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

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

All Implemented Interfaces:: PrimalDualHybridGradientParamsOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, java.lang.Cloneable

Enclosing class:: PrimalDualHybridGradientParams

public static final class PrimalDualHybridGradientParams.Builder
extends com.google.protobuf.GeneratedMessage.Builder<PrimalDualHybridGradientParams.Builder>
implements PrimalDualHybridGradientParamsOrBuilder

 Parameters for PrimalDualHybridGradient() in primal_dual_hybrid_gradient.h.
 While the defaults are generally good, it is usually worthwhile to perform a
 parameter sweep to find good settings for a particular family of problems.
 The following parameters should be considered for tuning:
 - restart_strategy (jointly with major_iteration_frequency)
 - primal_weight_update_smoothing (jointly with initial_primal_weight)
 - presolve_options.use_glop
 - l_inf_ruiz_iterations
 - l2_norm_rescaling
 In addition, tune num_threads to speed up the solve.

Protobuf type operations_research.pdlp.PrimalDualHybridGradientParams

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`PrimalDualHybridGradientParams.Builder`	`addAllRandomProjectionSeeds(java.lang.Iterable<? extends java.lang.Integer> values)` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`PrimalDualHybridGradientParams.Builder`	`addRandomProjectionSeeds(int value)` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`PrimalDualHybridGradientParams`	`build()`
`PrimalDualHybridGradientParams`	`buildPartial()`
`PrimalDualHybridGradientParams.Builder`	`clear()`
`PrimalDualHybridGradientParams.Builder`	`clearAdaptiveLinesearchParameters()` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`PrimalDualHybridGradientParams.Builder`	`clearDiagonalQpTrustRegionSolverTolerance()` The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter.
`PrimalDualHybridGradientParams.Builder`	`clearHandleSomePrimalGradientsOnFiniteBoundsAsResiduals()` See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
`PrimalDualHybridGradientParams.Builder`	`clearInfiniteConstraintBoundThreshold()` Constraint bounds with absolute value at least this threshold are replaced with infinities.
`PrimalDualHybridGradientParams.Builder`	`clearInitialPrimalWeight()` The initial value of the primal weight (i.e., the ratio of primal and dual step sizes).
`PrimalDualHybridGradientParams.Builder`	`clearInitialStepSizeScaling()` Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
`PrimalDualHybridGradientParams.Builder`	`clearL2NormRescaling()` If true, applies L_2 norm rescaling after the Ruiz rescaling.
`PrimalDualHybridGradientParams.Builder`	`clearLinesearchRule()` Linesearch rule applied at each major iteration.
`PrimalDualHybridGradientParams.Builder`	`clearLInfRuizIterations()` Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix.
`PrimalDualHybridGradientParams.Builder`	`clearLogIntervalSeconds()` Time between iteration-level statistics logging (if `verbosity_level > 1`).
`PrimalDualHybridGradientParams.Builder`	`clearMajorIterationFrequency()` The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight.
`PrimalDualHybridGradientParams.Builder`	`clearMalitskyPockParameters()` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`PrimalDualHybridGradientParams.Builder`	`clearNecessaryReductionForRestart()` For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function by this amount triggers a restart if, additionally, the quality of the iterates appears to be getting worse.
`PrimalDualHybridGradientParams.Builder`	`clearNumShards()` For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards.
`PrimalDualHybridGradientParams.Builder`	`clearNumThreads()` The number of threads to use.
`PrimalDualHybridGradientParams.Builder`	`clearPresolveOptions()` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`PrimalDualHybridGradientParams.Builder`	`clearPrimalWeightUpdateSmoothing()` This parameter controls exponential smoothing of log(primal_weight) when a primal weight update occurs (i.e., when the ratio of primal and dual step sizes is adjusted).
`PrimalDualHybridGradientParams.Builder`	`clearRandomProjectionSeeds()` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`PrimalDualHybridGradientParams.Builder`	`clearRecordIterationStats()` If true, the iteration_stats field of the SolveLog output will be populated at every iteration.
`PrimalDualHybridGradientParams.Builder`	`clearRestartStrategy()` NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
`PrimalDualHybridGradientParams.Builder`	`clearSchedulerType()` The type of scheduler used for CPU multi-threading.
`PrimalDualHybridGradientParams.Builder`	`clearSufficientReductionForRestart()` For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart.
`PrimalDualHybridGradientParams.Builder`	`clearTerminationCheckFrequency()` The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats.
`PrimalDualHybridGradientParams.Builder`	`clearTerminationCriteria()` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`PrimalDualHybridGradientParams.Builder`	`clearUseDiagonalQpTrustRegionSolver()` When solving QPs with diagonal objective matrices, this option can be turned on to enable an experimental solver that avoids linearization of the quadratic term.
`PrimalDualHybridGradientParams.Builder`	`clearUseFeasibilityPolishing()` If true, periodically runs feasibility polishing, which attempts to move from latest average iterate to one that is closer to feasibility (i.e., has smaller primal and dual residuals) while probably increasing the objective gap.
`PrimalDualHybridGradientParams.Builder`	`clearVerbosityLevel()` The verbosity of logging. 0: No informational logging.
`AdaptiveLinesearchParams`	`getAdaptiveLinesearchParameters()` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`AdaptiveLinesearchParams.Builder`	`getAdaptiveLinesearchParametersBuilder()` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`AdaptiveLinesearchParamsOrBuilder`	`getAdaptiveLinesearchParametersOrBuilder()` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`PrimalDualHybridGradientParams`	`getDefaultInstanceForType()`
`static com.google.protobuf.Descriptors.Descriptor`	`getDescriptor()`
`com.google.protobuf.Descriptors.Descriptor`	`getDescriptorForType()`
`double`	`getDiagonalQpTrustRegionSolverTolerance()` The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter.
`boolean`	`getHandleSomePrimalGradientsOnFiniteBoundsAsResiduals()` See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
`double`	`getInfiniteConstraintBoundThreshold()` Constraint bounds with absolute value at least this threshold are replaced with infinities.
`double`	`getInitialPrimalWeight()` The initial value of the primal weight (i.e., the ratio of primal and dual step sizes).
`double`	`getInitialStepSizeScaling()` Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
`boolean`	`getL2NormRescaling()` If true, applies L_2 norm rescaling after the Ruiz rescaling.
`PrimalDualHybridGradientParams.LinesearchRule`	`getLinesearchRule()` Linesearch rule applied at each major iteration.
`int`	`getLInfRuizIterations()` Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix.
`double`	`getLogIntervalSeconds()` Time between iteration-level statistics logging (if `verbosity_level > 1`).
`int`	`getMajorIterationFrequency()` The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight.
`MalitskyPockParams`	`getMalitskyPockParameters()` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`MalitskyPockParams.Builder`	`getMalitskyPockParametersBuilder()` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`MalitskyPockParamsOrBuilder`	`getMalitskyPockParametersOrBuilder()` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`double`	`getNecessaryReductionForRestart()` For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function by this amount triggers a restart if, additionally, the quality of the iterates appears to be getting worse.
`int`	`getNumShards()` For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards.
`int`	`getNumThreads()` The number of threads to use.
`PrimalDualHybridGradientParams.PresolveOptions`	`getPresolveOptions()` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`PrimalDualHybridGradientParams.PresolveOptions.Builder`	`getPresolveOptionsBuilder()` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`PrimalDualHybridGradientParams.PresolveOptionsOrBuilder`	`getPresolveOptionsOrBuilder()` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`double`	`getPrimalWeightUpdateSmoothing()` This parameter controls exponential smoothing of log(primal_weight) when a primal weight update occurs (i.e., when the ratio of primal and dual step sizes is adjusted).
`int`	`getRandomProjectionSeeds(int index)` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`int`	`getRandomProjectionSeedsCount()` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`java.util.List<java.lang.Integer>`	`getRandomProjectionSeedsList()` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`boolean`	`getRecordIterationStats()` If true, the iteration_stats field of the SolveLog output will be populated at every iteration.
`PrimalDualHybridGradientParams.RestartStrategy`	`getRestartStrategy()` NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
`SchedulerType`	`getSchedulerType()` The type of scheduler used for CPU multi-threading.
`double`	`getSufficientReductionForRestart()` For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart.
`int`	`getTerminationCheckFrequency()` The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats.
`TerminationCriteria`	`getTerminationCriteria()` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`TerminationCriteria.Builder`	`getTerminationCriteriaBuilder()` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`TerminationCriteriaOrBuilder`	`getTerminationCriteriaOrBuilder()` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`boolean`	`getUseDiagonalQpTrustRegionSolver()` When solving QPs with diagonal objective matrices, this option can be turned on to enable an experimental solver that avoids linearization of the quadratic term.
