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

java.lang.Object
- com.google.protobuf.AbstractMessageLite
- - com.google.protobuf.AbstractMessage
  - - com.google.protobuf.GeneratedMessage
    - - com.google.ortools.glop.GlopParameters

All Implemented Interfaces:

GlopParametersOrBuilder, com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, java.io.Serializable
```
public final class GlopParameters
extends com.google.protobuf.GeneratedMessage
implements GlopParametersOrBuilder
```
```
 next id = 72
 
```
Protobuf type operations_research.glop.GlopParameters
See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`GlopParameters.Builder` next id = 72 Protobuf type `operations_research.glop.GlopParameters`
`static class`	`GlopParameters.CostScalingAlgorithm` This is only used if use_scaling is true.
`static class`	`GlopParameters.InitialBasisHeuristic` Heuristics to use in the primal simplex to remove fixed slack variables from the initial basis.
`static class`	`GlopParameters.PricingRule` General strategy used during pricing.
`static class`	`GlopParameters.ScalingAlgorithm` Supported algorithms for scaling: EQUILIBRATION - progressive scaling by row and column norms until the marginal difference passes below a threshold.
`static class`	`GlopParameters.SolverBehavior` Like a Boolean with an extra value to let the algorithm decide what is the best choice.

Nested classes/interfaces inherited from class com.google.protobuf.GeneratedMessage
com.google.protobuf.GeneratedMessage.ExtendableBuilder<MessageT extends com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT>,BuilderT extends com.google.protobuf.GeneratedMessage.ExtendableBuilder<MessageT,BuilderT>>, com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT extends com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessage.ExtendableMessageOrBuilder<MessageT extends com.google.protobuf.GeneratedMessage.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessage.FieldAccessorTable, com.google.protobuf.GeneratedMessage.GeneratedExtension<ContainingT extends com.google.protobuf.Message,T>, com.google.protobuf.GeneratedMessage.UnusedPrivateParameter

Nested classes/interfaces inherited from class com.google.protobuf.AbstractMessage
com.google.protobuf.AbstractMessage.BuilderParent

Nested classes/interfaces inherited from class com.google.protobuf.AbstractMessageLite
com.google.protobuf.AbstractMessageLite.InternalOneOfEnum

