.pdlp with parameters of type GlopParameters.Builder

Modifier and Type	Method	Description
Solvers.PrimalDualHybridGradientParams.PresolveOptions.Builder	Solvers.PrimalDualHybridGradientParams.PresolveOptions.Builder.setGlopParameters(GlopParameters.Builder builderForValue)	Parameters to control glop's presolver.