`boolean`	`getUseFeasibilityPolishing()` If true, periodically runs feasibility polishing, which attempts to move from latest average iterate to one that is closer to feasibility (i.e., has smaller primal and dual residuals) while probably increasing the objective gap.
`int`	`getVerbosityLevel()` The verbosity of logging. 0: No informational logging.
`boolean`	`hasAdaptiveLinesearchParameters()` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`boolean`	`hasDiagonalQpTrustRegionSolverTolerance()` The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter.
`boolean`	`hasHandleSomePrimalGradientsOnFiniteBoundsAsResiduals()` See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
`boolean`	`hasInfiniteConstraintBoundThreshold()` Constraint bounds with absolute value at least this threshold are replaced with infinities.
`boolean`	`hasInitialPrimalWeight()` The initial value of the primal weight (i.e., the ratio of primal and dual step sizes).
`boolean`	`hasInitialStepSizeScaling()` Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
`boolean`	`hasL2NormRescaling()` If true, applies L_2 norm rescaling after the Ruiz rescaling.
`boolean`	`hasLinesearchRule()` Linesearch rule applied at each major iteration.
`boolean`	`hasLInfRuizIterations()` Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix.
`boolean`	`hasLogIntervalSeconds()` Time between iteration-level statistics logging (if `verbosity_level > 1`).
`boolean`	`hasMajorIterationFrequency()` The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight.
`boolean`	`hasMalitskyPockParameters()` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`boolean`	`hasNecessaryReductionForRestart()` For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function by this amount triggers a restart if, additionally, the quality of the iterates appears to be getting worse.
`boolean`	`hasNumShards()` For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards.
`boolean`	`hasNumThreads()` The number of threads to use.
`boolean`	`hasPresolveOptions()` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`boolean`	`hasPrimalWeightUpdateSmoothing()` This parameter controls exponential smoothing of log(primal_weight) when a primal weight update occurs (i.e., when the ratio of primal and dual step sizes is adjusted).
`boolean`	`hasRecordIterationStats()` If true, the iteration_stats field of the SolveLog output will be populated at every iteration.
`boolean`	`hasRestartStrategy()` NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
`boolean`	`hasSchedulerType()` The type of scheduler used for CPU multi-threading.
`boolean`	`hasSufficientReductionForRestart()` For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart.
`boolean`	`hasTerminationCheckFrequency()` The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats.
`boolean`	`hasTerminationCriteria()` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`boolean`	`hasUseDiagonalQpTrustRegionSolver()` When solving QPs with diagonal objective matrices, this option can be turned on to enable an experimental solver that avoids linearization of the quadratic term.
`boolean`	`hasUseFeasibilityPolishing()` If true, periodically runs feasibility polishing, which attempts to move from latest average iterate to one that is closer to feasibility (i.e., has smaller primal and dual residuals) while probably increasing the objective gap.
`boolean`	`hasVerbosityLevel()` The verbosity of logging. 0: No informational logging.
`protected com.google.protobuf.GeneratedMessage.FieldAccessorTable`	`internalGetFieldAccessorTable()`
`boolean`	`isInitialized()`
`PrimalDualHybridGradientParams.Builder`	`mergeAdaptiveLinesearchParameters(AdaptiveLinesearchParams value)` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`PrimalDualHybridGradientParams.Builder`	`mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`PrimalDualHybridGradientParams.Builder`	`mergeFrom(com.google.protobuf.Message other)`
`PrimalDualHybridGradientParams.Builder`	`mergeFrom(PrimalDualHybridGradientParams other)`
`PrimalDualHybridGradientParams.Builder`	`mergeMalitskyPockParameters(MalitskyPockParams value)` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`PrimalDualHybridGradientParams.Builder`	`mergePresolveOptions(PrimalDualHybridGradientParams.PresolveOptions value)` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`PrimalDualHybridGradientParams.Builder`	`mergeTerminationCriteria(TerminationCriteria value)` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`PrimalDualHybridGradientParams.Builder`	`setAdaptiveLinesearchParameters(AdaptiveLinesearchParams.Builder builderForValue)` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`PrimalDualHybridGradientParams.Builder`	`setAdaptiveLinesearchParameters(AdaptiveLinesearchParams value)` `optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;`
`PrimalDualHybridGradientParams.Builder`	`setDiagonalQpTrustRegionSolverTolerance(double value)` The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter.
`PrimalDualHybridGradientParams.Builder`	`setHandleSomePrimalGradientsOnFiniteBoundsAsResiduals(boolean value)` See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
`PrimalDualHybridGradientParams.Builder`	`setInfiniteConstraintBoundThreshold(double value)` Constraint bounds with absolute value at least this threshold are replaced with infinities.
`PrimalDualHybridGradientParams.Builder`	`setInitialPrimalWeight(double value)` The initial value of the primal weight (i.e., the ratio of primal and dual step sizes).
`PrimalDualHybridGradientParams.Builder`	`setInitialStepSizeScaling(double value)` Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
`PrimalDualHybridGradientParams.Builder`	`setL2NormRescaling(boolean value)` If true, applies L_2 norm rescaling after the Ruiz rescaling.
`PrimalDualHybridGradientParams.Builder`	`setLinesearchRule(PrimalDualHybridGradientParams.LinesearchRule value)` Linesearch rule applied at each major iteration.
`PrimalDualHybridGradientParams.Builder`	`setLInfRuizIterations(int value)` Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix.
`PrimalDualHybridGradientParams.Builder`	`setLogIntervalSeconds(double value)` Time between iteration-level statistics logging (if `verbosity_level > 1`).
`PrimalDualHybridGradientParams.Builder`	`setMajorIterationFrequency(int value)` The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight.
`PrimalDualHybridGradientParams.Builder`	`setMalitskyPockParameters(MalitskyPockParams.Builder builderForValue)` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`PrimalDualHybridGradientParams.Builder`	`setMalitskyPockParameters(MalitskyPockParams value)` `optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;`
`PrimalDualHybridGradientParams.Builder`	`setNecessaryReductionForRestart(double value)` For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function by this amount triggers a restart if, additionally, the quality of the iterates appears to be getting worse.
`PrimalDualHybridGradientParams.Builder`	`setNumShards(int value)` For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards.
`PrimalDualHybridGradientParams.Builder`	`setNumThreads(int value)` The number of threads to use.
`PrimalDualHybridGradientParams.Builder`	`setPresolveOptions(PrimalDualHybridGradientParams.PresolveOptions.Builder builderForValue)` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`PrimalDualHybridGradientParams.Builder`	`setPresolveOptions(PrimalDualHybridGradientParams.PresolveOptions value)` `optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;`
`PrimalDualHybridGradientParams.Builder`	`setPrimalWeightUpdateSmoothing(double value)` This parameter controls exponential smoothing of log(primal_weight) when a primal weight update occurs (i.e., when the ratio of primal and dual step sizes is adjusted).
`PrimalDualHybridGradientParams.Builder`	`setRandomProjectionSeeds(int index, int value)` Seeds for generating (pseudo-)random projections of iterates during termination checks.
`PrimalDualHybridGradientParams.Builder`	`setRecordIterationStats(boolean value)` If true, the iteration_stats field of the SolveLog output will be populated at every iteration.
`PrimalDualHybridGradientParams.Builder`	`setRestartStrategy(PrimalDualHybridGradientParams.RestartStrategy value)` NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
`PrimalDualHybridGradientParams.Builder`	`setSchedulerType(SchedulerType value)` The type of scheduler used for CPU multi-threading.
`PrimalDualHybridGradientParams.Builder`	`setSufficientReductionForRestart(double value)` For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart.
`PrimalDualHybridGradientParams.Builder`	`setTerminationCheckFrequency(int value)` The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats.
`PrimalDualHybridGradientParams.Builder`	`setTerminationCriteria(TerminationCriteria.Builder builderForValue)` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`PrimalDualHybridGradientParams.Builder`	`setTerminationCriteria(TerminationCriteria value)` `optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;`
`PrimalDualHybridGradientParams.Builder`	`setUseDiagonalQpTrustRegionSolver(boolean value)` When solving QPs with diagonal objective matrices, this option can be turned on to enable an experimental solver that avoids linearization of the quadratic term.
`PrimalDualHybridGradientParams.Builder`	`setUseFeasibilityPolishing(boolean value)` If true, periodically runs feasibility polishing, which attempts to move from latest average iterate to one that is closer to feasibility (i.e., has smaller primal and dual residuals) while probably increasing the objective gap.
`PrimalDualHybridGradientParams.Builder`	`setVerbosityLevel(int value)` The verbosity of logging. 0: No informational logging.