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`ALLOW_SIMPLEX_ALGORITHM_CHANGE_FIELD_NUMBER`
`static int`	`BASIS_REFACTORIZATION_PERIOD_FIELD_NUMBER`
`static int`	`CHANGE_STATUS_TO_IMPRECISE_FIELD_NUMBER`
`static int`	`COST_SCALING_FIELD_NUMBER`
`static int`	`CROSSOVER_BOUND_SNAPPING_DISTANCE_FIELD_NUMBER`
`static int`	`DEGENERATE_MINISTEP_FACTOR_FIELD_NUMBER`
`static int`	`DEVEX_WEIGHTS_RESET_PERIOD_FIELD_NUMBER`
`static int`	`DROP_MAGNITUDE_FIELD_NUMBER`
`static int`	`DROP_TOLERANCE_FIELD_NUMBER`
`static int`	`DUAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER`
`static int`	`DUAL_PRICE_PRIORITIZE_NORM_FIELD_NUMBER`
`static int`	`DUAL_SMALL_PIVOT_THRESHOLD_FIELD_NUMBER`
`static int`	`DUALIZER_THRESHOLD_FIELD_NUMBER`
`static int`	`DYNAMICALLY_ADJUST_REFACTORIZATION_PERIOD_FIELD_NUMBER`
`static int`	`EXPLOIT_SINGLETON_COLUMN_IN_INITIAL_BASIS_FIELD_NUMBER`
`static int`	`FEASIBILITY_RULE_FIELD_NUMBER`
`static int`	`HARRIS_TOLERANCE_RATIO_FIELD_NUMBER`
`static int`	`INITIAL_BASIS_FIELD_NUMBER`
`static int`	`INITIAL_CONDITION_NUMBER_THRESHOLD_FIELD_NUMBER`
`static int`	`INITIALIZE_DEVEX_WITH_COLUMN_NORMS_FIELD_NUMBER`
`static int`	`LOG_SEARCH_PROGRESS_FIELD_NUMBER`
`static int`	`LOG_TO_STDOUT_FIELD_NUMBER`
`static int`	`LU_FACTORIZATION_PIVOT_THRESHOLD_FIELD_NUMBER`
`static int`	`MARKOWITZ_SINGULARITY_THRESHOLD_FIELD_NUMBER`
`static int`	`MARKOWITZ_ZLATEV_PARAMETER_FIELD_NUMBER`
`static int`	`MAX_DETERMINISTIC_TIME_FIELD_NUMBER`
`static int`	`MAX_NUMBER_OF_ITERATIONS_FIELD_NUMBER`
`static int`	`MAX_NUMBER_OF_REOPTIMIZATIONS_FIELD_NUMBER`
`static int`	`MAX_TIME_IN_SECONDS_FIELD_NUMBER`
`static int`	`MAX_VALID_MAGNITUDE_FIELD_NUMBER`
`static int`	`MINIMUM_ACCEPTABLE_PIVOT_FIELD_NUMBER`
`static int`	`NUM_OMP_THREADS_FIELD_NUMBER`
`static int`	`OBJECTIVE_LOWER_LIMIT_FIELD_NUMBER`
`static int`	`OBJECTIVE_UPPER_LIMIT_FIELD_NUMBER`
`static int`	`OPTIMIZATION_RULE_FIELD_NUMBER`
`static int`	`PERTURB_COSTS_IN_DUAL_SIMPLEX_FIELD_NUMBER`
`static int`	`PREPROCESSOR_ZERO_TOLERANCE_FIELD_NUMBER`
`static int`	`PRIMAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER`
`static int`	`PROVIDE_STRONG_OPTIMAL_GUARANTEE_FIELD_NUMBER`
`static int`	`PUSH_TO_VERTEX_FIELD_NUMBER`
`static int`	`RANDOM_SEED_FIELD_NUMBER`
`static int`	`RATIO_TEST_ZERO_THRESHOLD_FIELD_NUMBER`
`static int`	`RECOMPUTE_EDGES_NORM_THRESHOLD_FIELD_NUMBER`
`static int`	`RECOMPUTE_REDUCED_COSTS_THRESHOLD_FIELD_NUMBER`
`static int`	`REFACTORIZATION_THRESHOLD_FIELD_NUMBER`
`static int`	`RELATIVE_COST_PERTURBATION_FIELD_NUMBER`
`static int`	`RELATIVE_MAX_COST_PERTURBATION_FIELD_NUMBER`
`static int`	`SCALING_METHOD_FIELD_NUMBER`
`static int`	`SMALL_PIVOT_THRESHOLD_FIELD_NUMBER`
`static int`	`SOLUTION_FEASIBILITY_TOLERANCE_FIELD_NUMBER`
`static int`	`SOLVE_DUAL_PROBLEM_FIELD_NUMBER`
`static int`	`USE_DEDICATED_DUAL_FEASIBILITY_ALGORITHM_FIELD_NUMBER`
`static int`	`USE_DUAL_SIMPLEX_FIELD_NUMBER`
`static int`	`USE_IMPLIED_FREE_PREPROCESSOR_FIELD_NUMBER`
`static int`	`USE_MIDDLE_PRODUCT_FORM_UPDATE_FIELD_NUMBER`
`static int`	`USE_PREPROCESSING_FIELD_NUMBER`
`static int`	`USE_SCALING_FIELD_NUMBER`
`static int`	`USE_TRANSPOSED_MATRIX_FIELD_NUMBER`

Fields inherited from class com.google.protobuf.GeneratedMessage
alwaysUseFieldBuilders, unknownFields

Fields inherited from class com.google.protobuf.AbstractMessage
memoizedSize

Fields inherited from class com.google.protobuf.AbstractMessageLite
memoizedHashCode

