Package com.google.ortools.sat

Interface CpModelProtoOrBuilder

  • All Superinterfaces:
    com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
    All Known Implementing Classes:
    CpModelProto, CpModelProto.Builder
    public interface CpModelProtoOrBuilder
extends com.google.protobuf.MessageOrBuilder

    • Method Detail

      • getName

        java.lang.String getName()
         For debug/logging only. Can be empty.
        string name = 1;
        Returns:
        The name.

      • getNameBytes

        com.google.protobuf.ByteString getNameBytes()
         For debug/logging only. Can be empty.
        string name = 1;
        Returns:
        The bytes for name.

      • getVariablesList

        java.util.List<IntegerVariableProto> getVariablesList()
         The associated Protos should be referred by their index in these fields.
        repeated .operations_research.sat.IntegerVariableProto variables = 2;

      • getVariables

        IntegerVariableProto getVariables​(int index)
         The associated Protos should be referred by their index in these fields.
        repeated .operations_research.sat.IntegerVariableProto variables = 2;

      • getVariablesCount

        int getVariablesCount()
         The associated Protos should be referred by their index in these fields.
        repeated .operations_research.sat.IntegerVariableProto variables = 2;

      • getVariablesOrBuilderList

        java.util.List<? extends IntegerVariableProtoOrBuilder> getVariablesOrBuilderList()
         The associated Protos should be referred by their index in these fields.
        repeated .operations_research.sat.IntegerVariableProto variables = 2;

      • getVariablesOrBuilder

        IntegerVariableProtoOrBuilder getVariablesOrBuilder​(int index)
         The associated Protos should be referred by their index in these fields.
        repeated .operations_research.sat.IntegerVariableProto variables = 2;

      • getConstraintsList

        java.util.List<ConstraintProto> getConstraintsList()
        repeated .operations_research.sat.ConstraintProto constraints = 3;

      • getConstraints

        ConstraintProto getConstraints​(int index)
        repeated .operations_research.sat.ConstraintProto constraints = 3;

      • getConstraintsCount

        int getConstraintsCount()
        repeated .operations_research.sat.ConstraintProto constraints = 3;

      • getConstraintsOrBuilderList

        java.util.List<? extends ConstraintProtoOrBuilder> getConstraintsOrBuilderList()
        repeated .operations_research.sat.ConstraintProto constraints = 3;

      • getConstraintsOrBuilder

        ConstraintProtoOrBuilder getConstraintsOrBuilder​(int index)
        repeated .operations_research.sat.ConstraintProto constraints = 3;

      • hasObjective

        boolean hasObjective()
         The objective to minimize. Can be empty for pure decision problems.
        .operations_research.sat.CpObjectiveProto objective = 4;
        Returns:
        Whether the objective field is set.

      • getObjective

        CpObjectiveProto getObjective()
         The objective to minimize. Can be empty for pure decision problems.
        .operations_research.sat.CpObjectiveProto objective = 4;
        Returns:
        The objective.

      • getObjectiveOrBuilder

        CpObjectiveProtoOrBuilder getObjectiveOrBuilder()
         The objective to minimize. Can be empty for pure decision problems.
        .operations_research.sat.CpObjectiveProto objective = 4;

      • hasFloatingPointObjective

        boolean hasFloatingPointObjective()
         Advanced usage.
 It is invalid to have both an objective and a floating point objective.

 The objective of the model, in floating point format. The solver will
 automatically scale this to integer during expansion and thus convert it to
 a normal CpObjectiveProto. See the mip* parameters to control how this is
 scaled. In most situation the precision will be good enough, but you can
 see the logs to see what are the precision guaranteed when this is
 converted to a fixed point representation.

 Note that even if the precision is bad, the returned objective_value and
 best_objective_bound will be computed correctly. So at the end of the solve
 you can check the gap if you only want precise optimal.
        .operations_research.sat.FloatObjectiveProto floating_point_objective = 9;
        Returns:
        Whether the floatingPointObjective field is set.

      • getFloatingPointObjective

        FloatObjectiveProto getFloatingPointObjective()
         Advanced usage.
 It is invalid to have both an objective and a floating point objective.

 The objective of the model, in floating point format. The solver will
 automatically scale this to integer during expansion and thus convert it to
 a normal CpObjectiveProto. See the mip* parameters to control how this is
 scaled. In most situation the precision will be good enough, but you can
 see the logs to see what are the precision guaranteed when this is
 converted to a fixed point representation.