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

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

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

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

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

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

Method Detail

getDescriptor

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

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

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

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

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

Specified by:

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

Overrides:

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

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

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

Specified by:

getDescriptorForType in interface com.google.protobuf.MessageOrBuilder

Overrides:

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

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

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

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

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

Specified by:

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

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

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

Specified by:

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

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

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

Overrides:

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

mergeFrom

public PrimalDualHybridGradientParams.Builder mergeFrom(PrimalDualHybridGradientParams other)

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

isInitialized in interface com.google.protobuf.MessageLiteOrBuilder

Overrides:

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

mergeFrom

public PrimalDualHybridGradientParams.Builder mergeFrom(com.google.protobuf.CodedInputStream input,
                                                        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                 throws java.io.IOException

Specified by:: mergeFrom in interface com.google.protobuf.Message.Builder
Specified by:: mergeFrom in interface com.google.protobuf.MessageLite.Builder
Overrides:: mergeFrom in class com.google.protobuf.AbstractMessage.Builder<PrimalDualHybridGradientParams.Builder>
Throws:: java.io.IOException

hasTerminationCriteria
```
public boolean hasTerminationCriteria()
```
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

Specified by:

hasTerminationCriteria in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the terminationCriteria field is set.

getTerminationCriteria
```
public TerminationCriteria getTerminationCriteria()
```
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

Specified by:

getTerminationCriteria in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The terminationCriteria.

setTerminationCriteria

public PrimalDualHybridGradientParams.Builder setTerminationCriteria(TerminationCriteria value)

optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

setTerminationCriteria

public PrimalDualHybridGradientParams.Builder setTerminationCriteria(TerminationCriteria.Builder builderForValue)

optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

mergeTerminationCriteria

public PrimalDualHybridGradientParams.Builder mergeTerminationCriteria(TerminationCriteria value)

optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

clearTerminationCriteria
```
public PrimalDualHybridGradientParams.Builder clearTerminationCriteria()
```
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

getTerminationCriteriaBuilder
```
public TerminationCriteria.Builder getTerminationCriteriaBuilder()
```
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

getTerminationCriteriaOrBuilder
```
public TerminationCriteriaOrBuilder getTerminationCriteriaOrBuilder()
```
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;

Specified by:

getTerminationCriteriaOrBuilder in interface PrimalDualHybridGradientParamsOrBuilder

hasNumThreads

public boolean hasNumThreads()

 The number of threads to use. Must be positive.
 Try various values of num_threads, up to the number of physical cores.
 Performance may not be monotonically increasing with the number of threads
 because of memory bandwidth limitations.

optional int32 num_threads = 2 [default = 1];

Specified by:: hasNumThreads in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the numThreads field is set.

getNumThreads

public int getNumThreads()

 The number of threads to use. Must be positive.
 Try various values of num_threads, up to the number of physical cores.
 Performance may not be monotonically increasing with the number of threads
 because of memory bandwidth limitations.

