GlopParameters.Builder (com.google.ortools:ortools-java 9.12.9999 API)

java.lang.Object
- com.google.protobuf.AbstractMessageLite.Builder
- - com.google.protobuf.AbstractMessage.Builder<BuilderT>
  - - com.google.protobuf.GeneratedMessage.Builder<GlopParameters.Builder>
    - - com.google.ortools.glop.GlopParameters.Builder

All Implemented Interfaces:

GlopParametersOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, java.lang.Cloneable

Enclosing class:

GlopParameters
```
public static final class GlopParameters.Builder
extends com.google.protobuf.GeneratedMessage.Builder<GlopParameters.Builder>
implements GlopParametersOrBuilder
```
```
 next id = 72
 
```
Protobuf type operations_research.glop.GlopParameters

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`GlopParameters`	`build()`
`GlopParameters`	`buildPartial()`
`GlopParameters.Builder`	`clear()`
`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`	`clearBasisRefactorizationPeriod()` Number of iterations between two basis refactorizations.
`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`	`clearCostScaling()` `optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];`
`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`	`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`	`clearDevexWeightsResetPeriod()` Devex weights will be reset to 1.0 after that number of updates.
`GlopParameters.Builder`	`clearDropMagnitude()` Value in the input LP lower than this will be ignored.
`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`	`clearDualFeasibilityTolerance()` Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates.
`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`	`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`	`clearDualSmallPivotThreshold()` Like small_pivot_threshold but for the dual simplex.
`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`	`clearExploitSingletonColumnInInitialBasis()` Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
`GlopParameters.Builder`	`clearFeasibilityRule()` PricingRule to use during the feasibility phase.
`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`	`clearInitialBasis()` What heuristic is used to try to replace the fixed slack columns in the initial basis of the primal simplex.
`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`	`clearInitializeDevexWithColumnNorms()` Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
`GlopParameters.Builder`	`clearLogSearchProgress()` If true, logs the progress of a solve to LOG(INFO).
`GlopParameters.Builder`	`clearLogToStdout()` If true, logs will be displayed to stdout instead of using Google log info.
`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`	`clearMarkowitzSingularityThreshold()` If a pivot magnitude is smaller than this during the Markowitz LU factorization, then the matrix is assumed to be singular.
`GlopParameters.Builder`	`clearMarkowitzZlatevParameter()` How many columns do we look at in the Markowitz pivoting rule to find a good pivot.
`GlopParameters.Builder`	`clearMaxDeterministicTime()` Maximum deterministic time allowed to solve a problem.
`GlopParameters.Builder`	`clearMaxNumberOfIterations()` Maximum number of simplex iterations to solve a problem.
`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`	`clearMaxTimeInSeconds()` Maximum time allowed in seconds to solve a problem.
`GlopParameters.Builder`	`clearMaxValidMagnitude()` Any finite values in the input LP must be below this threshold, otherwise the model will be reported invalid.
`GlopParameters.Builder`	`clearMinimumAcceptablePivot()` We never follow a basis change with a pivot under this threshold.
`GlopParameters.Builder`	`clearNumOmpThreads()` Number of threads in the OMP parallel sections.
`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`	`clearObjectiveUpperLimit()` `optional double objective_upper_limit = 41 [default = inf];`
`GlopParameters.Builder`	`clearOptimizationRule()` PricingRule to use during the optimization phase.
`GlopParameters.Builder`	`clearPerturbCostsInDualSimplex()` When this is true, then the costs are randomly perturbed before the dual simplex is even started.
`GlopParameters.Builder`	`clearPreprocessorZeroTolerance()` A floating point tolerance used by the preprocessors.
`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`	`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`	`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`	`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`	`clearRatioTestZeroThreshold()` 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.
`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`	`clearRecomputeReducedCostsThreshold()` We estimate the accuracy of the iteratively computed reduced costs.
`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`	`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); `optional double relative_cost_perturbation = 54 [default = 1e-05];`
`GlopParameters.Builder`	`clearRelativeMaxCostPerturbation()` `optional double relative_max_cost_perturbation = 55 [default = 1e-07];`
`GlopParameters.Builder`	`clearScalingMethod()` `optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];`
`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`	`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`	`clearSolveDualProblem()` Whether or not we solve the dual of the given problem.
`GlopParameters.Builder`	`clearUseDedicatedDualFeasibilityAlgorithm()` We have two possible dual phase I algorithms.
`GlopParameters.Builder`	`clearUseDualSimplex()` Whether or not we use the dual simplex algorithm instead of the primal.
`GlopParameters.Builder`	`clearUseImpliedFreePreprocessor()` If presolve runs, include the pass that detects implied free variables.
`GlopParameters.Builder`	`clearUseMiddleProductFormUpdate()` Whether or not to use the middle product form update rather than the standard eta LU update.
`GlopParameters.Builder`	`clearUsePreprocessing()` Whether or not we use advanced preprocessing techniques.
`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`	`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.
`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.
`GlopParameters`	`getDefaultInstanceForType()`
`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).
`static com.google.protobuf.Descriptors.Descriptor`	`getDescriptor()`
`com.google.protobuf.Descriptors.Descriptor`	`getDescriptorForType()`
`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); `optional double relative_cost_perturbation = 54 [default = 1e-05];`
`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`	`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); `optional double relative_cost_perturbation = 54 [default = 1e-05];`
`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`	`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.
`protected com.google.protobuf.GeneratedMessage.FieldAccessorTable`	`internalGetFieldAccessorTable()`
`boolean`	`isInitialized()`
`GlopParameters.Builder`	`mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`GlopParameters.Builder`	`mergeFrom(GlopParameters other)`
`GlopParameters.Builder`	`mergeFrom(com.google.protobuf.Message other)`
`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`	`setBasisRefactorizationPeriod(int value)` Number of iterations between two basis refactorizations.
`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`	`setCostScaling(GlopParameters.CostScalingAlgorithm value)` `optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];`
`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`	`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`	`setDevexWeightsResetPeriod(int value)` Devex weights will be reset to 1.0 after that number of updates.
`GlopParameters.Builder`	`setDropMagnitude(double value)` Value in the input LP lower than this will be ignored.
`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`	`setDualFeasibilityTolerance(double value)` Variables whose reduced costs have an absolute value smaller than this tolerance are not considered as entering candidates.
`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`	`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`	`setDualSmallPivotThreshold(double value)` Like small_pivot_threshold but for the dual simplex.
`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`	`setExploitSingletonColumnInInitialBasis(boolean value)` Whether or not we exploit the singleton columns already present in the problem when we create the initial basis.
`GlopParameters.Builder`	`setFeasibilityRule(GlopParameters.PricingRule value)` PricingRule to use during the feasibility phase.
`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`	`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`	`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`	`setInitializeDevexWithColumnNorms(boolean value)` Whether we initialize devex weights to 1.0 or to the norms of the matrix columns.
`GlopParameters.Builder`	`setLogSearchProgress(boolean value)` If true, logs the progress of a solve to LOG(INFO).
`GlopParameters.Builder`	`setLogToStdout(boolean value)` If true, logs will be displayed to stdout instead of using Google log info.
`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`	`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`	`setMarkowitzZlatevParameter(int value)` How many columns do we look at in the Markowitz pivoting rule to find a good pivot.
`GlopParameters.Builder`	`setMaxDeterministicTime(double value)` Maximum deterministic time allowed to solve a problem.
`GlopParameters.Builder`	`setMaxNumberOfIterations(long value)` Maximum number of simplex iterations to solve a problem.
`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`	`setMaxTimeInSeconds(double value)` Maximum time allowed in seconds to solve a problem.
`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`	`setMinimumAcceptablePivot(double value)` We never follow a basis change with a pivot under this threshold.
`GlopParameters.Builder`	`setNumOmpThreads(int value)` Number of threads in the OMP parallel sections.
`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`	`setObjectiveUpperLimit(double value)` `optional double objective_upper_limit = 41 [default = inf];`
`GlopParameters.Builder`	`setOptimizationRule(GlopParameters.PricingRule value)` PricingRule to use during the optimization phase.
`GlopParameters.Builder`	`setPerturbCostsInDualSimplex(boolean value)` When this is true, then the costs are randomly perturbed before the dual simplex is even started.
`GlopParameters.Builder`	`setPreprocessorZeroTolerance(double value)` A floating point tolerance used by the preprocessors.
`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`	`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`	`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`	`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`	`setRatioTestZeroThreshold(double value)` 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.
`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`	`setRecomputeReducedCostsThreshold(double value)` We estimate the accuracy of the iteratively computed reduced costs.
`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`	`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); `optional double relative_cost_perturbation = 54 [default = 1e-05];`
`GlopParameters.Builder`	`setRelativeMaxCostPerturbation(double value)` `optional double relative_max_cost_perturbation = 55 [default = 1e-07];`
`GlopParameters.Builder`	`setScalingMethod(GlopParameters.ScalingAlgorithm value)` `optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];`
`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`	`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`	`setSolveDualProblem(GlopParameters.SolverBehavior value)` Whether or not we solve the dual of the given problem.
`GlopParameters.Builder`	`setUseDedicatedDualFeasibilityAlgorithm(boolean value)` We have two possible dual phase I algorithms.
`GlopParameters.Builder`	`setUseDualSimplex(boolean value)` Whether or not we use the dual simplex algorithm instead of the primal.
`GlopParameters.Builder`	`setUseImpliedFreePreprocessor(boolean value)` If presolve runs, include the pass that detects implied free variables.
`GlopParameters.Builder`	`setUseMiddleProductFormUpdate(boolean value)` Whether or not to use the middle product form update rather than the standard eta LU update.
`GlopParameters.Builder`	`setUsePreprocessing(boolean value)` Whether or not we use advanced preprocessing techniques.
`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`	`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.

