""" @generated by mypy-protobuf. Do not edit manually! isort:skip_file Copyright 2010-2025 Google LLC Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. LINT: LEGACY_NAMES """ 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 sys import typing if sys.version_info >= (3, 10): import typing as typing_extensions else: import typing_extensions DESCRIPTOR: google.protobuf.descriptor.FileDescriptor @typing.final class SatParameters(google.protobuf.message.Message): """Contains the definitions for all the sat algorithm parameters and their default values. NEXT TAG: 356 """ DESCRIPTOR: google.protobuf.descriptor.Descriptor class _VariableOrder: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _VariableOrderEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._VariableOrder.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor IN_ORDER: SatParameters._VariableOrder.ValueType # 0 """As specified by the problem.""" IN_REVERSE_ORDER: SatParameters._VariableOrder.ValueType # 1 IN_RANDOM_ORDER: SatParameters._VariableOrder.ValueType # 2 class VariableOrder(_VariableOrder, metaclass=_VariableOrderEnumTypeWrapper): """========================================================================== Branching and polarity ========================================================================== Variables without activity (i.e. at the beginning of the search) will be tried in this preferred order. """ IN_ORDER: SatParameters.VariableOrder.ValueType # 0 """As specified by the problem.""" IN_REVERSE_ORDER: SatParameters.VariableOrder.ValueType # 1 IN_RANDOM_ORDER: SatParameters.VariableOrder.ValueType # 2 class _Polarity: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _PolarityEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._Polarity.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor POLARITY_TRUE: SatParameters._Polarity.ValueType # 0 POLARITY_FALSE: SatParameters._Polarity.ValueType # 1 POLARITY_RANDOM: SatParameters._Polarity.ValueType # 2 class Polarity(_Polarity, metaclass=_PolarityEnumTypeWrapper): """Specifies the initial polarity (true/false) when the solver branches on a variable. This can be modified later by the user, or the phase saving heuristic. Note(user): POLARITY_FALSE is usually a good choice because of the "natural" way to express a linear boolean problem. """ POLARITY_TRUE: SatParameters.Polarity.ValueType # 0 POLARITY_FALSE: SatParameters.Polarity.ValueType # 1 POLARITY_RANDOM: SatParameters.Polarity.ValueType # 2 class _ConflictMinimizationAlgorithm: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _ConflictMinimizationAlgorithmEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._ConflictMinimizationAlgorithm.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor NONE: SatParameters._ConflictMinimizationAlgorithm.ValueType # 0 SIMPLE: SatParameters._ConflictMinimizationAlgorithm.ValueType # 1 RECURSIVE: SatParameters._ConflictMinimizationAlgorithm.ValueType # 2 class ConflictMinimizationAlgorithm(_ConflictMinimizationAlgorithm, metaclass=_ConflictMinimizationAlgorithmEnumTypeWrapper): """========================================================================== Conflict analysis ========================================================================== Do we try to minimize conflicts (greedily) when creating them. """ NONE: SatParameters.ConflictMinimizationAlgorithm.ValueType # 0 SIMPLE: SatParameters.ConflictMinimizationAlgorithm.ValueType # 1 RECURSIVE: SatParameters.ConflictMinimizationAlgorithm.ValueType # 2 class _BinaryMinizationAlgorithm: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _BinaryMinizationAlgorithmEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._BinaryMinizationAlgorithm.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor NO_BINARY_MINIMIZATION: SatParameters._BinaryMinizationAlgorithm.ValueType # 0 BINARY_MINIMIZATION_FROM_UIP: SatParameters._BinaryMinizationAlgorithm.ValueType # 1 BINARY_MINIMIZATION_FROM_UIP_AND_DECISIONS: SatParameters._BinaryMinizationAlgorithm.ValueType # 5 class BinaryMinizationAlgorithm(_BinaryMinizationAlgorithm, metaclass=_BinaryMinizationAlgorithmEnumTypeWrapper): """Whether to expoit the binary clause to minimize learned clauses further.""" NO_BINARY_MINIMIZATION: SatParameters.BinaryMinizationAlgorithm.ValueType # 0 BINARY_MINIMIZATION_FROM_UIP: SatParameters.BinaryMinizationAlgorithm.ValueType # 1 BINARY_MINIMIZATION_FROM_UIP_AND_DECISIONS: SatParameters.BinaryMinizationAlgorithm.ValueType # 5 class _ClauseOrdering: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _ClauseOrderingEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._ClauseOrdering.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor CLAUSE_ACTIVITY: SatParameters._ClauseOrdering.ValueType # 0 """Order clause by decreasing activity, then by increasing LBD.""" CLAUSE_LBD: SatParameters._ClauseOrdering.ValueType # 1 """Order clause by increasing LBD, then by decreasing activity.""" class ClauseOrdering(_ClauseOrdering, metaclass=_ClauseOrderingEnumTypeWrapper): """The clauses that will be kept during a cleanup are the ones that come first under this order. We always keep or exclude ties together. """ CLAUSE_ACTIVITY: SatParameters.ClauseOrdering.ValueType # 0 """Order clause by decreasing activity, then by increasing LBD.""" CLAUSE_LBD: SatParameters.ClauseOrdering.ValueType # 1 """Order clause by increasing LBD, then by decreasing activity.""" class _RestartAlgorithm: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _RestartAlgorithmEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._RestartAlgorithm.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor NO_RESTART: SatParameters._RestartAlgorithm.ValueType # 0 LUBY_RESTART: SatParameters._RestartAlgorithm.ValueType # 1 """Just follow a Luby sequence times restart_period.""" DL_MOVING_AVERAGE_RESTART: SatParameters._RestartAlgorithm.ValueType # 2 """Moving average restart based on the decision level of conflicts.""" LBD_MOVING_AVERAGE_RESTART: SatParameters._RestartAlgorithm.ValueType # 3 """Moving average restart based on the LBD of conflicts.""" FIXED_RESTART: SatParameters._RestartAlgorithm.ValueType # 4 """Fixed period restart every restart period.""" class RestartAlgorithm(_RestartAlgorithm, metaclass=_RestartAlgorithmEnumTypeWrapper): """========================================================================== Restart ========================================================================== Restart algorithms. A reference for the more advanced ones is: Gilles Audemard, Laurent Simon, "Refining Restarts Strategies for SAT and UNSAT", Principles and Practice of Constraint Programming Lecture Notes in Computer Science 2012, pp 118-126 """ NO_RESTART: SatParameters.RestartAlgorithm.ValueType # 0 LUBY_RESTART: SatParameters.RestartAlgorithm.ValueType # 1 """Just follow a Luby sequence times restart_period.""" DL_MOVING_AVERAGE_RESTART: SatParameters.RestartAlgorithm.ValueType # 2 """Moving average restart based on the decision level of conflicts.""" LBD_MOVING_AVERAGE_RESTART: SatParameters.RestartAlgorithm.ValueType # 3 """Moving average restart based on the LBD of conflicts.""" FIXED_RESTART: SatParameters.RestartAlgorithm.ValueType # 4 """Fixed period restart every restart period.""" class _MaxSatAssumptionOrder: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _MaxSatAssumptionOrderEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._MaxSatAssumptionOrder.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor DEFAULT_ASSUMPTION_ORDER: SatParameters._MaxSatAssumptionOrder.ValueType # 0 ORDER_ASSUMPTION_BY_DEPTH: SatParameters._MaxSatAssumptionOrder.ValueType # 1 ORDER_ASSUMPTION_BY_WEIGHT: SatParameters._MaxSatAssumptionOrder.ValueType # 2 class MaxSatAssumptionOrder(_MaxSatAssumptionOrder, metaclass=_MaxSatAssumptionOrderEnumTypeWrapper): """In what order do we add the assumptions in a core-based max-sat algorithm""" DEFAULT_ASSUMPTION_ORDER: SatParameters.MaxSatAssumptionOrder.ValueType # 0 ORDER_ASSUMPTION_BY_DEPTH: SatParameters.MaxSatAssumptionOrder.ValueType # 1 ORDER_ASSUMPTION_BY_WEIGHT: SatParameters.MaxSatAssumptionOrder.ValueType # 2 class _MaxSatStratificationAlgorithm: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _MaxSatStratificationAlgorithmEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._MaxSatStratificationAlgorithm.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor STRATIFICATION_NONE: SatParameters._MaxSatStratificationAlgorithm.ValueType # 0 """No stratification of the problem.""" STRATIFICATION_DESCENT: SatParameters._MaxSatStratificationAlgorithm.ValueType # 1 """Start with literals with the highest weight, and when SAT, add the literals with the next highest weight and so on. """ STRATIFICATION_ASCENT: SatParameters._MaxSatStratificationAlgorithm.ValueType # 2 """Start with all literals. Each time a core is found with a given minimum weight, do not consider literals with a lower weight for the next core computation. If the subproblem is SAT, do like in STRATIFICATION_DESCENT and just add the literals with the next highest weight. """ class MaxSatStratificationAlgorithm(_MaxSatStratificationAlgorithm, metaclass=_MaxSatStratificationAlgorithmEnumTypeWrapper): """What stratification algorithm we use in the presence of weight.""" STRATIFICATION_NONE: SatParameters.MaxSatStratificationAlgorithm.ValueType # 0 """No stratification of the problem.""" STRATIFICATION_DESCENT: SatParameters.MaxSatStratificationAlgorithm.ValueType # 1 """Start with literals with the highest weight, and when SAT, add the literals with the next highest weight and so on. """ STRATIFICATION_ASCENT: SatParameters.MaxSatStratificationAlgorithm.ValueType # 2 """Start with all literals. Each time a core is found with a given minimum weight, do not consider literals with a lower weight for the next core computation. If the subproblem is SAT, do like in STRATIFICATION_DESCENT and just add the literals with the next highest weight. """ class _SearchBranching: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _SearchBranchingEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._SearchBranching.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor AUTOMATIC_SEARCH: SatParameters._SearchBranching.ValueType # 0 """Try to fix all literals using the underlying SAT solver's heuristics, then generate and fix literals until integer variables are fixed. New literals on integer variables are generated using the fixed search specified by the user or our default one. """ FIXED_SEARCH: SatParameters._SearchBranching.ValueType # 1 """If used then all decisions taken by the solver are made using a fixed order as specified in the API or in the CpModelProto search_strategy field. """ PORTFOLIO_SEARCH: SatParameters._SearchBranching.ValueType # 2 """Simple portfolio search used by LNS workers.""" LP_SEARCH: SatParameters._SearchBranching.ValueType # 3 """If used, the solver will use heuristics from the LP relaxation. This exploit the reduced costs of the variables in the relaxation. """ PSEUDO_COST_SEARCH: SatParameters._SearchBranching.ValueType # 4 """If used, the solver uses the pseudo costs for branching. Pseudo costs are computed using the historical change in objective bounds when some decision are taken. Note that this works whether we use an LP or not. """ PORTFOLIO_WITH_QUICK_RESTART_SEARCH: SatParameters._SearchBranching.ValueType # 5 """Mainly exposed here for testing. This quickly tries a lot of randomized heuristics with a low conflict limit. It usually provides a good first solution. """ HINT_SEARCH: SatParameters._SearchBranching.ValueType # 6 """Mainly used internally. This is like FIXED_SEARCH, except we follow the solution_hint field of the CpModelProto rather than using the information provided in the search_strategy. """ PARTIAL_FIXED_SEARCH: SatParameters._SearchBranching.ValueType # 7 """Similar to FIXED_SEARCH, but differ in how the variable not listed into the fixed search heuristics are branched on. This will always start the search tree according to the specified fixed search strategy, but will complete it using the default automatic search. """ RANDOMIZED_SEARCH: SatParameters._SearchBranching.ValueType # 8 """Randomized search. Used to increase entropy in the search.""" class SearchBranching(_SearchBranching, metaclass=_SearchBranchingEnumTypeWrapper): """The search branching will be used to decide how to branch on unfixed nodes.""" AUTOMATIC_SEARCH: SatParameters.SearchBranching.ValueType # 0 """Try to fix all literals using the underlying SAT solver's heuristics, then generate and fix literals until integer variables are fixed. New literals on integer variables are generated using the fixed search specified by the user or our default one. """ FIXED_SEARCH: SatParameters.SearchBranching.ValueType # 1 """If used then all decisions taken by the solver are made using a fixed order as specified in the API or in the CpModelProto search_strategy field. """ PORTFOLIO_SEARCH: SatParameters.SearchBranching.ValueType # 2 """Simple portfolio search used by LNS workers.""" LP_SEARCH: SatParameters.SearchBranching.ValueType # 3 """If used, the solver will use heuristics from the LP relaxation. This exploit the reduced costs of the variables in the relaxation. """ PSEUDO_COST_SEARCH: SatParameters.SearchBranching.ValueType # 4 """If used, the solver uses the pseudo costs for branching. Pseudo costs are computed using the historical change in objective bounds when some decision are taken. Note that this works whether we use an LP or not. """ PORTFOLIO_WITH_QUICK_RESTART_SEARCH: SatParameters.SearchBranching.ValueType # 5 """Mainly exposed here for testing. This quickly tries a lot of randomized heuristics with a low conflict limit. It usually provides a good first solution. """ HINT_SEARCH: SatParameters.SearchBranching.ValueType # 6 """Mainly used internally. This is like FIXED_SEARCH, except we follow the solution_hint field of the CpModelProto rather than using the information provided in the search_strategy. """ PARTIAL_FIXED_SEARCH: SatParameters.SearchBranching.ValueType # 7 """Similar to FIXED_SEARCH, but differ in how the variable not listed into the fixed search heuristics are branched on. This will always start the search tree according to the specified fixed search strategy, but will complete it using the default automatic search. """ RANDOMIZED_SEARCH: SatParameters.SearchBranching.ValueType # 8 """Randomized search. Used to increase entropy in the search.""" class _SharedTreeSplitStrategy: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _SharedTreeSplitStrategyEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._SharedTreeSplitStrategy.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor SPLIT_STRATEGY_AUTO: SatParameters._SharedTreeSplitStrategy.ValueType # 0 """Uses the default strategy, currently equivalent to SPLIT_STRATEGY_DISCREPANCY. """ SPLIT_STRATEGY_DISCREPANCY: SatParameters._SharedTreeSplitStrategy.ValueType # 1 """Only accept splits if the node to be split's depth+discrepancy is minimal for the desired number of leaves. The preferred child for discrepancy calculation is the one with the lowest objective lower bound or the original branch direction if the bounds are equal. This rule allows twice as many workers to work in the preferred subtree as non-preferred. """ SPLIT_STRATEGY_OBJECTIVE_LB: SatParameters._SharedTreeSplitStrategy.ValueType # 2 """Only split nodes with an objective lb equal to the global lb. If there is no objective, this is equivalent to SPLIT_STRATEGY_FIRST_PROPOSAL. """ SPLIT_STRATEGY_BALANCED_TREE: SatParameters._SharedTreeSplitStrategy.ValueType # 3 """Attempt to keep the shared tree balanced.""" SPLIT_STRATEGY_FIRST_PROPOSAL: SatParameters._SharedTreeSplitStrategy.ValueType # 4 """Workers race to split their subtree, the winner's proposal is accepted.""" class SharedTreeSplitStrategy(_SharedTreeSplitStrategy, metaclass=_SharedTreeSplitStrategyEnumTypeWrapper): ... SPLIT_STRATEGY_AUTO: SatParameters.SharedTreeSplitStrategy.ValueType # 0 """Uses the default strategy, currently equivalent to SPLIT_STRATEGY_DISCREPANCY. """ SPLIT_STRATEGY_DISCREPANCY: SatParameters.SharedTreeSplitStrategy.ValueType # 1 """Only accept splits if the node to be split's depth+discrepancy is minimal for the desired number of leaves. The preferred child for discrepancy calculation is the one with the lowest objective lower bound or the original branch direction if the bounds are equal. This rule allows twice as many workers to work in the preferred subtree as non-preferred. """ SPLIT_STRATEGY_OBJECTIVE_LB: SatParameters.SharedTreeSplitStrategy.ValueType # 2 """Only split nodes with an objective lb equal to the global lb. If there is no objective, this is equivalent to SPLIT_STRATEGY_FIRST_PROPOSAL. """ SPLIT_STRATEGY_BALANCED_TREE: SatParameters.SharedTreeSplitStrategy.ValueType # 3 """Attempt to keep the shared tree balanced.""" SPLIT_STRATEGY_FIRST_PROPOSAL: SatParameters.SharedTreeSplitStrategy.ValueType # 4 """Workers race to split their subtree, the winner's proposal is accepted.""" class _FPRoundingMethod: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _FPRoundingMethodEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[SatParameters._FPRoundingMethod.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor NEAREST_INTEGER: SatParameters._FPRoundingMethod.ValueType # 0 """Rounds to the nearest integer value.""" LOCK_BASED: SatParameters._FPRoundingMethod.ValueType # 1 """Counts the number of linear constraints restricting the variable in the increasing values (up locks) and decreasing values (down locks). Rounds the variable in the direction of lesser locks. """ ACTIVE_LOCK_BASED: SatParameters._FPRoundingMethod.ValueType # 3 """Similar to lock based rounding except this only considers locks of active constraints from the last lp solve. """ PROPAGATION_ASSISTED: SatParameters._FPRoundingMethod.ValueType # 2 """This is expensive rounding algorithm. We round variables one by one and propagate the bounds in between. If none of the rounded values fall in the continuous domain specified by lower and upper bound, we use the current lower/upper bound (whichever one is closest) instead of rounding the fractional lp solution value. If both the rounded values are in the domain, we round to nearest integer. """ class FPRoundingMethod(_FPRoundingMethod, metaclass=_FPRoundingMethodEnumTypeWrapper): """Rounding method to use for feasibility pump.""" NEAREST_INTEGER: SatParameters.FPRoundingMethod.ValueType # 0 """Rounds to the nearest integer value.""" LOCK_BASED: SatParameters.FPRoundingMethod.ValueType # 1 """Counts the number of linear constraints restricting the variable in the increasing values (up locks) and decreasing values (down locks). Rounds the variable in the direction of lesser locks. """ ACTIVE_LOCK_BASED: SatParameters.FPRoundingMethod.ValueType # 3 """Similar to lock based rounding except this only considers locks of active constraints from the last lp solve. """ PROPAGATION_ASSISTED: SatParameters.FPRoundingMethod.ValueType # 2 """This is expensive rounding algorithm. We round variables one by one and propagate the bounds in between. If none of the rounded values fall in the continuous domain specified by lower and upper bound, we use the current lower/upper bound (whichever one is closest) instead of rounding the fractional lp solution value. If both the rounded values are in the domain, we round to nearest integer. """ NAME_FIELD_NUMBER: builtins.int PREFERRED_VARIABLE_ORDER_FIELD_NUMBER: builtins.int INITIAL_POLARITY_FIELD_NUMBER: builtins.int USE_PHASE_SAVING_FIELD_NUMBER: builtins.int POLARITY_REPHASE_INCREMENT_FIELD_NUMBER: builtins.int POLARITY_EXPLOIT_LS_HINTS_FIELD_NUMBER: builtins.int RANDOM_POLARITY_RATIO_FIELD_NUMBER: builtins.int RANDOM_BRANCHES_RATIO_FIELD_NUMBER: builtins.int USE_ERWA_HEURISTIC_FIELD_NUMBER: builtins.int INITIAL_VARIABLES_ACTIVITY_FIELD_NUMBER: builtins.int ALSO_BUMP_VARIABLES_IN_CONFLICT_REASONS_FIELD_NUMBER: builtins.int MINIMIZATION_ALGORITHM_FIELD_NUMBER: builtins.int BINARY_MINIMIZATION_ALGORITHM_FIELD_NUMBER: builtins.int SUBSUMPTION_DURING_CONFLICT_ANALYSIS_FIELD_NUMBER: builtins.int EXTRA_SUBSUMPTION_DURING_CONFLICT_ANALYSIS_FIELD_NUMBER: builtins.int DECISION_SUBSUMPTION_DURING_CONFLICT_ANALYSIS_FIELD_NUMBER: builtins.int EAGERLY_SUBSUME_LAST_N_CONFLICTS_FIELD_NUMBER: builtins.int SUBSUME_DURING_VIVIFICATION_FIELD_NUMBER: builtins.int USE_CHRONOLOGICAL_BACKTRACKING_FIELD_NUMBER: builtins.int MAX_BACKJUMP_LEVELS_FIELD_NUMBER: builtins.int CHRONOLOGICAL_BACKTRACK_MIN_CONFLICTS_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_PERIOD_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_PERIOD_INCREMENT_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_TARGET_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_RATIO_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_LBD_BOUND_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_LBD_TIER1_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_LBD_TIER2_FIELD_NUMBER: builtins.int CLAUSE_CLEANUP_ORDERING_FIELD_NUMBER: builtins.int PB_CLEANUP_INCREMENT_FIELD_NUMBER: builtins.int PB_CLEANUP_RATIO_FIELD_NUMBER: builtins.int VARIABLE_ACTIVITY_DECAY_FIELD_NUMBER: builtins.int MAX_VARIABLE_ACTIVITY_VALUE_FIELD_NUMBER: builtins.int GLUCOSE_MAX_DECAY_FIELD_NUMBER: builtins.int GLUCOSE_DECAY_INCREMENT_FIELD_NUMBER: builtins.int GLUCOSE_DECAY_INCREMENT_PERIOD_FIELD_NUMBER: builtins.int CLAUSE_ACTIVITY_DECAY_FIELD_NUMBER: builtins.int MAX_CLAUSE_ACTIVITY_VALUE_FIELD_NUMBER: builtins.int RESTART_ALGORITHMS_FIELD_NUMBER: builtins.int DEFAULT_RESTART_ALGORITHMS_FIELD_NUMBER: builtins.int RESTART_PERIOD_FIELD_NUMBER: builtins.int RESTART_RUNNING_WINDOW_SIZE_FIELD_NUMBER: builtins.int RESTART_DL_AVERAGE_RATIO_FIELD_NUMBER: builtins.int RESTART_LBD_AVERAGE_RATIO_FIELD_NUMBER: builtins.int USE_BLOCKING_RESTART_FIELD_NUMBER: builtins.int BLOCKING_RESTART_WINDOW_SIZE_FIELD_NUMBER: builtins.int BLOCKING_RESTART_MULTIPLIER_FIELD_NUMBER: builtins.int NUM_CONFLICTS_BEFORE_STRATEGY_CHANGES_FIELD_NUMBER: builtins.int STRATEGY_CHANGE_INCREASE_RATIO_FIELD_NUMBER: builtins.int MAX_TIME_IN_SECONDS_FIELD_NUMBER: builtins.int MAX_DETERMINISTIC_TIME_FIELD_NUMBER: builtins.int MAX_NUM_DETERMINISTIC_BATCHES_FIELD_NUMBER: builtins.int MAX_NUMBER_OF_CONFLICTS_FIELD_NUMBER: builtins.int MAX_MEMORY_IN_MB_FIELD_NUMBER: builtins.int ABSOLUTE_GAP_LIMIT_FIELD_NUMBER: builtins.int RELATIVE_GAP_LIMIT_FIELD_NUMBER: builtins.int RANDOM_SEED_FIELD_NUMBER: builtins.int PERMUTE_VARIABLE_RANDOMLY_FIELD_NUMBER: builtins.int PERMUTE_PRESOLVE_CONSTRAINT_ORDER_FIELD_NUMBER: builtins.int USE_ABSL_RANDOM_FIELD_NUMBER: builtins.int LOG_SEARCH_PROGRESS_FIELD_NUMBER: builtins.int LOG_SUBSOLVER_STATISTICS_FIELD_NUMBER: builtins.int LOG_PREFIX_FIELD_NUMBER: builtins.int LOG_TO_STDOUT_FIELD_NUMBER: builtins.int LOG_TO_RESPONSE_FIELD_NUMBER: builtins.int USE_PB_RESOLUTION_FIELD_NUMBER: builtins.int MINIMIZE_REDUCTION_DURING_PB_RESOLUTION_FIELD_NUMBER: builtins.int COUNT_ASSUMPTION_LEVELS_IN_LBD_FIELD_NUMBER: builtins.int PRESOLVE_BVE_THRESHOLD_FIELD_NUMBER: builtins.int FILTER_SAT_POSTSOLVE_CLAUSES_FIELD_NUMBER: builtins.int PRESOLVE_BVE_CLAUSE_WEIGHT_FIELD_NUMBER: builtins.int PROBING_DETERMINISTIC_TIME_LIMIT_FIELD_NUMBER: builtins.int PRESOLVE_PROBING_DETERMINISTIC_TIME_LIMIT_FIELD_NUMBER: builtins.int PRESOLVE_BLOCKED_CLAUSE_FIELD_NUMBER: builtins.int PRESOLVE_USE_BVA_FIELD_NUMBER: builtins.int PRESOLVE_BVA_THRESHOLD_FIELD_NUMBER: builtins.int MAX_PRESOLVE_ITERATIONS_FIELD_NUMBER: builtins.int CP_MODEL_PRESOLVE_FIELD_NUMBER: builtins.int CP_MODEL_PROBING_LEVEL_FIELD_NUMBER: builtins.int CP_MODEL_USE_SAT_PRESOLVE_FIELD_NUMBER: builtins.int LOAD_AT_MOST_ONES_IN_SAT_PRESOLVE_FIELD_NUMBER: builtins.int REMOVE_FIXED_VARIABLES_EARLY_FIELD_NUMBER: builtins.int DETECT_TABLE_WITH_COST_FIELD_NUMBER: builtins.int TABLE_COMPRESSION_LEVEL_FIELD_NUMBER: builtins.int EXPAND_ALLDIFF_CONSTRAINTS_FIELD_NUMBER: builtins.int MAX_ALLDIFF_DOMAIN_SIZE_FIELD_NUMBER: builtins.int EXPAND_RESERVOIR_CONSTRAINTS_FIELD_NUMBER: builtins.int MAX_DOMAIN_SIZE_FOR_LINEAR2_EXPANSION_FIELD_NUMBER: builtins.int EXPAND_RESERVOIR_USING_CIRCUIT_FIELD_NUMBER: builtins.int ENCODE_CUMULATIVE_AS_RESERVOIR_FIELD_NUMBER: builtins.int MAX_LIN_MAX_SIZE_FOR_EXPANSION_FIELD_NUMBER: builtins.int DISABLE_CONSTRAINT_EXPANSION_FIELD_NUMBER: builtins.int ENCODE_COMPLEX_LINEAR_CONSTRAINT_WITH_INTEGER_FIELD_NUMBER: builtins.int MERGE_NO_OVERLAP_WORK_LIMIT_FIELD_NUMBER: builtins.int MERGE_AT_MOST_ONE_WORK_LIMIT_FIELD_NUMBER: builtins.int PRESOLVE_SUBSTITUTION_LEVEL_FIELD_NUMBER: builtins.int PRESOLVE_EXTRACT_INTEGER_ENFORCEMENT_FIELD_NUMBER: builtins.int PRESOLVE_INCLUSION_WORK_LIMIT_FIELD_NUMBER: builtins.int IGNORE_NAMES_FIELD_NUMBER: builtins.int INFER_ALL_DIFFS_FIELD_NUMBER: builtins.int FIND_BIG_LINEAR_OVERLAP_FIELD_NUMBER: builtins.int FIND_CLAUSES_THAT_ARE_EXACTLY_ONE_FIELD_NUMBER: builtins.int USE_SAT_INPROCESSING_FIELD_NUMBER: builtins.int INPROCESSING_DTIME_RATIO_FIELD_NUMBER: builtins.int INPROCESSING_PROBING_DTIME_FIELD_NUMBER: builtins.int INPROCESSING_MINIMIZATION_DTIME_FIELD_NUMBER: builtins.int INPROCESSING_MINIMIZATION_USE_CONFLICT_ANALYSIS_FIELD_NUMBER: builtins.int INPROCESSING_MINIMIZATION_USE_ALL_ORDERINGS_FIELD_NUMBER: builtins.int INPROCESSING_USE_CONGRUENCE_CLOSURE_FIELD_NUMBER: builtins.int INPROCESSING_USE_SAT_SWEEPING_FIELD_NUMBER: builtins.int NUM_WORKERS_FIELD_NUMBER: builtins.int NUM_SEARCH_WORKERS_FIELD_NUMBER: builtins.int NUM_FULL_SUBSOLVERS_FIELD_NUMBER: builtins.int SUBSOLVERS_FIELD_NUMBER: builtins.int EXTRA_SUBSOLVERS_FIELD_NUMBER: builtins.int IGNORE_SUBSOLVERS_FIELD_NUMBER: builtins.int FILTER_SUBSOLVERS_FIELD_NUMBER: builtins.int SUBSOLVER_PARAMS_FIELD_NUMBER: builtins.int INTERLEAVE_SEARCH_FIELD_NUMBER: builtins.int INTERLEAVE_BATCH_SIZE_FIELD_NUMBER: builtins.int SHARE_OBJECTIVE_BOUNDS_FIELD_NUMBER: builtins.int SHARE_LEVEL_ZERO_BOUNDS_FIELD_NUMBER: builtins.int SHARE_LINEAR2_BOUNDS_FIELD_NUMBER: builtins.int SHARE_BINARY_CLAUSES_FIELD_NUMBER: builtins.int SHARE_GLUE_CLAUSES_FIELD_NUMBER: builtins.int MINIMIZE_SHARED_CLAUSES_FIELD_NUMBER: builtins.int SHARE_GLUE_CLAUSES_DTIME_FIELD_NUMBER: builtins.int CHECK_LRAT_PROOF_FIELD_NUMBER: builtins.int CHECK_MERGED_LRAT_PROOF_FIELD_NUMBER: builtins.int OUTPUT_LRAT_PROOF_FIELD_NUMBER: builtins.int CHECK_DRAT_PROOF_FIELD_NUMBER: builtins.int OUTPUT_DRAT_PROOF_FIELD_NUMBER: builtins.int MAX_DRAT_TIME_IN_SECONDS_FIELD_NUMBER: builtins.int DEBUG_POSTSOLVE_WITH_FULL_SOLVER_FIELD_NUMBER: builtins.int DEBUG_MAX_NUM_PRESOLVE_OPERATIONS_FIELD_NUMBER: builtins.int DEBUG_CRASH_ON_BAD_HINT_FIELD_NUMBER: builtins.int DEBUG_CRASH_IF_PRESOLVE_BREAKS_HINT_FIELD_NUMBER: builtins.int DEBUG_CRASH_IF_LRAT_CHECK_FAILS_FIELD_NUMBER: builtins.int USE_OPTIMIZATION_HINTS_FIELD_NUMBER: builtins.int CORE_MINIMIZATION_LEVEL_FIELD_NUMBER: builtins.int FIND_MULTIPLE_CORES_FIELD_NUMBER: builtins.int COVER_OPTIMIZATION_FIELD_NUMBER: builtins.int MAX_SAT_ASSUMPTION_ORDER_FIELD_NUMBER: builtins.int MAX_SAT_REVERSE_ASSUMPTION_ORDER_FIELD_NUMBER: builtins.int MAX_SAT_STRATIFICATION_FIELD_NUMBER: builtins.int PROPAGATION_LOOP_DETECTION_FACTOR_FIELD_NUMBER: builtins.int USE_PRECEDENCES_IN_DISJUNCTIVE_CONSTRAINT_FIELD_NUMBER: builtins.int TRANSITIVE_PRECEDENCES_WORK_LIMIT_FIELD_NUMBER: builtins.int MAX_SIZE_TO_CREATE_PRECEDENCE_LITERALS_IN_DISJUNCTIVE_FIELD_NUMBER: builtins.int USE_STRONG_PROPAGATION_IN_DISJUNCTIVE_FIELD_NUMBER: builtins.int USE_DYNAMIC_PRECEDENCE_IN_DISJUNCTIVE_FIELD_NUMBER: builtins.int USE_DYNAMIC_PRECEDENCE_IN_CUMULATIVE_FIELD_NUMBER: builtins.int USE_OVERLOAD_CHECKER_IN_CUMULATIVE_FIELD_NUMBER: builtins.int USE_CONSERVATIVE_SCALE_OVERLOAD_CHECKER_FIELD_NUMBER: builtins.int USE_TIMETABLE_EDGE_FINDING_IN_CUMULATIVE_FIELD_NUMBER: builtins.int MAX_NUM_INTERVALS_FOR_TIMETABLE_EDGE_FINDING_FIELD_NUMBER: builtins.int USE_HARD_PRECEDENCES_IN_CUMULATIVE_FIELD_NUMBER: builtins.int EXPLOIT_ALL_PRECEDENCES_FIELD_NUMBER: builtins.int USE_DISJUNCTIVE_CONSTRAINT_IN_CUMULATIVE_FIELD_NUMBER: builtins.int NO_OVERLAP_2D_BOOLEAN_RELATIONS_LIMIT_FIELD_NUMBER: builtins.int USE_TIMETABLING_IN_NO_OVERLAP_2D_FIELD_NUMBER: builtins.int USE_ENERGETIC_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER: builtins.int USE_AREA_ENERGETIC_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER: builtins.int USE_TRY_EDGE_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER: builtins.int MAX_PAIRS_PAIRWISE_REASONING_IN_NO_OVERLAP_2D_FIELD_NUMBER: builtins.int MAXIMUM_REGIONS_TO_SPLIT_IN_DISCONNECTED_NO_OVERLAP_2D_FIELD_NUMBER: builtins.int USE_LINEAR3_FOR_NO_OVERLAP_2D_PRECEDENCES_FIELD_NUMBER: builtins.int USE_DUAL_SCHEDULING_HEURISTICS_FIELD_NUMBER: builtins.int USE_ALL_DIFFERENT_FOR_CIRCUIT_FIELD_NUMBER: builtins.int ROUTING_CUT_SUBSET_SIZE_FOR_BINARY_RELATION_BOUND_FIELD_NUMBER: builtins.int ROUTING_CUT_SUBSET_SIZE_FOR_TIGHT_BINARY_RELATION_BOUND_FIELD_NUMBER: builtins.int ROUTING_CUT_SUBSET_SIZE_FOR_EXACT_BINARY_RELATION_BOUND_FIELD_NUMBER: builtins.int ROUTING_CUT_SUBSET_SIZE_FOR_SHORTEST_PATHS_BOUND_FIELD_NUMBER: builtins.int ROUTING_CUT_DP_EFFORT_FIELD_NUMBER: builtins.int ROUTING_CUT_MAX_INFEASIBLE_PATH_LENGTH_FIELD_NUMBER: builtins.int SEARCH_BRANCHING_FIELD_NUMBER: builtins.int HINT_CONFLICT_LIMIT_FIELD_NUMBER: builtins.int REPAIR_HINT_FIELD_NUMBER: builtins.int FIX_VARIABLES_TO_THEIR_HINTED_VALUE_FIELD_NUMBER: builtins.int USE_PROBING_SEARCH_FIELD_NUMBER: builtins.int USE_EXTENDED_PROBING_FIELD_NUMBER: builtins.int PROBING_NUM_COMBINATIONS_LIMIT_FIELD_NUMBER: builtins.int SHAVING_DETERMINISTIC_TIME_IN_PROBING_SEARCH_FIELD_NUMBER: builtins.int SHAVING_SEARCH_DETERMINISTIC_TIME_FIELD_NUMBER: builtins.int SHAVING_SEARCH_THRESHOLD_FIELD_NUMBER: builtins.int USE_OBJECTIVE_LB_SEARCH_FIELD_NUMBER: builtins.int USE_OBJECTIVE_SHAVING_SEARCH_FIELD_NUMBER: builtins.int VARIABLES_SHAVING_LEVEL_FIELD_NUMBER: builtins.int PSEUDO_COST_RELIABILITY_THRESHOLD_FIELD_NUMBER: builtins.int OPTIMIZE_WITH_CORE_FIELD_NUMBER: builtins.int OPTIMIZE_WITH_LB_TREE_SEARCH_FIELD_NUMBER: builtins.int SAVE_LP_BASIS_IN_LB_TREE_SEARCH_FIELD_NUMBER: builtins.int BINARY_SEARCH_NUM_CONFLICTS_FIELD_NUMBER: builtins.int OPTIMIZE_WITH_MAX_HS_FIELD_NUMBER: builtins.int USE_FEASIBILITY_JUMP_FIELD_NUMBER: builtins.int USE_LS_ONLY_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_DECAY_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_LINEARIZATION_LEVEL_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_RESTART_FACTOR_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_BATCH_DTIME_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_VAR_RANDOMIZATION_PROBABILITY_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_VAR_PERBURBATION_RANGE_RATIO_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_ENABLE_RESTARTS_FIELD_NUMBER: builtins.int FEASIBILITY_JUMP_MAX_EXPANDED_CONSTRAINT_SIZE_FIELD_NUMBER: builtins.int NUM_VIOLATION_LS_FIELD_NUMBER: builtins.int VIOLATION_LS_PERTURBATION_PERIOD_FIELD_NUMBER: builtins.int VIOLATION_LS_COMPOUND_MOVE_PROBABILITY_FIELD_NUMBER: builtins.int SHARED_TREE_NUM_WORKERS_FIELD_NUMBER: builtins.int USE_SHARED_TREE_SEARCH_FIELD_NUMBER: builtins.int SHARED_TREE_WORKER_MIN_RESTARTS_PER_SUBTREE_FIELD_NUMBER: builtins.int SHARED_TREE_WORKER_ENABLE_TRAIL_SHARING_FIELD_NUMBER: builtins.int SHARED_TREE_WORKER_ENABLE_PHASE_SHARING_FIELD_NUMBER: builtins.int SHARED_TREE_OPEN_LEAVES_PER_WORKER_FIELD_NUMBER: builtins.int SHARED_TREE_MAX_NODES_PER_WORKER_FIELD_NUMBER: builtins.int SHARED_TREE_SPLIT_STRATEGY_FIELD_NUMBER: builtins.int SHARED_TREE_BALANCE_TOLERANCE_FIELD_NUMBER: builtins.int SHARED_TREE_SPLIT_MIN_DTIME_FIELD_NUMBER: builtins.int ENUMERATE_ALL_SOLUTIONS_FIELD_NUMBER: builtins.int KEEP_ALL_FEASIBLE_SOLUTIONS_IN_PRESOLVE_FIELD_NUMBER: builtins.int FILL_TIGHTENED_DOMAINS_IN_RESPONSE_FIELD_NUMBER: builtins.int FILL_ADDITIONAL_SOLUTIONS_IN_RESPONSE_FIELD_NUMBER: builtins.int INSTANTIATE_ALL_VARIABLES_FIELD_NUMBER: builtins.int AUTO_DETECT_GREATER_THAN_AT_LEAST_ONE_OF_FIELD_NUMBER: builtins.int STOP_AFTER_FIRST_SOLUTION_FIELD_NUMBER: builtins.int STOP_AFTER_PRESOLVE_FIELD_NUMBER: builtins.int STOP_AFTER_ROOT_PROPAGATION_FIELD_NUMBER: builtins.int LNS_INITIAL_DIFFICULTY_FIELD_NUMBER: builtins.int LNS_INITIAL_DETERMINISTIC_LIMIT_FIELD_NUMBER: builtins.int USE_LNS_FIELD_NUMBER: builtins.int USE_LNS_ONLY_FIELD_NUMBER: builtins.int SOLUTION_POOL_SIZE_FIELD_NUMBER: builtins.int SOLUTION_POOL_DIVERSITY_LIMIT_FIELD_NUMBER: builtins.int ALTERNATIVE_POOL_SIZE_FIELD_NUMBER: builtins.int USE_RINS_LNS_FIELD_NUMBER: builtins.int USE_FEASIBILITY_PUMP_FIELD_NUMBER: builtins.int USE_LB_RELAX_LNS_FIELD_NUMBER: builtins.int LB_RELAX_NUM_WORKERS_THRESHOLD_FIELD_NUMBER: builtins.int FP_ROUNDING_FIELD_NUMBER: builtins.int DIVERSIFY_LNS_PARAMS_FIELD_NUMBER: builtins.int RANDOMIZE_SEARCH_FIELD_NUMBER: builtins.int SEARCH_RANDOM_VARIABLE_POOL_SIZE_FIELD_NUMBER: builtins.int PUSH_ALL_TASKS_TOWARD_START_FIELD_NUMBER: builtins.int USE_OPTIONAL_VARIABLES_FIELD_NUMBER: builtins.int USE_EXACT_LP_REASON_FIELD_NUMBER: builtins.int USE_COMBINED_NO_OVERLAP_FIELD_NUMBER: builtins.int AT_MOST_ONE_MAX_EXPANSION_SIZE_FIELD_NUMBER: builtins.int CATCH_SIGINT_SIGNAL_FIELD_NUMBER: builtins.int USE_IMPLIED_BOUNDS_FIELD_NUMBER: builtins.int POLISH_LP_SOLUTION_FIELD_NUMBER: builtins.int LP_PRIMAL_TOLERANCE_FIELD_NUMBER: builtins.int LP_DUAL_TOLERANCE_FIELD_NUMBER: builtins.int CONVERT_INTERVALS_FIELD_NUMBER: builtins.int SYMMETRY_LEVEL_FIELD_NUMBER: builtins.int USE_SYMMETRY_IN_LP_FIELD_NUMBER: builtins.int KEEP_SYMMETRY_IN_PRESOLVE_FIELD_NUMBER: builtins.int SYMMETRY_DETECTION_DETERMINISTIC_TIME_LIMIT_FIELD_NUMBER: builtins.int NEW_LINEAR_PROPAGATION_FIELD_NUMBER: builtins.int LINEAR_SPLIT_SIZE_FIELD_NUMBER: builtins.int LINEARIZATION_LEVEL_FIELD_NUMBER: builtins.int BOOLEAN_ENCODING_LEVEL_FIELD_NUMBER: builtins.int MAX_DOMAIN_SIZE_WHEN_ENCODING_EQ_NEQ_CONSTRAINTS_FIELD_NUMBER: builtins.int MAX_NUM_CUTS_FIELD_NUMBER: builtins.int CUT_LEVEL_FIELD_NUMBER: builtins.int ONLY_ADD_CUTS_AT_LEVEL_ZERO_FIELD_NUMBER: builtins.int ADD_OBJECTIVE_CUT_FIELD_NUMBER: builtins.int ADD_CG_CUTS_FIELD_NUMBER: builtins.int ADD_MIR_CUTS_FIELD_NUMBER: builtins.int ADD_ZERO_HALF_CUTS_FIELD_NUMBER: builtins.int ADD_CLIQUE_CUTS_FIELD_NUMBER: builtins.int ADD_RLT_CUTS_FIELD_NUMBER: builtins.int MAX_ALL_DIFF_CUT_SIZE_FIELD_NUMBER: builtins.int ADD_LIN_MAX_CUTS_FIELD_NUMBER: builtins.int MAX_INTEGER_ROUNDING_SCALING_FIELD_NUMBER: builtins.int ADD_LP_CONSTRAINTS_LAZILY_FIELD_NUMBER: builtins.int ROOT_LP_ITERATIONS_FIELD_NUMBER: builtins.int MIN_ORTHOGONALITY_FOR_LP_CONSTRAINTS_FIELD_NUMBER: builtins.int MAX_CUT_ROUNDS_AT_LEVEL_ZERO_FIELD_NUMBER: builtins.int MAX_CONSECUTIVE_INACTIVE_COUNT_FIELD_NUMBER: builtins.int CUT_MAX_ACTIVE_COUNT_VALUE_FIELD_NUMBER: builtins.int CUT_ACTIVE_COUNT_DECAY_FIELD_NUMBER: builtins.int CUT_CLEANUP_TARGET_FIELD_NUMBER: builtins.int NEW_CONSTRAINTS_BATCH_SIZE_FIELD_NUMBER: builtins.int EXPLOIT_INTEGER_LP_SOLUTION_FIELD_NUMBER: builtins.int EXPLOIT_ALL_LP_SOLUTION_FIELD_NUMBER: builtins.int EXPLOIT_BEST_SOLUTION_FIELD_NUMBER: builtins.int EXPLOIT_RELAXATION_SOLUTION_FIELD_NUMBER: builtins.int EXPLOIT_OBJECTIVE_FIELD_NUMBER: builtins.int DETECT_LINEARIZED_PRODUCT_FIELD_NUMBER: builtins.int USE_NEW_INTEGER_CONFLICT_RESOLUTION_FIELD_NUMBER: builtins.int CREATE_1UIP_BOOLEAN_DURING_ICR_FIELD_NUMBER: builtins.int MIP_MAX_BOUND_FIELD_NUMBER: builtins.int MIP_VAR_SCALING_FIELD_NUMBER: builtins.int MIP_SCALE_LARGE_DOMAIN_FIELD_NUMBER: builtins.int MIP_AUTOMATICALLY_SCALE_VARIABLES_FIELD_NUMBER: builtins.int ONLY_SOLVE_IP_FIELD_NUMBER: builtins.int MIP_WANTED_PRECISION_FIELD_NUMBER: builtins.int MIP_MAX_ACTIVITY_EXPONENT_FIELD_NUMBER: builtins.int MIP_CHECK_PRECISION_FIELD_NUMBER: builtins.int MIP_COMPUTE_TRUE_OBJECTIVE_BOUND_FIELD_NUMBER: builtins.int MIP_MAX_VALID_MAGNITUDE_FIELD_NUMBER: builtins.int MIP_TREAT_HIGH_MAGNITUDE_BOUNDS_AS_INFINITY_FIELD_NUMBER: builtins.int MIP_DROP_TOLERANCE_FIELD_NUMBER: builtins.int MIP_PRESOLVE_LEVEL_FIELD_NUMBER: builtins.int name: builtins.str """In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose. """ preferred_variable_order: Global___SatParameters.VariableOrder.ValueType initial_polarity: Global___SatParameters.Polarity.ValueType use_phase_saving: builtins.bool """If this is true, then the polarity of a variable will be the last value it was assigned to, or its default polarity if it was never assigned since the call to ResetDecisionHeuristic(). Actually, we use a newer version where we follow the last value in the longest non-conflicting partial assignment in the current phase. This is called 'literal phase saving'. For details see 'A Lightweight Component Caching Scheme for Satisfiability Solvers' K. Pipatsrisawat and A.Darwiche, In 10th International Conference on Theory and Applications of Satisfiability Testing, 2007. """ polarity_rephase_increment: builtins.int """If non-zero, then we change the polarity heuristic after that many number of conflicts in an arithmetically increasing fashion. So x the first time, 2 * x the second time, etc... """ polarity_exploit_ls_hints: builtins.bool """If true and we have first solution LS workers, tries in some phase to follow a LS solutions that violates has litle constraints as possible. """ random_polarity_ratio: builtins.float """The proportion of polarity chosen at random. Note that this take precedence over the phase saving heuristic. This is different from initial_polarity:POLARITY_RANDOM because it will select a new random polarity each time the variable is branched upon instead of selecting one initially and then always taking this choice. """ random_branches_ratio: builtins.float """A number between 0 and 1 that indicates the proportion of branching variables that are selected randomly instead of choosing the first variable from the given variable_ordering strategy. """ use_erwa_heuristic: builtins.bool """Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as described in "Learning Rate Based Branching Heuristic for SAT solvers", J.H.Liang, V. Ganesh, P. Poupart, K.Czarnecki, SAT 2016. """ initial_variables_activity: builtins.float """The initial value of the variables activity. A non-zero value only make sense when use_erwa_heuristic is true. Experiments with a value of 1e-2 together with the ERWA heuristic showed slighthly better result than simply using zero. The idea is that when the "learning rate" of a variable becomes lower than this value, then we prefer to branch on never explored before variables. This is not in the ERWA paper. """ also_bump_variables_in_conflict_reasons: builtins.bool """When this is true, then the variables that appear in any of the reason of the variables in a conflict have their activity bumped. This is addition to the variables in the conflict, and the one that were used during conflict resolution. """ minimization_algorithm: Global___SatParameters.ConflictMinimizationAlgorithm.ValueType binary_minimization_algorithm: Global___SatParameters.BinaryMinizationAlgorithm.ValueType subsumption_during_conflict_analysis: builtins.bool """At a really low cost, during the 1-UIP conflict computation, it is easy to detect if some of the involved reasons are subsumed by the current conflict. When this is true, such clauses are detached and later removed from the problem. """ extra_subsumption_during_conflict_analysis: builtins.bool """It is possible that "intermediate" clauses during conflict resolution subsumes some of the clauses that propagated. This is quite cheap to detect and result in more subsumption/strengthening of clauses. """ decision_subsumption_during_conflict_analysis: builtins.bool """Try even more subsumption options during conflict analysis.""" eagerly_subsume_last_n_conflicts: builtins.int """If >=0, each time we have a conflict, we try to subsume the last n learned clause with it. """ subsume_during_vivification: builtins.bool """If we remove clause that we now are "implied" by others. Note that this might not always be good as we might loose some propagation power. """ use_chronological_backtracking: builtins.bool """If true, try to backtrack as little as possible on conflict and re-imply the clauses later. This means we discard less propagation than traditional backjumping, but requites additional bookkeeping to handle reimplication. See: https://doi.org/10.1007/978-3-319-94144-8_7 """ max_backjump_levels: builtins.int """If chronological backtracking is enabled, this is the maximum number of levels we will backjump over, otherwise we will backtrack. """ chronological_backtrack_min_conflicts: builtins.int """If chronological backtracking is enabled, this is the minimum number of conflicts before we will consider backjumping. """ clause_cleanup_period: builtins.int """========================================================================== Clause database management ========================================================================== Trigger a cleanup when this number of "deletable" clauses is learned. """ clause_cleanup_period_increment: builtins.int """Increase clause_cleanup_period by this amount after each cleanup.""" clause_cleanup_target: builtins.int """During a cleanup, we will always keep that number of "deletable" clauses. Note that this doesn't include the "protected" clauses. """ clause_cleanup_ratio: builtins.float """During a cleanup, if clause_cleanup_target is 0, we will delete the clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed target of clauses to keep. """ clause_cleanup_lbd_bound: builtins.int """All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept. Note that the LBD of a clause that just propagated is 1 + number of different decision levels of its literals. So that the "classic" LBD of a learned conflict is the same as its LBD when we backjump and then propagate it. """ clause_cleanup_lbd_tier1: builtins.int """All the clause with a LBD lower or equal to this will be kept except if its activity hasn't been bumped in the last 32 cleanup phase. Note that this has no effect if it is <= clause_cleanup_lbd_bound. """ clause_cleanup_lbd_tier2: builtins.int """All the clause with a LBD lower or equal to this will be kept except if its activity hasn't been bumped since the previous cleanup phase. Note that this has no effect if it is <= clause_cleanup_lbd_bound or <= clause_cleanup_lbd_tier1. """ clause_cleanup_ordering: Global___SatParameters.ClauseOrdering.ValueType pb_cleanup_increment: builtins.int """Same as for the clauses, but for the learned pseudo-Boolean constraints.""" pb_cleanup_ratio: builtins.float variable_activity_decay: builtins.float """========================================================================== Variable and clause activities ========================================================================== Each time a conflict is found, the activities of some variables are increased by one. Then, the activity of all variables are multiplied by variable_activity_decay. To implement this efficiently, the activity of all the variables is not decayed at each conflict. Instead, the activity increment is multiplied by 1 / decay. When an activity reach max_variable_activity_value, all the activity are multiplied by 1 / max_variable_activity_value. """ max_variable_activity_value: builtins.float glucose_max_decay: builtins.float """The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until 0.95. This "hack" seems to work well and comes from: Glucose 2.3 in the SAT 2013 Competition - SAT Competition 2013 http://edacc4.informatik.uni-ulm.de/SC13/solver-description-download/136 """ glucose_decay_increment: builtins.float glucose_decay_increment_period: builtins.int clause_activity_decay: builtins.float """Clause activity parameters (same effect as the one on the variables).""" max_clause_activity_value: builtins.float default_restart_algorithms: builtins.str restart_period: builtins.int """Restart period for the FIXED_RESTART strategy. This is also the multiplier used by the LUBY_RESTART strategy. """ restart_running_window_size: builtins.int """Size of the window for the moving average restarts.""" restart_dl_average_ratio: builtins.float """In the moving average restart algorithms, a restart is triggered if the window average times this ratio is greater that the global average. """ restart_lbd_average_ratio: builtins.float use_blocking_restart: builtins.bool """Block a moving restart algorithm if the trail size of the current conflict is greater than the multiplier times the moving average of the trail size at the previous conflicts. """ blocking_restart_window_size: builtins.int blocking_restart_multiplier: builtins.float num_conflicts_before_strategy_changes: builtins.int """After each restart, if the number of conflict since the last strategy change is greater that this, then we increment a "strategy_counter" that can be use to change the search strategy used by the following restarts. """ strategy_change_increase_ratio: builtins.float """The parameter num_conflicts_before_strategy_changes is increased by that much after each strategy change. """ max_time_in_seconds: builtins.float """========================================================================== Limits ========================================================================== Maximum time allowed in seconds to solve a problem. The counter will starts at the beginning of the Solve() call. """ max_deterministic_time: builtins.float """Maximum time allowed in deterministic time to solve a problem. The deterministic time should be correlated with the real time used by the solver, the time unit being as close as possible to a second. """ max_num_deterministic_batches: builtins.int """Stops after that number of batches has been scheduled. This only make sense when interleave_search is true. """ max_number_of_conflicts: builtins.int """Maximum number of conflicts allowed to solve a problem. TODO(user): Maybe change the way the conflict limit is enforced? currently it is enforced on each independent internal SAT solve, rather than on the overall number of conflicts across all solves. So in the context of an optimization problem, this is not really usable directly by a client. kint64max """ max_memory_in_mb: builtins.int """Maximum memory allowed for the whole thread containing the solver. The solver will abort as soon as it detects that this limit is crossed. As a result, this limit is approximative, but usually the solver will not go too much over. TODO(user): This is only used by the pure SAT solver, generalize to CP-SAT. """ absolute_gap_limit: builtins.float """Stop the search when the gap between the best feasible objective (O) and our best objective bound (B) is smaller than a limit. The exact definition is: - Absolute: abs(O - B) - Relative: abs(O - B) / max(1, abs(O)). Important: The relative gap depends on the objective offset! If you artificially shift the objective, you will get widely different value of the relative gap. Note that if the gap is reached, the search status will be OPTIMAL. But one can check the best objective bound to see the actual gap. If the objective is integer, then any absolute gap < 1 will lead to a true optimal. If the objective is floating point, a gap of zero make little sense so is is why we use a non-zero default value. At the end of the search, we will display a warning if OPTIMAL is reported yet the gap is greater than this absolute gap. """ relative_gap_limit: builtins.float random_seed: builtins.int """========================================================================== Other parameters ========================================================================== At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed. If you change the random seed, the solver may make different choices during the solving process. For some problems, the running time may vary a lot depending on small change in the solving algorithm. Running the solver with different seeds enables to have more robust benchmarks when evaluating new features. """ permute_variable_randomly: builtins.bool """This is mainly here to test the solver variability. Note that in tests, if not explicitly set to false, all 3 options will be set to true so that clients do not rely on the solver returning a specific solution if they are many equivalent optimal solutions. """ permute_presolve_constraint_order: builtins.bool use_absl_random: builtins.bool log_search_progress: builtins.bool """Whether the solver should log the search progress. This is the maing logging parameter and if this is false, none of the logging (callbacks, log_to_stdout, log_to_response, ...) will do anything. """ log_subsolver_statistics: builtins.bool """Whether the solver should display per sub-solver search statistics. This is only useful is log_search_progress is set to true, and if the number of search workers is > 1. Note that in all case we display a bit of stats with one line per subsolver. """ log_prefix: builtins.str """Add a prefix to all logs.""" log_to_stdout: builtins.bool """Log to stdout.""" log_to_response: builtins.bool """Log to response proto.""" use_pb_resolution: builtins.bool """Experimental. This is an old experiment, it might cause crashes in multi-thread and you should double check the solver result. It can still be used if you only care about feasible solutions (these are checked) and it gives good result on your problem. We might revive it at some point. Whether to use pseudo-Boolean resolution to analyze a conflict. Note that this option only make sense if your problem is modelized using pseudo-Boolean constraints. If you only have clauses, this shouldn't change anything (except slow the solver down). """ minimize_reduction_during_pb_resolution: builtins.bool """A different algorithm during PB resolution. It minimizes the number of calls to ReduceCoefficients() which can be time consuming. However, the search space will be different and if the coefficients are large, this may lead to integer overflows that could otherwise be prevented. """ count_assumption_levels_in_lbd: builtins.bool """Whether or not the assumption levels are taken into account during the LBD computation. According to the reference below, not counting them improves the solver in some situation. Note that this only impact solves under assumptions. Gilles Audemard, Jean-Marie Lagniez, Laurent Simon, "Improving Glucose for Incremental SAT Solving with Assumptions: Application to MUS Extraction" Theory and Applications of Satisfiability Testing - SAT 2013, Lecture Notes in Computer Science Volume 7962, 2013, pp 309-317. """ presolve_bve_threshold: builtins.int """========================================================================== Presolve ========================================================================== During presolve, only try to perform the bounded variable elimination (BVE) of a variable x if the number of occurrences of x times the number of occurrences of not(x) is not greater than this parameter. """ filter_sat_postsolve_clauses: builtins.bool """Internal parameter. During BVE, if we eliminate a variable x, by default we will push all clauses containing x and all clauses containing not(x) to the postsolve. However, it is possible to write the postsolve code so that only one such set is needed. The idea is that, if we push the set containing a literal l, is to set l to false except if it is needed to satisfy one of the clause in the set. This is always beneficial, but for historical reason, not all our postsolve algorithm support this. """ presolve_bve_clause_weight: builtins.int """During presolve, we apply BVE only if this weight times the number of clauses plus the number of clause literals is not increased. """ probing_deterministic_time_limit: builtins.float """The maximum "deterministic" time limit to spend in probing. A value of zero will disable the probing. TODO(user): Clean up. The first one is used in CP-SAT, the other in pure SAT presolve. """ presolve_probing_deterministic_time_limit: builtins.float presolve_blocked_clause: builtins.bool """Whether we use an heuristic to detect some basic case of blocked clause in the SAT presolve. """ presolve_use_bva: builtins.bool """Whether or not we use Bounded Variable Addition (BVA) in the presolve.""" presolve_bva_threshold: builtins.int """Apply Bounded Variable Addition (BVA) if the number of clauses is reduced by stricly more than this threshold. The algorithm described in the paper uses 0, but quick experiments showed that 1 is a good value. It may not be worth it to add a new variable just to remove one clause. """ max_presolve_iterations: builtins.int """In case of large reduction in a presolve iteration, we perform multiple presolve iterations. This parameter controls the maximum number of such presolve iterations. """ cp_model_presolve: builtins.bool """Whether we presolve the cp_model before solving it.""" cp_model_probing_level: builtins.int """How much effort do we spend on probing. 0 disables it completely.""" cp_model_use_sat_presolve: builtins.bool """Whether we also use the sat presolve when cp_model_presolve is true.""" load_at_most_ones_in_sat_presolve: builtins.bool """If we try to load at most ones and exactly ones constraints when running the pure SAT presolve. Or if we just ignore them. If one detects at_most_one via merge_at_most_one_work_limit or exactly one with find_clauses_that_are_exactly_one, it might be good to also set this to true. """ remove_fixed_variables_early: builtins.bool """If cp_model_presolve is true and there is a large proportion of fixed variable after the first model copy, remap all the model to a dense set of variable before the full presolve even starts. This should help for LNS on large models. """ detect_table_with_cost: builtins.bool """If true, we detect variable that are unique to a table constraint and only there to encode a cost on each tuple. This is usually the case when a WCSP (weighted constraint program) is encoded into CP-SAT format. This can lead to a dramatic speed-up for such problems but is still experimental at this point. """ table_compression_level: builtins.int """How much we try to "compress" a table constraint. Compressing more leads to less Booleans and faster propagation but can reduced the quality of the lp relaxation. Values goes from 0 to 3 where we always try to fully compress a table. At 2, we try to automatically decide if it is worth it. """ expand_alldiff_constraints: builtins.bool """If true, expand all_different constraints that are not permutations. Permutations (#Variables = #Values) are always expanded. """ max_alldiff_domain_size: builtins.int """Max domain size for all_different constraints to be expanded.""" expand_reservoir_constraints: builtins.bool """If true, expand the reservoir constraints by creating booleans for all possible precedences between event and encoding the constraint. """ max_domain_size_for_linear2_expansion: builtins.int """Max domain size for expanding linear2 constraints (ax + by ==/!= c).""" expand_reservoir_using_circuit: builtins.bool """Mainly useful for testing. If this and expand_reservoir_constraints is true, we use a different encoding of the reservoir constraint using circuit instead of precedences. Note that this is usually slower, but can exercise different part of the solver. Note that contrary to the precedence encoding, this easily support variable demands. WARNING: with this encoding, the constraint takes a slightly different meaning. There must exist a permutation of the events occurring at the same time such that the level is within the reservoir after each of these events (in this permuted order). So we cannot have +100 and -100 at the same time if the level must be between 0 and 10 (as authorized by the reservoir constraint). """ encode_cumulative_as_reservoir: builtins.bool """Encore cumulative with fixed demands and capacity as a reservoir constraint. The only reason you might want to do that is to test the reservoir propagation code! """ max_lin_max_size_for_expansion: builtins.int """If the number of expressions in the lin_max is less that the max size parameter, model expansion replaces target = max(xi) by linear constraint with the introduction of new booleans bi such that bi => target == xi. This is mainly for experimenting compared to a custom lin_max propagator. """ disable_constraint_expansion: builtins.bool """If true, it disable all constraint expansion. This should only be used to test the presolve of expanded constraints. """ encode_complex_linear_constraint_with_integer: builtins.bool """Linear constraint with a complex right hand side (more than a single interval) need to be expanded, there is a couple of way to do that. """ merge_no_overlap_work_limit: builtins.float """During presolve, we use a maximum clique heuristic to merge together no-overlap constraints or at most one constraints. This code can be slow, so we have a limit in place on the number of explored nodes in the underlying graph. The internal limit is an int64, but we use double here to simplify manual input. """ merge_at_most_one_work_limit: builtins.float presolve_substitution_level: builtins.int """How much substitution (also called free variable aggregation in MIP litterature) should we perform at presolve. This currently only concerns variable appearing only in linear constraints. For now the value 0 turns it off and any positive value performs substitution. """ presolve_extract_integer_enforcement: builtins.bool """If true, we will extract from linear constraints, enforcement literals of the form "integer variable at bound => simplified constraint". This should always be beneficial except that we don't always handle them as efficiently as we could for now. This causes problem on manna81.mps (LP relaxation not as tight it seems) and on neos-3354841-apure.mps.gz (too many literals created this way). """ presolve_inclusion_work_limit: builtins.int """A few presolve operations involve detecting constraints included in other constraint. Since there can be a quadratic number of such pairs, and processing them usually involve scanning them, the complexity of these operations can be big. This enforce a local deterministic limit on the number of entries scanned. Default is 1e8. A value of zero will disable these presolve rules completely. """ ignore_names: builtins.bool """If true, we don't keep names in our internal copy of the user given model.""" infer_all_diffs: builtins.bool """Run a max-clique code amongst all the x != y we can find and try to infer set of variables that are all different. This allows to close neos16.mps for instance. Note that we only run this code if there is no all_diff already in the model so that if a user want to add some all_diff, we assume it is well done and do not try to add more. This will also detect and add no_overlap constraints, if all the relations x != y have "offsets" between them. I.e. x > y + offset. """ find_big_linear_overlap: builtins.bool """Try to find large "rectangle" in the linear constraint matrix with identical lines. If such rectangle is big enough, we can introduce a new integer variable corresponding to the common expression and greatly reduce the number of non-zero. """ find_clauses_that_are_exactly_one: builtins.bool """By propagating (or just using binary clauses), one can detect that all literal of a clause are actually in at most one relationship. Thus this constraint can be promoted to an exactly one constraints. This should help as it convey more structure. Note that this is expensive, so we have a deterministic limit in place. """ use_sat_inprocessing: builtins.bool """========================================================================== Inprocessing ========================================================================== Enable or disable "inprocessing" which is some SAT presolving done at each restart to the root level. """ inprocessing_dtime_ratio: builtins.float """Proportion of deterministic time we should spend on inprocessing. At each "restart", if the proportion is below this ratio, we will do some inprocessing, otherwise, we skip it for this restart. """ inprocessing_probing_dtime: builtins.float """The amount of dtime we should spend on probing for each inprocessing round.""" inprocessing_minimization_dtime: builtins.float """Parameters for an heuristic similar to the one described in "An effective learnt clause minimization approach for CDCL Sat Solvers", https://www.ijcai.org/proceedings/2017/0098.pdf This is the amount of dtime we should spend on this technique during each inprocessing phase. The minimization technique is the same as the one used to minimize core in max-sat. We also minimize problem clauses and not just the learned clause that we keep forever like in the paper. """ inprocessing_minimization_use_conflict_analysis: builtins.bool inprocessing_minimization_use_all_orderings: builtins.bool inprocessing_use_congruence_closure: builtins.bool """Whether we use the algorithm described in "Clausal Congruence closure", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2024. Note that we only have a basic version currently. """ inprocessing_use_sat_sweeping: builtins.bool """Whether we use the SAT sweeping algorithm described in "Clausal Equivalence Sweeping", Armin Biere, Katalin Fazekas, Mathias Fleury, Nils Froleyks, 2025. """ num_workers: builtins.int """========================================================================== Multithread ========================================================================== Specify the number of parallel workers (i.e. threads) to use during search. This should usually be lower than your number of available cpus + hyperthread in your machine. A value of 0 means the solver will try to use all cores on the machine. A number of 1 means no parallelism. Note that 'num_workers' is the preferred name, but if it is set to zero, we will still read the deprecated 'num_search_workers'. As of 2020-04-10, if you're using SAT via MPSolver (to solve integer programs) this field is overridden with a value of 8, if the field is not set *explicitly*. Thus, always set this field explicitly or via MPSolver::SetNumThreads(). """ num_search_workers: builtins.int num_full_subsolvers: builtins.int """We distinguish subsolvers that consume a full thread, and the ones that are always interleaved. If left at zero, we will fix this with a default formula that depends on num_workers. But if you start modifying what runs, you might want to fix that to a given value depending on the num_workers you use. """ interleave_search: builtins.bool """Experimental. If this is true, then we interleave all our major search strategy and distribute the work amongst num_workers. The search is deterministic (independently of num_workers!), and