optional int32 num_threads = 2 [default = 1];

Specified by:: getNumThreads in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The numThreads.

setNumThreads

public PrimalDualHybridGradientParams.Builder setNumThreads(int value)

 The number of threads to use. Must be positive.
 Try various values of num_threads, up to the number of physical cores.
 Performance may not be monotonically increasing with the number of threads
 because of memory bandwidth limitations.

optional int32 num_threads = 2 [default = 1];

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

clearNumThreads

public PrimalDualHybridGradientParams.Builder clearNumThreads()

 The number of threads to use. Must be positive.
 Try various values of num_threads, up to the number of physical cores.
 Performance may not be monotonically increasing with the number of threads
 because of memory bandwidth limitations.

optional int32 num_threads = 2 [default = 1];

Returns:: This builder for chaining.

hasNumShards

public boolean hasNumShards()

 For more efficient parallel computation, the matrices and vectors are
 divided (virtually) into num_shards shards. Results are computed
 independently for each shard and then combined. As a consequence, the order
 of computation, and hence floating point roundoff, depends on the number of
 shards so reproducible results require using the same value for num_shards.
 However, for efficiency num_shards should a be at least num_threads, and
 preferably at least 4*num_threads to allow better load balancing. If
 num_shards is positive, the computation will use that many shards.
 Otherwise a default that depends on num_threads will be used.

optional int32 num_shards = 27 [default = 0];

Specified by:: hasNumShards in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the numShards field is set.

getNumShards

public int getNumShards()

 For more efficient parallel computation, the matrices and vectors are
 divided (virtually) into num_shards shards. Results are computed
 independently for each shard and then combined. As a consequence, the order
 of computation, and hence floating point roundoff, depends on the number of
 shards so reproducible results require using the same value for num_shards.
 However, for efficiency num_shards should a be at least num_threads, and
 preferably at least 4*num_threads to allow better load balancing. If
 num_shards is positive, the computation will use that many shards.
 Otherwise a default that depends on num_threads will be used.

optional int32 num_shards = 27 [default = 0];

Specified by:: getNumShards in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The numShards.

setNumShards

public PrimalDualHybridGradientParams.Builder setNumShards(int value)

 For more efficient parallel computation, the matrices and vectors are
 divided (virtually) into num_shards shards. Results are computed
 independently for each shard and then combined. As a consequence, the order
 of computation, and hence floating point roundoff, depends on the number of
 shards so reproducible results require using the same value for num_shards.
 However, for efficiency num_shards should a be at least num_threads, and
 preferably at least 4*num_threads to allow better load balancing. If
 num_shards is positive, the computation will use that many shards.
 Otherwise a default that depends on num_threads will be used.

optional int32 num_shards = 27 [default = 0];

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

clearNumShards

public PrimalDualHybridGradientParams.Builder clearNumShards()

 For more efficient parallel computation, the matrices and vectors are
 divided (virtually) into num_shards shards. Results are computed
 independently for each shard and then combined. As a consequence, the order
 of computation, and hence floating point roundoff, depends on the number of
 shards so reproducible results require using the same value for num_shards.
 However, for efficiency num_shards should a be at least num_threads, and
 preferably at least 4*num_threads to allow better load balancing. If
 num_shards is positive, the computation will use that many shards.
 Otherwise a default that depends on num_threads will be used.

optional int32 num_shards = 27 [default = 0];

Returns:: This builder for chaining.

hasSchedulerType
```
public boolean hasSchedulerType()
```
```
 The type of scheduler used for CPU multi-threading. See the documentation
 of the corresponding enum for more details.
 
```
optional .operations_research.pdlp.SchedulerType scheduler_type = 32 [default = SCHEDULER_TYPE_GOOGLE_THREADPOOL];
Specified by:

hasSchedulerType in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the schedulerType field is set.

getSchedulerType
```
public SchedulerType getSchedulerType()
```
```
 The type of scheduler used for CPU multi-threading. See the documentation
 of the corresponding enum for more details.
 
```
optional .operations_research.pdlp.SchedulerType scheduler_type = 32 [default = SCHEDULER_TYPE_GOOGLE_THREADPOOL];
Specified by:

getSchedulerType in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The schedulerType.

setSchedulerType

public PrimalDualHybridGradientParams.Builder setSchedulerType(SchedulerType value)

 The type of scheduler used for CPU multi-threading. See the documentation
 of the corresponding enum for more details.

optional .operations_research.pdlp.SchedulerType scheduler_type = 32 [default = SCHEDULER_TYPE_GOOGLE_THREADPOOL];

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

clearSchedulerType

public PrimalDualHybridGradientParams.Builder clearSchedulerType()

 The type of scheduler used for CPU multi-threading. See the documentation
 of the corresponding enum for more details.

optional .operations_research.pdlp.SchedulerType scheduler_type = 32 [default = SCHEDULER_TYPE_GOOGLE_THREADPOOL];

Returns:: This builder for chaining.

hasRecordIterationStats

public boolean hasRecordIterationStats()

 If true, the iteration_stats field of the SolveLog output will be populated
 at every iteration. Note that we only compute solution statistics at
 termination checks. Setting this parameter to true may substantially
 increase the size of the output.

optional bool record_iteration_stats = 3;

Specified by:: hasRecordIterationStats in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the recordIterationStats field is set.

getRecordIterationStats

public boolean getRecordIterationStats()

 If true, the iteration_stats field of the SolveLog output will be populated
 at every iteration. Note that we only compute solution statistics at
 termination checks. Setting this parameter to true may substantially
 increase the size of the output.

optional bool record_iteration_stats = 3;

Specified by:: getRecordIterationStats in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The recordIterationStats.

setRecordIterationStats

public PrimalDualHybridGradientParams.Builder setRecordIterationStats(boolean value)

 If true, the iteration_stats field of the SolveLog output will be populated
 at every iteration. Note that we only compute solution statistics at
 termination checks. Setting this parameter to true may substantially
 increase the size of the output.

optional bool record_iteration_stats = 3;

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

clearRecordIterationStats

public PrimalDualHybridGradientParams.Builder clearRecordIterationStats()

 If true, the iteration_stats field of the SolveLog output will be populated
 at every iteration. Note that we only compute solution statistics at
 termination checks. Setting this parameter to true may substantially
 increase the size of the output.

optional bool record_iteration_stats = 3;

Returns:: This builder for chaining.

hasVerbosityLevel

public boolean hasVerbosityLevel()

 The verbosity of logging.
 0: No informational logging. (Errors are logged.)
 1: Summary statistics only. No iteration-level details.
 2: A table of iteration-level statistics is logged.
 (See ToShortString() in primal_dual_hybrid_gradient.cc).
 3: A more detailed table of iteration-level statistics is logged.
 (See ToString() in primal_dual_hybrid_gradient.cc).
 4: For iteration-level details, prints the statistics of both the average
 (prefixed with A) and the current iterate (prefixed with C). Also prints
 internal algorithmic state and details.
 Logging at levels 2-4 also includes messages from level 1.

