""" @generated by mypy-protobuf. Do not edit manually! isort:skip_file These proto messages are for collecting solve statistics, e.g., during experiments. """ import builtins import collections.abc import google.protobuf.descriptor import google.protobuf.internal.containers import google.protobuf.internal.enum_type_wrapper import google.protobuf.message import ortools.pdlp.solvers_pb2 import sys import typing if sys.version_info >= (3, 10): import typing as typing_extensions else: import typing_extensions DESCRIPTOR: google.protobuf.descriptor.FileDescriptor class _RestartChoice: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _RestartChoiceEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_RestartChoice.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor RESTART_CHOICE_UNSPECIFIED: _RestartChoice.ValueType # 0 RESTART_CHOICE_NO_RESTART: _RestartChoice.ValueType # 1 """No restart on this iteration.""" RESTART_CHOICE_WEIGHTED_AVERAGE_RESET: _RestartChoice.ValueType # 2 """The weighted average of iterates is cleared and reset to the current point. Note that from a mathematical perspective this can be equivalently viewed as restarting the algorithm but picking the restart point to be the current iterate. """ RESTART_CHOICE_RESTART_TO_AVERAGE: _RestartChoice.ValueType # 3 """The algorithm is restarted at the average of iterates since the last restart. """ class RestartChoice(_RestartChoice, metaclass=_RestartChoiceEnumTypeWrapper): """Specifies whether a restart was performed on a given iteration.""" RESTART_CHOICE_UNSPECIFIED: RestartChoice.ValueType # 0 RESTART_CHOICE_NO_RESTART: RestartChoice.ValueType # 1 """No restart on this iteration.""" RESTART_CHOICE_WEIGHTED_AVERAGE_RESET: RestartChoice.ValueType # 2 """The weighted average of iterates is cleared and reset to the current point. Note that from a mathematical perspective this can be equivalently viewed as restarting the algorithm but picking the restart point to be the current iterate. """ RESTART_CHOICE_RESTART_TO_AVERAGE: RestartChoice.ValueType # 3 """The algorithm is restarted at the average of iterates since the last restart. """ Global___RestartChoice: typing_extensions.TypeAlias = RestartChoice class _PointType: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _PointTypeEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_PointType.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor POINT_TYPE_UNSPECIFIED: _PointType.ValueType # 0 POINT_TYPE_CURRENT_ITERATE: _PointType.ValueType # 1 """Current iterate (x_k, y_k).""" POINT_TYPE_ITERATE_DIFFERENCE: _PointType.ValueType # 2 """Difference of iterates (x_{k+1} - x_k, y_{k+1} - y_k).""" POINT_TYPE_AVERAGE_ITERATE: _PointType.ValueType # 3 """Average of iterates since the last restart.""" POINT_TYPE_NONE: _PointType.ValueType # 4 """There is no corresponding point.""" POINT_TYPE_PRESOLVER_SOLUTION: _PointType.ValueType # 5 """Output of presolver.""" POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION: _PointType.ValueType # 6 """Combined solution from primal and dual feasibility polishing.""" class PointType(_PointType, metaclass=_PointTypeEnumTypeWrapper): """Identifies the type of point used to compute the fields in a given proto; see ConvergenceInformation and InfeasibilityInformation. """ POINT_TYPE_UNSPECIFIED: PointType.ValueType # 0 POINT_TYPE_CURRENT_ITERATE: PointType.ValueType # 1 """Current iterate (x_k, y_k).""" POINT_TYPE_ITERATE_DIFFERENCE: PointType.ValueType # 2 """Difference of iterates (x_{k+1} - x_k, y_{k+1} - y_k).""" POINT_TYPE_AVERAGE_ITERATE: PointType.ValueType # 3 """Average of iterates since the last restart.""" POINT_TYPE_NONE: PointType.ValueType # 4 """There is no corresponding point.""" POINT_TYPE_PRESOLVER_SOLUTION: PointType.ValueType # 5 """Output of presolver.""" POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION: PointType.ValueType # 6 """Combined solution from primal and dual feasibility polishing.""" Global___PointType: typing_extensions.TypeAlias = PointType class _TerminationReason: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _TerminationReasonEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_TerminationReason.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor TERMINATION_REASON_UNSPECIFIED: _TerminationReason.ValueType # 0 TERMINATION_REASON_OPTIMAL: _TerminationReason.ValueType # 1 TERMINATION_REASON_PRIMAL_INFEASIBLE: _TerminationReason.ValueType # 2 """Note in this situation the dual could be either unbounded or infeasible.""" TERMINATION_REASON_DUAL_INFEASIBLE: _TerminationReason.ValueType # 3 """Note in this situation the primal could be either unbounded or infeasible.""" TERMINATION_REASON_TIME_LIMIT: _TerminationReason.ValueType # 4 TERMINATION_REASON_ITERATION_LIMIT: _TerminationReason.ValueType # 5 TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT: _TerminationReason.ValueType # 8 TERMINATION_REASON_INTERRUPTED_BY_USER: _TerminationReason.ValueType # 12 TERMINATION_REASON_NUMERICAL_ERROR: _TerminationReason.ValueType # 6 TERMINATION_REASON_INVALID_PROBLEM: _TerminationReason.ValueType # 9 """Indicates that the solver detected invalid problem data, e.g., inconsistent bounds. """ TERMINATION_REASON_INVALID_INITIAL_SOLUTION: _TerminationReason.ValueType # 13 """Indicates that the solver detected that the initial solution that was provided was invalid, e.g., wrong size or containing NAN or inf. """ TERMINATION_REASON_INVALID_PARAMETER: _TerminationReason.ValueType # 10 """Indicates that an invalid value for the parameters was detected.""" TERMINATION_REASON_OTHER: _TerminationReason.ValueType # 7 TERMINATION_REASON_PRIMAL_OR_DUAL_INFEASIBLE: _TerminationReason.ValueType # 11 """Primal or dual infeasibility was detected (e.g. by presolve) but