we schedule and wait for interleave_batch_size task to be completed before synchronizing and scheduling the next batch of tasks. """ interleave_batch_size: builtins.int share_objective_bounds: builtins.bool """Allows objective sharing between workers.""" share_level_zero_bounds: builtins.bool """Allows sharing of the bounds of modified variables at level 0.""" share_linear2_bounds: builtins.bool """Allows sharing of the bounds on linear2 discovered at level 0. This is mainly interesting on scheduling type of problems when we branch on precedences. Warning: This currently non-deterministic. """ share_binary_clauses: builtins.bool """Allows sharing of new learned binary clause between workers.""" share_glue_clauses: builtins.bool """Allows sharing of short glue clauses between workers. Implicitly disabled if share_binary_clauses is false. """ minimize_shared_clauses: builtins.bool """Minimize and detect subsumption of shared clauses immediately after they are imported. """ share_glue_clauses_dtime: builtins.float """The amount of dtime between each export of shared glue clauses.""" check_lrat_proof: builtins.bool """========================================================================== Proofs ========================================================================== If true, inferred clauses are checked with an LRAT checker as they are learned, in presolve (reduced to trivial simplifications if cp_model_presolve is false), and in each worker. As of December 2025, this only works with pure SAT problems, with - cp_model_presolve = false, - linearization_level <= 1, - symmetry_level <= 1. """ check_merged_lrat_proof: builtins.bool """If true, and if output_lrat_proof is true and the problem is UNSAT, check that the merged proof file is valid, i.e., that clause sharing between workers is correct. This checks each inferred clause, so you might want to disable check_lrat_proof to avoid redundant work. As of November 2025, this only works for pure SAT problems, with num_workers = 1. """ output_lrat_proof: builtins.bool """If true, an LRAT proof that all the clauses inferred by the solver are valid is output to several files (one for presolve -- reduced to trivial simplifications if cp_model_presolve is false, one per worker, and one for the merged proof). As of December 2025, this only works for pure SAT problems, with - cp_model_presolve = false, - linearization_level <= 1, - symmetry_level <= 1. """ check_drat_proof: builtins.bool """If true, and if the problem is UNSAT, a DRAT proof of this UNSAT property is checked after the solver has finished. As of November 2025, this only works for pure SAT problems, with - num_workers = 1, - cp_model_presolve = false, - linearization_level <= 1, - symmetry_level <= 1. """ output_drat_proof: builtins.bool """If true, a DRAT proof that all the clauses inferred by the solver are valid is output to a file. As of December 2025, this only works for pure SAT problems, with - num_workers = 1, - cp_model_presolve = false, - linearization_level <= 1, - symmetry_level <= 1. """ max_drat_time_in_seconds: builtins.float """The maximum time allowed to check the DRAT proof (this can take more time than the solve itself). Only used if check_drat_proof is true. """ debug_postsolve_with_full_solver: builtins.bool """========================================================================== Debugging parameters ========================================================================== We have two different postsolve code. The default one should be better and it allows for a more powerful presolve, but it can be useful to postsolve using the full solver instead. """ debug_max_num_presolve_operations: builtins.int """If positive, try to stop just after that many presolve rules have been applied. This is mainly useful for debugging presolve. """ debug_crash_on_bad_hint: builtins.bool """Crash if we do not manage to complete the hint into a full solution.""" debug_crash_if_presolve_breaks_hint: builtins.bool """Crash if presolve breaks a feasible hint.""" debug_crash_if_lrat_check_fails: builtins.bool """Crash if the LRAT UNSAT proof is invalid.""" use_optimization_hints: builtins.bool """========================================================================== Max-sat parameters ========================================================================== For an optimization problem, whether we follow some hints in order to find a better first solution. For a variable with hint, the solver will always try to follow the hint. It will revert to the variable_branching default otherwise. """ core_minimization_level: builtins.int """If positive, we spend some effort on each core: - At level 1, we use a simple heuristic to try to minimize an UNSAT core. - At level 2, we use propagation to minimize the core but also identify literal in at most one relationship in this core. """ find_multiple_cores: builtins.bool """Whether we try to find more independent cores for a given set of assumptions in the core based max-SAT algorithms. """ cover_optimization: builtins.bool """If true, when the max-sat algo find a core, we compute the minimal number of literals in the core that needs to be true to have a feasible solution. This is also called core exhaustion in more recent max-SAT papers. """ max_sat_assumption_order: Global___SatParameters.MaxSatAssumptionOrder.ValueType max_sat_reverse_assumption_order: builtins.bool """If true, adds the assumption in the reverse order of the one defined by max_sat_assumption_order. """ max_sat_stratification: Global___SatParameters.MaxSatStratificationAlgorithm.ValueType propagation_loop_detection_factor: builtins.float """========================================================================== Constraint programming parameters ========================================================================== Some search decisions might cause a really large number of propagations to happen when integer variables with large domains are only reduced by 1 at each step. If we propagate more than the number of variable times this parameters we try to take counter-measure. Setting this to 0.0 disable this feature. TODO(user): Setting this to something like 10 helps in most cases, but the code is currently buggy and can cause the solve to enter a bad state where no progress is made. """ use_precedences_in_disjunctive_constraint: builtins.bool """When this is true, then a disjunctive constraint will try to use the precedence relations between time intervals to propagate their bounds further. For instance if task A and B are both before C and task A and B are in disjunction, then we can deduce that task C must start after duration(A) + duration(B) instead of simply max(duration(A), duration(B)), provided that the start time for all task was currently zero. This always result in better propagation, but it is usually slow, so depending on the problem, turning this off may lead to a faster solution. """ transitive_precedences_work_limit: builtins.int """At root level, we might compute the transitive closure of "precedences" relations so that we can exploit that in scheduling problems. Setting this to zero disable the feature. """ max_size_to_create_precedence_literals_in_disjunctive: builtins.int """Create one literal for each disjunction of two pairs of tasks. This slows down the solve time, but improves the lower bound of the objective in the makespan case. This will be triggered if the number of intervals is less or equal than the parameter and if use_strong_propagation_in_disjunctive is true. """ use_strong_propagation_in_disjunctive: builtins.bool """Enable stronger and more expensive propagation on no_overlap constraint.""" use_dynamic_precedence_in_disjunctive: builtins.bool """Whether we try to branch on decision "interval A before interval B" rather than on intervals bounds. This usually works better, but slow down a bit the time to find the first solution. These parameters are still EXPERIMENTAL, the result should be correct, but it some corner cases, they can cause some failing CHECK in the solver. """ use_dynamic_precedence_in_cumulative: builtins.bool use_overload_checker_in_cumulative: builtins.bool """When this is true, the cumulative constraint is reinforced with overload checking, i.e., an additional level of reasoning based on energy. This additional level supplements the default level of reasoning as well as timetable edge finding. This always result in better propagation, but it is usually slow, so depending on the problem, turning this off may lead to a faster solution. """ use_conservative_scale_overload_checker: builtins.bool """Enable a heuristic to solve cumulative constraints using a modified energy constraint. We modify the usual energy definition by applying a super-additive function (also called "conservative scale" or "dual-feasible function") to the demand and the durations of the tasks. This heuristic is fast but for most problems it does not help much to find a solution. """ use_timetable_edge_finding_in_cumulative: builtins.bool """When this is true, the cumulative constraint is reinforced with timetable edge finding, i.e., an additional level of reasoning based on the conjunction of energy and mandatory parts. This additional level supplements the default level of reasoning as well as overload_checker. This always result in better propagation, but it is usually slow, so depending on the problem, turning this off may lead to a faster solution. """ max_num_intervals_for_timetable_edge_finding: builtins.int """Max number of intervals for the timetable_edge_finding algorithm to propagate. A value of 0 disables the constraint. """ use_hard_precedences_in_cumulative: builtins.bool """If true, detect and create constraint for integer variable that are "after" a set of intervals in the same cumulative constraint. Experimental: by default we just use "direct" precedences. If exploit_all_precedences is true, we explore the full precedence graph. This assumes we have a DAG otherwise it fails. """ exploit_all_precedences: builtins.bool use_disjunctive_constraint_in_cumulative: builtins.bool """When this is true, the cumulative constraint is reinforced with propagators from the disjunctive constraint to improve the inference on a set of tasks that are disjunctive at the root of the problem. This additional level supplements the default level of reasoning. Propagators of the cumulative constraint will not be used at all if all the tasks are disjunctive at root node. This always result in better propagation, but it is usually slow, so depending on the problem, turning this off may lead to a faster solution. """ no_overlap_2d_boolean_relations_limit: builtins.int """If less than this number of boxes are present in a no-overlap 2d, we create 4 Booleans per pair of boxes: - Box 2 is after Box 1 on x. - Box 1 is after Box 2 on x. - Box 2 is after Box 1 on y. - Box 1 is after Box 2 on y. Note that at least one of them must be true, and at most one on x and one on y can be true. This can significantly help in closing small problem. The SAT reasoning can be a lot more powerful when we take decision on such positional relations. """ use_timetabling_in_no_overlap_2d: builtins.bool """When this is true, the no_overlap_2d constraint is reinforced with propagators from the cumulative constraints. It consists of ignoring the position of rectangles in one position and projecting the no_overlap_2d on the other dimension to create a cumulative constraint. This is done on both axis. This additional level supplements the default level of reasoning. """ use_energetic_reasoning_in_no_overlap_2d: builtins.bool """When this is true, the no_overlap_2d constraint is reinforced with energetic reasoning. This additional level supplements the default level of reasoning. """ use_area_energetic_reasoning_in_no_overlap_2d: builtins.bool """When this is true, the no_overlap_2d constraint is reinforced with an energetic reasoning that uses an area-based energy. This can be combined with the two other overlap heuristics above. """ use_try_edge_reasoning_in_no_overlap_2d: builtins.bool max_pairs_pairwise_reasoning_in_no_overlap_2d: builtins.int """If the number of pairs to look is below this threshold, do an extra step of propagation in the no_overlap_2d constraint by looking at all pairs of intervals. """ maximum_regions_to_split_in_disconnected_no_overlap_2d: builtins.int """Detects when the space where items of a no_overlap_2d constraint can placed is disjoint (ie., fixed boxes split the domain). When it is the case, we can introduce a boolean for each pair encoding whether the item is in the component or not. Then we replace the original no_overlap_2d constraint by one no_overlap_2d constraint for each component, with the new booleans as the enforcement_literal of the intervals. This is equivalent to expanding the original no_overlap_2d constraint into a bin packing problem with each connected component being a bin. This heuristic is only done when the number of regions to split is less than this parameter and <= 1 disables it. """ use_linear3_for_no_overlap_2d_precedences: builtins.bool """When set, this activates a propagator for the no_overlap_2d constraint that uses any eventual linear constraints of the model in the form `{start interval 1} - {end interval 2} + c*w <= ub` to detect that two intervals must overlap in one dimension for some values of `w`. This is particularly useful for problems where the distance between two boxes is part of the model. """ use_dual_scheduling_heuristics: builtins.bool """When set, it activates a few scheduling parameters to improve the lower bound of scheduling problems. This is only effective with multiple workers as it modifies the reduced_cost, lb_tree_search, and probing workers. """ use_all_different_for_circuit: builtins.bool """Turn on extra propagation for the circuit constraint. This can be quite slow. """ routing_cut_subset_size_for_binary_relation_bound: builtins.int """If the size of a subset of nodes of a RoutesConstraint is less than this value, use linear constraints of size 1 and 2 (such as capacity and time window constraints) enforced by the arc literals to compute cuts for this subset (unless the subset size is less than routing_cut_subset_size_for_tight_binary_relation_bound, in which case the corresponding algorithm is used instead). The algorithm for these cuts has a O(n^3) complexity, where n is the subset size. Hence the value of this parameter should not be too large (e.g. 10 or 20). """ routing_cut_subset_size_for_tight_binary_relation_bound: builtins.int """Similar to above, but with a different algorithm producing better cuts, at the price of a higher O(2^n) complexity, where n is the subset size. Hence the value of this parameter should be small (e.g. less than 10). """ routing_cut_subset_size_for_exact_binary_relation_bound: builtins.int """Similar to above, but with an even stronger algorithm in O(n!). We try to be defensive and abort early or not run that often. Still the value of that parameter shouldn't really be much more than 10. """ routing_cut_subset_size_for_shortest_paths_bound: builtins.int """Similar to routing_cut_subset_size_for_exact_binary_relation_bound but use a bound based on shortest path distances (which respect triangular inequality). This allows to derive bounds that are valid for any superset of a given subset. This is slow, so it shouldn't really be larger than 10. """ routing_cut_dp_effort: builtins.float """The amount of "effort" to spend in dynamic programming for computing routing cuts. This is in term of basic operations needed by the algorithm in the worst case, so a value like 1e8 should take less than a second to compute. """ routing_cut_max_infeasible_path_length: builtins.int """If the length of an infeasible path is less than this value, a cut will be added to exclude it. """ search_branching: Global___SatParameters.SearchBranching.ValueType hint_conflict_limit: builtins.int """Conflict limit used in the phase that exploit the solution hint.""" repair_hint: builtins.bool """If true, the solver tries to repair the solution given in the hint. This search terminates after the 'hint_conflict_limit' is reached and the solver switches to regular search. If false, then we do a FIXED_SEARCH using the hint until the hint_conflict_limit is reached. """ fix_variables_to_their_hinted_value: builtins.bool """If true, variables appearing in the solution hints will be fixed to their hinted value. """ use_probing_search: builtins.bool """If true, search will continuously probe Boolean variables, and integer variable bounds. This parameter is set to true in parallel on the probing worker. """ use_extended_probing: builtins.bool """Use extended probing (probe bool_or, at_most_one, exactly_one).""" probing_num_combinations_limit: builtins.int """How many combinations of pairs or triplets of variables we want to scan.""" shaving_deterministic_time_in_probing_search: builtins.float """Add a shaving phase (where the solver tries to prove that the lower or upper bound of a variable are infeasible) to the probing search. (<= 0 disables it). """ shaving_search_deterministic_time: builtins.float """Specifies the amount of deterministic time spent of each try at shaving a bound in the shaving search. """ shaving_search_threshold: builtins.int """Specifies the threshold between two modes in the shaving procedure. If the range of the variable/objective is less than this threshold, then the shaving procedure will try to remove values one by one. Otherwise, it will try to remove one range at a time. """ use_objective_lb_search: builtins.bool """If true, search will search in ascending max objective value (when minimizing) starting from the lower bound of the objective. """ use_objective_shaving_search: builtins.bool """This search differs from the previous search as it will not use assumptions to bound the objective, and it will recreate a full model with the hardcoded objective value. """ variables_shaving_level: builtins.int """This search takes all Boolean or integer variables, and maximize or minimize them in order to reduce their domain. -1 is automatic, otherwise value 0 disables it, and 1, 2, or 3 changes something. """ pseudo_cost_reliability_threshold: builtins.int """The solver ignores the pseudo costs of variables with number of recordings less than this threshold. """ optimize_with_core: builtins.bool """The default optimization method is a simple "linear scan", each time trying to find a better solution than the previous one. If this is true, then we use a core-based approach (like in max-SAT) when we try to increase the lower bound instead. """ optimize_with_lb_tree_search: builtins.bool """Do a more conventional tree search (by opposition to SAT based one) where we keep all the explored node in a tree. This is meant to be used in a portfolio and focus on improving the objective lower bound. Keeping the whole tree allow us to report a better objective lower bound coming from the worst open node in the tree. """ save_lp_basis_in_lb_tree_search: builtins.bool """Experimental. Save the current LP basis at each node of the search tree so that when we jump around, we can load it and reduce the number of LP iterations needed. It currently works okay if we do not change the lp with cuts or simplification... More work is needed to make it robust in all cases. """ binary_search_num_conflicts: builtins.int """If non-negative, perform a binary search on the objective variable in order to find an [min, max] interval outside of which the solver proved unsat/sat under this amount of conflict. This can quickly reduce the objective domain on some problems. """ optimize_with_max_hs: builtins.bool """This has no effect if optimize_with_core is false. If true, use a different core-based algorithm similar to the max-HS algo for max-SAT. This is a hybrid MIP/CP approach and it uses a MIP solver in addition to the CP/SAT one. This is also related to the PhD work of tobyodavies@ "Automatic Logic-Based Benders Decomposition with MiniZinc" http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14489 """ use_feasibility_jump: builtins.bool """Parameters for an heuristic similar to the one described in the paper: "Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation. """ use_ls_only: builtins.bool """Disable every other type of subsolver, setting this turns CP-SAT into a pure local-search solver. """ feasibility_jump_decay: builtins.float """On each restart, we randomly choose if we use decay (with this parameter) or no decay. """ feasibility_jump_linearization_level: builtins.int """How much do we linearize the problem in the local search code.""" feasibility_jump_restart_factor: builtins.int """This is a factor that directly influence the work before each restart. Increasing it leads to longer restart. """ feasibility_jump_batch_dtime: builtins.float """How much dtime for each LS batch.""" feasibility_jump_var_randomization_probability: builtins.float """Probability for a variable to have a non default value upon restarts or perturbations. """ feasibility_jump_var_perburbation_range_ratio: builtins.float """Max distance between the default value and the pertubated value relative to the range of the domain of the variable. """ feasibility_jump_enable_restarts: builtins.bool """When stagnating, feasibility jump will either restart from a default solution (with some possible randomization), or randomly pertubate the current solution. This parameter selects the first option. """ feasibility_jump_max_expanded_constraint_size: builtins.int """Maximum size of no_overlap or no_overlap_2d constraint for a quadratic expansion. This might look a lot, but by expanding such constraint, we get a linear time evaluation per single variable moves instead of a slow O(n log n) one. """ num_violation_ls: builtins.int """This will create incomplete subsolvers (that are not LNS subsolvers) that use the feasibility jump code to find improving solution, treating the objective improvement as a hard constraint. """ violation_ls_perturbation_period: builtins.int """How long violation_ls should wait before perturbating a solution.""" violation_ls_compound_move_probability: builtins.float """Probability of using compound move search each restart. TODO(user): Add reference to paper when published. """ shared_tree_num_workers: builtins.int """Enables shared tree search. If positive, start this many complete worker threads to explore a shared search tree. These workers communicate objective bounds and simple decision nogoods relating to the shared prefix of the tree, and will avoid exploring the same subtrees as one another. Specifying a negative number uses a heuristic to select an appropriate number of shared tree workeres based on the total number of workers. """ use_shared_tree_search: builtins.bool """Set on shared subtree workers. Users should not set this directly.""" shared_tree_worker_min_restarts_per_subtree: builtins.int """Minimum restarts before a worker will replace a subtree that looks "bad" based on the average LBD of learned clauses. """ shared_tree_worker_enable_trail_sharing: builtins.bool """If true, workers share more of the information from their local trail. Specifically, literals implied by the shared tree decisions. """ shared_tree_worker_enable_phase_sharing: builtins.bool """If true, shared tree workers share their target phase when returning an assigned subtree for the next worker to use. """ shared_tree_open_leaves_per_worker: builtins.float """How many open leaf nodes should the shared tree maintain per worker.""" shared_tree_max_nodes_per_worker: builtins.int """In order to limit total shared memory and communication overhead, limit the total number of nodes that may be generated in the shared tree. If the shared tree runs out of unassigned leaves, workers act as portfolio workers. Note: this limit includes interior nodes, not just leaves. """ shared_tree_split_strategy: Global___SatParameters.SharedTreeSplitStrategy.ValueType shared_tree_balance_tolerance: builtins.int """How much deeper compared to the ideal max depth of the tree is considered "balanced" enough to still accept a split. Without such a tolerance, sometimes the tree can only be split by a single worker, and they may not generate a split for some time. In contrast, with a tolerance of 1, at least half of all workers should be able to split the tree as soon as a split becomes required. This only has an effect on SPLIT_STRATEGY_BALANCED_TREE and SPLIT_STRATEGY_DISCREPANCY. """ shared_tree_split_min_dtime: builtins.float """How much dtime a worker will wait between proposing splits. This limits the contention in splitting the shared tree, and also reduces the number of too-easy subtrees that are generates. """ enumerate_all_solutions: builtins.bool """Whether we enumerate all solutions of a problem without objective. WARNING: - This can be used with num_workers > 1 but then each solutions can be found more than once, so it is up to the client to deduplicate them. - If keep_all_feasible_solutions_in_presolve is unset, we will set it to true as otherwise, many feasible solution can just be removed by the presolve. It is still possible to manually set this to false if one only wants to enumerate all solutions of the presolved model. """ keep_all_feasible_solutions_in_presolve: builtins.bool """If true, we disable the presolve reductions that remove feasible solutions from the search space. Such solution are usually dominated by a "better" solution that is kept, but depending on the situation, we might want to keep all solutions. A trivial example is when a variable is unused. If this is true, then the presolve will not fix it to an arbitrary value and it will stay in the search space. """ fill_tightened_domains_in_response: builtins.bool """If true, add information about the derived variable domains to the CpSolverResponse. It is an option because it makes the response slighly bigger and there is a bit more work involved during the postsolve to construct it, but it should still have a low overhead. See the tightened_variables field in CpSolverResponse for more details. """ fill_additional_solutions_in_response: builtins.bool """If true, the final response addition_solutions field will be filled with all solutions from our solutions pool. Note that if both this field and enumerate_all_solutions is true, we will copy to the pool all of the solution found. So if solution_pool_size is big enough, you can get all solutions this way instead of using the solution callback. Note that this only affect the "final" solution, not the one passed to the solution callbacks. """ instantiate_all_variables: builtins.bool """If true, the solver will add a default integer branching strategy to the already defined search strategy. If not, some variable might still not be fixed at the end of the search. For now we assume these variable can just be set to their lower bound. """ auto_detect_greater_than_at_least_one_of: builtins.bool """If true, then the precedences propagator try to detect for each variable if it has a set of "optional incoming arc" for which at least one of them is present. This is usually useful to have but can be slow on model with a lot of precedence. """ stop_after_first_solution: builtins.bool """For an optimization problem, stop the solver as soon as we have a solution.""" stop_after_presolve: builtins.bool """Mainly used when improving the presolver. When true, stops the solver after the presolve is complete (or after loading and root level propagation). """ stop_after_root_propagation: builtins.bool lns_initial_difficulty: builtins.float """LNS parameters. Initial parameters for neighborhood generation. """ lns_initial_deterministic_limit: builtins.float use_lns: builtins.bool """Testing parameters used to disable all lns workers.""" use_lns_only: builtins.bool """Experimental parameters to disable everything but lns.""" solution_pool_size: builtins.int """Size of the top-n different solutions kept by the solver. This parameter must be > 0. Currently, having this larger than one mainly impact the "base" solution chosen for a LNS/LS fragment. """ solution_pool_diversity_limit: builtins.int """If solution_pool_size is <= this, we will use DP to keep a "diverse" set of solutions (the one further apart via hamming distance) in the pool. Setting this to large value might be slow, especially if your solution are large. """ alternative_pool_size: builtins.int """In order to not get stuck in local optima, when this is non-zero, we try to also work on "older" solutions with a worse objective value so we get a chance to follow a different LS/LNS trajectory. """ use_rins_lns: builtins.bool """Turns on relaxation induced neighborhood generator.""" use_feasibility_pump: builtins.bool """Adds a feasibility pump subsolver along with lns subsolvers.""" use_lb_relax_lns: builtins.bool """Turns on neighborhood generator based on local branching LP. Based on Huang et al., "Local Branching Relaxation Heuristics for Integer Linear Programs", 2023. """ lb_relax_num_workers_threshold: builtins.int """Only use lb-relax if we have at least that many workers.""" fp_rounding: Global___SatParameters.FPRoundingMethod.ValueType diversify_lns_params: builtins.bool """If true, registers more lns subsolvers with different parameters.""" randomize_search: builtins.bool """Randomize fixed search.""" search_random_variable_pool_size: builtins.int """Search randomization will collect the top 'search_random_variable_pool_size' valued variables, and pick one randomly. The value of the variable is specific to each strategy. """ push_all_tasks_toward_start: builtins.bool """Experimental code: specify if the objective pushes all tasks toward the start of the schedule. """ use_optional_variables: builtins.bool """If true, we automatically detect variables whose constraint are always enforced by the same literal and we mark them as optional. This allows to propagate them as if they were present in some situation. TODO(user): This is experimental and seems to lead to wrong optimal in some situation. It should however gives correct solutions. Fix. """ use_exact_lp_reason: builtins.bool """The solver usually exploit the LP relaxation of a model. If this option is true, then whatever is infered by the LP will be used like an heuristic to compute EXACT propagation on the IP. So with this option, there is no numerical imprecision issues. """ use_combined_no_overlap: builtins.bool """This can be beneficial if there is a lot of no-overlap constraints but a relatively low number of different intervals in the problem. Like 1000 intervals, but 1M intervals in the no-overlap constraints covering them. """ at_most_one_max_expansion_size: builtins.int """All at_most_one constraints with a size <= param will be replaced by a quadratic number of binary implications. """ catch_sigint_signal: builtins.bool """Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals when calling solve. If set, catching the SIGINT signal will terminate the search gracefully, as if a time limit was reached. """ use_implied_bounds: builtins.bool """Stores and exploits "implied-bounds" in the solver. That is, relations of the form literal => (var >= bound). This is currently used to derive stronger cuts. """ polish_lp_solution: builtins.bool """Whether we try to do a few degenerate iteration at the end of an LP solve to minimize the fractionality of the integer variable in the basis. This helps on some problems, but not so much on others. It also cost of bit of time to do such polish step. """ lp_primal_tolerance: builtins.float """The internal LP tolerances used by CP-SAT. These applies to the internal and scaled problem. If the domains of your variables are large it might be good to use lower tolerances. If your problem is binary with low coefficients, it might be good to use higher ones to speed-up the lp solves. """ lp_dual_tolerance: builtins.float convert_intervals: builtins.bool """Temporary flag util the feature is more mature. This convert intervals to the newer proto format that support affine start/var/end instead of just variables. """ symmetry_level: builtins.int """Whether we try to automatically detect the symmetries in a model and exploit them. Currently, at level 1 we detect them in presolve and try to fix Booleans. At level 2, we also do some form of dynamic symmetry breaking during search. At level 3, we also detect symmetries for very large models, which can be slow. At level 4, we try to break as much symmetry as possible in presolve. """ use_symmetry_in_lp: builtins.bool """When we have symmetry, it is possible to "fold" all variables from the same orbit into a single variable, while having the same power of LP relaxation. This can help significantly on symmetric problem. However there is currently a bit of overhead as the rest of the solver need to do some translation between the folded LP and the rest of the problem. """ keep_symmetry_in_presolve: builtins.bool """Experimental. This will compute the symmetry of the problem once and for all. All presolve operations we do should keep the symmetry group intact or modify it properly. For now we have really little support for this. We will disable a bunch of presolve operations that could be supported. """ symmetry_detection_deterministic_time_limit: builtins.float """Deterministic time limit for symmetry detection.""" new_linear_propagation: builtins.bool """The new linear propagation code treat all constraints at once and use an adaptation of Bellman-Ford-Tarjan to propagate constraint in a smarter order and potentially detect propagation cycle earlier. """ linear_split_size: builtins.int """Linear constraints that are not pseudo-Boolean and that are longer than this size will be split into sqrt(size) intermediate sums in order to have faster propation in the CP engine. """ linearization_level: builtins.int """========================================================================== Linear programming relaxation ========================================================================== A non-negative level indicating the type of constraints we consider in the LP relaxation. At level zero, no LP relaxation is used. At level 1, only the linear constraint and full encoding are added. At level 2, we also add all the Boolean constraints. """ boolean_encoding_level: builtins.int """A non-negative level indicating how much we should try to fully encode Integer variables as Boolean. """ max_domain_size_when_encoding_eq_neq_constraints: builtins.int """When loading a*x + b*y ==/!= c when x and y are both fully encoded. The solver may decide to replace the linear equation by a set of clauses. This is triggered if the sizes of the domains of x and y are below the threshold. """ max_num_cuts: builtins.int """The limit on the number of cuts in our cut pool. When this is reached we do not generate cuts anymore. TODO(user): We should probably remove this parameters, and just always generate cuts but only keep the best n or something. """ cut_level: builtins.int """Control the global cut effort. Zero will turn off all cut. For now we just have one level. Note also that most cuts are only used at linearization level >= 2. """ only_add_cuts_at_level_zero: builtins.bool """For the cut that can be generated at any level, this control if we only try to generate them at the root node. """ add_objective_cut: builtins.bool """When the LP objective is fractional, do we add the cut that forces the linear objective expression to be greater or equal to this fractional value rounded up? We can always do that since our objective is integer, and combined with MIR heuristic to reduce the coefficient of such cut, it can help. """ add_cg_cuts: builtins.bool """Whether we generate and add Chvatal-Gomory cuts to the LP at root node. Note that for now, this is not heavily tuned. """ add_mir_cuts: builtins.bool """Whether we generate MIR cuts at root node. Note that for now, this is not heavily tuned. """ add_zero_half_cuts: builtins.bool """Whether we generate Zero-Half cuts at root node. Note that for now, this is not heavily tuned. """ add_clique_cuts: builtins.bool """Whether we generate clique cuts from the binary implication graph. Note that as the search goes on, this graph will contains new binary clauses learned by the SAT engine. """ add_rlt_cuts: builtins.bool """Whether we generate RLT cuts. This is still experimental but can help on binary problem with a lot of clauses of size 3. """ max_all_diff_cut_size: builtins.int """Cut generator for all diffs can add too many cuts for large all_diff constraints. This parameter restricts the large all_diff constraints to have a cut generator. """ add_lin_max_cuts: builtins.bool """For the lin max constraints, generates the cuts described in "Strong mixed-integer programming formulations for trained neural networks" by Ross Anderson et. (https://arxiv.org/pdf/1811.01988.pdf) """ max_integer_rounding_scaling: builtins.int """In the integer rounding procedure used for MIR and Gomory cut, the maximum "scaling" we use (must be positive). The lower this is, the lower the integer coefficients of the cut will be. Note that cut generated by lower values are not necessarily worse than cut generated by larger value. There is no strict dominance relationship. Setting this to 2 result in the "strong fractional rouding" of Letchford and Lodi. """ add_lp_constraints_lazily: builtins.bool """If true, we start by an empty LP, and only add constraints not satisfied by the current LP solution batch by batch. A constraint that is only added like this is known as a "lazy" constraint in the literature, except that we currently consider all constraints as lazy here. """ root_lp_iterations: builtins.int """Even at the root node, we do not want to spend too much time on the LP if it is "difficult". So we solve it in "chunks" of that many iterations. The solve will be continued down in the tree or the next time we go back to the root node. """ min_orthogonality_for_lp_constraints: builtins.float """While adding constraints, skip the constraints which have orthogonality less than 'min_orthogonality_for_lp_constraints' with already added constraints during current call. Orthogonality is defined as 1 - cosine(vector angle between constraints). A value of zero disable this feature. """ max_cut_rounds_at_level_zero: builtins.int """Max number of time we perform cut generation and resolve the LP at level 0.""" max_consecutive_inactive_count: builtins.int """If a constraint/cut in LP is not active for that many consecutive OPTIMAL solves, remove it from the LP. Note that it might be added again later if it become violated by the current LP solution. """ cut_max_active_count_value: builtins.float """These parameters are similar to sat clause management activity parameters. They are effective only if the number of generated cuts exceed the storage limit. Default values are based on a few experiments on miplib instances. """ cut_active_count_decay: builtins.float cut_cleanup_target: builtins.int """Target number of constraints to remove during cleanup.""" new_constraints_batch_size: builtins.int """Add that many lazy constraints (or cuts) at once in the LP. Note that at the beginning of the solve, we do add more than this. """ exploit_integer_lp_solution: builtins.bool """All the "exploit_*" parameters below work in the same way: when branching on an IntegerVariable, these parameters affect the value the variable is branched on. Currently the first heuristic that triggers win in the order in which they appear below. TODO(user): Maybe do like for the restart algorithm, introduce an enum and a repeated field that control the order on which these are applied? If true and the Lp relaxation of the problem has an integer optimal solution, try to exploit it. Note that since the LP relaxation may not contain all the constraints, such a solution is not necessarily a solution of the full problem. """ exploit_all_lp_solution: builtins.bool """If true and the Lp relaxation of the problem has a solution, try to exploit it. This is same as above except in this case the lp solution might not be an integer solution. """ exploit_best_solution: builtins.bool """When branching on a variable, follow the last best solution value.""" exploit_relaxation_solution: builtins.bool """When branching on a variable, follow the last best relaxation solution value. We use the relaxation with the tightest bound on the objective as the best relaxation solution. """ exploit_objective: builtins.bool """When branching an a variable that directly affect the objective, branch on the value that lead to the best objective first. """ detect_linearized_product: builtins.bool """Infer products of Boolean or of Boolean time IntegerVariable from the linear constrainst in the problem. This can be used in some cuts, altough for now we don't really exploit it. """ use_new_integer_conflict_resolution: builtins.bool """This should be better on integer problems. But it is still work in progress. """ create_1uip_boolean_during_icr: builtins.bool """If true, and during integer conflict resolution (icr) the 1-UIP is an integer literal for which we do not have an associated Boolean. Create one. """ mip_max_bound: builtins.float """========================================================================== MIP -> CP-SAT (i.e. IP with integer coeff) conversion parameters that are used by our automatic "scaling" algorithm. Note that it is hard to do a meaningful conversion automatically and if you have a model with continuous variables, it is best if you scale the domain of the variable yourself so that you have a relevant precision for the application at hand. Same for the coefficients and constraint