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`boolean`	`equals(java.lang.Object obj)`
`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.
`static GlopParameters`	`getDefaultInstance()`
`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()`
`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.
`com.google.protobuf.Parser<GlopParameters>`	`getParserForType()`
`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];`
`int`	`getSerializedSize()`
`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.
`int`	`hashCode()`
`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()`
`static GlopParameters.Builder`	`newBuilder()`
`static GlopParameters.Builder`	`newBuilder(GlopParameters prototype)`
`GlopParameters.Builder`	`newBuilderForType()`
`protected GlopParameters.Builder`	`newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent)`
`static GlopParameters`	`parseDelimitedFrom(java.io.InputStream input)`
`static GlopParameters`	`parseDelimitedFrom(java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`static GlopParameters`	`parseFrom(byte[] data)`
`static GlopParameters`	`parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`static GlopParameters`	`parseFrom(java.nio.ByteBuffer data)`
`static GlopParameters`	`parseFrom(java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`static GlopParameters`	`parseFrom(com.google.protobuf.ByteString data)`
`static GlopParameters`	`parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`static GlopParameters`	`parseFrom(com.google.protobuf.CodedInputStream input)`
`static GlopParameters`	`parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`static GlopParameters`	`parseFrom(java.io.InputStream input)`
`static GlopParameters`	`parseFrom(java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`static com.google.protobuf.Parser<GlopParameters>`	`parser()`
`GlopParameters.Builder`	`toBuilder()`
`void`	`writeTo(com.google.protobuf.CodedOutputStream output)`

Methods inherited from class com.google.protobuf.GeneratedMessage
canUseUnsafe, computeStringSize, computeStringSizeNoTag, emptyBooleanList, emptyDoubleList, emptyFloatList, emptyIntList, emptyList, emptyLongList, getAllFields, getDescriptorForType, getField, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, isStringEmpty, makeMutableCopy, makeMutableCopy, mergeFromAndMakeImmutableInternal, newFileScopedGeneratedExtension, newInstance, newMessageScopedGeneratedExtension, parseDelimitedWithIOException, parseDelimitedWithIOException, parseUnknownField, parseUnknownFieldProto3, parseWithIOException, parseWithIOException, parseWithIOException, parseWithIOException, serializeBooleanMapTo, serializeIntegerMapTo, serializeLongMapTo, serializeStringMapTo, writeReplace, writeString, writeStringNoTag

Methods inherited from class com.google.protobuf.AbstractMessage
findInitializationErrors, getInitializationErrorString, hashFields, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite
addAll, checkByteStringIsUtf8, toByteArray, toByteString, writeDelimitedTo, writeTo

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

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

Methods inherited from interface com.google.protobuf.MessageLite
toByteArray, toByteString, writeDelimitedTo, writeTo