optional int32 verbosity_level = 26 [default = 0];

Specified by:: hasVerbosityLevel in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the verbosityLevel field is set.

getVerbosityLevel

public int getVerbosityLevel()

 The verbosity of logging.
 0: No informational logging. (Errors are logged.)
 1: Summary statistics only. No iteration-level details.
 2: A table of iteration-level statistics is logged.
 (See ToShortString() in primal_dual_hybrid_gradient.cc).
 3: A more detailed table of iteration-level statistics is logged.
 (See ToString() in primal_dual_hybrid_gradient.cc).
 4: For iteration-level details, prints the statistics of both the average
 (prefixed with A) and the current iterate (prefixed with C). Also prints
 internal algorithmic state and details.
 Logging at levels 2-4 also includes messages from level 1.

optional int32 verbosity_level = 26 [default = 0];

Specified by:: getVerbosityLevel in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The verbosityLevel.

setVerbosityLevel

public PrimalDualHybridGradientParams.Builder setVerbosityLevel(int value)

 The verbosity of logging.
 0: No informational logging. (Errors are logged.)
 1: Summary statistics only. No iteration-level details.
 2: A table of iteration-level statistics is logged.
 (See ToShortString() in primal_dual_hybrid_gradient.cc).
 3: A more detailed table of iteration-level statistics is logged.
 (See ToString() in primal_dual_hybrid_gradient.cc).
 4: For iteration-level details, prints the statistics of both the average
 (prefixed with A) and the current iterate (prefixed with C). Also prints
 internal algorithmic state and details.
 Logging at levels 2-4 also includes messages from level 1.

optional int32 verbosity_level = 26 [default = 0];

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

clearVerbosityLevel

public PrimalDualHybridGradientParams.Builder clearVerbosityLevel()

 The verbosity of logging.
 0: No informational logging. (Errors are logged.)
 1: Summary statistics only. No iteration-level details.
 2: A table of iteration-level statistics is logged.
 (See ToShortString() in primal_dual_hybrid_gradient.cc).
 3: A more detailed table of iteration-level statistics is logged.
 (See ToString() in primal_dual_hybrid_gradient.cc).
 4: For iteration-level details, prints the statistics of both the average
 (prefixed with A) and the current iterate (prefixed with C). Also prints
 internal algorithmic state and details.
 Logging at levels 2-4 also includes messages from level 1.

optional int32 verbosity_level = 26 [default = 0];

Returns:: This builder for chaining.

hasLogIntervalSeconds

public boolean hasLogIntervalSeconds()

 Time between iteration-level statistics logging (if `verbosity_level > 1`).
 Since iteration-level statistics are only generated when performing
 termination checks, logs will be generated from next termination check
 after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the
 default) means log statistics at every termination check.

optional double log_interval_seconds = 31 [default = 0];

Specified by:: hasLogIntervalSeconds in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the logIntervalSeconds field is set.

getLogIntervalSeconds

public double getLogIntervalSeconds()

 Time between iteration-level statistics logging (if `verbosity_level > 1`).
 Since iteration-level statistics are only generated when performing
 termination checks, logs will be generated from next termination check
 after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the
 default) means log statistics at every termination check.

optional double log_interval_seconds = 31 [default = 0];

Specified by:: getLogIntervalSeconds in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The logIntervalSeconds.

setLogIntervalSeconds

public PrimalDualHybridGradientParams.Builder setLogIntervalSeconds(double value)

 Time between iteration-level statistics logging (if `verbosity_level > 1`).
 Since iteration-level statistics are only generated when performing
 termination checks, logs will be generated from next termination check
 after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the
 default) means log statistics at every termination check.

optional double log_interval_seconds = 31 [default = 0];

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

clearLogIntervalSeconds

public PrimalDualHybridGradientParams.Builder clearLogIntervalSeconds()

 Time between iteration-level statistics logging (if `verbosity_level > 1`).
 Since iteration-level statistics are only generated when performing
 termination checks, logs will be generated from next termination check
 after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the
 default) means log statistics at every termination check.

optional double log_interval_seconds = 31 [default = 0];

Returns:: This builder for chaining.

hasMajorIterationFrequency

public boolean hasMajorIterationFrequency()

 The frequency at which extra work is performed to make major algorithmic
 decisions, e.g., performing restarts and updating the primal weight. Major
 iterations also trigger a termination check. For best performance using the
 NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale
 grid search over this parameter, for example, over powers of two.
 ADAPTIVE_HEURISTIC is mostly insensitive to this value.

optional int32 major_iteration_frequency = 4 [default = 64];

Specified by:: hasMajorIterationFrequency in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the majorIterationFrequency field is set.

getMajorIterationFrequency

public int getMajorIterationFrequency()

 The frequency at which extra work is performed to make major algorithmic
 decisions, e.g., performing restarts and updating the primal weight. Major
 iterations also trigger a termination check. For best performance using the
 NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale
 grid search over this parameter, for example, over powers of two.
 ADAPTIVE_HEURISTIC is mostly insensitive to this value.

optional int32 major_iteration_frequency = 4 [default = 64];

Specified by:: getMajorIterationFrequency in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The majorIterationFrequency.

setMajorIterationFrequency

public PrimalDualHybridGradientParams.Builder setMajorIterationFrequency(int value)

 The frequency at which extra work is performed to make major algorithmic
 decisions, e.g., performing restarts and updating the primal weight. Major
 iterations also trigger a termination check. For best performance using the
 NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale
 grid search over this parameter, for example, over powers of two.
 ADAPTIVE_HEURISTIC is mostly insensitive to this value.

optional int32 major_iteration_frequency = 4 [default = 64];

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

clearMajorIterationFrequency

public PrimalDualHybridGradientParams.Builder clearMajorIterationFrequency()

 The frequency at which extra work is performed to make major algorithmic
 decisions, e.g., performing restarts and updating the primal weight. Major
 iterations also trigger a termination check. For best performance using the
 NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale
 grid search over this parameter, for example, over powers of two.
 ADAPTIVE_HEURISTIC is mostly insensitive to this value.

optional int32 major_iteration_frequency = 4 [default = 64];

Returns:: This builder for chaining.

hasTerminationCheckFrequency

public boolean hasTerminationCheckFrequency()

 The frequency (based on a counter reset every major iteration) to check for
 termination (involves extra work) and log iteration stats. Termination
 checks do not affect algorithmic progress unless termination is triggered.

optional int32 termination_check_frequency = 5 [default = 64];