Methods inherited from class com.google.protobuf.GeneratedMessage.Builder
addRepeatedField, clearField, clearOneof, clone, getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownFields, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setField, setRepeatedField, setUnknownFields, setUnknownFieldSetBuilder, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder
findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder
addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.Message.Builder
mergeDelimitedFrom, mergeDelimitedFrom

Method Detail

getDescriptor

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

internalGetFieldAccessorTable
```
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
```
Specified by:

internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<GlopParameters.Builder>

clear
```
public GlopParameters.Builder clear()
```
Specified by:

clear in interface com.google.protobuf.Message.Builder

Specified by:

clear in interface com.google.protobuf.MessageLite.Builder

Overrides:

clear in class com.google.protobuf.GeneratedMessage.Builder<GlopParameters.Builder>

getDescriptorForType
```
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
```
Specified by:

getDescriptorForType in interface com.google.protobuf.Message.Builder

Specified by:

getDescriptorForType in interface com.google.protobuf.MessageOrBuilder

Overrides:

getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<GlopParameters.Builder>

getDefaultInstanceForType
```
public GlopParameters getDefaultInstanceForType()
```
Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

build
```
public GlopParameters build()
```
Specified by:

build in interface com.google.protobuf.Message.Builder

Specified by:

build in interface com.google.protobuf.MessageLite.Builder

buildPartial
```
public GlopParameters buildPartial()
```
Specified by:

buildPartial in interface com.google.protobuf.Message.Builder

Specified by:

buildPartial in interface com.google.protobuf.MessageLite.Builder

mergeFrom
```
public GlopParameters.Builder mergeFrom(com.google.protobuf.Message other)
```
Specified by:

mergeFrom in interface com.google.protobuf.Message.Builder

Overrides:

mergeFrom in class com.google.protobuf.AbstractMessage.Builder<GlopParameters.Builder>

mergeFrom

public GlopParameters.Builder mergeFrom(GlopParameters other)

isInitialized
```
public final boolean isInitialized()
```
Specified by:

isInitialized in interface com.google.protobuf.MessageLiteOrBuilder

Overrides:

isInitialized in class com.google.protobuf.GeneratedMessage.Builder<GlopParameters.Builder>

mergeFrom
```
public GlopParameters.Builder mergeFrom(com.google.protobuf.CodedInputStream input,
                                        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                 throws java.io.IOException
```
Specified by:

mergeFrom in interface com.google.protobuf.Message.Builder

Specified by:

mergeFrom in interface com.google.protobuf.MessageLite.Builder

Overrides:

mergeFrom in class com.google.protobuf.AbstractMessage.Builder<GlopParameters.Builder>

Throws:

java.io.IOException

hasScalingMethod
```
public boolean hasScalingMethod()
```
optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Specified by:

hasScalingMethod in interface GlopParametersOrBuilder

Returns:

Whether the scalingMethod field is set.

getScalingMethod
```
public GlopParameters.ScalingAlgorithm getScalingMethod()
```
optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Specified by:

getScalingMethod in interface GlopParametersOrBuilder

Returns:

The scalingMethod.

setScalingMethod
```
public GlopParameters.Builder setScalingMethod(GlopParameters.ScalingAlgorithm value)
```
optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Parameters:

value - The scalingMethod to set.

Returns:

This builder for chaining.

clearScalingMethod
```
public GlopParameters.Builder clearScalingMethod()
```
optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Returns:

This builder for chaining.

hasFeasibilityRule
```
public boolean hasFeasibilityRule()
```
```
 PricingRule to use during the feasibility phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
Specified by:

hasFeasibilityRule in interface GlopParametersOrBuilder

Returns:

Whether the feasibilityRule field is set.

getFeasibilityRule
```
public GlopParameters.PricingRule getFeasibilityRule()
```
```
 PricingRule to use during the feasibility phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
Specified by:

getFeasibilityRule in interface GlopParametersOrBuilder

Returns:

The feasibilityRule.

setFeasibilityRule
```
public GlopParameters.Builder setFeasibilityRule(GlopParameters.PricingRule value)
```
```
 PricingRule to use during the feasibility phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
Parameters:

value - The feasibilityRule to set.

Returns:

This builder for chaining.

clearFeasibilityRule
```
public GlopParameters.Builder clearFeasibilityRule()
```
```
 PricingRule to use during the feasibility phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];
Returns:

This builder for chaining.

hasOptimizationRule
```
public boolean hasOptimizationRule()
```
```
 PricingRule to use during the optimization phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
Specified by:

hasOptimizationRule in interface GlopParametersOrBuilder

Returns:

Whether the optimizationRule field is set.

getOptimizationRule
```
public GlopParameters.PricingRule getOptimizationRule()
```
```
 PricingRule to use during the optimization phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
Specified by:

getOptimizationRule in interface GlopParametersOrBuilder

Returns:

The optimizationRule.