Field Detail
- SCALING_METHOD_FIELD_NUMBER
```
public static final int SCALING_METHOD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- FEASIBILITY_RULE_FIELD_NUMBER
```
public static final int FEASIBILITY_RULE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- OPTIMIZATION_RULE_FIELD_NUMBER
```
public static final int OPTIMIZATION_RULE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- REFACTORIZATION_THRESHOLD_FIELD_NUMBER
```
public static final int REFACTORIZATION_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- RECOMPUTE_REDUCED_COSTS_THRESHOLD_FIELD_NUMBER
```
public static final int RECOMPUTE_REDUCED_COSTS_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- RECOMPUTE_EDGES_NORM_THRESHOLD_FIELD_NUMBER
```
public static final int RECOMPUTE_EDGES_NORM_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- PRIMAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER
```
public static final int PRIMAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DUAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER
```
public static final int DUAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- RATIO_TEST_ZERO_THRESHOLD_FIELD_NUMBER
```
public static final int RATIO_TEST_ZERO_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- HARRIS_TOLERANCE_RATIO_FIELD_NUMBER
```
public static final int HARRIS_TOLERANCE_RATIO_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- SMALL_PIVOT_THRESHOLD_FIELD_NUMBER
```
public static final int SMALL_PIVOT_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MINIMUM_ACCEPTABLE_PIVOT_FIELD_NUMBER
```
public static final int MINIMUM_ACCEPTABLE_PIVOT_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DROP_TOLERANCE_FIELD_NUMBER
```
public static final int DROP_TOLERANCE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_SCALING_FIELD_NUMBER
```
public static final int USE_SCALING_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- COST_SCALING_FIELD_NUMBER
```
public static final int COST_SCALING_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- INITIAL_BASIS_FIELD_NUMBER
```
public static final int INITIAL_BASIS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_TRANSPOSED_MATRIX_FIELD_NUMBER
```
public static final int USE_TRANSPOSED_MATRIX_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- BASIS_REFACTORIZATION_PERIOD_FIELD_NUMBER
```
public static final int BASIS_REFACTORIZATION_PERIOD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DYNAMICALLY_ADJUST_REFACTORIZATION_PERIOD_FIELD_NUMBER
```
public static final int DYNAMICALLY_ADJUST_REFACTORIZATION_PERIOD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- SOLVE_DUAL_PROBLEM_FIELD_NUMBER
```
public static final int SOLVE_DUAL_PROBLEM_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DUALIZER_THRESHOLD_FIELD_NUMBER
```
public static final int DUALIZER_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- SOLUTION_FEASIBILITY_TOLERANCE_FIELD_NUMBER
```
public static final int SOLUTION_FEASIBILITY_TOLERANCE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- PROVIDE_STRONG_OPTIMAL_GUARANTEE_FIELD_NUMBER
```
public static final int PROVIDE_STRONG_OPTIMAL_GUARANTEE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- CHANGE_STATUS_TO_IMPRECISE_FIELD_NUMBER
```
public static final int CHANGE_STATUS_TO_IMPRECISE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MAX_NUMBER_OF_REOPTIMIZATIONS_FIELD_NUMBER
```
public static final int MAX_NUMBER_OF_REOPTIMIZATIONS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- LU_FACTORIZATION_PIVOT_THRESHOLD_FIELD_NUMBER
```
public static final int LU_FACTORIZATION_PIVOT_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MAX_TIME_IN_SECONDS_FIELD_NUMBER
```
public static final int MAX_TIME_IN_SECONDS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MAX_DETERMINISTIC_TIME_FIELD_NUMBER
```
public static final int MAX_DETERMINISTIC_TIME_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MAX_NUMBER_OF_ITERATIONS_FIELD_NUMBER
```
public static final int MAX_NUMBER_OF_ITERATIONS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MARKOWITZ_ZLATEV_PARAMETER_FIELD_NUMBER
```
public static final int MARKOWITZ_ZLATEV_PARAMETER_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MARKOWITZ_SINGULARITY_THRESHOLD_FIELD_NUMBER