Specified by:: hasTerminationCheckFrequency in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the terminationCheckFrequency field is set.

getTerminationCheckFrequency

public int getTerminationCheckFrequency()

 The frequency (based on a counter reset every major iteration) to check for
 termination (involves extra work) and log iteration stats. Termination
 checks do not affect algorithmic progress unless termination is triggered.

optional int32 termination_check_frequency = 5 [default = 64];

Specified by:: getTerminationCheckFrequency in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The terminationCheckFrequency.

setTerminationCheckFrequency

public PrimalDualHybridGradientParams.Builder setTerminationCheckFrequency(int value)

 The frequency (based on a counter reset every major iteration) to check for
 termination (involves extra work) and log iteration stats. Termination
 checks do not affect algorithmic progress unless termination is triggered.

optional int32 termination_check_frequency = 5 [default = 64];

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

clearTerminationCheckFrequency

public PrimalDualHybridGradientParams.Builder clearTerminationCheckFrequency()

 The frequency (based on a counter reset every major iteration) to check for
 termination (involves extra work) and log iteration stats. Termination
 checks do not affect algorithmic progress unless termination is triggered.

optional int32 termination_check_frequency = 5 [default = 64];

Returns:: This builder for chaining.

hasRestartStrategy
```
public boolean hasRestartStrategy()
```
```
 NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
 If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune
 major_iteration_frequency.
 
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];
Specified by:

hasRestartStrategy in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the restartStrategy field is set.

getRestartStrategy
```
public PrimalDualHybridGradientParams.RestartStrategy getRestartStrategy()
```
```
 NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
 If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune
 major_iteration_frequency.
 
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];
Specified by:

getRestartStrategy in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The restartStrategy.

setRestartStrategy

public PrimalDualHybridGradientParams.Builder setRestartStrategy(PrimalDualHybridGradientParams.RestartStrategy value)

 NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
 If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune
 major_iteration_frequency.

optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];

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

clearRestartStrategy

public PrimalDualHybridGradientParams.Builder clearRestartStrategy()

 NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.
 If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune
 major_iteration_frequency.

optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];

Returns:: This builder for chaining.

hasPrimalWeightUpdateSmoothing

public boolean hasPrimalWeightUpdateSmoothing()

 This parameter controls exponential smoothing of log(primal_weight) when a
 primal weight update occurs (i.e., when the ratio of primal and dual step
 sizes is adjusted). At 0.0, the primal weight will be frozen at its initial
 value and there will be no dynamic updates in the algorithm. At 1.0, there
 is no smoothing in the updates. The default of 0.5 generally performs well,
 but has been observed on occasion to trigger unstable swings in the primal
 weight. We recommend also trying 0.0 (disabling primal weight updates), in
 which case you must also tune initial_primal_weight.

optional double primal_weight_update_smoothing = 7 [default = 0.5];

Specified by:: hasPrimalWeightUpdateSmoothing in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the primalWeightUpdateSmoothing field is set.

getPrimalWeightUpdateSmoothing

public double getPrimalWeightUpdateSmoothing()

 This parameter controls exponential smoothing of log(primal_weight) when a
 primal weight update occurs (i.e., when the ratio of primal and dual step
 sizes is adjusted). At 0.0, the primal weight will be frozen at its initial
 value and there will be no dynamic updates in the algorithm. At 1.0, there
 is no smoothing in the updates. The default of 0.5 generally performs well,
 but has been observed on occasion to trigger unstable swings in the primal
 weight. We recommend also trying 0.0 (disabling primal weight updates), in
 which case you must also tune initial_primal_weight.

optional double primal_weight_update_smoothing = 7 [default = 0.5];

Specified by:: getPrimalWeightUpdateSmoothing in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The primalWeightUpdateSmoothing.

setPrimalWeightUpdateSmoothing

public PrimalDualHybridGradientParams.Builder setPrimalWeightUpdateSmoothing(double value)

 This parameter controls exponential smoothing of log(primal_weight) when a
 primal weight update occurs (i.e., when the ratio of primal and dual step
 sizes is adjusted). At 0.0, the primal weight will be frozen at its initial
 value and there will be no dynamic updates in the algorithm. At 1.0, there
 is no smoothing in the updates. The default of 0.5 generally performs well,
 but has been observed on occasion to trigger unstable swings in the primal
 weight. We recommend also trying 0.0 (disabling primal weight updates), in
 which case you must also tune initial_primal_weight.

optional double primal_weight_update_smoothing = 7 [default = 0.5];

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

clearPrimalWeightUpdateSmoothing

public PrimalDualHybridGradientParams.Builder clearPrimalWeightUpdateSmoothing()

 This parameter controls exponential smoothing of log(primal_weight) when a
 primal weight update occurs (i.e., when the ratio of primal and dual step
 sizes is adjusted). At 0.0, the primal weight will be frozen at its initial
 value and there will be no dynamic updates in the algorithm. At 1.0, there
 is no smoothing in the updates. The default of 0.5 generally performs well,
 but has been observed on occasion to trigger unstable swings in the primal
 weight. We recommend also trying 0.0 (disabling primal weight updates), in
 which case you must also tune initial_primal_weight.

optional double primal_weight_update_smoothing = 7 [default = 0.5];

Returns:: This builder for chaining.

hasInitialPrimalWeight

public boolean hasInitialPrimalWeight()

 The initial value of the primal weight (i.e., the ratio of primal and dual
 step sizes). The primal weight remains fixed throughout the solve if
 primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
 the norm of the objective vector to the L2 norm of the combined constraint
 bounds vector (as defined above). If this ratio is not finite and positive,
 then the default is 1.0 instead. For tuning, try powers of 10, for example,
 from 10^{-6} to 10^6.

optional double initial_primal_weight = 8;

Specified by:: hasInitialPrimalWeight in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the initialPrimalWeight field is set.

getInitialPrimalWeight

public double getInitialPrimalWeight()

 The initial value of the primal weight (i.e., the ratio of primal and dual
 step sizes). The primal weight remains fixed throughout the solve if
 primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
 the norm of the objective vector to the L2 norm of the combined constraint
 bounds vector (as defined above). If this ratio is not finite and positive,
 then the default is 1.0 instead. For tuning, try powers of 10, for example,
 from 10^{-6} to 10^6.

optional double initial_primal_weight = 8;

Specified by:: getInitialPrimalWeight in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The initialPrimalWeight.

setInitialPrimalWeight

public PrimalDualHybridGradientParams.Builder setInitialPrimalWeight(double value)

 The initial value of the primal weight (i.e., the ratio of primal and dual
 step sizes). The primal weight remains fixed throughout the solve if
 primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
 the norm of the objective vector to the L2 norm of the combined constraint
 bounds vector (as defined above). If this ratio is not finite and positive,
 then the default is 1.0 instead. For tuning, try powers of 10, for example,
 from 10^{-6} to 10^6.