setOptimizationRule
```
public GlopParameters.Builder setOptimizationRule(GlopParameters.PricingRule value)
```
```
 PricingRule to use during the optimization phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
Parameters:

value - The optimizationRule to set.

Returns:

This builder for chaining.

clearOptimizationRule
```
public GlopParameters.Builder clearOptimizationRule()
```
```
 PricingRule to use during the optimization phase.
 
```
optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];
Returns:

This builder for chaining.

hasRefactorizationThreshold

public 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];

Specified by:: hasRefactorizationThreshold in interface GlopParametersOrBuilder
Returns:: Whether the refactorizationThreshold field is set.

getRefactorizationThreshold

public 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];

Specified by:: getRefactorizationThreshold in interface GlopParametersOrBuilder
Returns:: The refactorizationThreshold.

setRefactorizationThreshold

public 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].
 - 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];

Parameters:: value - The refactorizationThreshold to set.
Returns:: This builder for chaining.

clearRefactorizationThreshold

public 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].
 - 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:: This builder for chaining.

hasRecomputeReducedCostsThreshold

public 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];

Specified by:: hasRecomputeReducedCostsThreshold in interface GlopParametersOrBuilder
Returns:: Whether the recomputeReducedCostsThreshold field is set.

getRecomputeReducedCostsThreshold

public 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];

Specified by:: getRecomputeReducedCostsThreshold in interface GlopParametersOrBuilder
Returns:: The recomputeReducedCostsThreshold.

setRecomputeReducedCostsThreshold

public GlopParameters.Builder setRecomputeReducedCostsThreshold(double value)

 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];

Parameters:: value - The recomputeReducedCostsThreshold to set.
Returns:: This builder for chaining.

clearRecomputeReducedCostsThreshold

public GlopParameters.Builder clearRecomputeReducedCostsThreshold()

 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:: This builder for chaining.

hasRecomputeEdgesNormThreshold

public 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];

Specified by:: hasRecomputeEdgesNormThreshold in interface GlopParametersOrBuilder
Returns:: Whether the recomputeEdgesNormThreshold field is set.

getRecomputeEdgesNormThreshold

public 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];

Specified by:: getRecomputeEdgesNormThreshold in interface GlopParametersOrBuilder
Returns:: The recomputeEdgesNormThreshold.

setRecomputeEdgesNormThreshold

public 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. 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];

Parameters:: value - The recomputeEdgesNormThreshold to set.
Returns:: This builder for chaining.

clearRecomputeEdgesNormThreshold

public 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. 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:: This builder for chaining.

hasPrimalFeasibilityTolerance

public 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];

Specified by:: hasPrimalFeasibilityTolerance in interface GlopParametersOrBuilder
Returns:: Whether the primalFeasibilityTolerance field is set.

getPrimalFeasibilityTolerance

public 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];

Specified by:: getPrimalFeasibilityTolerance in interface GlopParametersOrBuilder
Returns:: The primalFeasibilityTolerance.

setPrimalFeasibilityTolerance

public 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. 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];

Parameters:: value - The primalFeasibilityTolerance to set.
Returns:: This builder for chaining.

clearPrimalFeasibilityTolerance

public 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. 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:: This builder for chaining.

hasDualFeasibilityTolerance

public 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];

Specified by:: hasDualFeasibilityTolerance in interface GlopParametersOrBuilder
Returns:: Whether the dualFeasibilityTolerance field is set.

getDualFeasibilityTolerance

public 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];

Specified by:: getDualFeasibilityTolerance in interface GlopParametersOrBuilder
Returns:: The dualFeasibilityTolerance.

setDualFeasibilityTolerance

public GlopParameters.Builder setDualFeasibilityTolerance(double value)

 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];

Parameters:: value - The dualFeasibilityTolerance to set.
Returns:: This builder for chaining.

clearDualFeasibilityTolerance

public GlopParameters.Builder clearDualFeasibilityTolerance()

 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:: This builder for chaining.

hasRatioTestZeroThreshold

public 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];

Specified by:: hasRatioTestZeroThreshold in interface GlopParametersOrBuilder
Returns:: Whether the ratioTestZeroThreshold field is set.

getRatioTestZeroThreshold

public 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];

Specified by:: getRatioTestZeroThreshold in interface GlopParametersOrBuilder
Returns:: The ratioTestZeroThreshold.

setRatioTestZeroThreshold

public GlopParameters.Builder setRatioTestZeroThreshold(double value)

 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];

Parameters:: value - The ratioTestZeroThreshold to set.
Returns:: This builder for chaining.

clearRatioTestZeroThreshold

public GlopParameters.Builder clearRatioTestZeroThreshold()

 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:: This builder for chaining.

hasHarrisToleranceRatio

public 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];

Specified by:: hasHarrisToleranceRatio in interface GlopParametersOrBuilder
Returns:: Whether the harrisToleranceRatio field is set.

getHarrisToleranceRatio

public 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];

Specified by:: getHarrisToleranceRatio in interface GlopParametersOrBuilder
Returns:: The harrisToleranceRatio.

setHarrisToleranceRatio

public 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. 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];

Parameters:: value - The harrisToleranceRatio to set.
Returns:: This builder for chaining.

clearHarrisToleranceRatio

public 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. 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:: This builder for chaining.

hasSmallPivotThreshold

public 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];

Specified by:: hasSmallPivotThreshold in interface GlopParametersOrBuilder
Returns:: Whether the smallPivotThreshold field is set.

getSmallPivotThreshold

public 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];

Specified by:: getSmallPivotThreshold in interface GlopParametersOrBuilder
Returns:: The smallPivotThreshold.

setSmallPivotThreshold

public 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. 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];

Parameters:: value - The smallPivotThreshold to set.
Returns:: This builder for chaining.

clearSmallPivotThreshold

public 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. 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:: This builder for chaining.

hasMinimumAcceptablePivot
```
public boolean hasMinimumAcceptablePivot()
```
```
 We never follow a basis change with a pivot under this threshold.
 
```
optional double minimum_acceptable_pivot = 15 [default = 1e-06];
Specified by:

hasMinimumAcceptablePivot in interface GlopParametersOrBuilder

Returns:

Whether the minimumAcceptablePivot field is set.

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

getMinimumAcceptablePivot in interface GlopParametersOrBuilder

Returns:

The minimumAcceptablePivot.

setMinimumAcceptablePivot
```
public GlopParameters.Builder setMinimumAcceptablePivot(double value)
```
```
 We never follow a basis change with a pivot under this threshold.
 
```
optional double minimum_acceptable_pivot = 15 [default = 1e-06];
Parameters:

value - The minimumAcceptablePivot to set.

Returns:

This builder for chaining.

clearMinimumAcceptablePivot

public GlopParameters.Builder clearMinimumAcceptablePivot()

 We never follow a basis change with a pivot under this threshold.

optional double minimum_acceptable_pivot = 15 [default = 1e-06];

Returns:: This builder for chaining.

hasDropTolerance

public 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];

Specified by:: hasDropTolerance in interface GlopParametersOrBuilder
Returns:: Whether the dropTolerance field is set.

getDropTolerance

public 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];

Specified by:: getDropTolerance in interface GlopParametersOrBuilder
Returns:: The dropTolerance.

setDropTolerance

public 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). This parameter should be positive or zero.

optional double drop_tolerance = 52 [default = 1e-14];

Parameters:: value - The dropTolerance to set.
Returns:: This builder for chaining.

clearDropTolerance

public 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). This parameter should be positive or zero.

optional double drop_tolerance = 52 [default = 1e-14];

Returns:: This builder for chaining.

hasUseScaling
```
public 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];
Specified by:

hasUseScaling in interface GlopParametersOrBuilder

Returns:

Whether the useScaling field is set.

getUseScaling
```
public 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];
Specified by:

getUseScaling in interface GlopParametersOrBuilder

Returns:

The useScaling.

setUseScaling

public 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.

optional bool use_scaling = 16 [default = true];

Parameters:: value - The useScaling to set.
Returns:: This builder for chaining.

clearUseScaling

public 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.

optional bool use_scaling = 16 [default = true];

Returns:: This builder for chaining.

hasCostScaling
```
public boolean hasCostScaling()
```
optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Specified by:

hasCostScaling in interface GlopParametersOrBuilder

Returns:

Whether the costScaling field is set.

getCostScaling
```
public GlopParameters.CostScalingAlgorithm getCostScaling()
```
optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Specified by:

getCostScaling in interface GlopParametersOrBuilder

Returns:

The costScaling.

setCostScaling
```
public GlopParameters.Builder setCostScaling(GlopParameters.CostScalingAlgorithm value)
```
optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Parameters:

value - The costScaling to set.

Returns:

This builder for chaining.

clearCostScaling
```
public GlopParameters.Builder clearCostScaling()
```
optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Returns:

This builder for chaining.

hasInitialBasis
```
public 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];
Specified by:

hasInitialBasis in interface GlopParametersOrBuilder

Returns:

Whether the initialBasis field is set.

getInitialBasis
```
public 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];
Specified by:

getInitialBasis in interface GlopParametersOrBuilder

Returns:

The initialBasis.

setInitialBasis

public 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.

optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];

Parameters:: value - The initialBasis to set.
Returns:: This builder for chaining.

clearInitialBasis

public GlopParameters.Builder clearInitialBasis()

 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:: This builder for chaining.

hasUseTransposedMatrix
```
public 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];
Specified by:

hasUseTransposedMatrix in interface GlopParametersOrBuilder

Returns:

Whether the useTransposedMatrix field is set.

getUseTransposedMatrix

public 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];

Specified by:: getUseTransposedMatrix in interface GlopParametersOrBuilder
Returns:: The useTransposedMatrix.

setUseTransposedMatrix

public 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.

optional bool use_transposed_matrix = 18 [default = true];

Parameters:: value - The useTransposedMatrix to set.
Returns:: This builder for chaining.

clearUseTransposedMatrix

public 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.

optional bool use_transposed_matrix = 18 [default = true];

Returns:: This builder for chaining.

hasBasisRefactorizationPeriod

public 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];

Specified by:: hasBasisRefactorizationPeriod in interface GlopParametersOrBuilder
Returns:: Whether the basisRefactorizationPeriod field is set.

getBasisRefactorizationPeriod

public 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];

Specified by:: getBasisRefactorizationPeriod in interface GlopParametersOrBuilder
Returns:: The basisRefactorizationPeriod.

setBasisRefactorizationPeriod

public GlopParameters.Builder setBasisRefactorizationPeriod(int value)

 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];

Parameters:: value - The basisRefactorizationPeriod to set.
Returns:: This builder for chaining.

clearBasisRefactorizationPeriod

public GlopParameters.Builder clearBasisRefactorizationPeriod()

 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:: This builder for chaining.

hasDynamicallyAdjustRefactorizationPeriod

public 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];

Specified by:: hasDynamicallyAdjustRefactorizationPeriod in interface GlopParametersOrBuilder
Returns:: Whether the dynamicallyAdjustRefactorizationPeriod field is set.

getDynamicallyAdjustRefactorizationPeriod

public 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];

Specified by:: getDynamicallyAdjustRefactorizationPeriod in interface GlopParametersOrBuilder
Returns:: The dynamicallyAdjustRefactorizationPeriod.

setDynamicallyAdjustRefactorizationPeriod

public 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). 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];

Parameters:: value - The dynamicallyAdjustRefactorizationPeriod to set.
Returns:: This builder for chaining.

clearDynamicallyAdjustRefactorizationPeriod

public 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). 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:: This builder for chaining.

hasSolveDualProblem
```
public 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];
Specified by:

hasSolveDualProblem in interface GlopParametersOrBuilder

Returns:

Whether the solveDualProblem field is set.

getSolveDualProblem

public 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];

Specified by:: getSolveDualProblem in interface GlopParametersOrBuilder
Returns:: The solveDualProblem.

setSolveDualProblem

public GlopParameters.Builder setSolveDualProblem(GlopParameters.SolverBehavior value)

 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];

Parameters:: value - The solveDualProblem to set.
Returns:: This builder for chaining.

clearSolveDualProblem

public GlopParameters.Builder clearSolveDualProblem()

 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:: This builder for chaining.

hasDualizerThreshold

public 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];

Specified by:: hasDualizerThreshold in interface GlopParametersOrBuilder
Returns:: Whether the dualizerThreshold field is set.

getDualizerThreshold

public 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];

Specified by:: getDualizerThreshold in interface GlopParametersOrBuilder
Returns:: The dualizerThreshold.

setDualizerThreshold

public 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.

optional double dualizer_threshold = 21 [default = 1.5];

Parameters:: value - The dualizerThreshold to set.
Returns:: This builder for chaining.

clearDualizerThreshold

public 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.

optional double dualizer_threshold = 21 [default = 1.5];

Returns:: This builder for chaining.

hasSolutionFeasibilityTolerance

public 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];

Specified by:: hasSolutionFeasibilityTolerance in interface GlopParametersOrBuilder
Returns:: Whether the solutionFeasibilityTolerance field is set.

getSolutionFeasibilityTolerance

public 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];

Specified by:: getSolutionFeasibilityTolerance in interface GlopParametersOrBuilder
Returns:: The solutionFeasibilityTolerance.

setSolutionFeasibilityTolerance

public 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.

 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];

Parameters:: value - The solutionFeasibilityTolerance to set.
Returns:: This builder for chaining.

clearSolutionFeasibilityTolerance

public 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.

 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:: This builder for chaining.

hasProvideStrongOptimalGuarantee

public 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];

Specified by:: hasProvideStrongOptimalGuarantee in interface GlopParametersOrBuilder
Returns:: Whether the provideStrongOptimalGuarantee field is set.

getProvideStrongOptimalGuarantee

public 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];

Specified by:: getProvideStrongOptimalGuarantee in interface GlopParametersOrBuilder
Returns:: The provideStrongOptimalGuarantee.

setProvideStrongOptimalGuarantee

public 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.
 - 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];

Parameters:: value - The provideStrongOptimalGuarantee to set.
Returns:: This builder for chaining.

clearProvideStrongOptimalGuarantee

public 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.
 - 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:: This builder for chaining.

hasChangeStatusToImprecise
```
public 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];
Specified by:

hasChangeStatusToImprecise in interface GlopParametersOrBuilder

Returns:

Whether the changeStatusToImprecise field is set.