bounds. ========================================================================== We need to bound the maximum magnitude of the variables for CP-SAT, and that is the bound we use. If the MIP model expect larger variable value in the solution, then the converted model will likely not be relevant. """ mip_var_scaling: builtins.float """All continuous variable of the problem will be multiplied by this factor. By default, we don't do any variable scaling and rely on the MIP model to specify continuous variable domain with the wanted precision. """ mip_scale_large_domain: builtins.bool """If this is false, then mip_var_scaling is only applied to variables with "small" domain. If it is true, we scale all floating point variable independenlty of their domain. """ mip_automatically_scale_variables: builtins.bool """If true, some continuous variable might be automatically scaled. For now, this is only the case where we detect that a variable is actually an integer multiple of a constant. For instance, variables of the form k * 0.5 are quite frequent, and if we detect this, we will scale such variable domain by 2 to make it implied integer. """ only_solve_ip: builtins.bool """If one try to solve a MIP model with CP-SAT, because we assume all variable to be integer after scaling, we will not necessarily have the correct optimal. Note however that all feasible solutions are valid since we will just solve a more restricted version of the original problem. This parameters is here to prevent user to think the solution is optimal when it might not be. One will need to manually set this to false to solve a MIP model where the optimal might be different. Note that this is tested after some MIP presolve steps, so even if not all original variable are integer, we might end up with a pure IP after presolve and after implied integer detection. """ mip_wanted_precision: builtins.float """When scaling constraint with double coefficients to integer coefficients, we will multiply by a power of 2 and round the coefficients. We will choose the lowest power such that we have no potential overflow (see mip_max_activity_exponent) and the worst case constraint activity error does not exceed this threshold. Note that we also detect constraint with rational coefficients and scale them accordingly when it seems better instead of using a power of 2. We also relax all constraint bounds by this absolute value. For pure integer constraint, if this value if lower than one, this will not change anything. However it is needed when scaling MIP problems. If we manage to scale a constraint correctly, the maximum error we can make will be twice this value (once for the scaling error and once for the relaxed bounds). If we are not able to scale that well, we will display that fact but still scale as best as we can. """ mip_max_activity_exponent: builtins.int """To avoid integer overflow, we always force the maximum possible constraint activity (and objective value) according to the initial variable domain to be smaller than 2 to this given power. Because of this, we cannot always reach the "mip_wanted_precision" parameter above. This can go as high as 62, but some internal algo currently abort early if they might run into integer overflow, so it is better to keep it a bit lower than this. """ mip_check_precision: builtins.float """As explained in mip_precision and mip_max_activity_exponent, we cannot always reach the wanted precision during scaling. We use this threshold to enphasize in the logs when the precision seems bad. """ mip_compute_true_objective_bound: builtins.bool """Even if we make big error when scaling the objective, we can always derive a correct lower bound on the original objective by using the exact lower bound on the scaled integer version of the objective. This should be fast, but if you don't care about having a precise lower bound, you can turn it off. """ mip_max_valid_magnitude: builtins.float """Any finite values in the input MIP must be below this threshold, otherwise the model will be reported invalid. This is needed to avoid floating point overflow when evaluating bounds * coeff for instance. We are a bit more defensive, but in practice, users shouldn't use super large values in a MIP. """ mip_treat_high_magnitude_bounds_as_infinity: builtins.bool """By default, any variable/constraint bound with a finite value and a magnitude greater than the mip_max_valid_magnitude will result with a invalid model. This flags change the behavior such that such bounds are silently transformed to +∞ or -∞. It is recommended to keep it at false, and create valid bounds. """ mip_drop_tolerance: builtins.float """Any value in the input mip with a magnitude lower than this will be set to zero. This is to avoid some issue in LP presolving. """ mip_presolve_level: builtins.int """When solving a MIP, we do some basic floating point presolving before scaling the problem to integer to be handled by CP-SAT. This control how much of that presolve we do. It can help to better scale floating point model, but it is not always behaving nicely. """ @property def restart_algorithms(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[Global___SatParameters.RestartAlgorithm.ValueType]: """The restart strategies will change each time the strategy_counter is increased. The current strategy will simply be the one at index strategy_counter modulo the number of strategy. Note that if this list includes a NO_RESTART, nothing will change when it is reached because the strategy_counter will only increment after a restart. The idea of switching of search strategy tailored for SAT/UNSAT comes from Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/. But more generally, it seems REALLY beneficial to try different strategy. """ @property def subsolvers(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]: """In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters. This field indicates the names of the parameters that are used in multithread. This only applies to "full" subsolvers. See cp_model_search.cc to see a list of the names and the default value (if left empty) that looks like: - default_lp (linearization_level:1) - fixed (only if fixed search specified or scheduling) - no_lp (linearization_level:0) - max_lp (linearization_level:2) - pseudo_costs (only if objective, change search heuristic) - reduced_costs (only if objective, change search heuristic) - quick_restart (kind of probing) - quick_restart_no_lp (kind of probing with linearization_level:0) - lb_tree_search (to improve lower bound, MIP like tree search) - probing (continuous probing and shaving) Also, note that some set of parameters will be ignored if they do not make sense. For instance if there is no objective, pseudo_cost or reduced_cost search will be ignored. Core based search will only work if the objective has many terms. If there is no fixed strategy fixed will be ignored. And so on. The order is important, as only the first num_full_subsolvers will be scheduled. You can see in the log which one are selected for a given run. """ @property def extra_subsolvers(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]: """A convenient way to add more workers types. These will be added at the beginning of the list. """ @property def ignore_subsolvers(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]: """Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem or only keep specific ones for testing. Each string is interpreted as a "glob", so we support '*' and '?'. The way this work is that we will only accept a name that match a filter pattern (if non-empty) and do not match an ignore pattern. Note also that these fields work on LNS or LS names even if these are currently not specified via the subsolvers field. """ @property def filter_subsolvers(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]: ... @property def subsolver_params(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[Global___SatParameters]: """It is possible to specify additional subsolver configuration. These can be referred by their params.name() in the fields above. Note that only the specified field will "overwrite" the ones of the base parameter. If a subsolver_params has the name of an existing subsolver configuration, the named parameters will be merged into the subsolver configuration. """ def __init__( self, *, name: builtins.str | None = ..., preferred_variable_order: Global___SatParameters.VariableOrder.ValueType | None = ..., initial_polarity: Global___SatParameters.Polarity.ValueType | None = ..., use_phase_saving: builtins.bool | None = ..., polarity_rephase_increment: builtins.int | None = ..., polarity_exploit_ls_hints: builtins.bool | None = ..., random_polarity_ratio: builtins.float | None = ..., random_branches_ratio: builtins.float | None = ..., use_erwa_heuristic: builtins.bool | None = ..., initial_variables_activity: builtins.float | None = ..., also_bump_variables_in_conflict_reasons: builtins.bool | None = ..., minimization_algorithm: Global___SatParameters.ConflictMinimizationAlgorithm.ValueType | None = ..., binary_minimization_algorithm: Global___SatParameters.BinaryMinizationAlgorithm.ValueType | None = ..., subsumption_during_conflict_analysis: builtins.bool | None = ..., extra_subsumption_during_conflict_analysis: builtins.bool | None = ..., decision_subsumption_during_conflict_analysis: builtins.bool | None = ..., eagerly_subsume_last_n_conflicts: builtins.int | None = ..., subsume_during_vivification: builtins.bool | None = ..., use_chronological_backtracking: builtins.bool | None = ..., max_backjump_levels: builtins.int | None = ..., chronological_backtrack_min_conflicts: builtins.int | None = ..., clause_cleanup_period: builtins.int | None = ..., clause_cleanup_period_increment: builtins.int | None = ..., clause_cleanup_target: builtins.int | None = ..., clause_cleanup_ratio: builtins.float | None = ..., clause_cleanup_lbd_bound: builtins.int | None = ..., clause_cleanup_lbd_tier1: builtins.int | None = ..., clause_cleanup_lbd_tier2: builtins.int | None = ..., clause_cleanup_ordering: Global___SatParameters.ClauseOrdering.ValueType | None = ..., pb_cleanup_increment: builtins.int | None = ..., pb_cleanup_ratio: builtins.float | None = ..., variable_activity_decay: builtins.float | None = ..., max_variable_activity_value: builtins.float | None = ..., glucose_max_decay: builtins.float | None = ..., glucose_decay_increment: builtins.float | None = ..., glucose_decay_increment_period: builtins.int | None = ..., clause_activity_decay: builtins.float | None = ..., max_clause_activity_value: builtins.float | None = ..., restart_algorithms: collections.abc.Iterable[Global___SatParameters.RestartAlgorithm.ValueType] | None = ..., default_restart_algorithms: builtins.str | None = ..., restart_period: builtins.int | None = ..., restart_running_window_size: builtins.int | None = ..., restart_dl_average_ratio: builtins.float | None = ..., restart_lbd_average_ratio: builtins.float | None = ..., use_blocking_restart: builtins.bool | None = ..., blocking_restart_window_size: builtins.int | None = ..., blocking_restart_multiplier: builtins.float | None = ..., num_conflicts_before_strategy_changes: builtins.int | None = ..., strategy_change_increase_ratio: builtins.float | None = ..., max_time_in_seconds: builtins.float | None = ..., max_deterministic_time: builtins.float | None = ..., max_num_deterministic_batches: builtins.int | None = ..., max_number_of_conflicts: builtins.int | None = ..., max_memory_in_mb: builtins.int | None = ..., absolute_gap_limit: builtins.float | None = ..., relative_gap_limit: builtins.float | None = ..., random_seed: builtins.int | None = ..., permute_variable_randomly: builtins.bool | None = ..., permute_presolve_constraint_order: builtins.bool | None = ..., use_absl_random: builtins.bool | None = ..., log_search_progress: builtins.bool | None = ..., log_subsolver_statistics: builtins.bool | None = ..., log_prefix: builtins.str | None = ..., log_to_stdout: builtins.bool | None = ..., log_to_response: builtins.bool | None = ..., use_pb_resolution: builtins.bool | None = ..., minimize_reduction_during_pb_resolution: builtins.bool | None = ..., count_assumption_levels_in_lbd: builtins.bool | None = ..., presolve_bve_threshold: builtins.int | None = ..., filter_sat_postsolve_clauses: builtins.bool | None = ..., presolve_bve_clause_weight: builtins.int | None = ..., probing_deterministic_time_limit: builtins.float | None = ..., presolve_probing_deterministic_time_limit: builtins.float | None = ..., presolve_blocked_clause: builtins.bool | None = ..., presolve_use_bva: builtins.bool | None = ..., presolve_bva_threshold: builtins.int | None = ..., max_presolve_iterations: builtins.int | None = ..., cp_model_presolve: builtins.bool | None = ..., cp_model_probing_level: builtins.int | None = ..., cp_model_use_sat_presolve: builtins.bool | None = ..., load_at_most_ones_in_sat_presolve: builtins.bool | None = ..., remove_fixed_variables_early: builtins.bool | None = ..., detect_table_with_cost: builtins.bool | None = ..., table_compression_level: builtins.int | None = ..., expand_alldiff_constraints: builtins.bool | None = ..., max_alldiff_domain_size: builtins.int | None = ..., expand_reservoir_constraints: builtins.bool | None = ..., max_domain_size_for_linear2_expansion: builtins.int | None = ..., expand_reservoir_using_circuit: builtins.bool | None = ..., encode_cumulative_as_reservoir: builtins.bool | None = ..., max_lin_max_size_for_expansion: builtins.int | None = ..., disable_constraint_expansion: builtins.bool | None = ..., encode_complex_linear_constraint_with_integer: builtins.bool | None = ..., merge_no_overlap_work_limit: builtins.float | None = ..., merge_at_most_one_work_limit: builtins.float | None = ..., presolve_substitution_level: builtins.int | None = ..., presolve_extract_integer_enforcement: builtins.bool | None = ..., presolve_inclusion_work_limit: builtins.int | None = ..., ignore_names: builtins.bool | None = ..., infer_all_diffs: builtins.bool | None = ..., find_big_linear_overlap: builtins.bool | None = ..., find_clauses_that_are_exactly_one: builtins.bool | None = ..., use_sat_inprocessing: builtins.bool | None = ..., inprocessing_dtime_ratio: builtins.float | None = ..., inprocessing_probing_dtime: builtins.float | None = ..., inprocessing_minimization_dtime: builtins.float | None = ..., inprocessing_minimization_use_conflict_analysis: builtins.bool | None = ..., inprocessing_minimization_use_all_orderings: builtins.bool | None = ..., inprocessing_use_congruence_closure: builtins.bool | None = ..., inprocessing_use_sat_sweeping: builtins.bool | None = ..., num_workers: builtins.int | None = ..., num_search_workers: builtins.int | None = ..., num_full_subsolvers: builtins.int | None = ..., subsolvers: collections.abc.Iterable[builtins.str] | None = ..., extra_subsolvers: collections.abc.Iterable[builtins.str] | None = ..., ignore_subsolvers: collections.abc.Iterable[builtins.str] | None = ..., filter_subsolvers: collections.abc.Iterable[builtins.str] | None = ..., subsolver_params: collections.abc.Iterable[Global___SatParameters] | None = ..., interleave_search: builtins.bool | None = ..., interleave_batch_size: builtins.int | None = ..., share_objective_bounds: builtins.bool | None = ..., share_level_zero_bounds: builtins.bool | None = ..., share_linear2_bounds: builtins.bool | None = ..., share_binary_clauses: builtins.bool | None = ..., share_glue_clauses: builtins.bool | None = ..., minimize_shared_clauses: builtins.bool | None = ..., share_glue_clauses_dtime: builtins.float | None = ..., check_lrat_proof: builtins.bool | None = ..., check_merged_lrat_proof: builtins.bool | None = ..., output_lrat_proof: builtins.bool | None = ..., check_drat_proof: builtins.bool | None = ..., output_drat_proof: builtins.bool | None = ..., max_drat_time_in_seconds: builtins.float | None = ..., debug_postsolve_with_full_solver: builtins.bool | None = ..., debug_max_num_presolve_operations: builtins.int | None = ..., debug_crash_on_bad_hint: builtins.bool | None = ..., debug_crash_if_presolve_breaks_hint: builtins.bool | None = ..., debug_crash_if_lrat_check_fails: builtins.bool | None = ..., use_optimization_hints: builtins.bool | None = ..., core_minimization_level: builtins.int | None = ..., find_multiple_cores: builtins.bool | None = ..., cover_optimization: builtins.bool | None = ..., max_sat_assumption_order: Global___SatParameters.MaxSatAssumptionOrder.ValueType | None = ..., max_sat_reverse_assumption_order: builtins.bool | None = ..., max_sat_stratification: Global___SatParameters.MaxSatStratificationAlgorithm.ValueType | None = ..., propagation_loop_detection_factor: builtins.float | None = ..., use_precedences_in_disjunctive_constraint: builtins.bool | None = ..., transitive_precedences_work_limit: builtins.int | None = ..., max_size_to_create_precedence_literals_in_disjunctive: builtins.int | None = ..., use_strong_propagation_in_disjunctive: builtins.bool | None = ..., use_dynamic_precedence_in_disjunctive: builtins.bool | None = ..., use_dynamic_precedence_in_cumulative: builtins.bool | None = ..., use_overload_checker_in_cumulative: builtins.bool | None = ..., use_conservative_scale_overload_checker: builtins.bool | None = ..., use_timetable_edge_finding_in_cumulative: builtins.bool | None = ..., max_num_intervals_for_timetable_edge_finding: builtins.int | None = ..., use_hard_precedences_in_cumulative: builtins.bool | None = ..., exploit_all_precedences: builtins.bool | None = ..., use_disjunctive_constraint_in_cumulative: builtins.bool | None = ..., no_overlap_2d_boolean_relations_limit: builtins.int | None = ..., use_timetabling_in_no_overlap_2d: builtins.bool | None = ..., use_energetic_reasoning_in_no_overlap_2d: builtins.bool | None = ..., use_area_energetic_reasoning_in_no_overlap_2d: builtins.bool | None = ..., use_try_edge_reasoning_in_no_overlap_2d: builtins.bool | None = ..., max_pairs_pairwise_reasoning_in_no_overlap_2d: builtins.int | None = ..., maximum_regions_to_split_in_disconnected_no_overlap_2d: builtins.int | None = ..., use_linear3_for_no_overlap_2d_precedences: builtins.bool | None = ..., use_dual_scheduling_heuristics: builtins.bool | None = ..., use_all_different_for_circuit: builtins.bool | None = ..., routing_cut_subset_size_for_binary_relation_bound: builtins.int | None = ..., routing_cut_subset_size_for_tight_binary_relation_bound: builtins.int | None = ..., routing_cut_subset_size_for_exact_binary_relation_bound: builtins.int | None = ..., routing_cut_subset_size_for_shortest_paths_bound: builtins.int | None = ..., routing_cut_dp_effort: builtins.float | None = ..., routing_cut_max_infeasible_path_length: builtins.int | None = ..., search_branching: Global___SatParameters.SearchBranching.ValueType | None = ..., hint_conflict_limit: builtins.int | None = ..., repair_hint: builtins.bool | None = ..., fix_variables_to_their_hinted_value: builtins.bool | None = ..., use_probing_search: builtins.bool | None = ..., use_extended_probing: builtins.bool | None = ..., probing_num_combinations_limit: builtins.int | None = ..., shaving_deterministic_time_in_probing_search: builtins.float | None = ..., shaving_search_deterministic_time: builtins.float | None = ..., shaving_search_threshold: builtins.int | None = ..., use_objective_lb_search: builtins.bool | None = ..., use_objective_shaving_search: builtins.bool | None = ..., variables_shaving_level: builtins.int | None = ..., pseudo_cost_reliability_threshold: builtins.int | None = ..., optimize_with_core: builtins.bool | None = ..., optimize_with_lb_tree_search: builtins.bool | None = ..., save_lp_basis_in_lb_tree_search: builtins.bool | None = ..., binary_search_num_conflicts: builtins.int | None = ..., optimize_with_max_hs: builtins.bool | None = ..., use_feasibility_jump: builtins.bool | None = ..., use_ls_only: builtins.bool | None = ..., feasibility_jump_decay: builtins.float | None = ..., feasibility_jump_linearization_level: builtins.int | None = ..., feasibility_jump_restart_factor: builtins.int | None = ..., feasibility_jump_batch_dtime: builtins.float | None = ..., feasibility_jump_var_randomization_probability: builtins.float | None = ..., feasibility_jump_var_perburbation_range_ratio: builtins.float | None = ..., feasibility_jump_enable_restarts: builtins.bool | None = ..., feasibility_jump_max_expanded_constraint_size: builtins.int | None = ..., num_violation_ls: builtins.int | None = ..., violation_ls_perturbation_period: builtins.int | None = ..., violation_ls_compound_move_probability: builtins.float | None = ..., shared_tree_num_workers: builtins.int | None = ..., use_shared_tree_search: builtins.bool | None = ..., shared_tree_worker_min_restarts_per_subtree: builtins.int | None = ..., shared_tree_worker_enable_trail_sharing: builtins.bool | None = ..., shared_tree_worker_enable_phase_sharing: builtins.bool | None = ..., shared_tree_open_leaves_per_worker: builtins.float | None = ..., shared_tree_max_nodes_per_worker: builtins.int | None = ..., shared_tree_split_strategy: Global___SatParameters.SharedTreeSplitStrategy.ValueType | None = ..., shared_tree_balance_tolerance: builtins.int | None = ..., shared_tree_split_min_dtime: builtins.float | None = ..., enumerate_all_solutions: builtins.bool | None = ..., keep_all_feasible_solutions_in_presolve: builtins.bool | None = ..., fill_tightened_domains_in_response: builtins.bool | None = ..., fill_additional_solutions_in_response: builtins.bool | None = ..., instantiate_all_variables: builtins.bool | None = ..., auto_detect_greater_than_at_least_one_of: builtins.bool | None = ..., stop_after_first_solution: builtins.bool | None = ..., stop_after_presolve: builtins.bool | None = ..., stop_after_root_propagation: builtins.bool | None = ..., lns_initial_difficulty: builtins.float | None = ..., lns_initial_deterministic_limit: builtins.float | None = ..., use_lns: builtins.bool | None = ..., use_lns_only: builtins.bool | None = ..., solution_pool_size: builtins.int | None = ..., solution_pool_diversity_limit: builtins.int | None = ..., alternative_pool_size: builtins.int | None = ..., use_rins_lns: builtins.bool | None = ..., use_feasibility_pump: builtins.bool | None = ..., use_lb_relax_lns: builtins.bool | None = ..., lb_relax_num_workers_threshold: builtins.int | None = ..., fp_rounding: Global___SatParameters.FPRoundingMethod.ValueType | None = ..., diversify_lns_params: builtins.bool | None = ..., randomize_search: builtins.bool | None = ..., search_random_variable_pool_size: builtins.int | None = ..., push_all_tasks_toward_start: builtins.bool | None = ..., use_optional_variables: builtins.bool | None = ..., use_exact_lp_reason: builtins.bool | None = ..., use_combined_no_overlap: builtins.bool | None = ..., at_most_one_max_expansion_size: builtins.int | None = ..., catch_sigint_signal: builtins.bool | None = ..., use_implied_bounds: builtins.bool | None = ..., polish_lp_solution: builtins.bool | None = ..., lp_primal_tolerance: builtins.float | None = ..., lp_dual_tolerance: builtins.float | None = ..., convert_intervals: builtins.bool | None = ..., symmetry_level: builtins.int | None = ..., use_symmetry_in_lp: builtins.bool | None = ..., keep_symmetry_in_presolve: builtins.bool | None = ..., symmetry_detection_deterministic_time_limit: builtins.float | None = ..., new_linear_propagation: builtins.bool | None = ..., linear_split_size: builtins.int | None = ..., linearization_level: builtins.int | None = ..., boolean_encoding_level: builtins.int | None = ..., max_domain_size_when_encoding_eq_neq_constraints: builtins.int | None = ..., max_num_cuts: builtins.int | None = ..., cut_level: builtins.int | None = ..., only_add_cuts_at_level_zero: builtins.bool | None = ..., add_objective_cut: builtins.bool | None = ..., add_cg_cuts: builtins.bool | None = ..., add_mir_cuts: builtins.bool | None = ..., add_zero_half_cuts: builtins.bool | None = ..., add_clique_cuts: builtins.bool | None = ..., add_rlt_cuts: builtins.bool | None = ..., max_all_diff_cut_size: builtins.int | None = ..., add_lin_max_cuts: builtins.bool | None = ..., max_integer_rounding_scaling: builtins.int | None = ..., add_lp_constraints_lazily: builtins.bool | None = ..., root_lp_iterations: builtins.int | None = ..., min_orthogonality_for_lp_constraints: builtins.float | None = ..., max_cut_rounds_at_level_zero: builtins.int | None = ..., max_consecutive_inactive_count: builtins.int | None = ..., cut_max_active_count_value: builtins.float | None = ..., cut_active_count_decay: builtins.float | None = ..., cut_cleanup_target: builtins.int | None = ..., new_constraints_batch_size: builtins.int | None = ..., exploit_integer_lp_solution: builtins.bool | None = ..., exploit_all_lp_solution: builtins.bool | None = ..., exploit_best_solution: builtins.bool | None = ..., exploit_relaxation_solution: builtins.bool | None = ..., exploit_objective: builtins.bool | None = ..., detect_linearized_product: builtins.bool | None = ..., use_new_integer_conflict_resolution: builtins.bool | None = ..., create_1uip_boolean_during_icr: builtins.bool | None = ..., mip_max_bound: builtins.float | None = ..., mip_var_scaling: builtins.float | None = ..., mip_scale_large_domain: builtins.bool | None = ..., mip_automatically_scale_variables: builtins.bool | None = ..., only_solve_ip: builtins.bool | None = ..., mip_wanted_precision: builtins.float | None = ..., mip_max_activity_exponent: builtins.int | None = ..., mip_check_precision: builtins.float | None = ..., mip_compute_true_objective_bound: builtins.bool | None = ..., mip_max_valid_magnitude: builtins.float | None = ..., mip_treat_high_magnitude_bounds_as_infinity: builtins.bool | None = ..., mip_drop_tolerance: builtins.float | None = ..., mip_presolve_level: builtins.int | None = ..., ) -> None: ... _HasFieldArgType: typing_extensions.TypeAlias = typing.Literal["absolute_gap_limit", b"absolute_gap_limit", "add_cg_cuts", b"add_cg_cuts", "add_clique_cuts", b"add_clique_cuts", "add_lin_max_cuts", b"add_lin_max_cuts", "add_lp_constraints_lazily", b"add_lp_constraints_lazily", "add_mir_cuts", b"add_mir_cuts", "add_objective_cut", b"add_objective_cut", "add_rlt_cuts", b"add_rlt_cuts", "add_zero_half_cuts", b"add_zero_half_cuts", "also_bump_variables_in_conflict_reasons", b"also_bump_variables_in_conflict_reasons", "alternative_pool_size", b"alternative_pool_size", "at_most_one_max_expansion_size", b"at_most_one_max_expansion_size", "auto_detect_greater_than_at_least_one_of", b"auto_detect_greater_than_at_least_one_of", "binary_minimization_algorithm", b"binary_minimization_algorithm", "binary_search_num_conflicts", b"binary_search_num_conflicts", "blocking_restart_multiplier", b"blocking_restart_multiplier", "blocking_restart_window_size", b"blocking_restart_window_size", "boolean_encoding_level", b"boolean_encoding_level", "catch_sigint_signal", b"catch_sigint_signal", "check_drat_proof", b"check_drat_proof", "check_lrat_proof", b"check_lrat_proof", "check_merged_lrat_proof", b"check_merged_lrat_proof", "chronological_backtrack_min_conflicts", b"chronological_backtrack_min_conflicts", "clause_activity_decay", b"clause_activity_decay", "clause_cleanup_lbd_bound", b"clause_cleanup_lbd_bound", "clause_cleanup_lbd_tier1", b"clause_cleanup_lbd_tier1", "clause_cleanup_lbd_tier2", b"clause_cleanup_lbd_tier2", "clause_cleanup_ordering", b"clause_cleanup_ordering", "clause_cleanup_period", b"clause_cleanup_period", "clause_cleanup_period_increment", b"clause_cleanup_period_increment", "clause_cleanup_ratio", b"clause_cleanup_ratio", "clause_cleanup_target", b"clause_cleanup_target", "convert_intervals", b"convert_intervals", "core_minimization_level", b"core_minimization_level", "count_assumption_levels_in_lbd", b"count_assumption_levels_in_lbd", "cover_optimization", b"cover_optimization", "cp_model_presolve", b"cp_model_presolve", "cp_model_probing_level", b"cp_model_probing_level", "cp_model_use_sat_presolve", b"cp_model_use_sat_presolve", "create_1uip_boolean_during_icr", b"create_1uip_boolean_during_icr", "cut_active_count_decay", b"cut_active_count_decay", "cut_cleanup_target", b"cut_cleanup_target", "cut_level", b"cut_level", "cut_max_active_count_value", b"cut_max_active_count_value", "debug_crash_if_lrat_check_fails", b"debug_crash_if_lrat_check_fails", "debug_crash_if_presolve_breaks_hint", b"debug_crash_if_presolve_breaks_hint", "debug_crash_on_bad_hint", b"debug_crash_on_bad_hint", "debug_max_num_presolve_operations", b"debug_max_num_presolve_operations", "debug_postsolve_with_full_solver", b"debug_postsolve_with_full_solver", "decision_subsumption_during_conflict_analysis", b"decision_subsumption_during_conflict_analysis", "default_restart_algorithms", b"default_restart_algorithms", "detect_linearized_product", b"detect_linearized_product", "detect_table_with_cost", b"detect_table_with_cost", "disable_constraint_expansion", b"disable_constraint_expansion", "diversify_lns_params", b"diversify_lns_params", "eagerly_subsume_last_n_conflicts", b"eagerly_subsume_last_n_conflicts", "encode_complex_linear_constraint_with_integer", b"encode_complex_linear_constraint_with_integer", "encode_cumulative_as_reservoir", b"encode_cumulative_as_reservoir", "enumerate_all_solutions", b"enumerate_all_solutions", "expand_alldiff_constraints", b"expand_alldiff_constraints", "expand_reservoir_constraints", b"expand_reservoir_constraints", "expand_reservoir_using_circuit", b"expand_reservoir_using_circuit", "exploit_all_lp_solution", b"exploit_all_lp_solution", "exploit_all_precedences", b"exploit_all_precedences", "exploit_best_solution", b"exploit_best_solution", "exploit_integer_lp_solution", b"exploit_integer_lp_solution", "exploit_objective", b"exploit_objective", "exploit_relaxation_solution", b"exploit_relaxation_solution", "extra_subsumption_during_conflict_analysis", b"extra_subsumption_during_conflict_analysis", "feasibility_jump_batch_dtime", b"feasibility_jump_batch_dtime", "feasibility_jump_decay", b"feasibility_jump_decay", "feasibility_jump_enable_restarts", b"feasibility_jump_enable_restarts", "feasibility_jump_linearization_level", b"feasibility_jump_linearization_level", "feasibility_jump_max_expanded_constraint_size", b"feasibility_jump_max_expanded_constraint_size", "feasibility_jump_restart_factor", b"feasibility_jump_restart_factor", "feasibility_jump_var_perburbation_range_ratio", b"feasibility_jump_var_perburbation_range_ratio", "feasibility_jump_var_randomization_probability", b"feasibility_jump_var_randomization_probability", "fill_additional_solutions_in_response", b"fill_additional_solutions_in_response", "fill_tightened_domains_in_response", b"fill_tightened_domains_in_response", "filter_sat_postsolve_clauses", b"filter_sat_postsolve_clauses", "find_big_linear_overlap", b"find_big_linear_overlap", "find_clauses_that_are_exactly_one", b"find_clauses_that_are_exactly_one", "find_multiple_cores", b"find_multiple_cores", "fix_variables_to_their_hinted_value", b"fix_variables_to_their_hinted_value", "fp_rounding", b"fp_rounding", "glucose_decay_increment", b"glucose_decay_increment", "glucose_decay_increment_period", b"glucose_decay_increment_period", "glucose_max_decay", b"glucose_max_decay", "hint_conflict_limit", b"hint_conflict_limit", "ignore_names", b"ignore_names", "infer_all_diffs", b"infer_all_diffs", "initial_polarity", b"initial_polarity", "initial_variables_activity", b"initial_variables_activity", "inprocessing_dtime_ratio", b"inprocessing_dtime_ratio", "inprocessing_minimization_dtime", b"inprocessing_minimization_dtime", "inprocessing_minimization_use_all_orderings", b"inprocessing_minimization_use_all_orderings", "inprocessing_minimization_use_conflict_analysis", b"inprocessing_minimization_use_conflict_analysis", "inprocessing_probing_dtime", b"inprocessing_probing_dtime", "inprocessing_use_congruence_closure", b"inprocessing_use_congruence_closure", "inprocessing_use_sat_sweeping", b"inprocessing_use_sat_sweeping", "instantiate_all_variables", b"instantiate_all_variables", "interleave_batch_size", b"interleave_batch_size", "interleave_search", b"interleave_search", "keep_all_feasible_solutions_in_presolve", b"keep_all_feasible_solutions_in_presolve", "keep_symmetry_in_presolve", b"keep_symmetry_in_presolve", "lb_relax_num_workers_threshold", b"lb_relax_num_workers_threshold", "linear_split_size", b"linear_split_size", "linearization_level", b"linearization_level", "lns_initial_deterministic_limit", b"lns_initial_deterministic_limit", "lns_initial_difficulty", b"lns_initial_difficulty", "load_at_most_ones_in_sat_presolve", b"load_at_most_ones_in_sat_presolve", "log_prefix", b"log_prefix", "log_search_progress", b"log_search_progress", "log_subsolver_statistics", b"log_subsolver_statistics", "log_to_response", b"log_to_response", "log_to_stdout", b"log_to_stdout", "lp_dual_tolerance", b"lp_dual_tolerance", "lp_primal_tolerance", b"lp_primal_tolerance", "max_all_diff_cut_size", b"max_all_diff_cut_size", "max_alldiff_domain_size", b"max_alldiff_domain_size", "max_backjump_levels", b"max_backjump_levels", "max_clause_activity_value", b"max_clause_activity_value", "max_consecutive_inactive_count", b"max_consecutive_inactive_count", "max_cut_rounds_at_level_zero", b"max_cut_rounds_at_level_zero", "max_deterministic_time", b"max_deterministic_time", "max_domain_size_for_linear2_expansion", b"max_domain_size_for_linear2_expansion", "max_domain_size_when_encoding_eq_neq_constraints", b"max_domain_size_when_encoding_eq_neq_constraints", "max_drat_time_in_seconds", b"max_drat_time_in_seconds", "max_integer_rounding_scaling", b"max_integer_rounding_scaling", "max_lin_max_size_for_expansion", b"max_lin_max_size_for_expansion", "max_memory_in_mb", b"max_memory_in_mb", "max_num_cuts", b"max_num_cuts", "max_num_deterministic_batches", b"max_num_deterministic_batches", "max_num_intervals_for_timetable_edge_finding", b"max_num_intervals_for_timetable_edge_finding", "max_number_of_conflicts", b"max_number_of_conflicts", "max_pairs_pairwise_reasoning_in_no_overlap_2d", b"max_pairs_pairwise_reasoning_in_no_overlap_2d", "max_presolve_iterations", b"max_presolve_iterations", "max_sat_assumption_order", b"max_sat_assumption_order", "max_sat_reverse_assumption_order", b"max_sat_reverse_assumption_order", "max_sat_stratification", b"max_sat_stratification", "max_size_to_create_precedence_literals_in_disjunctive", b"max_size_to_create_precedence_literals_in_disjunctive", "max_time_in_seconds", b"max_time_in_seconds", "max_variable_activity_value", b"max_variable_activity_value", "maximum_regions_to_split_in_disconnected_no_overlap_2d", b"maximum_regions_to_split_in_disconnected_no_overlap_2d", "merge_at_most_one_work_limit", b"merge_at_most_one_work_limit", "merge_no_overlap_work_limit", b"merge_no_overlap_work_limit", "min_orthogonality_for_lp_constraints", b"min_orthogonality_for_lp_constraints", "minimization_algorithm", b"minimization_algorithm", "minimize_reduction_during_pb_resolution", b"minimize_reduction_during_pb_resolution", "minimize_shared_clauses", b"minimize_shared_clauses", "mip_automatically_scale_variables", b"mip_automatically_scale_variables", "mip_check_precision", b"mip_check_precision", "mip_compute_true_objective_bound", b"mip_compute_true_objective_bound", "mip_drop_tolerance", b"mip_drop_tolerance", "mip_max_activity_exponent", b"mip_max_activity_exponent", "mip_max_bound", b"mip_max_bound", "mip_max_valid_magnitude", b"mip_max_valid_magnitude", "mip_presolve_level", b"mip_presolve_level", "mip_scale_large_domain", b"mip_scale_large_domain", "mip_treat_high_magnitude_bounds_as_infinity", b"mip_treat_high_magnitude_bounds_as_infinity", "mip_var_scaling", b"mip_var_scaling", "mip_wanted_precision", b"mip_wanted_precision", "name", b"name", "new_constraints_batch_size", b"new_constraints_batch_size", "new_linear_propagation", b"new_linear_propagation", "no_overlap_2d_boolean_relations_limit", b"no_overlap_2d_boolean_relations_limit", "num_conflicts_before_strategy_changes", b"num_conflicts_before_strategy_changes", "num_full_subsolvers", b"num_full_subsolvers", "num_search_workers", b"num_search_workers", "num_violation_ls", b"num_violation_ls", "num_workers", b"num_workers", "only_add_cuts_at_level_zero", b"only_add_cuts_at_level_zero", "only_solve_ip", b"only_solve_ip", "optimize_with_core", b"optimize_with_core", "optimize_with_lb_tree_search", b"optimize_with_lb_tree_search", "optimize_with_max_hs", b"optimize_with_max_hs", "output_drat_proof", b"output_drat_proof", "output_lrat_proof", b"output_lrat_proof", "pb_cleanup_increment", b"pb_cleanup_increment", "pb_cleanup_ratio", b"pb_cleanup_ratio", "permute_presolve_constraint_order", b"permute_presolve_constraint_order", "permute_variable_randomly", b"permute_variable_randomly", "polarity_exploit_ls_hints", b"polarity_exploit_ls_hints", "polarity_rephase_increment", b"polarity_rephase_increment", "polish_lp_solution", b"polish_lp_solution", "preferred_variable_order", b"preferred_variable_order", "presolve_blocked_clause", b"presolve_blocked_clause", "presolve_bva_threshold", b"presolve_bva_threshold", "presolve_bve_clause_weight", b"presolve_bve_clause_weight", "presolve_bve_threshold", b"presolve_bve_threshold", "presolve_extract_integer_enforcement", b"presolve_extract_integer_enforcement", "presolve_inclusion_work_limit", b"presolve_inclusion_work_limit", "presolve_probing_deterministic_time_limit", b"presolve_probing_deterministic_time_limit", "presolve_substitution_level", b"presolve_substitution_level", "presolve_use_bva", b"presolve_use_bva", "probing_deterministic_time_limit", b"probing_deterministic_time_limit", "probing_num_combinations_limit", b"probing_num_combinations_limit", "propagation_loop_detection_factor", b"propagation_loop_detection_factor", "pseudo_cost_reliability_threshold", b"pseudo_cost_reliability_threshold", "push_all_tasks_toward_start", b"push_all_tasks_toward_start", "random_branches_ratio", b"random_branches_ratio", "random_polarity_ratio", b"random_polarity_ratio", "random_seed", b"random_seed", "randomize_search", b"randomize_search", "relative_gap_limit", b"relative_gap_limit", "remove_fixed_variables_early", b"remove_fixed_variables_early", "repair_hint", b"repair_hint", "restart_dl_average_ratio", b"restart_dl_average_ratio", "restart_lbd_average_ratio", b"restart_lbd_average_ratio", "restart_period", b"restart_period", "restart_running_window_size", b"restart_running_window_size", "root_lp_iterations", b"root_lp_iterations", "routing_cut_dp_effort", b"routing_cut_dp_effort", "routing_cut_max_infeasible_path_length", b"routing_cut_max_infeasible_path_length", "routing_cut_subset_size_for_binary_relation_bound", b"routing_cut_subset_size_for_binary_relation_bound", "routing_cut_subset_size_for_exact_binary_relation_bound", b"routing_cut_subset_size_for_exact_binary_relation_bound", "routing_cut_subset_size_for_shortest_paths_bound", b"routing_cut_subset_size_for_shortest_paths_bound", "routing_cut_subset_size_for_tight_binary_relation_bound", b"routing_cut_subset_size_for_tight_binary_relation_bound", "save_lp_basis_in_lb_tree_search", b"save_lp_basis_in_lb_tree_search", "search_branching", b"search_branching", "search_random_variable_pool_size", b"search_random_variable_pool_size", "share_binary_clauses", b"share_binary_clauses", "share_glue_clauses", b"share_glue_clauses", "share_glue_clauses_dtime", b"share_glue_clauses_dtime", "share_level_zero_bounds", b"share_level_zero_bounds", "share_linear2_bounds", b"share_linear2_bounds", "share_objective_bounds", b"share_objective_bounds", "shared_tree_balance_tolerance", b"shared_tree_balance_tolerance", "shared_tree_max_nodes_per_worker", b"shared_tree_max_nodes_per_worker", "shared_tree_num_workers", b"shared_tree_num_workers", "shared_tree_open_leaves_per_worker", b"shared_tree_open_leaves_per_worker", "shared_tree_split_min_dtime", b"shared_tree_split_min_dtime", "shared_tree_split_strategy", b"shared_tree_split_strategy", "shared_tree_worker_enable_phase_sharing", b"shared_tree_worker_enable_phase_sharing", "shared_tree_worker_enable_trail_sharing", b"shared_tree_worker_enable_trail_sharing", "shared_tree_worker_min_restarts_per_subtree", b"shared_tree_worker_min_restarts_per_subtree", "shaving_deterministic_time_in_probing_search", b"shaving_deterministic_time_in_probing_search", "shaving_search_deterministic_time", b"shaving_search_deterministic_time", "shaving_search_threshold", b"shaving_search_threshold", "solution_pool_diversity_limit", b"solution_pool_diversity_limit", "solution_pool_size", b"solution_pool_size", "stop_after_first_solution", b"stop_after_first_solution", "stop_after_presolve", b"stop_after_presolve", "stop_after_root_propagation", b"stop_after_root_propagation", "strategy_change_increase_ratio", b"strategy_change_increase_ratio", "subsume_during_vivification", b"subsume_during_vivification", "subsumption_during_conflict_analysis", b"subsumption_during_conflict_analysis", "symmetry_detection_deterministic_time_limit", b"symmetry_detection_deterministic_time_limit", "symmetry_level", b"symmetry_level", "table_compression_level", b"table_compression_level", "transitive_precedences_work_limit", b"transitive_precedences_work_limit", "use_absl_random", b"use_absl_random", "use_all_different_for_circuit", b"use_all_different_for_circuit", "use_area_energetic_reasoning_in_no_overlap_2d", b"use_area_energetic_reasoning_in_no_overlap_2d", "use_blocking_restart", b"use_blocking_restart", "use_chronological_backtracking", b"use_chronological_backtracking", "use_combined_no_overlap", b"use_combined_no_overlap", "use_conservative_scale_overload_checker", b"use_conservative_scale_overload_checker", "use_disjunctive_constraint_in_cumulative", b"use_disjunctive_constraint_in_cumulative", "use_dual_scheduling_heuristics", b"use_dual_scheduling_heuristics", "use_dynamic_precedence_in_cumulative", b"use_dynamic_precedence_in_cumulative", "use_dynamic_precedence_in_disjunctive", b"use_dynamic_precedence_in_disjunctive", "use_energetic_reasoning_in_no_overlap_2d", b"use_energetic_reasoning_in_no_overlap_2d", "use_erwa_heuristic", b"use_erwa_heuristic", "use_exact_lp_reason", b"use_exact_lp_reason", "use_extended_probing", b"use_extended_probing", "use_feasibility_jump", b"use_feasibility_jump", "use_feasibility_pump", b"use_feasibility_pump", "use_hard_precedences_in_cumulative", b"use_hard_precedences_in_cumulative", "use_implied_bounds", b"use_implied_bounds", "use_lb_relax_lns", b"use_lb_relax_lns", "use_linear3_for_no_overlap_2d_precedences", b"use_linear3_for_no_overlap_2d_precedences", "use_lns", b"use_lns", "use_lns_only", b"use_lns_only", "use_ls_only", b"use_ls_only", "use_new_integer_conflict_resolution", b"use_new_integer_conflict_resolution", "use_objective_lb_search", b"use_objective_lb_search", "use_objective_shaving_search", b"use_objective_shaving_search", "use_optimization_hints", b"use_optimization_hints", "use_optional_variables", b"use_optional_variables", "use_overload_checker_in_cumulative", b"use_overload_checker_in_cumulative", "use_pb_resolution", b"use_pb_resolution", "use_phase_saving", b"use_phase_saving", "use_precedences_in_disjunctive_constraint", b"use_precedences_in_disjunctive_constraint", "use_probing_search", b"use_probing_search", "use_rins_lns", b"use_rins_lns", "use_sat_inprocessing", b"use_sat_inprocessing", "use_shared_tree_search", b"use_shared_tree_search", "use_strong_propagation_in_disjunctive", b"use_strong_propagation_in_disjunctive", "use_symmetry_in_lp", b"use_symmetry_in_lp", "use_timetable_edge_finding_in_cumulative", b"use_timetable_edge_finding_in_cumulative", "use_timetabling_in_no_overlap_2d", b"use_timetabling_in_no_overlap_2d", "use_try_edge_reasoning_in_no_overlap_2d", b"use_try_edge_reasoning_in_no_overlap_2d", "variable_activity_decay", b"variable_activity_decay", "variables_shaving_level", b"variables_shaving_level", "violation_ls_compound_move_probability", b"violation_ls_compound_move_probability", "violation_ls_perturbation_period", b"violation_ls_perturbation_period"] def HasField(self, field_name: _HasFieldArgType) -> builtins.bool: ... _ClearFieldArgType: typing_extensions.TypeAlias = typing.Literal["absolute_gap_limit", b"absolute_gap_limit", "add_cg_cuts", b"add_cg_cuts", "add_clique_cuts", b"add_clique_cuts", "add_lin_max_cuts", b"add_lin_max_cuts", "add_lp_constraints_lazily", b"add_lp_constraints_lazily", "add_mir_cuts", b"add_mir_cuts", "add_objective_cut", b"add_objective_cut", "add_rlt_cuts", b"add_rlt_cuts", "add_zero_half_cuts", b"add_zero_half_cuts", "also_bump_variables_in_conflict_reasons", b"also_bump_variables_in_conflict_reasons", "alternative_pool_size", b"alternative_pool_size", "at_most_one_max_expansion_size", b"at_most_one_max_expansion_size", "auto_detect_greater_than_at_least_one_of", b"auto_detect_greater_than_at_least_one_of", "binary_minimization_algorithm", b"binary_minimization_algorithm", "binary_search_num_conflicts", b"binary_search_num_conflicts", "blocking_restart_multiplier", b"blocking_restart_multiplier", "blocking_restart_window_size", b"blocking_restart_window_size", "boolean_encoding_level", b"boolean_encoding_level", "catch_sigint_signal", b"catch_sigint_signal", "check_drat_proof", b"check_drat_proof", "check_lrat_proof", b"check_lrat_proof", "check_merged_lrat_proof", b"check_merged_lrat_proof", "chronological_backtrack_min_conflicts", b"chronological_backtrack_min_conflicts", "clause_activity_decay", b"clause_activity_decay", "clause_cleanup_lbd_bound", b"clause_cleanup_lbd_bound", "clause_cleanup_lbd_tier1", b"clause_cleanup_lbd_tier1", "clause_cleanup_lbd_tier2", b"clause_cleanup_lbd_tier2", "clause_cleanup_ordering", b"clause_cleanup_ordering", "clause_cleanup_period", b"clause_cleanup_period", "clause_cleanup_period_increment", b"clause_cleanup_period_increment", "clause_cleanup_ratio", b"clause_cleanup_ratio", "clause_cleanup_target", b"clause_cleanup_target", "convert_intervals", b"convert_intervals", "core_minimization_level", b"core_minimization_level", "count_assumption_levels_in_lbd", b"count_assumption_levels_in_lbd", "cover_optimization", b"cover_optimization", "cp_model_presolve", b"cp_model_presolve", "cp_model_probing_level", b"cp_model_probing_level", "cp_model_use_sat_presolve", b"cp_model_use_sat_presolve", "create_1uip_boolean_during_icr", b"create_1uip_boolean_during_icr", "cut_active_count_decay", b"cut_active_count_decay", "cut_cleanup_target", b"cut_cleanup_target", "cut_level", b"cut_level", "cut_max_active_count_value", b"cut_max_active_count_value", "debug_crash_if_lrat_check_fails", b"debug_crash_if_lrat_check_fails", "debug_crash_if_presolve_breaks_hint", b"debug_crash_if_presolve_breaks_hint", "debug_crash_on_bad_hint", b"debug_crash_on_bad_hint", "debug_max_num_presolve_operations", b"debug_max_num_presolve_operations", "debug_postsolve_with_full_solver", b"debug_postsolve_with_full_solver", "decision_subsumption_during_conflict_analysis", b"decision_subsumption_during_conflict_analysis", "default_restart_algorithms", b"default_restart_algorithms", "detect_linearized_product", b"detect_linearized_product", "detect_table_with_cost", b"detect_table_with_cost", "disable_constraint_expansion", b"disable_constraint_expansion", "diversify_lns_params", b"diversify_lns_params", "eagerly_subsume_last_n_conflicts", b"eagerly_subsume_last_n_conflicts", "encode_complex_linear_constraint_with_integer", b"encode_complex_linear_constraint_with_integer", "encode_cumulative_as_reservoir", b"encode_cumulative_as_reservoir", "enumerate_all_solutions", b"enumerate_all_solutions", "expand_alldiff_constraints", b"expand_alldiff_constraints", "expand_reservoir_constraints", b"expand_reservoir_constraints", "expand_reservoir_using_circuit", b"expand_reservoir_using_circuit", "exploit_all_lp_solution", b"exploit_all_lp_solution", "exploit_all_precedences", b"exploit_all_precedences", "exploit_best_solution", b"exploit_best_solution", "exploit_integer_lp_solution", b"exploit_integer_lp_solution", "exploit_objective", b"exploit_objective", "exploit_relaxation_solution", b"exploit_relaxation_solution", "extra_subsolvers", b"extra_subsolvers", "extra_subsumption_during_conflict_analysis", b"extra_subsumption_during_conflict_analysis", "feasibility_jump_batch_dtime", b"feasibility_jump_batch_dtime", "feasibility_jump_decay", b"feasibility_jump_decay", "feasibility_jump_enable_restarts", b"feasibility_jump_enable_restarts", "feasibility_jump_linearization_level", b"feasibility_jump_linearization_level", "feasibility_jump_max_expanded_constraint_size", b"feasibility_jump_max_expanded_constraint_size", "feasibility_jump_restart_factor", b"feasibility_jump_restart_factor", "feasibility_jump_var_perburbation_range_ratio", b"feasibility_jump_var_perburbation_range_ratio", "feasibility_jump_var_randomization_probability", b"feasibility_jump_var_randomization_probability", "fill_additional_solutions_in_response", b"fill_additional_solutions_in_response", "fill_tightened_domains_in_response", b"fill_tightened_domains_in_response", "filter_sat_postsolve_clauses", b"filter_sat_postsolve_clauses", "filter_subsolvers", b"filter_subsolvers", "find_big_linear_overlap", b"find_big_linear_overlap", "find_clauses_that_are_exactly_one", b"find_clauses_that_are_exactly_one", "find_multiple_cores", b"find_multiple_cores", "fix_variables_to_their_hinted_value", b"fix_variables_to_their_hinted_value", "fp_rounding", b"fp_rounding", "glucose_decay_increment", b"glucose_decay_increment", "glucose_decay_increment_period", b"glucose_decay_increment_period", "glucose_max_decay", b"glucose_max_decay", "hint_conflict_limit", b"hint_conflict_limit", "ignore_names", b"ignore_names", "ignore_subsolvers", b"ignore_subsolvers", "infer_all_diffs", b"infer_all_diffs", "initial_polarity", b"initial_polarity", "initial_variables_activity", b"initial_variables_activity", "inprocessing_dtime_ratio", b"inprocessing_dtime_ratio", "inprocessing_minimization_dtime", b"inprocessing_minimization_dtime", "inprocessing_minimization_use_all_orderings", b"inprocessing_minimization_use_all_orderings", "inprocessing_minimization_use_conflict_analysis", b"inprocessing_minimization_use_conflict_analysis", "inprocessing_probing_dtime", b"inprocessing_probing_dtime", "inprocessing_use_congruence_closure", b"inprocessing_use_congruence_closure", "inprocessing_use_sat_sweeping", b"inprocessing_use_sat_sweeping", "instantiate_all_variables", b"instantiate_all_variables", "interleave_batch_size", b"interleave_batch_size", "interleave_search", b"interleave_search", "keep_all_feasible_solutions_in_presolve", b"keep_all_feasible_solutions_in_presolve", "keep_symmetry_in_presolve", b"keep_symmetry_in_presolve", "lb_relax_num_workers_threshold", b"lb_relax_num_workers_threshold", "linear_split_size", b"linear_split_size", "linearization_level", b"linearization_level", "lns_initial_deterministic_limit", b"lns_initial_deterministic_limit", "lns_initial_difficulty", b"lns_initial_difficulty", "load_at_most_ones_in_sat_presolve", b"load_at_most_ones_in_sat_presolve", "log_prefix", b"log_prefix", "log_search_progress", b"log_search_progress", "log_subsolver_statistics", b"log_subsolver_statistics", "log_to_response", b"log_to_response", "log_to_stdout", b"log_to_stdout", "lp_dual_tolerance", b"lp_dual_tolerance", "lp_primal_tolerance", b"lp_primal_tolerance", "max_all_diff_cut_size", b"max_all_diff_cut_size", "max_alldiff_domain_size", b"max_alldiff_domain_size", "max_backjump_levels", b"max_backjump_levels", "max_clause_activity_value", b"max_clause_activity_value", "max_consecutive_inactive_count", b"max_consecutive_inactive_count", "max_cut_rounds_at_level_zero", b"max_cut_rounds_at_level_zero", "max_deterministic_time", b"max_deterministic_time", "max_domain_size_for_linear2_expansion", b"max_domain_size_for_linear2_expansion", "max_domain_size_when_encoding_eq_neq_constraints", b"max_domain_size_when_encoding_eq_neq_constraints", "max_drat_time_in_seconds", b"max_drat_time_in_seconds", "max_integer_rounding_scaling", b"max_integer_rounding_scaling", "max_lin_max_size_for_expansion", b"max_lin_max_size_for_expansion", "max_memory_in_mb", b"max_memory_in_mb", "max_num_cuts", b"max_num_cuts", "max_num_deterministic_batches", b"max_num_deterministic_batches", "max_num_intervals_for_timetable_edge_finding", b"max_num_intervals_for_timetable_edge_finding", "max_number_of_conflicts", b"max_number_of_conflicts", "max_pairs_pairwise_reasoning_in_no_overlap_2d", b"max_pairs_pairwise_reasoning_in_no_overlap_2d", "max_presolve_iterations", b"max_presolve_iterations", "max_sat_assumption_order", b"max_sat_assumption_order", "max_sat_reverse_assumption_order", b"max_sat_reverse_assumption_order", "max_sat_stratification", b"max_sat_stratification", "max_size_to_create_precedence_literals_in_disjunctive", b"max_size_to_create_precedence_literals_in_disjunctive", "max_time_in_seconds", b"max_time_in_seconds", "max_variable_activity_value", b"max_variable_activity_value", "maximum_regions_to_split_in_disconnected_no_overlap_2d", b"maximum_regions_to_split_in_disconnected_no_overlap_2d", "merge_at_most_one_work_limit", b"merge_at_most_one_work_limit", "merge_no_overlap_work_limit", b"merge_no_overlap_work_limit", "min_orthogonality_for_lp_constraints", b"min_orthogonality_for_lp_constraints", "minimization_algorithm", b"minimization_algorithm", "minimize_reduction_during_pb_resolution", b"minimize_reduction_during_pb_resolution", "minimize_shared_clauses", b"minimize_shared_clauses", "mip_automatically_scale_variables", b"mip_automatically_scale_variables", "mip_check_precision", b"mip_check_precision", "mip_compute_true_objective_bound", b"mip_compute_true_objective_bound", "mip_drop_tolerance", b"mip_drop_tolerance", "mip_max_activity_exponent", b"mip_max_activity_exponent", "mip_max_bound", b"mip_max_bound", "mip_max_valid_magnitude", b"mip_max_valid_magnitude", "mip_presolve_level", b"mip_presolve_level", "mip_scale_large_domain", b"mip_scale_large_domain", "mip_treat_high_magnitude_bounds_as_infinity", b"mip_treat_high_magnitude_bounds_as_infinity", "mip_var_scaling", b"mip_var_scaling", "mip_wanted_precision", b"mip_wanted_precision", "name", b"name", "new_constraints_batch_size", b"new_constraints_batch_size", "new_linear_propagation", b"new_linear_propagation", "no_overlap_2d_boolean_relations_limit", b"no_overlap_2d_boolean_relations_limit", "num_conflicts_before_strategy_changes", b"num_conflicts_before_strategy_changes", "num_full_subsolvers", b"num_full_subsolvers", "num_search_workers", b"num_search_workers", "num_violation_ls", b"num_violation_ls", "num_workers", b"num_workers", "only_add_cuts_at_level_zero", b"only_add_cuts_at_level_zero", "only_solve_ip", b"only_solve_ip", "optimize_with_core", b"optimize_with_core", "optimize_with_lb_tree_search", b"optimize_with_lb_tree_search", "optimize_with_max_hs", b"optimize_with_max_hs", "output_drat_proof", b"output_drat_proof", "output_lrat_proof", b"output_lrat_proof", "pb_cleanup_increment", b"pb_cleanup_increment", "pb_cleanup_ratio", b"pb_cleanup_ratio", "permute_presolve_constraint_order", b"permute_presolve_constraint_order", "permute_variable_randomly", b"permute_variable_randomly", "polarity_exploit_ls_hints", b"polarity_exploit_ls_hints", "polarity_rephase_increment", b"polarity_rephase_increment", "polish_lp_solution", b"polish_lp_solution", "preferred_variable_order", b"preferred_variable_order", "presolve_blocked_clause", b"presolve_blocked_clause", "presolve_bva_threshold", b"presolve_bva_threshold", "presolve_bve_clause_weight", b"presolve_bve_clause_weight", "presolve_bve_threshold", b"presolve_bve_threshold", "presolve_extract_integer_enforcement", b"presolve_extract_integer_enforcement", "presolve_inclusion_work_limit", b"presolve_inclusion_work_limit", "presolve_probing_deterministic_time_limit", b"presolve_probing_deterministic_time_limit", "presolve_substitution_level", b"presolve_substitution_level", "presolve_use_bva", b"presolve_use_bva", "probing_deterministic_time_limit", b"probing_deterministic_time_limit", "probing_num_combinations_limit", b"probing_num_combinations_limit", "propagation_loop_detection_factor", b"propagation_loop_detection_factor", "pseudo_cost_reliability_threshold", b"pseudo_cost_reliability_threshold", "push_all_tasks_toward_start", b"push_all_tasks_toward_start", "random_branches_ratio", b"random_branches_ratio", "random_polarity_ratio", b"random_polarity_ratio", "random_seed", b"random_seed", "randomize_search", b"randomize_search", "relative_gap_limit", b"relative_gap_limit", "remove_fixed_variables_early", b"remove_fixed_variables_early", "repair_hint", b"repair_hint", "restart_algorithms", b"restart_algorithms", "restart_dl_average_ratio", b"restart_dl_average_ratio", "restart_lbd_average_ratio", b"restart_lbd_average_ratio", "restart_period", b"restart_period", "restart_running_window_size", b"restart_running_window_size", "root_lp_iterations", b"root_lp_iterations", "routing_cut_dp_effort", b"routing_cut_dp_effort", "routing_cut_max_infeasible_path_length", b"routing_cut_max_infeasible_path_length", "routing_cut_subset_size_for_binary_relation_bound", b"routing_cut_subset_size_for_binary_relation_bound", "routing_cut_subset_size_for_exact_binary_relation_bound", b"routing_cut_subset_size_for_exact_binary_relation_bound", "routing_cut_subset_size_for_shortest_paths_bound", b"routing_cut_subset_size_for_shortest_paths_bound", "routing_cut_subset_size_for_tight_binary_relation_bound", b"routing_cut_subset_size_for_tight_binary_relation_bound", "save_lp_basis_in_lb_tree_search", b"save_lp_basis_in_lb_tree_search", "search_branching", b"search_branching", "search_random_variable_pool_size", b"search_random_variable_pool_size", "share_binary_clauses", b"share_binary_clauses", "share_glue_clauses", b"share_glue_clauses", "share_glue_clauses_dtime", b"share_glue_clauses_dtime", "share_level_zero_bounds", b"share_level_zero_bounds", "share_linear2_bounds", b"share_linear2_bounds", "share_objective_bounds", b"share_objective_bounds", "shared_tree_balance_tolerance", b"shared_tree_balance_tolerance", "shared_tree_max_nodes_per_worker", b"shared_tree_max_nodes_per_worker", "shared_tree_num_workers", b"shared_tree_num_workers", "shared_tree_open_leaves_per_worker", b"shared_tree_open_leaves_per_worker", "shared_tree_split_min_dtime", b"shared_tree_split_min_dtime", "shared_tree_split_strategy", b"shared_tree_split_strategy", "shared_tree_worker_enable_phase_sharing", b"shared_tree_worker_enable_phase_sharing", "shared_tree_worker_enable_trail_sharing", b"shared_tree_worker_enable_trail_sharing", "shared_tree_worker_min_restarts_per_subtree", b"shared_tree_worker_min_restarts_per_subtree", "shaving_deterministic_time_in_probing_search", b"shaving_deterministic_time_in_probing_search", "shaving_search_deterministic_time", b"shaving_search_deterministic_time", "shaving_search_threshold", b"shaving_search_threshold", "solution_pool_diversity_limit", b"solution_pool_diversity_limit", "solution_pool_size", b"solution_pool_size", "stop_after_first_solution", b"stop_after_first_solution", "stop_after_presolve", b"stop_after_presolve", "stop_after_root_propagation", b"stop_after_root_propagation", "strategy_change_increase_ratio", b"strategy_change_increase_ratio", "subsolver_params", b"subsolver_params", "subsolvers", b"subsolvers", "subsume_during_vivification", b"subsume_during_vivification", "subsumption_during_conflict_analysis", b"subsumption_during_conflict_analysis", "symmetry_detection_deterministic_time_limit", b"symmetry_detection_deterministic_time_limit", "symmetry_level", b"symmetry_level", "table_compression_level", b"table_compression_level", "transitive_precedences_work_limit", b"transitive_precedences_work_limit", "use_absl_random", b"use_absl_random", "use_all_different_for_circuit", b"use_all_different_for_circuit", "use_area_energetic_reasoning_in_no_overlap_2d", b"use_area_energetic_reasoning_in_no_overlap_2d", "use_blocking_restart", b"use_blocking_restart", "use_chronological_backtracking", b"use_chronological_backtracking", "use_combined_no_overlap", b"use_combined_no_overlap", "use_conservative_scale_overload_checker", b"use_conservative_scale_overload_checker", "use_disjunctive_constraint_in_cumulative", b"use_disjunctive_constraint_in_cumulative", "use_dual_scheduling_heuristics", b"use_dual_scheduling_heuristics", "use_dynamic_precedence_in_cumulative", b"use_dynamic_precedence_in_cumulative", "use_dynamic_precedence_in_disjunctive", b"use_dynamic_precedence_in_disjunctive", "use_energetic_reasoning_in_no_overlap_2d", b"use_energetic_reasoning_in_no_overlap_2d", "use_erwa_heuristic", b"use_erwa_heuristic", "use_exact_lp_reason", b"use_exact_lp_reason", "use_extended_probing", b"use_extended_probing", "use_feasibility_jump", b"use_feasibility_jump", "use_feasibility_pump", b"use_feasibility_pump", "use_hard_precedences_in_cumulative", b"use_hard_precedences_in_cumulative", "use_implied_bounds", b"use_implied_bounds", "use_lb_relax_lns", b"use_lb_relax_lns", "use_linear3_for_no_overlap_2d_precedences", b"use_linear3_for_no_overlap_2d_precedences", "use_lns", b"use_lns", "use_lns_only", b"use_lns_only", "use_ls_only", b"use_ls_only", "use_new_integer_conflict_resolution", b"use_new_integer_conflict_resolution", "use_objective_lb_search", b"use_objective_lb_search", "use_objective_shaving_search", b"use_objective_shaving_search", "use_optimization_hints", b"use_optimization_hints", "use_optional_variables", b"use_optional_variables", "use_overload_checker_in_cumulative", b"use_overload_checker_in_cumulative", "use_pb_resolution", b"use_pb_resolution", "use_phase_saving", b"use_phase_saving", "use_precedences_in_disjunctive_constraint", b"use_precedences_in_disjunctive_constraint", "use_probing_search", b"use_probing_search", "use_rins_lns", b"use_rins_lns", "use_sat_inprocessing", b"use_sat_inprocessing", "use_shared_tree_search", b"use_shared_tree_search", "use_strong_propagation_in_disjunctive", b"use_strong_propagation_in_disjunctive", "use_symmetry_in_lp", b"use_symmetry_in_lp", "use_timetable_edge_finding_in_cumulative", b"use_timetable_edge_finding_in_cumulative", "use_timetabling_in_no_overlap_2d", b"use_timetabling_in_no_overlap_2d", "use_try_edge_reasoning_in_no_overlap_2d", b"use_try_edge_reasoning_in_no_overlap_2d", "variable_activity_decay", b"variable_activity_decay", "variables_shaving_level", b"variables_shaving_level", "violation_ls_compound_move_probability", b"violation_ls_compound_move_probability", "violation_ls_perturbation_period", b"violation_ls_perturbation_period"] def ClearField(self, field_name: _ClearFieldArgType) -> None: ... Global___SatParameters: typing_extensions.TypeAlias = SatParameters