Package operations_research.pdlp

Class Solvers.PrimalDualHybridGradientParams

  • All Implemented Interfaces:
    com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, java.io.Serializable, Solvers.PrimalDualHybridGradientParamsOrBuilder
    Enclosing class:
    Solvers
    public static final class Solvers.PrimalDualHybridGradientParams
extends com.google.protobuf.GeneratedMessageV3
implements Solvers.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
    See Also:
    Serialized Form

    • Field Detail

      • TERMINATION_CRITERIA_FIELD_NUMBER

        public static final int TERMINATION_CRITERIA_FIELD_NUMBER
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      • NUM_THREADS_FIELD_NUMBER

        public static final int NUM_THREADS_FIELD_NUMBER
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      • NUM_SHARDS_FIELD_NUMBER

        public static final int NUM_SHARDS_FIELD_NUMBER
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      • RECORD_ITERATION_STATS_FIELD_NUMBER

        public static final int RECORD_ITERATION_STATS_FIELD_NUMBER
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      • VERBOSITY_LEVEL_FIELD_NUMBER

        public static final int VERBOSITY_LEVEL_FIELD_NUMBER
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      • LOG_INTERVAL_SECONDS_FIELD_NUMBER

        public static final int LOG_INTERVAL_SECONDS_FIELD_NUMBER
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      • MAJOR_ITERATION_FREQUENCY_FIELD_NUMBER

        public static final int MAJOR_ITERATION_FREQUENCY_FIELD_NUMBER
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      • TERMINATION_CHECK_FREQUENCY_FIELD_NUMBER

        public static final int TERMINATION_CHECK_FREQUENCY_FIELD_NUMBER
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      • RESTART_STRATEGY_FIELD_NUMBER

        public static final int RESTART_STRATEGY_FIELD_NUMBER
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      • PRIMAL_WEIGHT_UPDATE_SMOOTHING_FIELD_NUMBER

        public static final int PRIMAL_WEIGHT_UPDATE_SMOOTHING_FIELD_NUMBER
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      • INITIAL_PRIMAL_WEIGHT_FIELD_NUMBER

        public static final int INITIAL_PRIMAL_WEIGHT_FIELD_NUMBER
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      • PRESOLVE_OPTIONS_FIELD_NUMBER

        public static final int PRESOLVE_OPTIONS_FIELD_NUMBER
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      • L_INF_RUIZ_ITERATIONS_FIELD_NUMBER

        public static final int L_INF_RUIZ_ITERATIONS_FIELD_NUMBER
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      • L2_NORM_RESCALING_FIELD_NUMBER

        public static final int L2_NORM_RESCALING_FIELD_NUMBER
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      • SUFFICIENT_REDUCTION_FOR_RESTART_FIELD_NUMBER

        public static final int SUFFICIENT_REDUCTION_FOR_RESTART_FIELD_NUMBER
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      • NECESSARY_REDUCTION_FOR_RESTART_FIELD_NUMBER

        public static final int NECESSARY_REDUCTION_FOR_RESTART_FIELD_NUMBER
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      • LINESEARCH_RULE_FIELD_NUMBER

        public static final int LINESEARCH_RULE_FIELD_NUMBER
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      • ADAPTIVE_LINESEARCH_PARAMETERS_FIELD_NUMBER

        public static final int ADAPTIVE_LINESEARCH_PARAMETERS_FIELD_NUMBER
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      • MALITSKY_POCK_PARAMETERS_FIELD_NUMBER

        public static final int MALITSKY_POCK_PARAMETERS_FIELD_NUMBER
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      • INITIAL_STEP_SIZE_SCALING_FIELD_NUMBER

        public static final int INITIAL_STEP_SIZE_SCALING_FIELD_NUMBER
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      • RANDOM_PROJECTION_SEEDS_FIELD_NUMBER

        public static final int RANDOM_PROJECTION_SEEDS_FIELD_NUMBER
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      • INFINITE_CONSTRAINT_BOUND_THRESHOLD_FIELD_NUMBER

        public static final int INFINITE_CONSTRAINT_BOUND_THRESHOLD_FIELD_NUMBER
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      • HANDLE_SOME_PRIMAL_GRADIENTS_ON_FINITE_BOUNDS_AS_RESIDUALS_FIELD_NUMBER