getChangeStatusToImprecise
```
public 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];
Specified by:

getChangeStatusToImprecise in interface GlopParametersOrBuilder

Returns:

The changeStatusToImprecise.

setChangeStatusToImprecise

public 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.

optional bool change_status_to_imprecise = 58 [default = true];

Parameters:: value - The changeStatusToImprecise to set.
Returns:: This builder for chaining.

clearChangeStatusToImprecise

public GlopParameters.Builder clearChangeStatusToImprecise()

 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:: This builder for chaining.

hasMaxNumberOfReoptimizations

public 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];

Specified by:: hasMaxNumberOfReoptimizations in interface GlopParametersOrBuilder
Returns:: Whether the maxNumberOfReoptimizations field is set.

getMaxNumberOfReoptimizations

public 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];

Specified by:: getMaxNumberOfReoptimizations in interface GlopParametersOrBuilder
Returns:: The maxNumberOfReoptimizations.

setMaxNumberOfReoptimizations

public 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). We repeat
 such re-optimization until the solution is precise, or we hit this limit.

optional double max_number_of_reoptimizations = 56 [default = 40];

Parameters:: value - The maxNumberOfReoptimizations to set.
Returns:: This builder for chaining.

clearMaxNumberOfReoptimizations

public 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). 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:: This builder for chaining.

hasLuFactorizationPivotThreshold

public 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];

Specified by:: hasLuFactorizationPivotThreshold in interface GlopParametersOrBuilder
Returns:: Whether the luFactorizationPivotThreshold field is set.

getLuFactorizationPivotThreshold

public 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];

Specified by:: getLuFactorizationPivotThreshold in interface GlopParametersOrBuilder
Returns:: The luFactorizationPivotThreshold.

setLuFactorizationPivotThreshold

public 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. The value must be in [0,1].

optional double lu_factorization_pivot_threshold = 25 [default = 0.01];

Parameters:: value - The luFactorizationPivotThreshold to set.
Returns:: This builder for chaining.

clearLuFactorizationPivotThreshold

public 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. The value must be in [0,1].

optional double lu_factorization_pivot_threshold = 25 [default = 0.01];

Returns:: This builder for chaining.

hasMaxTimeInSeconds
```
public boolean hasMaxTimeInSeconds()
```
```
 Maximum time allowed in seconds to solve a problem.
 
```
optional double max_time_in_seconds = 26 [default = inf];
Specified by:

hasMaxTimeInSeconds in interface GlopParametersOrBuilder

Returns:

Whether the maxTimeInSeconds field is set.

getMaxTimeInSeconds
```
public double getMaxTimeInSeconds()
```
```
 Maximum time allowed in seconds to solve a problem.
 
```
optional double max_time_in_seconds = 26 [default = inf];
Specified by:

getMaxTimeInSeconds in interface GlopParametersOrBuilder

Returns:

The maxTimeInSeconds.

setMaxTimeInSeconds
```
public GlopParameters.Builder setMaxTimeInSeconds(double value)
```
```
 Maximum time allowed in seconds to solve a problem.
 
```
optional double max_time_in_seconds = 26 [default = inf];
Parameters:

value - The maxTimeInSeconds to set.

Returns:

This builder for chaining.

clearMaxTimeInSeconds

public GlopParameters.Builder clearMaxTimeInSeconds()

 Maximum time allowed in seconds to solve a problem.

optional double max_time_in_seconds = 26 [default = inf];

Returns:: This builder for chaining.

hasMaxDeterministicTime

public 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];

Specified by:: hasMaxDeterministicTime in interface GlopParametersOrBuilder
Returns:: Whether the maxDeterministicTime field is set.

getMaxDeterministicTime

public 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];

Specified by:: getMaxDeterministicTime in interface GlopParametersOrBuilder
Returns:: The maxDeterministicTime.

setMaxDeterministicTime

public GlopParameters.Builder setMaxDeterministicTime(double value)

 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];

Parameters:: value - The maxDeterministicTime to set.
Returns:: This builder for chaining.

clearMaxDeterministicTime

public GlopParameters.Builder clearMaxDeterministicTime()

 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:: This builder for chaining.

hasMaxNumberOfIterations
```
public 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];
Specified by:

hasMaxNumberOfIterations in interface GlopParametersOrBuilder

Returns:

Whether the maxNumberOfIterations field is set.

getMaxNumberOfIterations
```
public 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];
Specified by:

getMaxNumberOfIterations in interface GlopParametersOrBuilder

Returns:

The maxNumberOfIterations.

setMaxNumberOfIterations
```
public GlopParameters.Builder setMaxNumberOfIterations(long value)
```
```
 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];
Parameters:

value - The maxNumberOfIterations to set.

Returns:

This builder for chaining.

clearMaxNumberOfIterations

public GlopParameters.Builder clearMaxNumberOfIterations()

 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:: This builder for chaining.

hasMarkowitzZlatevParameter
```
public 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];
Specified by:

hasMarkowitzZlatevParameter in interface GlopParametersOrBuilder

Returns:

Whether the markowitzZlatevParameter field is set.

getMarkowitzZlatevParameter
```
public 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];
Specified by:

getMarkowitzZlatevParameter in interface GlopParametersOrBuilder

Returns:

The markowitzZlatevParameter.

setMarkowitzZlatevParameter

public GlopParameters.Builder setMarkowitzZlatevParameter(int value)

 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];

Parameters:: value - The markowitzZlatevParameter to set.
Returns:: This builder for chaining.

clearMarkowitzZlatevParameter

public GlopParameters.Builder clearMarkowitzZlatevParameter()

 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:: This builder for chaining.

hasMarkowitzSingularityThreshold

public 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];

Specified by:: hasMarkowitzSingularityThreshold in interface GlopParametersOrBuilder
Returns:: Whether the markowitzSingularityThreshold field is set.

getMarkowitzSingularityThreshold

public 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];

Specified by:: getMarkowitzSingularityThreshold in interface GlopParametersOrBuilder
Returns:: The markowitzSingularityThreshold.

setMarkowitzSingularityThreshold

public 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. 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];

Parameters:: value - The markowitzSingularityThreshold to set.
Returns:: This builder for chaining.

clearMarkowitzSingularityThreshold

public GlopParameters.Builder clearMarkowitzSingularityThreshold()

 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:: This builder for chaining.

hasUseDualSimplex
```
public boolean hasUseDualSimplex()
```
```
 Whether or not we use the dual simplex algorithm instead of the primal.
 
```
optional bool use_dual_simplex = 31 [default = false];
Specified by:

hasUseDualSimplex in interface GlopParametersOrBuilder

Returns:

Whether the useDualSimplex field is set.

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

getUseDualSimplex in interface GlopParametersOrBuilder

Returns:

The useDualSimplex.

setUseDualSimplex
```
public GlopParameters.Builder setUseDualSimplex(boolean value)
```
```
 Whether or not we use the dual simplex algorithm instead of the primal.
 
```
optional bool use_dual_simplex = 31 [default = false];
Parameters:

value - The useDualSimplex to set.

Returns:

This builder for chaining.

clearUseDualSimplex

public GlopParameters.Builder clearUseDualSimplex()

 Whether or not we use the dual simplex algorithm instead of the primal.

optional bool use_dual_simplex = 31 [default = false];

Returns:: This builder for chaining.

hasAllowSimplexAlgorithmChange

public 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];

Specified by:: hasAllowSimplexAlgorithmChange in interface GlopParametersOrBuilder
Returns:: Whether the allowSimplexAlgorithmChange field is set.

getAllowSimplexAlgorithmChange

public 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];

Specified by:: getAllowSimplexAlgorithmChange in interface GlopParametersOrBuilder
Returns:: The allowSimplexAlgorithmChange.

setAllowSimplexAlgorithmChange

public 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. 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];

Parameters:: value - The allowSimplexAlgorithmChange to set.
Returns:: This builder for chaining.

clearAllowSimplexAlgorithmChange

public 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. 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:: This builder for chaining.

hasDevexWeightsResetPeriod
```
public boolean hasDevexWeightsResetPeriod()
```
```
 Devex weights will be reset to 1.0 after that number of updates.
 