```
public static final int MARKOWITZ_SINGULARITY_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_DUAL_SIMPLEX_FIELD_NUMBER
```
public static final int USE_DUAL_SIMPLEX_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- ALLOW_SIMPLEX_ALGORITHM_CHANGE_FIELD_NUMBER
```
public static final int ALLOW_SIMPLEX_ALGORITHM_CHANGE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DEVEX_WEIGHTS_RESET_PERIOD_FIELD_NUMBER
```
public static final int DEVEX_WEIGHTS_RESET_PERIOD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_PREPROCESSING_FIELD_NUMBER
```
public static final int USE_PREPROCESSING_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_MIDDLE_PRODUCT_FORM_UPDATE_FIELD_NUMBER
```
public static final int USE_MIDDLE_PRODUCT_FORM_UPDATE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- INITIALIZE_DEVEX_WITH_COLUMN_NORMS_FIELD_NUMBER
```
public static final int INITIALIZE_DEVEX_WITH_COLUMN_NORMS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- EXPLOIT_SINGLETON_COLUMN_IN_INITIAL_BASIS_FIELD_NUMBER
```
public static final int EXPLOIT_SINGLETON_COLUMN_IN_INITIAL_BASIS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DUAL_SMALL_PIVOT_THRESHOLD_FIELD_NUMBER
```
public static final int DUAL_SMALL_PIVOT_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- PREPROCESSOR_ZERO_TOLERANCE_FIELD_NUMBER
```
public static final int PREPROCESSOR_ZERO_TOLERANCE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- OBJECTIVE_LOWER_LIMIT_FIELD_NUMBER
```
public static final int OBJECTIVE_LOWER_LIMIT_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- OBJECTIVE_UPPER_LIMIT_FIELD_NUMBER
```
public static final int OBJECTIVE_UPPER_LIMIT_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DEGENERATE_MINISTEP_FACTOR_FIELD_NUMBER
```
public static final int DEGENERATE_MINISTEP_FACTOR_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- RANDOM_SEED_FIELD_NUMBER
```
public static final int RANDOM_SEED_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- NUM_OMP_THREADS_FIELD_NUMBER
```
public static final int NUM_OMP_THREADS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- PERTURB_COSTS_IN_DUAL_SIMPLEX_FIELD_NUMBER
```
public static final int PERTURB_COSTS_IN_DUAL_SIMPLEX_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_DEDICATED_DUAL_FEASIBILITY_ALGORITHM_FIELD_NUMBER
```
public static final int USE_DEDICATED_DUAL_FEASIBILITY_ALGORITHM_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- RELATIVE_COST_PERTURBATION_FIELD_NUMBER
```
public static final int RELATIVE_COST_PERTURBATION_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- RELATIVE_MAX_COST_PERTURBATION_FIELD_NUMBER
```
public static final int RELATIVE_MAX_COST_PERTURBATION_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- INITIAL_CONDITION_NUMBER_THRESHOLD_FIELD_NUMBER
```
public static final int INITIAL_CONDITION_NUMBER_THRESHOLD_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- LOG_SEARCH_PROGRESS_FIELD_NUMBER
```
public static final int LOG_SEARCH_PROGRESS_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- LOG_TO_STDOUT_FIELD_NUMBER
```
public static final int LOG_TO_STDOUT_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- CROSSOVER_BOUND_SNAPPING_DISTANCE_FIELD_NUMBER
```
public static final int CROSSOVER_BOUND_SNAPPING_DISTANCE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- PUSH_TO_VERTEX_FIELD_NUMBER
```
public static final int PUSH_TO_VERTEX_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- USE_IMPLIED_FREE_PREPROCESSOR_FIELD_NUMBER
```
public static final int USE_IMPLIED_FREE_PREPROCESSOR_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- MAX_VALID_MAGNITUDE_FIELD_NUMBER
```
public static final int MAX_VALID_MAGNITUDE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DROP_MAGNITUDE_FIELD_NUMBER
```
public static final int DROP_MAGNITUDE_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values
- DUAL_PRICE_PRIORITIZE_NORM_FIELD_NUMBER
```
public static final int DUAL_PRICE_PRIORITIZE_NORM_FIELD_NUMBER
```
  See Also:
  
  Constant Field Values

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

isInitialized in interface com.google.protobuf.MessageLiteOrBuilder