        public static final int HANDLE_SOME_PRIMAL_GRADIENTS_ON_FINITE_BOUNDS_AS_RESIDUALS_FIELD_NUMBER
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      • USE_DIAGONAL_QP_TRUST_REGION_SOLVER_FIELD_NUMBER

        public static final int USE_DIAGONAL_QP_TRUST_REGION_SOLVER_FIELD_NUMBER
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      • DIAGONAL_QP_TRUST_REGION_SOLVER_TOLERANCE_FIELD_NUMBER

        public static final int DIAGONAL_QP_TRUST_REGION_SOLVER_TOLERANCE_FIELD_NUMBER
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      • USE_FEASIBILITY_POLISHING_FIELD_NUMBER

        public static final int USE_FEASIBILITY_POLISHING_FIELD_NUMBER
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    • Method Detail

      • newInstance

        protected java.lang.Object newInstance​(com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)
        Overrides:
        newInstance in class com.google.protobuf.GeneratedMessageV3

      • getDescriptor

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

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The numThreads.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The numShards.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The recordIterationStats.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The verbosityLevel.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The logIntervalSeconds.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The majorIterationFrequency.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The terminationCheckFrequency.

      • 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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the restartStrategy field is set.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The primalWeightUpdateSmoothing.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The initialPrimalWeight.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The lInfRuizIterations.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The l2NormRescaling.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The sufficientReductionForRestart.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The necessaryReductionForRestart.

      • 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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the linesearchRule field is set.

      • 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 Solvers.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 Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The initialStepSizeScaling.

      • getRandomProjectionSeedsList

        public java.util.List<java.lang.Integer> getRandomProjectionSeedsList()
         Seeds for generating (pseudo-)random projections of iterates during
 termination checks. For each seed, the projection of the primal and dual
 solutions onto random planes in primal and dual space will be computed and
 added the IterationStats if record_iteration_stats is true. The random
 planes generated will be determined by the seeds, the primal and dual
 dimensions, and num_threads.
        repeated int32 random_projection_seeds = 28 [packed = true];
        Specified by:
        getRandomProjectionSeedsList in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        A list containing the randomProjectionSeeds.

      • getRandomProjectionSeedsCount

        public int getRandomProjectionSeedsCount()
         Seeds for generating (pseudo-)random projections of iterates during
 termination checks. For each seed, the projection of the primal and dual
 solutions onto random planes in primal and dual space will be computed and
 added the IterationStats if record_iteration_stats is true. The random
 planes generated will be determined by the seeds, the primal and dual
 dimensions, and num_threads.
        repeated int32 random_projection_seeds = 28 [packed = true];
        Specified by:
        getRandomProjectionSeedsCount in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The count of randomProjectionSeeds.

      • getRandomProjectionSeeds

        public int getRandomProjectionSeeds​(int index)
         Seeds for generating (pseudo-)random projections of iterates during
 termination checks. For each seed, the projection of the primal and dual
 solutions onto random planes in primal and dual space will be computed and
 added the IterationStats if record_iteration_stats is true. The random
 planes generated will be determined by the seeds, the primal and dual
 dimensions, and num_threads.
        repeated int32 random_projection_seeds = 28 [packed = true];
        Specified by:
        getRandomProjectionSeeds in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Parameters:
        index - The index of the element to return.
        Returns:
        The randomProjectionSeeds at the given index.

      • hasInfiniteConstraintBoundThreshold

        public boolean hasInfiniteConstraintBoundThreshold()
         Constraint bounds with absolute value at least this threshold are replaced
 with infinities.
 NOTE: This primarily affects the relative convergence criteria. A smaller
 value makes the relative convergence criteria stronger. It also affects the
 problem statistics LOG()ed at the start of the run, and the default initial
 primal weight, since that is based on the norm of the bounds.
        optional double infinite_constraint_bound_threshold = 22 [default = inf];
        Specified by:
        hasInfiniteConstraintBoundThreshold in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the infiniteConstraintBoundThreshold field is set.