```
optional int32 devex_weights_reset_period = 33 [default = 150];
Specified by:

hasDevexWeightsResetPeriod in interface GlopParametersOrBuilder

Returns:

Whether the devexWeightsResetPeriod field is set.

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

getDevexWeightsResetPeriod in interface GlopParametersOrBuilder

Returns:

The devexWeightsResetPeriod.

setDevexWeightsResetPeriod
```
public GlopParameters.Builder setDevexWeightsResetPeriod(int value)
```
```
 Devex weights will be reset to 1.0 after that number of updates.
 
```
optional int32 devex_weights_reset_period = 33 [default = 150];
Parameters:

value - The devexWeightsResetPeriod to set.

Returns:

This builder for chaining.

clearDevexWeightsResetPeriod

public GlopParameters.Builder clearDevexWeightsResetPeriod()

 Devex weights will be reset to 1.0 after that number of updates.

optional int32 devex_weights_reset_period = 33 [default = 150];

Returns:: This builder for chaining.

hasUsePreprocessing
```
public boolean hasUsePreprocessing()
```
```
 Whether or not we use advanced preprocessing techniques.
 
```
optional bool use_preprocessing = 34 [default = true];
Specified by:

hasUsePreprocessing in interface GlopParametersOrBuilder

Returns:

Whether the usePreprocessing field is set.

getUsePreprocessing
```
public boolean getUsePreprocessing()
```
```
 Whether or not we use advanced preprocessing techniques.
 
```
optional bool use_preprocessing = 34 [default = true];
Specified by:

getUsePreprocessing in interface GlopParametersOrBuilder

Returns:

The usePreprocessing.

setUsePreprocessing
```
public GlopParameters.Builder setUsePreprocessing(boolean value)
```
```
 Whether or not we use advanced preprocessing techniques.
 
```
optional bool use_preprocessing = 34 [default = true];
Parameters:

value - The usePreprocessing to set.

Returns:

This builder for chaining.

clearUsePreprocessing

public GlopParameters.Builder clearUsePreprocessing()

 Whether or not we use advanced preprocessing techniques.

optional bool use_preprocessing = 34 [default = true];

Returns:: This builder for chaining.

hasUseMiddleProductFormUpdate

public 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];

Specified by:: hasUseMiddleProductFormUpdate in interface GlopParametersOrBuilder
Returns:: Whether the useMiddleProductFormUpdate field is set.

getUseMiddleProductFormUpdate

public 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];

Specified by:: getUseMiddleProductFormUpdate in interface GlopParametersOrBuilder
Returns:: The useMiddleProductFormUpdate.

setUseMiddleProductFormUpdate

public GlopParameters.Builder setUseMiddleProductFormUpdate(boolean value)

 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];

Parameters:: value - The useMiddleProductFormUpdate to set.
Returns:: This builder for chaining.

clearUseMiddleProductFormUpdate

public GlopParameters.Builder clearUseMiddleProductFormUpdate()

 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:: This builder for chaining.

hasInitializeDevexWithColumnNorms
```
public 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];
Specified by:

hasInitializeDevexWithColumnNorms in interface GlopParametersOrBuilder

Returns:

Whether the initializeDevexWithColumnNorms field is set.

getInitializeDevexWithColumnNorms
```
public 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];
Specified by:

getInitializeDevexWithColumnNorms in interface GlopParametersOrBuilder

Returns:

The initializeDevexWithColumnNorms.

setInitializeDevexWithColumnNorms
```
public GlopParameters.Builder setInitializeDevexWithColumnNorms(boolean value)
```
```
 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];
Parameters:

value - The initializeDevexWithColumnNorms to set.

Returns:

This builder for chaining.

clearInitializeDevexWithColumnNorms

public GlopParameters.Builder clearInitializeDevexWithColumnNorms()

 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:: This builder for chaining.

hasExploitSingletonColumnInInitialBasis
```
public 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];
Specified by:

hasExploitSingletonColumnInInitialBasis in interface GlopParametersOrBuilder

Returns:

Whether the exploitSingletonColumnInInitialBasis field is set.

getExploitSingletonColumnInInitialBasis
```
public 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];
Specified by:

getExploitSingletonColumnInInitialBasis in interface GlopParametersOrBuilder

Returns:

The exploitSingletonColumnInInitialBasis.

setExploitSingletonColumnInInitialBasis
```
public GlopParameters.Builder setExploitSingletonColumnInInitialBasis(boolean value)
```
```
 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];
Parameters:

value - The exploitSingletonColumnInInitialBasis to set.

Returns:

This builder for chaining.

clearExploitSingletonColumnInInitialBasis

public GlopParameters.Builder clearExploitSingletonColumnInInitialBasis()

 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:: This builder for chaining.

hasDualSmallPivotThreshold

public 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];

Specified by:: hasDualSmallPivotThreshold in interface GlopParametersOrBuilder
Returns:: Whether the dualSmallPivotThreshold field is set.

getDualSmallPivotThreshold

public 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];

Specified by:: getDualSmallPivotThreshold in interface GlopParametersOrBuilder
Returns:: The dualSmallPivotThreshold.

setDualSmallPivotThreshold

public GlopParameters.Builder setDualSmallPivotThreshold(double value)

 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];

Parameters:: value - The dualSmallPivotThreshold to set.
Returns:: This builder for chaining.

clearDualSmallPivotThreshold

public GlopParameters.Builder clearDualSmallPivotThreshold()

 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:: This builder for chaining.

hasPreprocessorZeroTolerance

public 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];

Specified by:: hasPreprocessorZeroTolerance in interface GlopParametersOrBuilder
Returns:: Whether the preprocessorZeroTolerance field is set.

getPreprocessorZeroTolerance

public 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];

Specified by:: getPreprocessorZeroTolerance in interface GlopParametersOrBuilder
Returns:: The preprocessorZeroTolerance.

setPreprocessorZeroTolerance

public GlopParameters.Builder setPreprocessorZeroTolerance(double value)

 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];

Parameters:: value - The preprocessorZeroTolerance to set.
Returns:: This builder for chaining.

clearPreprocessorZeroTolerance

public GlopParameters.Builder clearPreprocessorZeroTolerance()

 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:: This builder for chaining.

hasObjectiveLowerLimit

public 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];

Specified by:: hasObjectiveLowerLimit in interface GlopParametersOrBuilder
Returns:: Whether the objectiveLowerLimit field is set.

getObjectiveLowerLimit

public 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];

Specified by:: getObjectiveLowerLimit in interface GlopParametersOrBuilder
Returns:: The objectiveLowerLimit.

setObjectiveLowerLimit

public 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. 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];

Parameters:: value - The objectiveLowerLimit to set.
Returns:: This builder for chaining.

clearObjectiveLowerLimit

public 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. 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:: This builder for chaining.

hasObjectiveUpperLimit
```
public boolean hasObjectiveUpperLimit()
```
optional double objective_upper_limit = 41 [default = inf];

Specified by:

hasObjectiveUpperLimit in interface GlopParametersOrBuilder

Returns:

Whether the objectiveUpperLimit field is set.

getObjectiveUpperLimit
```
public double getObjectiveUpperLimit()
```
optional double objective_upper_limit = 41 [default = inf];

Specified by:

getObjectiveUpperLimit in interface GlopParametersOrBuilder

Returns:

The objectiveUpperLimit.

setObjectiveUpperLimit
```
public GlopParameters.Builder setObjectiveUpperLimit(double value)
```
optional double objective_upper_limit = 41 [default = inf];

Parameters:

value - The objectiveUpperLimit to set.

