Package operations_research.pdlp
package operations_research.pdlp
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ClassDescriptionInformation measuring how close a candidate is to establishing feasibility and optimality; see also TerminationCriteria.Information measuring how close a candidate is to establishing feasibility and optimality; see also TerminationCriteria.Details about one primal feasibility or dual feasibility polishing phase within a solve with `use_feasibility_polishing`.Details about one primal feasibility or dual feasibility polishing phase within a solve with `use_feasibility_polishing`.Information measuring how close a point is to establishing primal or dual infeasibility (i.e.Information measuring how close a point is to establishing primal or dual infeasibility (i.e.All values in IterationStats assume that the primal quadratic program is a minimization problem and the dual is a maximization problem.All values in IterationStats assume that the primal quadratic program is a minimization problem and the dual is a maximization problem.Protobuf type
operations_research.pdlp.PointMetadataProtobuf typeoperations_research.pdlp.PointMetadataIdentifies the type of point used to compute the fields in a given proto; see ConvergenceInformation and InfeasibilityInformation.Protobuf enumoperations_research.pdlp.PolishingPhaseTypeEasy-to-compute statistics for the quadratic program.Easy-to-compute statistics for the quadratic program.Specifies whether a restart was performed on a given iteration.Protobuf typeoperations_research.pdlp.SolveLogProtobuf typeoperations_research.pdlp.SolveLogProtobuf enumoperations_research.pdlp.TerminationReasonAt the end of each iteration, regardless of whether the step was accepted or not, the adaptive rule updates the step_size as the minimum of two potential step sizes defined by the following two exponents.At the end of each iteration, regardless of whether the step was accepted or not, the adaptive rule updates the step_size as the minimum of two potential step sizes defined by the following two exponents.Protobuf typeoperations_research.pdlp.MalitskyPockParamsProtobuf typeoperations_research.pdlp.MalitskyPockParamsProtobuf enumoperations_research.pdlp.OptimalityNormParameters for PrimalDualHybridGradient() in primal_dual_hybrid_gradient.h.Parameters for PrimalDualHybridGradient() in primal_dual_hybrid_gradient.h.Protobuf enumoperations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRuleProtobuf typeoperations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptionsProtobuf typeoperations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptionsProtobuf enumoperations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategyRelevant readings on infeasibility certificates: (1) https://docs.mosek.com/modeling-cookbook/qcqo.html provides references explaining why the primal rays imply dual infeasibility and dual rays imply primal infeasibility.Relevant readings on infeasibility certificates: (1) https://docs.mosek.com/modeling-cookbook/qcqo.html provides references explaining why the primal rays imply dual infeasibility and dual rays imply primal infeasibility.Protobuf typeoperations_research.pdlp.TerminationCriteria.DetailedOptimalityCriteriaProtobuf typeoperations_research.pdlp.TerminationCriteria.DetailedOptimalityCriteriaProtobuf typeoperations_research.pdlp.TerminationCriteria.SimpleOptimalityCriteriaProtobuf typeoperations_research.pdlp.TerminationCriteria.SimpleOptimalityCriteria