optional double initial_primal_weight = 8;

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

clearInitialPrimalWeight

public PrimalDualHybridGradientParams.Builder clearInitialPrimalWeight()

 The initial value of the primal weight (i.e., the ratio of primal and dual
 step sizes). The primal weight remains fixed throughout the solve if
 primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
 the norm of the objective vector to the L2 norm of the combined constraint
 bounds vector (as defined above). If this ratio is not finite and positive,
 then the default is 1.0 instead. For tuning, try powers of 10, for example,
 from 10^{-6} to 10^6.

optional double initial_primal_weight = 8;

Returns:: This builder for chaining.

hasPresolveOptions
```
public boolean hasPresolveOptions()
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

Specified by:

hasPresolveOptions in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the presolveOptions field is set.

getPresolveOptions
```
public PrimalDualHybridGradientParams.PresolveOptions getPresolveOptions()
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

Specified by:

getPresolveOptions in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The presolveOptions.

setPresolveOptions

public PrimalDualHybridGradientParams.Builder setPresolveOptions(PrimalDualHybridGradientParams.PresolveOptions value)

optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

setPresolveOptions

public PrimalDualHybridGradientParams.Builder setPresolveOptions(PrimalDualHybridGradientParams.PresolveOptions.Builder builderForValue)

optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

mergePresolveOptions

public PrimalDualHybridGradientParams.Builder mergePresolveOptions(PrimalDualHybridGradientParams.PresolveOptions value)

optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

clearPresolveOptions
```
public PrimalDualHybridGradientParams.Builder clearPresolveOptions()
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

getPresolveOptionsBuilder
```
public PrimalDualHybridGradientParams.PresolveOptions.Builder getPresolveOptionsBuilder()
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

getPresolveOptionsOrBuilder
```
public PrimalDualHybridGradientParams.PresolveOptionsOrBuilder getPresolveOptionsOrBuilder()
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;

Specified by:

getPresolveOptionsOrBuilder in interface PrimalDualHybridGradientParamsOrBuilder

hasLInfRuizIterations

public boolean hasLInfRuizIterations()

 Number of L_infinity Ruiz rescaling iterations to apply to the constraint
 matrix. Zero disables this rescaling pass. Recommended values to try when
 tuning are 0, 5, and 10.

optional int32 l_inf_ruiz_iterations = 9 [default = 5];

Specified by:: hasLInfRuizIterations in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the lInfRuizIterations field is set.

getLInfRuizIterations

public int getLInfRuizIterations()

 Number of L_infinity Ruiz rescaling iterations to apply to the constraint
 matrix. Zero disables this rescaling pass. Recommended values to try when
 tuning are 0, 5, and 10.

optional int32 l_inf_ruiz_iterations = 9 [default = 5];

Specified by:: getLInfRuizIterations in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The lInfRuizIterations.

setLInfRuizIterations

public PrimalDualHybridGradientParams.Builder setLInfRuizIterations(int value)

 Number of L_infinity Ruiz rescaling iterations to apply to the constraint
 matrix. Zero disables this rescaling pass. Recommended values to try when
 tuning are 0, 5, and 10.

optional int32 l_inf_ruiz_iterations = 9 [default = 5];

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

clearLInfRuizIterations

public PrimalDualHybridGradientParams.Builder clearLInfRuizIterations()

 Number of L_infinity Ruiz rescaling iterations to apply to the constraint
 matrix. Zero disables this rescaling pass. Recommended values to try when
 tuning are 0, 5, and 10.

optional int32 l_inf_ruiz_iterations = 9 [default = 5];

Returns:: This builder for chaining.

hasL2NormRescaling
```
public boolean hasL2NormRescaling()
```
```
 If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically
 this has been found to help convergence.
 
```
optional bool l2_norm_rescaling = 10 [default = true];
Specified by:

hasL2NormRescaling in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the l2NormRescaling field is set.

getL2NormRescaling
```
public boolean getL2NormRescaling()
```
```
 If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically
 this has been found to help convergence.
 
```
optional bool l2_norm_rescaling = 10 [default = true];
Specified by:

getL2NormRescaling in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The l2NormRescaling.

setL2NormRescaling

public PrimalDualHybridGradientParams.Builder setL2NormRescaling(boolean value)

 If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically
 this has been found to help convergence.

optional bool l2_norm_rescaling = 10 [default = true];

Parameters:: value - The l2NormRescaling to set.
Returns:: This builder for chaining.

clearL2NormRescaling

public PrimalDualHybridGradientParams.Builder clearL2NormRescaling()

 If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically
 this has been found to help convergence.

optional bool l2_norm_rescaling = 10 [default = true];

Returns:: This builder for chaining.

hasSufficientReductionForRestart
```
public boolean hasSufficientReductionForRestart()
```
```
 For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative
 reduction in the potential function by this amount always triggers a
 restart. Must be between 0.0 and 1.0.
 
```
optional double sufficient_reduction_for_restart = 11 [default = 0.1];
Specified by:

hasSufficientReductionForRestart in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the sufficientReductionForRestart field is set.

getSufficientReductionForRestart
```
public double getSufficientReductionForRestart()
```
```
 For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative
 reduction in the potential function by this amount always triggers a
 restart. Must be between 0.0 and 1.0.
 
```
optional double sufficient_reduction_for_restart = 11 [default = 0.1];
Specified by:

getSufficientReductionForRestart in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The sufficientReductionForRestart.

setSufficientReductionForRestart

public PrimalDualHybridGradientParams.Builder setSufficientReductionForRestart(double value)

 For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative
 reduction in the potential function by this amount always triggers a
 restart. Must be between 0.0 and 1.0.

optional double sufficient_reduction_for_restart = 11 [default = 0.1];

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

clearSufficientReductionForRestart

public PrimalDualHybridGradientParams.Builder clearSufficientReductionForRestart()

 For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative
 reduction in the potential function by this amount always triggers a
 restart. Must be between 0.0 and 1.0.

optional double sufficient_reduction_for_restart = 11 [default = 0.1];

Returns:: This builder for chaining.

hasNecessaryReductionForRestart

public boolean hasNecessaryReductionForRestart()

 For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function
 by this amount triggers a restart if, additionally, the quality of the
 iterates appears to be getting worse. The value must be in the interval
 [sufficient_reduction_for_restart, 1). Smaller values make restarts less
 frequent, and larger values make them more frequent.

optional double necessary_reduction_for_restart = 17 [default = 0.9];

Specified by:: hasNecessaryReductionForRestart in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: Whether the necessaryReductionForRestart field is set.

getNecessaryReductionForRestart

public double getNecessaryReductionForRestart()

 For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function
 by this amount triggers a restart if, additionally, the quality of the
 iterates appears to be getting worse. The value must be in the interval
 [sufficient_reduction_for_restart, 1). Smaller values make restarts less
 frequent, and larger values make them more frequent.

optional double necessary_reduction_for_restart = 17 [default = 0.9];

Specified by:: getNecessaryReductionForRestart in interface PrimalDualHybridGradientParamsOrBuilder
Returns:: The necessaryReductionForRestart.

setNecessaryReductionForRestart

public PrimalDualHybridGradientParams.Builder setNecessaryReductionForRestart(double value)

 For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function
 by this amount triggers a restart if, additionally, the quality of the
 iterates appears to be getting worse. The value must be in the interval
 [sufficient_reduction_for_restart, 1). Smaller values make restarts less
 frequent, and larger values make them more frequent.

optional double necessary_reduction_for_restart = 17 [default = 0.9];

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

clearNecessaryReductionForRestart

public PrimalDualHybridGradientParams.Builder clearNecessaryReductionForRestart()

 For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function
 by this amount triggers a restart if, additionally, the quality of the
 iterates appears to be getting worse. The value must be in the interval
 [sufficient_reduction_for_restart, 1). Smaller values make restarts less
 frequent, and larger values make them more frequent.

optional double necessary_reduction_for_restart = 17 [default = 0.9];

Returns:: This builder for chaining.

hasLinesearchRule
```
public boolean hasLinesearchRule()
```
```
 Linesearch rule applied at each major iteration.
 