Returns:

This builder for chaining.

clearObjectiveUpperLimit
```
public GlopParameters.Builder clearObjectiveUpperLimit()
```
optional double objective_upper_limit = 41 [default = inf];

Returns:

This builder for chaining.

hasDegenerateMinistepFactor

public 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];

Specified by:: hasDegenerateMinistepFactor in interface GlopParametersOrBuilder
Returns:: Whether the degenerateMinistepFactor field is set.

getDegenerateMinistepFactor

public 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];

Specified by:: getDegenerateMinistepFactor in interface GlopParametersOrBuilder
Returns:: The degenerateMinistepFactor.

setDegenerateMinistepFactor

public 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).
 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];

Parameters:: value - The degenerateMinistepFactor to set.
Returns:: This builder for chaining.

clearDegenerateMinistepFactor

public 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).
 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:: This builder for chaining.

hasRandomSeed

public 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];

Specified by:: hasRandomSeed in interface GlopParametersOrBuilder
Returns:: Whether the randomSeed field is set.

getRandomSeed

public 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];

Specified by:: getRandomSeed in interface GlopParametersOrBuilder
Returns:: The randomSeed.

setRandomSeed

public 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. 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];

Parameters:: value - The randomSeed to set.
Returns:: This builder for chaining.

clearRandomSeed

public 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. 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:: This builder for chaining.

hasNumOmpThreads

public 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];

Specified by:: hasNumOmpThreads in interface GlopParametersOrBuilder
Returns:: Whether the numOmpThreads field is set.

getNumOmpThreads

public 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];

Specified by:: getNumOmpThreads in interface GlopParametersOrBuilder
Returns:: The numOmpThreads.

setNumOmpThreads

public GlopParameters.Builder setNumOmpThreads(int value)

 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];

Parameters:: value - The numOmpThreads to set.
Returns:: This builder for chaining.

clearNumOmpThreads

public GlopParameters.Builder clearNumOmpThreads()

 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:: This builder for chaining.

hasPerturbCostsInDualSimplex

public 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];

Specified by:: hasPerturbCostsInDualSimplex in interface GlopParametersOrBuilder
Returns:: Whether the perturbCostsInDualSimplex field is set.

getPerturbCostsInDualSimplex

public 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];

Specified by:: getPerturbCostsInDualSimplex in interface GlopParametersOrBuilder
Returns:: The perturbCostsInDualSimplex.

setPerturbCostsInDualSimplex

public GlopParameters.Builder setPerturbCostsInDualSimplex(boolean value)

 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];

Parameters:: value - The perturbCostsInDualSimplex to set.
Returns:: This builder for chaining.

clearPerturbCostsInDualSimplex

public GlopParameters.Builder clearPerturbCostsInDualSimplex()

 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:: This builder for chaining.

hasUseDedicatedDualFeasibilityAlgorithm

public 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];

Specified by:: hasUseDedicatedDualFeasibilityAlgorithm in interface GlopParametersOrBuilder
Returns:: Whether the useDedicatedDualFeasibilityAlgorithm field is set.

getUseDedicatedDualFeasibilityAlgorithm

public 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];

Specified by:: getUseDedicatedDualFeasibilityAlgorithm in interface GlopParametersOrBuilder
Returns:: The useDedicatedDualFeasibilityAlgorithm.

setUseDedicatedDualFeasibilityAlgorithm

public GlopParameters.Builder setUseDedicatedDualFeasibilityAlgorithm(boolean value)

 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];

Parameters:: value - The useDedicatedDualFeasibilityAlgorithm to set.
Returns:: This builder for chaining.

clearUseDedicatedDualFeasibilityAlgorithm

public GlopParameters.Builder clearUseDedicatedDualFeasibilityAlgorithm()

 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:: This builder for chaining.

hasRelativeCostPerturbation
```
public 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];
Specified by:

hasRelativeCostPerturbation in interface GlopParametersOrBuilder

Returns:

Whether the relativeCostPerturbation field is set.

getRelativeCostPerturbation

public 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];

Specified by:: getRelativeCostPerturbation in interface GlopParametersOrBuilder
Returns:: The relativeCostPerturbation.

setRelativeCostPerturbation

public 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);

optional double relative_cost_perturbation = 54 [default = 1e-05];

Parameters:: value - The relativeCostPerturbation to set.
Returns:: This builder for chaining.

clearRelativeCostPerturbation

public 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);

optional double relative_cost_perturbation = 54 [default = 1e-05];

Returns:: This builder for chaining.

hasRelativeMaxCostPerturbation
```
public boolean hasRelativeMaxCostPerturbation()
```
optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Specified by:

hasRelativeMaxCostPerturbation in interface GlopParametersOrBuilder

Returns:

Whether the relativeMaxCostPerturbation field is set.

getRelativeMaxCostPerturbation
```
public double getRelativeMaxCostPerturbation()
```
optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Specified by:

getRelativeMaxCostPerturbation in interface GlopParametersOrBuilder

Returns:

The relativeMaxCostPerturbation.

setRelativeMaxCostPerturbation
```
public GlopParameters.Builder setRelativeMaxCostPerturbation(double value)
```
optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Parameters:

value - The relativeMaxCostPerturbation to set.

Returns:

This builder for chaining.

clearRelativeMaxCostPerturbation
```
public GlopParameters.Builder clearRelativeMaxCostPerturbation()
```
optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Returns:

This builder for chaining.

hasInitialConditionNumberThreshold
```
public 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];
Specified by:

hasInitialConditionNumberThreshold in interface GlopParametersOrBuilder

Returns:

Whether the initialConditionNumberThreshold field is set.