 Note that even if the precision is bad, the returned objective_value and
 best_objective_bound will be computed correctly. So at the end of the solve
 you can check the gap if you only want precise optimal.
        .operations_research.sat.FloatObjectiveProto floating_point_objective = 9;
        Returns:
        The floatingPointObjective.

      • getFloatingPointObjectiveOrBuilder

        FloatObjectiveProtoOrBuilder getFloatingPointObjectiveOrBuilder()
         Advanced usage.
 It is invalid to have both an objective and a floating point objective.

 The objective of the model, in floating point format. The solver will
 automatically scale this to integer during expansion and thus convert it to
 a normal CpObjectiveProto. See the mip* parameters to control how this is
 scaled. In most situation the precision will be good enough, but you can
 see the logs to see what are the precision guaranteed when this is
 converted to a fixed point representation.

 Note that even if the precision is bad, the returned objective_value and
 best_objective_bound will be computed correctly. So at the end of the solve
 you can check the gap if you only want precise optimal.
        .operations_research.sat.FloatObjectiveProto floating_point_objective = 9;

      • getSearchStrategyList

        java.util.List<DecisionStrategyProto> getSearchStrategyList()
         Defines the strategy that the solver should follow when the
 search_branching parameter is set to FIXED_SEARCH. Note that this strategy
 is also used as a heuristic when we are not in fixed search.

 Advanced Usage: if not all variables appears and the parameter
 "instantiate_all_variables" is set to false, then the solver will not try
 to instantiate the variables that do not appear. Thus, at the end of the
 search, not all variables may be fixed. Currently, we will set them to
 their lower bound in the solution.
        repeated .operations_research.sat.DecisionStrategyProto search_strategy = 5;

      • getSearchStrategy

        DecisionStrategyProto getSearchStrategy​(int index)
         Defines the strategy that the solver should follow when the
 search_branching parameter is set to FIXED_SEARCH. Note that this strategy
 is also used as a heuristic when we are not in fixed search.

 Advanced Usage: if not all variables appears and the parameter
 "instantiate_all_variables" is set to false, then the solver will not try
 to instantiate the variables that do not appear. Thus, at the end of the
 search, not all variables may be fixed. Currently, we will set them to
 their lower bound in the solution.
        repeated .operations_research.sat.DecisionStrategyProto search_strategy = 5;

      • getSearchStrategyCount

        int getSearchStrategyCount()
         Defines the strategy that the solver should follow when the
 search_branching parameter is set to FIXED_SEARCH. Note that this strategy
 is also used as a heuristic when we are not in fixed search.

 Advanced Usage: if not all variables appears and the parameter
 "instantiate_all_variables" is set to false, then the solver will not try
 to instantiate the variables that do not appear. Thus, at the end of the
 search, not all variables may be fixed. Currently, we will set them to
 their lower bound in the solution.
        repeated .operations_research.sat.DecisionStrategyProto search_strategy = 5;

      • getSearchStrategyOrBuilderList

        java.util.List<? extends DecisionStrategyProtoOrBuilder> getSearchStrategyOrBuilderList()
         Defines the strategy that the solver should follow when the
 search_branching parameter is set to FIXED_SEARCH. Note that this strategy
 is also used as a heuristic when we are not in fixed search.

 Advanced Usage: if not all variables appears and the parameter
 "instantiate_all_variables" is set to false, then the solver will not try
 to instantiate the variables that do not appear. Thus, at the end of the
 search, not all variables may be fixed. Currently, we will set them to
 their lower bound in the solution.
        repeated .operations_research.sat.DecisionStrategyProto search_strategy = 5;

      • getSearchStrategyOrBuilder

        DecisionStrategyProtoOrBuilder getSearchStrategyOrBuilder​(int index)
         Defines the strategy that the solver should follow when the
 search_branching parameter is set to FIXED_SEARCH. Note that this strategy
 is also used as a heuristic when we are not in fixed search.

 Advanced Usage: if not all variables appears and the parameter
 "instantiate_all_variables" is set to false, then the solver will not try
 to instantiate the variables that do not appear. Thus, at the end of the
 search, not all variables may be fixed. Currently, we will set them to
 their lower bound in the solution.
        repeated .operations_research.sat.DecisionStrategyProto search_strategy = 5;

      • hasSolutionHint

        boolean hasSolutionHint()
         Solution hint.

 If a feasible or almost-feasible solution to the problem is already known,
 it may be helpful to pass it to the solver so that it can be used. The
 solver will try to use this information to create its initial feasible
 solution.