no certificate is available. """ class TerminationReason(_TerminationReason, metaclass=_TerminationReasonEnumTypeWrapper): ... TERMINATION_REASON_UNSPECIFIED: TerminationReason.ValueType # 0 TERMINATION_REASON_OPTIMAL: TerminationReason.ValueType # 1 TERMINATION_REASON_PRIMAL_INFEASIBLE: TerminationReason.ValueType # 2 """Note in this situation the dual could be either unbounded or infeasible.""" TERMINATION_REASON_DUAL_INFEASIBLE: TerminationReason.ValueType # 3 """Note in this situation the primal could be either unbounded or infeasible.""" TERMINATION_REASON_TIME_LIMIT: TerminationReason.ValueType # 4 TERMINATION_REASON_ITERATION_LIMIT: TerminationReason.ValueType # 5 TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT: TerminationReason.ValueType # 8 TERMINATION_REASON_INTERRUPTED_BY_USER: TerminationReason.ValueType # 12 TERMINATION_REASON_NUMERICAL_ERROR: TerminationReason.ValueType # 6 TERMINATION_REASON_INVALID_PROBLEM: TerminationReason.ValueType # 9 """Indicates that the solver detected invalid problem data, e.g., inconsistent bounds. """ TERMINATION_REASON_INVALID_INITIAL_SOLUTION: TerminationReason.ValueType # 13 """Indicates that the solver detected that the initial solution that was provided was invalid, e.g., wrong size or containing NAN or inf. """ TERMINATION_REASON_INVALID_PARAMETER: TerminationReason.ValueType # 10 """Indicates that an invalid value for the parameters was detected.""" TERMINATION_REASON_OTHER: TerminationReason.ValueType # 7 TERMINATION_REASON_PRIMAL_OR_DUAL_INFEASIBLE: TerminationReason.ValueType # 11 """Primal or dual infeasibility was detected (e.g. by presolve) but no certificate is available. """ Global___TerminationReason: typing_extensions.TypeAlias = TerminationReason class _PolishingPhaseType: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _PolishingPhaseTypeEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_PolishingPhaseType.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor POLISHING_PHASE_TYPE_UNSPECIFIED: _PolishingPhaseType.ValueType # 0 POLISHING_PHASE_TYPE_PRIMAL_FEASIBILITY: _PolishingPhaseType.ValueType # 1 POLISHING_PHASE_TYPE_DUAL_FEASIBILITY: _PolishingPhaseType.ValueType # 2 class PolishingPhaseType(_PolishingPhaseType, metaclass=_PolishingPhaseTypeEnumTypeWrapper): ... POLISHING_PHASE_TYPE_UNSPECIFIED: PolishingPhaseType.ValueType # 0 POLISHING_PHASE_TYPE_PRIMAL_FEASIBILITY: PolishingPhaseType.ValueType # 1 POLISHING_PHASE_TYPE_DUAL_FEASIBILITY: PolishingPhaseType.ValueType # 2 Global___PolishingPhaseType: typing_extensions.TypeAlias = PolishingPhaseType @typing.final class QuadraticProgramStats(google.protobuf.message.Message): """Easy-to-compute statistics for the quadratic program.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor NUM_VARIABLES_FIELD_NUMBER: builtins.int NUM_CONSTRAINTS_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_COL_MIN_L_INF_NORM_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_ROW_MIN_L_INF_NORM_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_NUM_NONZEROS_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_ABS_MAX_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_ABS_MIN_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_ABS_AVG_FIELD_NUMBER: builtins.int CONSTRAINT_MATRIX_L2_NORM_FIELD_NUMBER: builtins.int COMBINED_BOUNDS_MAX_FIELD_NUMBER: builtins.int COMBINED_BOUNDS_MIN_FIELD_NUMBER: builtins.int COMBINED_BOUNDS_AVG_FIELD_NUMBER: builtins.int COMBINED_BOUNDS_L2_NORM_FIELD_NUMBER: builtins.int COMBINED_VARIABLE_BOUNDS_MAX_FIELD_NUMBER: builtins.int COMBINED_VARIABLE_BOUNDS_MIN_FIELD_NUMBER: builtins.int COMBINED_VARIABLE_BOUNDS_AVG_FIELD_NUMBER: builtins.int COMBINED_VARIABLE_BOUNDS_L2_NORM_FIELD_NUMBER: builtins.int VARIABLE_BOUND_GAPS_NUM_FINITE_FIELD_NUMBER: builtins.int VARIABLE_BOUND_GAPS_MAX_FIELD_NUMBER: builtins.int VARIABLE_BOUND_GAPS_MIN_FIELD_NUMBER: builtins.int VARIABLE_BOUND_GAPS_AVG_FIELD_NUMBER: builtins.int VARIABLE_BOUND_GAPS_L2_NORM_FIELD_NUMBER: builtins.int OBJECTIVE_VECTOR_ABS_MAX_FIELD_NUMBER: builtins.int OBJECTIVE_VECTOR_ABS_MIN_FIELD_NUMBER: builtins.int OBJECTIVE_VECTOR_ABS_AVG_FIELD_NUMBER: builtins.int OBJECTIVE_VECTOR_L2_NORM_FIELD_NUMBER: builtins.int OBJECTIVE_MATRIX_NUM_NONZEROS_FIELD_NUMBER: builtins.int OBJECTIVE_MATRIX_ABS_MAX_FIELD_NUMBER: builtins.int OBJECTIVE_MATRIX_ABS_MIN_FIELD_NUMBER: builtins.int OBJECTIVE_MATRIX_ABS_AVG_FIELD_NUMBER: builtins.int OBJECTIVE_MATRIX_L2_NORM_FIELD_NUMBER: builtins.int num_variables: builtins.int num_constraints: builtins.int constraint_matrix_col_min_l_inf_norm: builtins.float """Minimum row and column infinity norms of the constraint matrix. All-zero rows and columns are excluded. If the constraint matrix contains no nonzero entries, the values returned are 0.0. """ constraint_matrix_row_min_l_inf_norm: builtins.float constraint_matrix_num_nonzeros: builtins.int """The number of (finite) nonzero entries in the constraint matrix.""" constraint_matrix_abs_max: builtins.float """Max/min/mean/l2_norm of absolute values of (finite) elements in constraint matrix. Explicit zeros are included in the mean, but excluded from the min. Note that the maximum absolute value is also equal to the maximal row and column infinity norms of the constraint matrix. If the constraint matrix is empty, the values returned are 0.0 for the maximum, minimum, and l2_norm, and NaN for the average. """ constraint_matrix_abs_min: builtins.float constraint_matrix_abs_avg: builtins.float constraint_matrix_l2_norm: builtins.float combined_bounds_max: builtins.float """Statistics of the combined vector of the constraint lower and upper bounds. Given parallel lower and upper bounds vectors, the "combined bounds" vector takes the maximum absolute value of each pair of bounds, ignoring