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Specified by:

hasLinesearchRule in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the linesearchRule field is set.

getLinesearchRule
```
public PrimalDualHybridGradientParams.LinesearchRule getLinesearchRule()
```
```
 Linesearch rule applied at each major iteration.
 
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Specified by:

getLinesearchRule in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The linesearchRule.

setLinesearchRule
```
public PrimalDualHybridGradientParams.Builder setLinesearchRule(PrimalDualHybridGradientParams.LinesearchRule value)
```
```
 Linesearch rule applied at each major iteration.
 
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Parameters:

value - The linesearchRule to set.

Returns:

This builder for chaining.

clearLinesearchRule
```
public PrimalDualHybridGradientParams.Builder clearLinesearchRule()
```
```
 Linesearch rule applied at each major iteration.
 
```
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Returns:

This builder for chaining.

hasAdaptiveLinesearchParameters
```
public boolean hasAdaptiveLinesearchParameters()
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

Specified by:

hasAdaptiveLinesearchParameters in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the adaptiveLinesearchParameters field is set.

getAdaptiveLinesearchParameters
```
public AdaptiveLinesearchParams getAdaptiveLinesearchParameters()
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

Specified by:

getAdaptiveLinesearchParameters in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The adaptiveLinesearchParameters.

setAdaptiveLinesearchParameters
```
public PrimalDualHybridGradientParams.Builder setAdaptiveLinesearchParameters(AdaptiveLinesearchParams value)
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

setAdaptiveLinesearchParameters

public PrimalDualHybridGradientParams.Builder setAdaptiveLinesearchParameters(AdaptiveLinesearchParams.Builder builderForValue)

optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

mergeAdaptiveLinesearchParameters
```
public PrimalDualHybridGradientParams.Builder mergeAdaptiveLinesearchParameters(AdaptiveLinesearchParams value)
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

clearAdaptiveLinesearchParameters
```
public PrimalDualHybridGradientParams.Builder clearAdaptiveLinesearchParameters()
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

getAdaptiveLinesearchParametersBuilder
```
public AdaptiveLinesearchParams.Builder getAdaptiveLinesearchParametersBuilder()
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

getAdaptiveLinesearchParametersOrBuilder
```
public AdaptiveLinesearchParamsOrBuilder getAdaptiveLinesearchParametersOrBuilder()
```
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;

Specified by:

getAdaptiveLinesearchParametersOrBuilder in interface PrimalDualHybridGradientParamsOrBuilder

hasMalitskyPockParameters
```
public boolean hasMalitskyPockParameters()
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

Specified by:

hasMalitskyPockParameters in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the malitskyPockParameters field is set.

getMalitskyPockParameters
```
public MalitskyPockParams getMalitskyPockParameters()
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

Specified by:

getMalitskyPockParameters in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The malitskyPockParameters.

setMalitskyPockParameters
```
public PrimalDualHybridGradientParams.Builder setMalitskyPockParameters(MalitskyPockParams value)
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

setMalitskyPockParameters

public PrimalDualHybridGradientParams.Builder setMalitskyPockParameters(MalitskyPockParams.Builder builderForValue)

optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

mergeMalitskyPockParameters
```
public PrimalDualHybridGradientParams.Builder mergeMalitskyPockParameters(MalitskyPockParams value)
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

clearMalitskyPockParameters
```
public PrimalDualHybridGradientParams.Builder clearMalitskyPockParameters()
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

getMalitskyPockParametersBuilder
```
public MalitskyPockParams.Builder getMalitskyPockParametersBuilder()
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

getMalitskyPockParametersOrBuilder
```
public MalitskyPockParamsOrBuilder getMalitskyPockParametersOrBuilder()
```
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;

Specified by:

getMalitskyPockParametersOrBuilder in interface PrimalDualHybridGradientParamsOrBuilder

hasInitialStepSizeScaling
```
public boolean hasInitialStepSizeScaling()
```
```
 Scaling factor applied to the initial step size (all step sizes if
 linesearch_rule == CONSTANT_STEP_SIZE_RULE).
 
```
optional double initial_step_size_scaling = 25 [default = 1];
Specified by:

hasInitialStepSizeScaling in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

Whether the initialStepSizeScaling field is set.

getInitialStepSizeScaling
```
public double getInitialStepSizeScaling()
```
```
 Scaling factor applied to the initial step size (all step sizes if
 linesearch_rule == CONSTANT_STEP_SIZE_RULE).
 
```
optional double initial_step_size_scaling = 25 [default = 1];
Specified by:

getInitialStepSizeScaling in interface PrimalDualHybridGradientParamsOrBuilder

Returns:

The initialStepSizeScaling.

setInitialStepSizeScaling

public PrimalDualHybridGradientParams.Builder setInitialStepSizeScaling(double value)

 Scaling factor applied to the initial step size (all step sizes if
 linesearch_rule == CONSTANT_STEP_SIZE_RULE).

optional double initial_step_size_scaling = 25 [default = 1];

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

clearInitialStepSizeScaling

public PrimalDualHybridGradientParams.Builder clearInitialStepSizeScaling()

 Scaling factor applied to the initial step size (all step sizes if
 linesearch_rule == CONSTANT_STEP_SIZE_RULE).

optional double initial_step_size_scaling = 25 [default = 1];

Returns:: This builder for chaining.