 Note that it may not always be faster to give a hint like this to the
 solver. There is also no guarantee that the solver will use this hint or
 try to return a solution "close" to this assignment in case of multiple
 optimal solutions.
        .operations_research.sat.PartialVariableAssignment solution_hint = 6;
        Returns:
        Whether the solutionHint field is set.

      • getSolutionHint

        PartialVariableAssignment getSolutionHint()
         Solution hint.

 If a feasible or almost-feasible solution to the problem is already known,
 it may be helpful to pass it to the solver so that it can be used. The
 solver will try to use this information to create its initial feasible
 solution.

 Note that it may not always be faster to give a hint like this to the
 solver. There is also no guarantee that the solver will use this hint or
 try to return a solution "close" to this assignment in case of multiple
 optimal solutions.
        .operations_research.sat.PartialVariableAssignment solution_hint = 6;
        Returns:
        The solutionHint.

      • getSolutionHintOrBuilder

        PartialVariableAssignmentOrBuilder getSolutionHintOrBuilder()
         Solution hint.

 If a feasible or almost-feasible solution to the problem is already known,
 it may be helpful to pass it to the solver so that it can be used. The
 solver will try to use this information to create its initial feasible
 solution.

 Note that it may not always be faster to give a hint like this to the
 solver. There is also no guarantee that the solver will use this hint or
 try to return a solution "close" to this assignment in case of multiple
 optimal solutions.
        .operations_research.sat.PartialVariableAssignment solution_hint = 6;

      • getAssumptionsList

        java.util.List<java.lang.Integer> getAssumptionsList()
         A list of literals. The model will be solved assuming all these literals
 are true. Compared to just fixing the domain of these literals, using this
 mechanism is slower but allows in case the model is INFEASIBLE to get a
 potentially small subset of them that can be used to explain the
 infeasibility.

 Think (IIS), except when you are only concerned by the provided
 assumptions. This is powerful as it allows to group a set of logically
 related constraint under only one enforcement literal which can potentially
 give you a good and interpretable explanation for infeasiblity.

 Such infeasibility explanation will be available in the
 sufficient_assumptions_for_infeasibility response field.
        repeated int32 assumptions = 7;
        Returns:
        A list containing the assumptions.

      • getAssumptionsCount

        int getAssumptionsCount()
         A list of literals. The model will be solved assuming all these literals
 are true. Compared to just fixing the domain of these literals, using this
 mechanism is slower but allows in case the model is INFEASIBLE to get a
 potentially small subset of them that can be used to explain the
 infeasibility.

 Think (IIS), except when you are only concerned by the provided
 assumptions. This is powerful as it allows to group a set of logically
 related constraint under only one enforcement literal which can potentially
 give you a good and interpretable explanation for infeasiblity.

 Such infeasibility explanation will be available in the
 sufficient_assumptions_for_infeasibility response field.
        repeated int32 assumptions = 7;
        Returns:
        The count of assumptions.

      • getAssumptions

        int getAssumptions​(int index)
         A list of literals. The model will be solved assuming all these literals
 are true. Compared to just fixing the domain of these literals, using this
 mechanism is slower but allows in case the model is INFEASIBLE to get a
 potentially small subset of them that can be used to explain the
 infeasibility.

 Think (IIS), except when you are only concerned by the provided
 assumptions. This is powerful as it allows to group a set of logically
 related constraint under only one enforcement literal which can potentially
 give you a good and interpretable explanation for infeasiblity.

 Such infeasibility explanation will be available in the
 sufficient_assumptions_for_infeasibility response field.
        repeated int32 assumptions = 7;
        Parameters:
        index - The index of the element to return.
        Returns:
        The assumptions at the given index.

      • hasSymmetry

        boolean hasSymmetry()
         For now, this is not meant to be filled by a client writing a model, but
 by our preprocessing step.

 Information about the symmetries of the feasible solution space.
 These usually leaves the objective invariant.
        .operations_research.sat.SymmetryProto symmetry = 8;
        Returns:
        Whether the symmetry field is set.

      • getSymmetry

        SymmetryProto getSymmetry()
         For now, this is not meant to be filled by a client writing a model, but
 by our preprocessing step.

 Information about the symmetries of the feasible solution space.
 These usually leaves the objective invariant.
        .operations_research.sat.SymmetryProto symmetry = 8;
        Returns:
        The symmetry.

      • getSymmetryOrBuilder

        SymmetryProtoOrBuilder getSymmetryOrBuilder()
         For now, this is not meant to be filled by a client writing a model, but
 by our preprocessing step.

 Information about the symmetries of the feasible solution space.
 These usually leaves the objective invariant.
        .operations_research.sat.SymmetryProto symmetry = 8;