all non- finite values. The comment in solvers.proto:TerminationCriteria provides an example of the combined bounds vector. The min is over the nonzero combined bounds. If there are no constraints, the values returned are 0 for the maximum, minimum, and l2 norm and NaN for the average. """ combined_bounds_min: builtins.float combined_bounds_avg: builtins.float combined_bounds_l2_norm: builtins.float combined_variable_bounds_max: builtins.float """Statistics of the combined vector of the variable lower and upper bounds. See the comment before `combined_bounds_max` for a description of the "combined bounds" vector. The min is over the nonzero combined bounds. If there are no variables, the values returned are 0 for the maximum, minimum, and l2 norm and NaN for the average. """ combined_variable_bounds_min: builtins.float combined_variable_bounds_avg: builtins.float combined_variable_bounds_l2_norm: builtins.float variable_bound_gaps_num_finite: builtins.int """Number of finite variable bound gaps, which are the elementwise difference between the upper and lower bounds on primal feasible solutions. """ variable_bound_gaps_max: builtins.float """Max/min/mean/l2_norm over all finite variable bound gaps. The min excludes zero bound gaps (i.e., fixed variables). When there are no finite gaps, the values returned are 0 for the maximum, minimum, and l2_norm, and NaN for the average. """ variable_bound_gaps_min: builtins.float variable_bound_gaps_avg: builtins.float variable_bound_gaps_l2_norm: builtins.float objective_vector_abs_max: builtins.float """Statistics of the objective vector. The min is over the nonzero terms.""" objective_vector_abs_min: builtins.float objective_vector_abs_avg: builtins.float objective_vector_l2_norm: builtins.float objective_matrix_num_nonzeros: builtins.int objective_matrix_abs_max: builtins.float """Max/min/mean/l2_norm of absolute values of elements of the objective matrix. The min is over nonzero terms. If the objective matrix is empty, the returned values are 0.0, 0.0, NaN, and 0.0 respectively. """ objective_matrix_abs_min: builtins.float objective_matrix_abs_avg: builtins.float objective_matrix_l2_norm: builtins.float def __init__( self, *, num_variables: builtins.int | None = ..., num_constraints: builtins.int | None = ..., constraint_matrix_col_min_l_inf_norm: builtins.float | None = ..., constraint_matrix_row_min_l_inf_norm: builtins.float | None = ..., constraint_matrix_num_nonzeros: builtins.int | None = ..., constraint_matrix_abs_max: builtins.float | None = ..., constraint_matrix_abs_min: builtins.float | None = ..., constraint_matrix_abs_avg: builtins.float | None = ..., constraint_matrix_l2_norm: builtins.float | None = ..., combined_bounds_max: builtins.float | None = ..., combined_bounds_min: builtins.float | None = ..., combined_bounds_avg: builtins.float | None = ..., combined_bounds_l2_norm: builtins.float | None = ..., combined_variable_bounds_max: builtins.float | None = ..., combined_variable_bounds_min: builtins.float | None = ..., combined_variable_bounds_avg: builtins.float | None = ..., combined_variable_bounds_l2_norm: builtins.float | None = ..., variable_bound_gaps_num_finite: builtins.int | None = ..., variable_bound_gaps_max: builtins.float | None = ..., variable_bound_gaps_min: builtins.float | None = ..., variable_bound_gaps_avg: builtins.float | None = ..., variable_bound_gaps_l2_norm: builtins.float | None = ..., objective_vector_abs_max: builtins.float | None = ..., objective_vector_abs_min: builtins.float | None = ..., objective_vector_abs_avg: builtins.float | None = ..., objective_vector_l2_norm: builtins.float | None = ..., objective_matrix_num_nonzeros: builtins.int | None = ..., objective_matrix_abs_max: builtins.float | None = ..., objective_matrix_abs_min: builtins.float | None = ..., objective_matrix_abs_avg: builtins.float | None = ..., objective_matrix_l2_norm: builtins.float | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["combined_bounds_avg", b"combined_bounds_avg", "combined_bounds_l2_norm", b"combined_bounds_l2_norm", "combined_bounds_max", b"combined_bounds_max", "combined_bounds_min", b"combined_bounds_min", "combined_variable_bounds_avg", b"combined_variable_bounds_avg", "combined_variable_bounds_l2_norm", b"combined_variable_bounds_l2_norm", "combined_variable_bounds_max", b"combined_variable_bounds_max", "combined_variable_bounds_min", b"combined_variable_bounds_min", "constraint_matrix_abs_avg", b"constraint_matrix_abs_avg", "constraint_matrix_abs_max", b"constraint_matrix_abs_max", "constraint_matrix_abs_min", b"constraint_matrix_abs_min", "constraint_matrix_col_min_l_inf_norm", b"constraint_matrix_col_min_l_inf_norm", "constraint_matrix_l2_norm", b"constraint_matrix_l2_norm", "constraint_matrix_num_nonzeros", b"constraint_matrix_num_nonzeros", "constraint_matrix_row_min_l_inf_norm", b"constraint_matrix_row_min_l_inf_norm", "num_constraints", b"num_constraints", "num_variables", b"num_variables", "objective_matrix_abs_avg", b"objective_matrix_abs_avg", "objective_matrix_abs_max", b"objective_matrix_abs_max", "objective_matrix_abs_min", b"objective_matrix_abs_min", "objective_matrix_l2_norm", b"objective_matrix_l2_norm", "objective_matrix_num_nonzeros", b"objective_matrix_num_nonzeros", "objective_vector_abs_avg", b"objective_vector_abs_avg", "objective_vector_abs_max", b"objective_vector_abs_max", "objective_vector_abs_min", b"objective_vector_abs_min", "objective_vector_l2_norm", b"objective_vector_l2_norm", "variable_bound_gaps_avg", b"variable_bound_gaps_avg", "variable_bound_gaps_l2_norm", b"variable_bound_gaps_l2_norm", "variable_bound_gaps_max", b"variable_bound_gaps_max", "variable_bound_gaps_min", b"variable_bound_gaps_min", "variable_bound_gaps_num