Overrides:

isInitialized in class com.google.protobuf.GeneratedMessage

writeTo
```
public void writeTo(com.google.protobuf.CodedOutputStream output)
             throws java.io.IOException
```
Specified by:

writeTo in interface com.google.protobuf.MessageLite

Overrides:

writeTo in class com.google.protobuf.GeneratedMessage

Throws:

java.io.IOException

getSerializedSize
```
public int getSerializedSize()
```
Specified by:

getSerializedSize in interface com.google.protobuf.MessageLite

Overrides:

getSerializedSize in class com.google.protobuf.GeneratedMessage

equals
```
public boolean equals(java.lang.Object obj)
```
Specified by:

equals in interface com.google.protobuf.Message

Overrides:

equals in class com.google.protobuf.AbstractMessage

hashCode
```
public int hashCode()
```
Specified by:

hashCode in interface com.google.protobuf.Message

Overrides:

hashCode in class com.google.protobuf.AbstractMessage

parseFrom

public static GlopParameters parseFrom(java.nio.ByteBuffer data)
                                throws com.google.protobuf.InvalidProtocolBufferException

Throws:: com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static GlopParameters parseFrom(java.nio.ByteBuffer data,
                                       com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                throws com.google.protobuf.InvalidProtocolBufferException

Throws:: com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static GlopParameters parseFrom(com.google.protobuf.ByteString data)
                                throws com.google.protobuf.InvalidProtocolBufferException

Throws:: com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static GlopParameters parseFrom(com.google.protobuf.ByteString data,
                                       com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                throws com.google.protobuf.InvalidProtocolBufferException

Throws:: com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static GlopParameters parseFrom(byte[] data)
                                throws com.google.protobuf.InvalidProtocolBufferException

Throws:: com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static GlopParameters parseFrom(byte[] data,
                                       com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                throws com.google.protobuf.InvalidProtocolBufferException

Throws:: com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static GlopParameters parseFrom(java.io.InputStream input)
                                throws java.io.IOException

Throws:: java.io.IOException

parseFrom

public static GlopParameters parseFrom(java.io.InputStream input,
                                       com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                throws java.io.IOException

Throws:: java.io.IOException

parseDelimitedFrom

public static GlopParameters parseDelimitedFrom(java.io.InputStream input)
                                         throws java.io.IOException

Throws:: java.io.IOException

parseDelimitedFrom

public static GlopParameters parseDelimitedFrom(java.io.InputStream input,
                                                com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                         throws java.io.IOException

Throws:: java.io.IOException

parseFrom

public static GlopParameters parseFrom(com.google.protobuf.CodedInputStream input)
                                throws java.io.IOException

Throws:: java.io.IOException

parseFrom

public static GlopParameters parseFrom(com.google.protobuf.CodedInputStream input,
                                       com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                throws java.io.IOException

Throws:: java.io.IOException

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

newBuilderForType in interface com.google.protobuf.Message

Specified by:

newBuilderForType in interface com.google.protobuf.MessageLite

newBuilder

public static GlopParameters.Builder newBuilder()

newBuilder

public static GlopParameters.Builder newBuilder(GlopParameters prototype)

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