getInitialConditionNumberThreshold
```
public 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];
Specified by:

getInitialConditionNumberThreshold in interface GlopParametersOrBuilder

Returns:

The initialConditionNumberThreshold.

setInitialConditionNumberThreshold

public 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.

optional double initial_condition_number_threshold = 59 [default = 1e+50];

Parameters:: value - The initialConditionNumberThreshold to set.
Returns:: This builder for chaining.

clearInitialConditionNumberThreshold

public 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.

optional double initial_condition_number_threshold = 59 [default = 1e+50];

Returns:: This builder for chaining.

hasLogSearchProgress

public 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];

Specified by:: hasLogSearchProgress in interface GlopParametersOrBuilder
Returns:: Whether the logSearchProgress field is set.

getLogSearchProgress

public 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];

Specified by:: getLogSearchProgress in interface GlopParametersOrBuilder
Returns:: The logSearchProgress.

setLogSearchProgress

public GlopParameters.Builder setLogSearchProgress(boolean value)

 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];

Parameters:: value - The logSearchProgress to set.
Returns:: This builder for chaining.

clearLogSearchProgress

public GlopParameters.Builder clearLogSearchProgress()

 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:: This builder for chaining.

hasLogToStdout
```
public boolean hasLogToStdout()
```
```
 If true, logs will be displayed to stdout instead of using Google log info.
 
```
optional bool log_to_stdout = 66 [default = true];
Specified by:

hasLogToStdout in interface GlopParametersOrBuilder

Returns:

Whether the logToStdout field is set.

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

getLogToStdout in interface GlopParametersOrBuilder

Returns:

The logToStdout.

setLogToStdout

public GlopParameters.Builder setLogToStdout(boolean value)

 If true, logs will be displayed to stdout instead of using Google log info.

optional bool log_to_stdout = 66 [default = true];

Parameters:: value - The logToStdout to set.
Returns:: This builder for chaining.

clearLogToStdout

public GlopParameters.Builder clearLogToStdout()

 If true, logs will be displayed to stdout instead of using Google log info.

optional bool log_to_stdout = 66 [default = true];

Returns:: This builder for chaining.

hasCrossoverBoundSnappingDistance

public 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];

Specified by:: hasCrossoverBoundSnappingDistance in interface GlopParametersOrBuilder
Returns:: Whether the crossoverBoundSnappingDistance field is set.

getCrossoverBoundSnappingDistance

public 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];

Specified by:: getCrossoverBoundSnappingDistance in interface GlopParametersOrBuilder
Returns:: The crossoverBoundSnappingDistance.

setCrossoverBoundSnappingDistance

public 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.

 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];

Parameters:: value - The crossoverBoundSnappingDistance to set.
Returns:: This builder for chaining.

clearCrossoverBoundSnappingDistance

public 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.

 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:: This builder for chaining.

hasPushToVertex

public 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];

Specified by:: hasPushToVertex in interface GlopParametersOrBuilder
Returns:: Whether the pushToVertex field is set.

getPushToVertex

public 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];

Specified by:: getPushToVertex in interface GlopParametersOrBuilder
Returns:: The pushToVertex.

setPushToVertex

public 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. Note
 this situation can happen only if a starting value was specified via
 SetStartingVariableValuesForNextSolve().

optional bool push_to_vertex = 65 [default = true];

Parameters:: value - The pushToVertex to set.
Returns:: This builder for chaining.

clearPushToVertex

public 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. Note
 this situation can happen only if a starting value was specified via
 SetStartingVariableValuesForNextSolve().

optional bool push_to_vertex = 65 [default = true];

Returns:: This builder for chaining.

hasUseImpliedFreePreprocessor
```
public boolean hasUseImpliedFreePreprocessor()
```
```
 If presolve runs, include the pass that detects implied free variables.
 
```
optional bool use_implied_free_preprocessor = 67 [default = true];
Specified by:

hasUseImpliedFreePreprocessor in interface GlopParametersOrBuilder

Returns:

Whether the useImpliedFreePreprocessor field is set.

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

getUseImpliedFreePreprocessor in interface GlopParametersOrBuilder

Returns:

The useImpliedFreePreprocessor.

setUseImpliedFreePreprocessor
```
public GlopParameters.Builder setUseImpliedFreePreprocessor(boolean value)
```
```
 If presolve runs, include the pass that detects implied free variables.
 
```
optional bool use_implied_free_preprocessor = 67 [default = true];
Parameters:

value - The useImpliedFreePreprocessor to set.

Returns:

This builder for chaining.

clearUseImpliedFreePreprocessor

public GlopParameters.Builder clearUseImpliedFreePreprocessor()

 If presolve runs, include the pass that detects implied free variables.

optional bool use_implied_free_preprocessor = 67 [default = true];

Returns:: This builder for chaining.

hasMaxValidMagnitude

public 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];

Specified by:: hasMaxValidMagnitude in interface GlopParametersOrBuilder
Returns:: Whether the maxValidMagnitude field is set.

getMaxValidMagnitude

public 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];

Specified by:: getMaxValidMagnitude in interface GlopParametersOrBuilder
Returns:: The maxValidMagnitude.

setMaxValidMagnitude

public GlopParameters.Builder setMaxValidMagnitude(double value)

 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];

Parameters:: value - The maxValidMagnitude to set.
Returns:: This builder for chaining.

clearMaxValidMagnitude

public GlopParameters.Builder clearMaxValidMagnitude()

 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:: This builder for chaining.

hasDropMagnitude

public 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];

Specified by:: hasDropMagnitude in interface GlopParametersOrBuilder
Returns:: Whether the dropMagnitude field is set.

getDropMagnitude

public 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];

Specified by:: getDropMagnitude in interface GlopParametersOrBuilder
Returns:: The dropMagnitude.

setDropMagnitude

public GlopParameters.Builder setDropMagnitude(double value)

 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];

Parameters:: value - The dropMagnitude to set.
Returns:: This builder for chaining.

clearDropMagnitude

public GlopParameters.Builder clearDropMagnitude()

 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:: This builder for chaining.

hasDualPricePrioritizeNorm
```
public 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];
Specified by:

hasDualPricePrioritizeNorm in interface GlopParametersOrBuilder

Returns:

Whether the dualPricePrioritizeNorm field is set.

getDualPricePrioritizeNorm
```
public 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];
Specified by:

getDualPricePrioritizeNorm in interface GlopParametersOrBuilder

Returns:

The dualPricePrioritizeNorm.

setDualPricePrioritizeNorm

public 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.

optional bool dual_price_prioritize_norm = 69 [default = false];

Parameters:: value - The dualPricePrioritizeNorm to set.
Returns:: This builder for chaining.

clearDualPricePrioritizeNorm

public 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.

optional bool dual_price_prioritize_norm = 69 [default = false];

Returns:: This builder for chaining.

Class GlopParameters.Builder

Method Summary

Methods inherited from class com.google.protobuf.GeneratedMessage.Builder

Methods inherited from class com.google.protobuf.AbstractMessage.Builder

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

Methods inherited from class java.lang.Object

Methods inherited from interface com.google.protobuf.MessageOrBuilder

Methods inherited from interface com.google.protobuf.Message.Builder

Method Detail

getDescriptor

internalGetFieldAccessorTable

clear

getDescriptorForType

getDefaultInstanceForType

build

buildPartial

mergeFrom

mergeFrom

isInitialized

mergeFrom

hasScalingMethod

getScalingMethod

setScalingMethod

clearScalingMethod

hasFeasibilityRule

getFeasibilityRule

setFeasibilityRule

clearFeasibilityRule

hasOptimizationRule

getOptimizationRule

setOptimizationRule

clearOptimizationRule

hasRefactorizationThreshold

getRefactorizationThreshold

setRefactorizationThreshold

clearRefactorizationThreshold

hasRecomputeReducedCostsThreshold

getRecomputeReducedCostsThreshold

setRecomputeReducedCostsThreshold

clearRecomputeReducedCostsThreshold

hasRecomputeEdgesNormThreshold

getRecomputeEdgesNormThreshold

setRecomputeEdgesNormThreshold

clearRecomputeEdgesNormThreshold

hasPrimalFeasibilityTolerance

getPrimalFeasibilityTolerance

setPrimalFeasibilityTolerance

clearPrimalFeasibilityTolerance

hasDualFeasibilityTolerance

getDualFeasibilityTolerance

setDualFeasibilityTolerance

clearDualFeasibilityTolerance

hasRatioTestZeroThreshold

getRatioTestZeroThreshold

setRatioTestZeroThreshold

clearRatioTestZeroThreshold

hasHarrisToleranceRatio

getHarrisToleranceRatio

setHarrisToleranceRatio

clearHarrisToleranceRatio

hasSmallPivotThreshold

getSmallPivotThreshold

setSmallPivotThreshold

clearSmallPivotThreshold

hasMinimumAcceptablePivot

getMinimumAcceptablePivot

setMinimumAcceptablePivot

clearMinimumAcceptablePivot

hasDropTolerance

getDropTolerance

setDropTolerance

clearDropTolerance

hasUseScaling

getUseScaling

setUseScaling

clearUseScaling

hasCostScaling

getCostScaling

setCostScaling

clearCostScaling