_finite", b"variable_bound_gaps_num_finite"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["combined_bounds_avg", b"combined_bounds_avg", "combined_bounds_l2_norm", b"combined_bounds_l2_norm", "combined_bounds_max", b"combined_bounds_max", "combined_bounds_min", b"combined_bounds_min", "combined_variable_bounds_avg", b"combined_variable_bounds_avg", "combined_variable_bounds_l2_norm", b"combined_variable_bounds_l2_norm", "combined_variable_bounds_max", b"combined_variable_bounds_max", "combined_variable_bounds_min", b"combined_variable_bounds_min", "constraint_matrix_abs_avg", b"constraint_matrix_abs_avg", "constraint_matrix_abs_max", b"constraint_matrix_abs_max", "constraint_matrix_abs_min", b"constraint_matrix_abs_min", "constraint_matrix_col_min_l_inf_norm", b"constraint_matrix_col_min_l_inf_norm", "constraint_matrix_l2_norm", b"constraint_matrix_l2_norm", "constraint_matrix_num_nonzeros", b"constraint_matrix_num_nonzeros", "constraint_matrix_row_min_l_inf_norm", b"constraint_matrix_row_min_l_inf_norm", "num_constraints", b"num_constraints", "num_variables", b"num_variables", "objective_matrix_abs_avg", b"objective_matrix_abs_avg", "objective_matrix_abs_max", b"objective_matrix_abs_max", "objective_matrix_abs_min", b"objective_matrix_abs_min", "objective_matrix_l2_norm", b"objective_matrix_l2_norm", "objective_matrix_num_nonzeros", b"objective_matrix_num_nonzeros", "objective_vector_abs_avg", b"objective_vector_abs_avg", "objective_vector_abs_max", b"objective_vector_abs_max", "objective_vector_abs_min", b"objective_vector_abs_min", "objective_vector_l2_norm", b"objective_vector_l2_norm", "variable_bound_gaps_avg", b"variable_bound_gaps_avg", "variable_bound_gaps_l2_norm", b"variable_bound_gaps_l2_norm", "variable_bound_gaps_max", b"variable_bound_gaps_max", "variable_bound_gaps_min", b"variable_bound_gaps_min", "variable_bound_gaps_num_finite", b"variable_bound_gaps_num_finite"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___QuadraticProgramStats: typing_extensions.TypeAlias = QuadraticProgramStats @typing.final class ConvergenceInformation(google.protobuf.message.Message): """Information measuring how close a candidate is to establishing feasibility and optimality; see also TerminationCriteria. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor CANDIDATE_TYPE_FIELD_NUMBER: builtins.int PRIMAL_OBJECTIVE_FIELD_NUMBER: builtins.int DUAL_OBJECTIVE_FIELD_NUMBER: builtins.int CORRECTED_DUAL_OBJECTIVE_FIELD_NUMBER: builtins.int L_INF_PRIMAL_RESIDUAL_FIELD_NUMBER: builtins.int L2_PRIMAL_RESIDUAL_FIELD_NUMBER: builtins.int L_INF_COMPONENTWISE_PRIMAL_RESIDUAL_FIELD_NUMBER: builtins.int L_INF_DUAL_RESIDUAL_FIELD_NUMBER: builtins.int L2_DUAL_RESIDUAL_FIELD_NUMBER: builtins.int L_INF_COMPONENTWISE_DUAL_RESIDUAL_FIELD_NUMBER: builtins.int L_INF_PRIMAL_VARIABLE_FIELD_NUMBER: builtins.int L2_PRIMAL_VARIABLE_FIELD_NUMBER: builtins.int L_INF_DUAL_VARIABLE_FIELD_NUMBER: builtins.int L2_DUAL_VARIABLE_FIELD_NUMBER: builtins.int candidate_type: Global___PointType.ValueType """Type of the candidate point described by this ConvergenceInformation.""" primal_objective: builtins.float """The primal objective. The primal need not be feasible.""" dual_objective: builtins.float """The dual objective. The dual need not be feasible. The dual objective includes the contributions from reduced costs. NOTE: The definition of dual_objective changed in OR-tools version 9.8. See https://developers.google.com/optimization/lp/pdlp_math#reduced_costs_dual_residuals_and_the_corrected_dual_objective for details. """ corrected_dual_objective: builtins.float """If possible (e.g., when all primal variables have lower and upper bounds), a correct dual bound. The value is negative infinity if no corrected dual bound is available. """ l_inf_primal_residual: builtins.float """The maximum violation of any primal constraint, i.e., the l_∞ norm of the violations. """ l2_primal_residual: builtins.float """The l_2 norm of the violations of primal constraints.""" l_inf_componentwise_primal_residual: builtins.float """The maximum relative violation of any primal constraint, with an absolute offset, i.e., the l_∞ norm of [violation / (eps_ratio + |bound|)] where eps_ratio = eps_optimal_primal_residual_absolute / eps_optimal_primal_residual_relative and bound is the violated bound. """ l_inf_dual_residual: builtins.float """The maximum violation of any dual constraint, i.e., the l_∞ norm of the violations. """ l2_dual_residual: builtins.float """The l_2 norm of the violations of dual constraints.""" l_inf_componentwise_dual_residual: builtins.float """The maximum relative violation of any dual constraint, with an absolute offset, i.e., the l_∞ norm of [violation / (eps_ratio + |objective|)] where eps_ratio = eps_optimal_dual_residual_absolute / eps_optimal_dual_residual_relative """ l_inf_primal_variable: builtins.float """The maximum absolute value of the primal variables, i.e., the l_∞ norm. This is useful to detect when the primal iterates are diverging. Divergence of the primal variables could be an algorithmic issue, or indicate that the dual is infeasible. """ l2_primal_variable: builtins.float """The l_2 norm of the primal variables.""" l_inf_dual_variable: builtins.float """The maximum absolute value of the dual variables, i.e., the l_∞ norm. This is useful to detect when the dual iterates are diverging. Divergence of the dual variables could be an algorithmic issue, or indicate the primal is infeasible. """ l2_dual_variable: builtins.float """The l_2 norm of the dual variables.""" def __init__( self, *, candidate_type: Global___PointType.ValueType | None = ..., primal_objective: builtins.float | None = ..., dual_objective: builtins.float | None = ..., corrected_dual_objective: builtins.float | None = ..., l_inf_primal_residual: builtins.float | None = ..., l2_primal_residual: builtins.float | None = ..., l_inf_componentwise_primal_residual: builtins.float | None = ..., l_inf_dual_residual: builtins.float | None = ..., l2_dual_residual: builtins.float | None = ..., l_inf_componentwise_dual_residual: builtins.float | None = ..., l_inf_primal_variable: builtins.float | None = ..., l2_primal_variable: builtins.float | None = ..., l_inf_dual_variable: builtins.float | None = ..., l2_dual_variable: builtins.float | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["candidate_type", b"candidate_type", "corrected_dual_objective", b"corrected_dual_objective", "dual_objective", b"dual_objective", "l2_dual_residual", b"l2_dual_residual", "l2_dual_variable", b"l2_dual_variable", "l2_primal_residual", b"l2_primal_residual", "l2_primal_variable", b"l2_primal_variable", "l_inf_componentwise_dual_residual", b"l_inf_componentwise_dual_residual", "l_inf_componentwise_primal_residual", b"l_inf_componentwise_primal_residual", "l_inf_dual_residual", b"l_inf_dual_residual", "l_inf_dual_variable", b"l_inf_dual_variable", "l_inf_primal_residual", b"l_inf_primal_residual", "l_inf_primal_variable", b"l_inf_primal_variable", "primal_objective", b"primal_objective"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["candidate_type", b"candidate_type", "corrected_dual_objective", b"corrected_dual_objective", "dual_objective", b"dual_objective", "l2_dual_residual", b"l2_dual_residual", "l2_dual_variable", b"l2_dual_variable", "l2_primal_residual", b"l2_primal_residual", "l2_primal_variable", b"l2_primal_variable", "l_inf_componentwise_dual_residual", b"l_inf_componentwise_dual_residual", "l_inf_componentwise_primal_residual", b"l_inf_componentwise_primal_residual", "l_inf_dual_residual", b"l_inf_dual_residual", "l_inf_dual_variable", b"l_inf_dual_variable", "l_inf_primal_residual", b"l_inf_primal_residual", "l_inf_primal_variable", b"l_inf_primal_variable", "primal_objective", b"primal_objective"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___ConvergenceInformation: typing_extensions.TypeAlias = ConvergenceInformation @typing.final class InfeasibilityInformation(google.protobuf.message.Message): """Information measuring how close a point is to establishing primal or dual infeasibility (i.e. has no solution); see also TerminationCriteria. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor MAX_PRIMAL_RAY_INFEASIBILITY_FIELD_NUMBER: builtins.int PRIMAL_RAY_LINEAR_OBJECTIVE_FIELD_NUMBER: builtins.int PRIMAL_RAY_QUADRATIC_NORM_FIELD_NUMBER: builtins.int MAX_DUAL_RAY_INFEASIBILITY_FIELD_NUMBER: builtins.int DUAL_RAY_OBJECTIVE_FIELD_NUMBER: builtins.int CANDIDATE_TYPE_FIELD_NUMBER: builtins.int max_primal_ray_infeasibility: builtins.float """Let x_ray be the algorithm's estimate of the primal extreme ray where x_ray is a vector that satisfies the sign constraints for a ray, scaled such that its infinity norm is one (the sign constraints are the variable bound constraints, with all finite bounds mapped to zero). A simple and typical choice of x_ray is x_ray = x / | x |_∞ where x is the current primal iterate projected onto the primal ray sign constraints. For this value compute the maximum absolute error in the primal linear program with the right hand side set to zero. """ primal_ray_linear_objective: builtins.float """The value of the linear part of the primal objective (ignoring additive constants) evaluated at x_ray, i.e., c' * x_ray where c is the objective coefficient vector. """ primal_ray_quadratic_norm: builtins.float """The l_∞ norm of the vector resulting from taking the quadratic matrix from primal objective and multiplying it by the primal variables. For linear programming problems this is zero. """ max_dual_ray_infeasibility: builtins.float """Let (y_ray, r_ray) be the algorithm's estimate of the dual and reduced cost extreme ray where (y_ray, r_ray) is a vector (satisfying the dual variable constraints) scaled such that its infinity norm is one. A simple and typical choice of y_ray is (y_ray, r_ray) = (y, r) / max(| y |_∞, | r |_∞) where y is the current dual iterate and r is the current dual reduced costs. Consider the quadratic program we are solving but with the objective (both quadratic and linear terms) set to zero. This forms a linear program (label this linear program (1)) with no objective. Take the dual of (1) and compute the maximum absolute value of the constraint error for (y_ray, r_ray) to obtain the value of max_dual_ray_infeasibility. """ dual_ray_objective: builtins.float """The objective of the linear program labeled (1) in the previous paragraph.""" candidate_type: Global___PointType.ValueType """Type of the point used to compute the InfeasibilityInformation.""" def __init__( self, *, max_primal_ray_infeasibility: builtins.float | None = ..., primal_ray_linear_objective: builtins.float | None = ..., primal_ray_quadratic_norm: builtins.float | None = ..., max_dual_ray_infeasibility: builtins.float | None = ..., dual_ray_objective: builtins.float | None = ..., candidate_type: Global___PointType.ValueType | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["candidate_type", b"candidate_type", "dual_ray_objective", b"dual_ray_objective", "max_dual_ray_infeasibility", b"max_dual_ray_infeasibility", "max_primal_ray_infeasibility", b"max_primal_ray_infeasibility", "primal_ray_linear_objective", b"primal_ray_linear_objective", "primal_ray_quadratic_norm", b"primal_ray_quadratic_norm"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["candidate_type", b"candidate_type", "dual_ray_objective", b"dual_ray_objective", "max_dual_ray_infeasibility", b"max_dual_ray_infeasibility", "max_primal_ray_infeasibility", b"max_primal_ray_infeasibility", "primal_ray_linear_objective", b"primal_ray_linear_objective", "primal_ray_quadratic_norm", b"primal_ray_quadratic_norm"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___InfeasibilityInformation: typing_extensions.TypeAlias = InfeasibilityInformation @typing.final class PointMetadata(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor POINT_TYPE_FIELD_NUMBER: builtins.int RANDOM_PRIMAL_PROJECTIONS_FIELD_NUMBER: builtins.int RANDOM_DUAL_PROJECTIONS_FIELD_NUMBER: builtins.int ACTIVE_PRIMAL_VARIABLE_COUNT_FIELD_NUMBER: builtins.int ACTIVE_DUAL_VARIABLE_COUNT_FIELD_NUMBER: builtins.int ACTIVE_PRIMAL_VARIABLE_CHANGE_FIELD_NUMBER: builtins.int ACTIVE_DUAL_VARIABLE_CHANGE_FIELD_NUMBER: builtins.int point_type: Global___PointType.ValueType """Type of the point that this metadata corresponds to.""" active_primal_variable_count: builtins.int """The number of primal variables that are not at their bounds.""" active_dual_variable_count: builtins.int """The number of dual variables that are not at their bounds.""" active_primal_variable_change: builtins.int """The number of primal variables that have a different bound status than they did at the last restart. """ active_dual_variable_change: builtins.int """The number of dual variables that have a different bound status than they did at the last restart. """ @property def random_primal_projections(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: """Projections of the primal solution onto random planes.""" @property def random_dual_projections(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: """Projections of the dual solution onto random planes.""" def __init__( self, *, point_type: Global___PointType.ValueType | None = ..., random_primal_projections: collections.abc.Iterable[builtins.float] | None = ..., random_dual_projections: collections.abc.Iterable[builtins.float] | None = ..., active_primal_variable_count: builtins.int | None = ..., active_dual_variable_count: builtins.int | None = ..., active_primal_variable_change: builtins.int | None = ..., active_dual_variable_change: builtins.int | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["active_dual_variable_change", b"active_dual_variable_change", "active_dual_variable_count", b"active_dual_variable_count", "active_primal_variable_change", b"active_primal_variable_change", "active_primal_variable_count", b"active_primal_variable_count", "point_type", b"point_type"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["active_dual_variable_change", b"active_dual_variable_change", "active_dual_variable_count", b"active_dual_variable_count", "active_primal_variable_change", b"active_primal_variable_change", "active_primal_variable_count", b"active_primal_variable_count", "point_type", b"point_type", "random_dual_projections", b"random_dual_projections", "random_primal_projections", b"random_primal_projections"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___PointMetadata: typing_extensions.TypeAlias = PointMetadata @typing.final class IterationStats(google.protobuf.message.Message): """All values in IterationStats assume that the primal quadratic program is a minimization problem and the dual is a maximization problem. Problems should be transformed to this form if they are not already in this form. The dual vector is defined to be the vector of multipliers on the linear constraints, that is, excluding dual multipliers on variable bounds (reduced costs). """ DESCRIPTOR: google.protobuf.descriptor.Descriptor ITERATION_NUMBER_FIELD_NUMBER: builtins.int CONVERGENCE_INFORMATION_FIELD_NUMBER: builtins.int INFEASIBILITY_INFORMATION_FIELD_NUMBER: builtins.int POINT_METADATA_FIELD_NUMBER: builtins.int CUMULATIVE_KKT_MATRIX_PASSES_FIELD_NUMBER: builtins.int CUMULATIVE_REJECTED_STEPS_FIELD_NUMBER: builtins.int CUMULATIVE_TIME_SEC_FIELD_NUMBER: builtins.int RESTART_USED_FIELD_NUMBER: builtins.int STEP_SIZE_FIELD_NUMBER: builtins.int PRIMAL_WEIGHT_FIELD_NUMBER: builtins.int iteration_number: builtins.int """The iteration number at which these stats were recorded. By convention, iteration counts start at 1, and the stats correspond to the solution *after* the iteration. Therefore stats from iteration 0 are the stats at the starting point. """ cumulative_kkt_matrix_passes: builtins.float """The cumulative number of passes through the KKT matrix since the start of the solve. One pass is a multply by the constraint matrix, its transpose and the matrix that defines the quadratic part of the objective. For example, each iteration of mirror saddle prox contributes 2.0 to this sum. This is a float because it can include fractional passes through the data. For example, in an active set method we may only use a submatrix with 20% of the nonzeros of the KKT matrix at each iteration in which case 0.2 would be added to the total. """ cumulative_rejected_steps: builtins.int """The total number of rejected steps (e.g., within a line search procedure) since the start of the solve. """ cumulative_time_sec: builtins.float """The amount of time passed since we started solving the problem (see solver log `solve_time_sec` which records total time). """ restart_used: Global___RestartChoice.ValueType """The kind of restart that occurred at this iteration, or NO_RESTART if a restart did not occur. """ step_size: builtins.float """Step size used at this iteration. Note that the step size used for the primal update is step_size / primal_weight, while the one used for the dual update is step_size * primal_weight. """ primal_weight: builtins.float """Primal weight controlling the relation between primal and dual step sizes. See field 'step_size' for a detailed description. """ @property def convergence_information(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___ConvergenceInformation]: """A set of statistics measuring how close a point is to establishing primal and dual feasibility and optimality. This field is repeated since there might be several different points that are considered. """ @property def infeasibility_information(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___InfeasibilityInformation]: """A set of statistics measuring how close a point is to establishing primal or dual infeasibility (i.e., has no solution). This field is repeated since there might be several different points that could establish infeasibility. """ @property def point_metadata(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___PointMetadata]: """Auxiliary statistics for each type of point.""" def __init__( self, *, iteration_number: builtins.int | None = ..., convergence_information: collections.abc.Iterable[Global___ConvergenceInformation] | None = ..., infeasibility_information: collections.abc.Iterable[Global___InfeasibilityInformation] | None = ..., point_metadata: collections.abc.Iterable[Global___PointMetadata] | None = ..., cumulative_kkt_matrix_passes: builtins.float | None = ..., cumulative_rejected_steps: builtins.int | None = ..., cumulative_time_sec: builtins.float | None = ..., restart_used: Global___RestartChoice.ValueType | None = ..., step_size: builtins.float | None = ..., primal_weight: builtins.float | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["cumulative_kkt_matrix_passes", b"cumulative_kkt_matrix_passes", "cumulative_rejected_steps", b"cumulative_rejected_steps", "cumulative_time_sec", b"cumulative_time_sec", "iteration_number", b"iteration_number", "primal_weight", b"primal_weight", "restart_used", b"restart_used", "step_size", b"step_size"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["convergence_information", b"convergence_information", "cumulative_kkt_matrix_passes", b"cumulative_kkt_matrix_passes", "cumulative_rejected_steps", b"cumulative_rejected_steps", "cumulative_time_sec", b"cumulative_time_sec", "infeasibility_information", b"infeasibility_information", "iteration_number", b"iteration_number", "point_metadata", b"point_metadata", "primal_weight", b"primal_weight", "restart_used", b"restart_used", "step_size", b"step_size"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___IterationStats: typing_extensions.TypeAlias = IterationStats @typing.final class FeasibilityPolishingDetails(google.protobuf.message.Message): """Details about one primal feasibility or dual feasibility polishing phase within a solve with `use_feasibility_polishing`. See `SolveLog` for descriptions of the fields with the same name. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor POLISHING_PHASE_TYPE_FIELD_NUMBER: builtins.int MAIN_ITERATION_COUNT_FIELD_NUMBER: builtins.int PARAMS_FIELD_NUMBER: builtins.int TERMINATION_REASON_FIELD_NUMBER: builtins.int ITERATION_COUNT_FIELD_NUMBER: builtins.int SOLVE_TIME_SEC_FIELD_NUMBER: builtins.int SOLUTION_STATS_FIELD_NUMBER: builtins.int SOLUTION_TYPE_FIELD_NUMBER: builtins.int ITERATION_STATS_FIELD_NUMBER: builtins.int polishing_phase_type: Global___PolishingPhaseType.ValueType main_iteration_count: builtins.int """The iteration count for the main iteration when this feasibility polishing phase was triggered. """ termination_reason: Global___TerminationReason.ValueType iteration_count: builtins.int solve_time_sec: builtins.float solution_type: Global___PointType.ValueType @property def params(self) -> ortools.pdlp.solvers_pb2.PrimalDualHybridGradientParams: ... @property def solution_stats(self) -> Global___IterationStats: ... @property def iteration_stats(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___IterationStats]: ... def __init__( self, *, polishing_phase_type: Global___PolishingPhaseType.ValueType | None = ..., main_iteration_count: builtins.int | None = ..., params: ortools.pdlp.solvers_pb2.PrimalDualHybridGradientParams | None = ..., termination_reason: Global___TerminationReason.ValueType | None = ..., iteration_count: builtins.int | None = ..., solve_time_sec: builtins.float | None = ..., solution_stats: Global___IterationStats | None = ..., solution_type: Global___PointType.ValueType | None = ..., iteration_stats: collections.abc.Iterable[Global___IterationStats] | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["iteration_count", b"iteration_count", "main_iteration_count", b"main_iteration_count", "params", b"params", "polishing_phase_type", b"polishing_phase_type", "solution_stats", b"solution_stats", "solution_type", b"solution_type", "solve_time_sec", b"solve_time_sec", "termination_reason", b"termination_reason"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["iteration_count", b"iteration_count", "iteration_stats", b"iteration_stats", "main_iteration_count", b"main_iteration_count", "params", b"params", "polishing_phase_type", b"polishing_phase_type", "solution_stats", b"solution_stats", "solution_type", b"solution_type", "solve_time_sec", b"solve_time_sec", "termination_reason", b"termination_reason"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___FeasibilityPolishingDetails: typing_extensions.TypeAlias = FeasibilityPolishingDetails @typing.final class SolveLog(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor INSTANCE_NAME_FIELD_NUMBER: builtins.int PARAMS_FIELD_NUMBER: builtins.int TERMINATION_REASON_FIELD_NUMBER: builtins.int TERMINATION_STRING_FIELD_NUMBER: builtins.int ITERATION_COUNT_FIELD_NUMBER: builtins.int PREPROCESSING_TIME_SEC_FIELD_NUMBER: builtins.int SOLVE_TIME_SEC_FIELD_NUMBER: builtins.int SOLUTION_STATS_FIELD_NUMBER: builtins.int SOLUTION_TYPE_FIELD_NUMBER: builtins.int ITERATION_STATS_FIELD_NUMBER: builtins.int ORIGINAL_PROBLEM_STATS_FIELD_NUMBER: builtins.int PREPROCESSED_PROBLEM_STATS_FIELD_NUMBER: builtins.int FEASIBILITY_POLISHING_DETAILS_FIELD_NUMBER: builtins.int instance_name: builtins.str """The name of the optimization problem.""" termination_reason: Global___TerminationReason.ValueType """The reason that the solve terminated.""" termination_string: builtins.str """Optional extra information about the termination reason.""" iteration_count: builtins.int """The total number of iterations during the solve. For a solve with `use_feasibility_polishing` this count includes the iterations from the feasibility polishing phases. """ preprocessing_time_sec: builtins.float """Time for preprocessing (everything before iteration 0). This is also included in `solve_time_sec`. """ solve_time_sec: builtins.float """The runtime of the solve. Note: This should not be used for comparing methods unless care is taken to control for noise in runtime measurement. For a solve with `use_feasibility_polishing` this count includes the iterations from the feasibility polishing phases. """ solution_type: Global___PointType.ValueType """The type of the output point that the solver returned. The quality of the point is reported in the corresponding entry of solution_stats.convergence_information and/or solution_stats.infeasibility_information. If termination_reason is TERMINATION_REASON_OPTIMAL, it's guaranteed that the corresponding entry of solution_stats.convergence_information satisfies the optimality conditions. Similarly, if termination_reason is either TERMINATION_REASON_PRIMAL_INFEASIBLE or TERMINATION_REASON_DUAL_INFEASIBLE the corresponding entry of solution_stats.infeasibility_information satisifes conditions for declaring primal or dual infeasibility, respectively. If termination_reason is anything else, e.g. TERMINATION_REASON_TIME_LIMIT or TERMINATION_REASON_PRIMAL_OR_DUAL_INFEASIBLE, the solution may not satisfy the optimality or infeasibility conditions. """ @property def params(self) -> ortools.pdlp.solvers_pb2.PrimalDualHybridGradientParams: """If solved with PDLP, the parameters for this solve.""" @property def solution_stats(self) -> Global___IterationStats: """The `IterationStats` for the final iteration of the solver. For a solve with `use_feasibility_polishing`, the work metrics (iteration_count, cumulative_kkt_matrix_passes, etc.) will include the work done in the feasibility polishing phases. NOTE: Regardless of preprocessing (i.e. scaling or presolve) the optimality or infeasibility information is evaluated with respect to the original problem. """ @property def iteration_stats(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___IterationStats]: """A history of iteration stats for the solve. The iteration_number fields should be in increasing order. The frequency at which these stats should be recorded is not specified. This field is "more" optional than the others because it often significantly increases the size of the message, and because the information may not be available for third-party solvers. For a solve with `use_feasibility_polishing`, these iteration stats will only reflect the work done in the main iterations (not the feasibility polishing phases). """ @property def original_problem_stats(self) -> Global___QuadraticProgramStats: """Statistics of the original problem.""" @property def preprocessed_problem_stats(self) -> Global___QuadraticProgramStats: """Statistics of the problem after preprocessing.""" @property def feasibility_polishing_details(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___FeasibilityPolishingDetails]: """If solving with `use_feasibility_polishing`, details about the primal and dual feasibility polishing phases. """ def __init__( self, *, instance_name: builtins.str | None = ..., params: ortools.pdlp.solvers_pb2.PrimalDualHybridGradientParams | None = ..., termination_reason: Global___TerminationReason.ValueType | None = ..., termination_string: builtins.str | None = ..., iteration_count: builtins.int | None = ..., preprocessing_time_sec: builtins.float | None = ..., solve_time_sec: builtins.float | None = ..., solution_stats: Global___IterationStats | None = ..., solution_type: Global___PointType.ValueType | None = ..., iteration_stats: collections.abc.Iterable[Global___IterationStats] | None = ..., original_problem_stats: Global___QuadraticProgramStats | None = ..., preprocessed_problem_stats: Global___QuadraticProgramStats | None = ..., feasibility_polishing_details: collections.abc.Iterable[Global___FeasibilityPolishingDetails] | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["instance_name", b"instance_name", "iteration_count", b"iteration_count", "original_problem_stats", b"original_problem_stats", "params", b"params", "preprocessed_problem_stats", b"preprocessed_problem_stats", "preprocessing_time_sec", b"preprocessing_time_sec", "solution_stats", b"solution_stats", "solution_type", b"solution_type", "solve_time_sec", b"solve_time_sec", "termination_reason", b"termination_reason", "termination_string", b"termination_string"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["feasibility_polishing_details", b"feasibility_polishing_details", "instance_name", b"instance_name", "iteration_count", b"iteration_count", "iteration_stats", b"iteration_stats", "original_problem_stats", b"original_problem_stats", "params", b"params", "preprocessed_problem_stats", b"preprocessed_problem_stats", "preprocessing_time_sec", b"preprocessing_time_sec", "solution_stats", b"solution_stats", "solution_type", b"solution_type", "solve_time_sec", b"solve_time_sec", "termination_reason", b"termination_reason", "termination_string", b"termination_string"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___SolveLog: typing_extensions.TypeAlias = SolveLog