      • getInfiniteConstraintBoundThreshold

        public double getInfiniteConstraintBoundThreshold()
         Constraint bounds with absolute value at least this threshold are replaced
 with infinities.
 NOTE: This primarily affects the relative convergence criteria. A smaller
 value makes the relative convergence criteria stronger. It also affects the
 problem statistics LOG()ed at the start of the run, and the default initial
 primal weight, since that is based on the norm of the bounds.
        optional double infinite_constraint_bound_threshold = 22 [default = inf];
        Specified by:
        getInfiniteConstraintBoundThreshold in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The infiniteConstraintBoundThreshold.

      • hasHandleSomePrimalGradientsOnFiniteBoundsAsResiduals

        public boolean hasHandleSomePrimalGradientsOnFiniteBoundsAsResiduals()
         See
 https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite
 for a description of this flag.
        optional bool handle_some_primal_gradients_on_finite_bounds_as_residuals = 29 [default = true];
        Specified by:
        hasHandleSomePrimalGradientsOnFiniteBoundsAsResiduals in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the handleSomePrimalGradientsOnFiniteBoundsAsResiduals field is set.

      • getHandleSomePrimalGradientsOnFiniteBoundsAsResiduals

        public boolean getHandleSomePrimalGradientsOnFiniteBoundsAsResiduals()
         See
 https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite
 for a description of this flag.
        optional bool handle_some_primal_gradients_on_finite_bounds_as_residuals = 29 [default = true];
        Specified by:
        getHandleSomePrimalGradientsOnFiniteBoundsAsResiduals in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The handleSomePrimalGradientsOnFiniteBoundsAsResiduals.

      • hasUseDiagonalQpTrustRegionSolver

        public 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. The `diagonal_qp_solver_accuracy` parameter controls the
 solve accuracy.
 TODO(user): Turn this option on by default for quadratic
 programs after numerical evaluation.
        optional bool use_diagonal_qp_trust_region_solver = 23 [default = false];
        Specified by:
        hasUseDiagonalQpTrustRegionSolver in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the useDiagonalQpTrustRegionSolver field is set.

      • getUseDiagonalQpTrustRegionSolver

        public 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. The `diagonal_qp_solver_accuracy` parameter controls the
 solve accuracy.
 TODO(user): Turn this option on by default for quadratic
 programs after numerical evaluation.
        optional bool use_diagonal_qp_trust_region_solver = 23 [default = false];
        Specified by:
        getUseDiagonalQpTrustRegionSolver in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The useDiagonalQpTrustRegionSolver.

      • hasDiagonalQpTrustRegionSolverTolerance

        public 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. Smaller values imply smaller relative error of the final
 solution vector.
 TODO(user): Find an expression for the final relative error.
        optional double diagonal_qp_trust_region_solver_tolerance = 24 [default = 1e-08];
        Specified by:
        hasDiagonalQpTrustRegionSolverTolerance in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the diagonalQpTrustRegionSolverTolerance field is set.

      • getDiagonalQpTrustRegionSolverTolerance

        public 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. Smaller values imply smaller relative error of the final
 solution vector.
 TODO(user): Find an expression for the final relative error.
        optional double diagonal_qp_trust_region_solver_tolerance = 24 [default = 1e-08];
        Specified by:
        getDiagonalQpTrustRegionSolverTolerance in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The diagonalQpTrustRegionSolverTolerance.

      • hasUseFeasibilityPolishing

        public 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. This is useful primarily when the feasibility tolerances are fairly
 tight and the objective gap tolerance is somewhat looser. Note that this
 does not change the termination criteria, but rather can help achieve the
 termination criteria more quickly when the objective gap is not as
 important as feasibility.

 `use_feasibility_polishing` cannot be used with glop presolve, and requires
 `handle_some_primal_gradients_on_finite_bounds_as_residuals == false`.
 `use_feasibility_polishing` can only be used with linear programs.