toBuilder in interface com.google.protobuf.Message

Specified by:

toBuilder in interface com.google.protobuf.MessageLite

newBuilderForType

protected GlopParameters.Builder newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent)

Overrides:: newBuilderForType in class com.google.protobuf.AbstractMessage

getDefaultInstance

public static GlopParameters getDefaultInstance()

parser

public static com.google.protobuf.Parser<GlopParameters> parser()

getParserForType
```
public com.google.protobuf.Parser<GlopParameters> getParserForType()
```
Specified by:

getParserForType in interface com.google.protobuf.Message

Specified by:

getParserForType in interface com.google.protobuf.MessageLite

Overrides:

getParserForType in class com.google.protobuf.GeneratedMessage

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

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

Class GlopParameters

Nested Class Summary

Nested classes/interfaces inherited from class com.google.protobuf.GeneratedMessage

Nested classes/interfaces inherited from class com.google.protobuf.AbstractMessage

Nested classes/interfaces inherited from class com.google.protobuf.AbstractMessageLite

Field Summary

Fields inherited from class com.google.protobuf.GeneratedMessage

Fields inherited from class com.google.protobuf.AbstractMessage

Fields inherited from class com.google.protobuf.AbstractMessageLite

Method Summary

Methods inherited from class com.google.protobuf.GeneratedMessage

Methods inherited from class com.google.protobuf.AbstractMessage

Methods inherited from class com.google.protobuf.AbstractMessageLite

Methods inherited from class java.lang.Object

Methods inherited from interface com.google.protobuf.MessageOrBuilder

Methods inherited from interface com.google.protobuf.MessageLite

Field Detail

SCALING_METHOD_FIELD_NUMBER

FEASIBILITY_RULE_FIELD_NUMBER

OPTIMIZATION_RULE_FIELD_NUMBER

REFACTORIZATION_THRESHOLD_FIELD_NUMBER

RECOMPUTE_REDUCED_COSTS_THRESHOLD_FIELD_NUMBER

RECOMPUTE_EDGES_NORM_THRESHOLD_FIELD_NUMBER

PRIMAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER

DUAL_FEASIBILITY_TOLERANCE_FIELD_NUMBER

RATIO_TEST_ZERO_THRESHOLD_FIELD_NUMBER

HARRIS_TOLERANCE_RATIO_FIELD_NUMBER

SMALL_PIVOT_THRESHOLD_FIELD_NUMBER

MINIMUM_ACCEPTABLE_PIVOT_FIELD_NUMBER

DROP_TOLERANCE_FIELD_NUMBER

USE_SCALING_FIELD_NUMBER

COST_SCALING_FIELD_NUMBER

INITIAL_BASIS_FIELD_NUMBER

USE_TRANSPOSED_MATRIX_FIELD_NUMBER

BASIS_REFACTORIZATION_PERIOD_FIELD_NUMBER

DYNAMICALLY_ADJUST_REFACTORIZATION_PERIOD_FIELD_NUMBER

SOLVE_DUAL_PROBLEM_FIELD_NUMBER

DUALIZER_THRESHOLD_FIELD_NUMBER

SOLUTION_FEASIBILITY_TOLERANCE_FIELD_NUMBER

PROVIDE_STRONG_OPTIMAL_GUARANTEE_FIELD_NUMBER

CHANGE_STATUS_TO_IMPRECISE_FIELD_NUMBER

MAX_NUMBER_OF_REOPTIMIZATIONS_FIELD_NUMBER

LU_FACTORIZATION_PIVOT_THRESHOLD_FIELD_NUMBER

MAX_TIME_IN_SECONDS_FIELD_NUMBER

MAX_DETERMINISTIC_TIME_FIELD_NUMBER

MAX_NUMBER_OF_ITERATIONS_FIELD_NUMBER

MARKOWITZ_ZLATEV_PARAMETER_FIELD_NUMBER

MARKOWITZ_SINGULARITY_THRESHOLD_FIELD_NUMBER

USE_DUAL_SIMPLEX_FIELD_NUMBER

ALLOW_SIMPLEX_ALGORITHM_CHANGE_FIELD_NUMBER

DEVEX_WEIGHTS_RESET_PERIOD_FIELD_NUMBER

USE_PREPROCESSING_FIELD_NUMBER

USE_MIDDLE_PRODUCT_FORM_UPDATE_FIELD_NUMBER

INITIALIZE_DEVEX_WITH_COLUMN_NORMS_FIELD_NUMBER

EXPLOIT_SINGLETON_COLUMN_IN_INITIAL_BASIS_FIELD_NUMBER

DUAL_SMALL_PIVOT_THRESHOLD_FIELD_NUMBER

PREPROCESSOR_ZERO_TOLERANCE_FIELD_NUMBER

OBJECTIVE_LOWER_LIMIT_FIELD_NUMBER

OBJECTIVE_UPPER_LIMIT_FIELD_NUMBER

DEGENERATE_MINISTEP_FACTOR_FIELD_NUMBER

RANDOM_SEED_FIELD_NUMBER

NUM_OMP_THREADS_FIELD_NUMBER

PERTURB_COSTS_IN_DUAL_SIMPLEX_FIELD_NUMBER

USE_DEDICATED_DUAL_FEASIBILITY_ALGORITHM_FIELD_NUMBER

RELATIVE_COST_PERTURBATION_FIELD_NUMBER

RELATIVE_MAX_COST_PERTURBATION_FIELD_NUMBER

INITIAL_CONDITION_NUMBER_THRESHOLD_FIELD_NUMBER

LOG_SEARCH_PROGRESS_FIELD_NUMBER

LOG_TO_STDOUT_FIELD_NUMBER

CROSSOVER_BOUND_SNAPPING_DISTANCE_FIELD_NUMBER

PUSH_TO_VERTEX_FIELD_NUMBER

USE_IMPLIED_FREE_PREPROCESSOR_FIELD_NUMBER

MAX_VALID_MAGNITUDE_FIELD_NUMBER

DROP_MAGNITUDE_FIELD_NUMBER

DUAL_PRICE_PRIORITIZE_NORM_FIELD_NUMBER

Method Detail

getDescriptor

internalGetFieldAccessorTable

hasScalingMethod

getScalingMethod