 Feasibility polishing runs two separate phases, primal feasibility and dual
 feasibility. The primal feasibility phase runs PDHG on the primal
 feasibility problem (obtained by changing the objective vector to all
 zeros), using the average primal iterate and zero dual (which is optimal
 for the primal feasibility problem) as the initial solution. The dual
 feasibility phase runs PDHG on the dual feasibility problem (obtained by
 changing all finite variable and constraint bounds to zero), using the
 average dual iterate and zero primal (which is optimal for the dual
 feasibility problem) as the initial solution. The primal solution from the
 primal feasibility phase and dual solution from the dual feasibility phase
 are then combined (forming a solution of type
 `POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION`) and checked against the
 termination criteria.
        optional bool use_feasibility_polishing = 30 [default = false];
        Specified by:
        hasUseFeasibilityPolishing in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        Whether the useFeasibilityPolishing field is set.

      • getUseFeasibilityPolishing

        public 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. This is useful primarily when the feasibility tolerances are fairly
 tight and the objective gap tolerance is somewhat looser. Note that this
 does not change the termination criteria, but rather can help achieve the
 termination criteria more quickly when the objective gap is not as
 important as feasibility.

 `use_feasibility_polishing` cannot be used with glop presolve, and requires
 `handle_some_primal_gradients_on_finite_bounds_as_residuals == false`.
 `use_feasibility_polishing` can only be used with linear programs.

 Feasibility polishing runs two separate phases, primal feasibility and dual
 feasibility. The primal feasibility phase runs PDHG on the primal
 feasibility problem (obtained by changing the objective vector to all
 zeros), using the average primal iterate and zero dual (which is optimal
 for the primal feasibility problem) as the initial solution. The dual
 feasibility phase runs PDHG on the dual feasibility problem (obtained by
 changing all finite variable and constraint bounds to zero), using the
 average dual iterate and zero primal (which is optimal for the dual
 feasibility problem) as the initial solution. The primal solution from the
 primal feasibility phase and dual solution from the dual feasibility phase
 are then combined (forming a solution of type
 `POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION`) and checked against the
 termination criteria.
        optional bool use_feasibility_polishing = 30 [default = false];
        Specified by:
        getUseFeasibilityPolishing in interface Solvers.PrimalDualHybridGradientParamsOrBuilder
        Returns:
        The useFeasibilityPolishing.

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3

      • 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.GeneratedMessageV3
        Throws:
        java.io.IOException

      • getSerializedSize

        public int getSerializedSize()
        Specified by:
        getSerializedSize in interface com.google.protobuf.MessageLite
        Overrides:
        getSerializedSize in class com.google.protobuf.GeneratedMessageV3

      • 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 Solvers.PrimalDualHybridGradientParams parseFrom​(java.nio.ByteBuffer data)
                                                        throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(java.nio.ByteBuffer data,
                                                               com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                        throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(com.google.protobuf.ByteString data)
                                                        throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(com.google.protobuf.ByteString data,
                                                               com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                        throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(byte[] data)
                                                        throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(byte[] data,
                                                               com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                        throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(java.io.InputStream input,
                                                               com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                        throws java.io.IOException
        Throws:
        java.io.IOException

      • parseDelimitedFrom

        public static Solvers.PrimalDualHybridGradientParams parseDelimitedFrom​(java.io.InputStream input,
                                                                        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                                 throws java.io.IOException
        Throws:
        java.io.IOException

      • parseFrom

        public static Solvers.PrimalDualHybridGradientParams parseFrom​(com.google.protobuf.CodedInputStream input,
                                                               com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                        throws java.io.IOException
        Throws:
        java.io.IOException

      • newBuilderForType

        public Solvers.PrimalDualHybridGradientParams.Builder newBuilderForType()
        Specified by:
        newBuilderForType in interface com.google.protobuf.Message
        Specified by:
        newBuilderForType in interface com.google.protobuf.MessageLite

      • newBuilderForType

        protected Solvers.PrimalDualHybridGradientParams.Builder newBuilderForType​(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)
        Specified by:
        newBuilderForType in class com.google.protobuf.GeneratedMessageV3

      • getParserForType

        public com.google.protobuf.Parser<Solvers.PrimalDualHybridGradientParams> 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.GeneratedMessageV3

      • getDefaultInstanceForType

        public Solvers.PrimalDualHybridGradientParams getDefaultInstanceForType()
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder