Interface CpSolverResponseOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
CpSolverResponse, CpSolverResponse.Builder
@Generated public interface CpSolverResponseOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • getStatusValue

      int getStatusValue()
       The status of the solve.
      .operations_research.sat.CpSolverStatus status = 1;
      Returns:
      The enum numeric value on the wire for status.

    • getStatus

      CpSolverStatus getStatus()
       The status of the solve.
      .operations_research.sat.CpSolverStatus status = 1;
      Returns:
      The status.

    • getSolutionList

      List<Long> getSolutionList()
       A feasible solution to the given problem. Depending on the returned status
 it may be optimal or just feasible. This is in one-to-one correspondence
 with a CpModelProto::variables repeated field and list the values of all
 the variables.
      repeated int64 solution = 2;
      Returns:
      A list containing the solution.

    • getSolutionCount

      int getSolutionCount()
       A feasible solution to the given problem. Depending on the returned status
 it may be optimal or just feasible. This is in one-to-one correspondence
 with a CpModelProto::variables repeated field and list the values of all
 the variables.
      repeated int64 solution = 2;
      Returns:
      The count of solution.

    • getSolution

      long getSolution(int index)
       A feasible solution to the given problem. Depending on the returned status
 it may be optimal or just feasible. This is in one-to-one correspondence
 with a CpModelProto::variables repeated field and list the values of all
 the variables.
      repeated int64 solution = 2;
      Parameters:
      index - The index of the element to return.
      Returns:
      The solution at the given index.

    • getObjectiveValue

      double getObjectiveValue()
       Only make sense for an optimization problem. The objective value of the
 returned solution if it is non-empty. If there is no solution, then for a
 minimization problem, this will be an upper-bound of the objective of any
 feasible solution, and a lower-bound for a maximization problem.
      double objective_value = 3;
      Returns:
      The objectiveValue.

    • getBestObjectiveBound

      double getBestObjectiveBound()
       Only make sense for an optimization problem. A proven lower-bound on the
 objective for a minimization problem, or a proven upper-bound for a
 maximization problem.
      double best_objective_bound = 4;
      Returns:
      The bestObjectiveBound.

    • getAdditionalSolutionsList

      List<CpSolverSolution> getAdditionalSolutionsList()
       If the parameter fill_additional_solutions_in_response is set, then we
 copy all the solutions from our internal solution pool here.

 Note that the one returned in the solution field will likely appear here
 too. Do not rely on the solutions order as it depends on our internal
 representation (after postsolve).
      repeated .operations_research.sat.CpSolverSolution additional_solutions = 27;

    • getAdditionalSolutions

      CpSolverSolution getAdditionalSolutions(int index)
       If the parameter fill_additional_solutions_in_response is set, then we
 copy all the solutions from our internal solution pool here.

 Note that the one returned in the solution field will likely appear here
 too. Do not rely on the solutions order as it depends on our internal
 representation (after postsolve).
      repeated .operations_research.sat.CpSolverSolution additional_solutions = 27;

    • getAdditionalSolutionsCount

      int getAdditionalSolutionsCount()
       If the parameter fill_additional_solutions_in_response is set, then we
 copy all the solutions from our internal solution pool here.

 Note that the one returned in the solution field will likely appear here
 too. Do not rely on the solutions order as it depends on our internal
 representation (after postsolve).
      repeated .operations_research.sat.CpSolverSolution additional_solutions = 27;

    • getAdditionalSolutionsOrBuilderList

      List<? extends CpSolverSolutionOrBuilder> getAdditionalSolutionsOrBuilderList()
       If the parameter fill_additional_solutions_in_response is set, then we
 copy all the solutions from our internal solution pool here.

 Note that the one returned in the solution field will likely appear here
 too. Do not rely on the solutions order as it depends on our internal
 representation (after postsolve).
      repeated .operations_research.sat.CpSolverSolution additional_solutions = 27;

    • getAdditionalSolutionsOrBuilder

      CpSolverSolutionOrBuilder getAdditionalSolutionsOrBuilder(int index)
       If the parameter fill_additional_solutions_in_response is set, then we
 copy all the solutions from our internal solution pool here.

 Note that the one returned in the solution field will likely appear here
 too. Do not rely on the solutions order as it depends on our internal
 representation (after postsolve).
      repeated .operations_research.sat.CpSolverSolution additional_solutions = 27;

    • getTightenedVariablesList

      List<IntegerVariableProto> getTightenedVariablesList()
       Advanced usage.

 If the option fill_tightened_domains_in_response is set, then this field
 will be a copy of the CpModelProto.variables where each domain has been
 reduced using the information the solver was able to derive. Note that this
 is only filled with the info derived during a normal search and we do not
 have any dedicated algorithm to improve it.

 Warning: if you didn't set keep_all_feasible_solutions_in_presolve, then
 these domains might exclude valid feasible solution. Otherwise for a
 feasibility problem, all feasible solution should be there.

 Warning: For an optimization problem, these will correspond to valid bounds
 for the problem of finding an improving solution to the best one found so
 far. It might be better to solve a feasibility version if one just want to
 explore the feasible region.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getTightenedVariables

      IntegerVariableProto getTightenedVariables(int index)
       Advanced usage.

 If the option fill_tightened_domains_in_response is set, then this field
 will be a copy of the CpModelProto.variables where each domain has been
 reduced using the information the solver was able to derive. Note that this
 is only filled with the info derived during a normal search and we do not
 have any dedicated algorithm to improve it.

 Warning: if you didn't set keep_all_feasible_solutions_in_presolve, then
 these domains might exclude valid feasible solution. Otherwise for a
 feasibility problem, all feasible solution should be there.

 Warning: For an optimization problem, these will correspond to valid bounds
 for the problem of finding an improving solution to the best one found so
 far. It might be better to solve a feasibility version if one just want to
 explore the feasible region.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getTightenedVariablesCount

      int getTightenedVariablesCount()
       Advanced usage.

 If the option fill_tightened_domains_in_response is set, then this field
 will be a copy of the CpModelProto.variables where each domain has been
 reduced using the information the solver was able to derive. Note that this
 is only filled with the info derived during a normal search and we do not
 have any dedicated algorithm to improve it.

 Warning: if you didn't set keep_all_feasible_solutions_in_presolve, then
 these domains might exclude valid feasible solution. Otherwise for a
 feasibility problem, all feasible solution should be there.

 Warning: For an optimization problem, these will correspond to valid bounds
 for the problem of finding an improving solution to the best one found so
 far. It might be better to solve a feasibility version if one just want to
 explore the feasible region.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getTightenedVariablesOrBuilderList

      List<? extends IntegerVariableProtoOrBuilder> getTightenedVariablesOrBuilderList()
       Advanced usage.

 If the option fill_tightened_domains_in_response is set, then this field
 will be a copy of the CpModelProto.variables where each domain has been
 reduced using the information the solver was able to derive. Note that this
 is only filled with the info derived during a normal search and we do not
 have any dedicated algorithm to improve it.

 Warning: if you didn't set keep_all_feasible_solutions_in_presolve, then
 these domains might exclude valid feasible solution. Otherwise for a
 feasibility problem, all feasible solution should be there.

 Warning: For an optimization problem, these will correspond to valid bounds
 for the problem of finding an improving solution to the best one found so
 far. It might be better to solve a feasibility version if one just want to
 explore the feasible region.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getTightenedVariablesOrBuilder

      IntegerVariableProtoOrBuilder getTightenedVariablesOrBuilder(int index)
       Advanced usage.

 If the option fill_tightened_domains_in_response is set, then this field
 will be a copy of the CpModelProto.variables where each domain has been
 reduced using the information the solver was able to derive. Note that this
 is only filled with the info derived during a normal search and we do not
 have any dedicated algorithm to improve it.

 Warning: if you didn't set keep_all_feasible_solutions_in_presolve, then
 these domains might exclude valid feasible solution. Otherwise for a
 feasibility problem, all feasible solution should be there.

 Warning: For an optimization problem, these will correspond to valid bounds
 for the problem of finding an improving solution to the best one found so
 far. It might be better to solve a feasibility version if one just want to
 explore the feasible region.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getSufficientAssumptionsForInfeasibilityList

      List<Integer> getSufficientAssumptionsForInfeasibilityList()
       A subset of the model "assumptions" field. This will only be filled if the
 status is INFEASIBLE. This subset of assumption will be enough to still get
 an infeasible problem.

 This is related to what is called the irreducible inconsistent subsystem or
 IIS. Except one is only concerned by the provided assumptions. There is
 also no guarantee that we return an irreducible (aka minimal subset).
 However, this is based on SAT explanation and there is a good chance it is
 not too large.

 If you really want a minimal subset, a possible way to get one is by
 changing your model to minimize the number of assumptions at false, but
 this is likely an harder problem to solve.

 Important: Currently, this is minimized only in single-thread and if the
 problem is not an optimization problem, otherwise, it will always include
 all the assumptions.

 TODO(user): Allows for returning multiple core at once.
      repeated int32 sufficient_assumptions_for_infeasibility = 23;
      Returns:
      A list containing the sufficientAssumptionsForInfeasibility.

    • getSufficientAssumptionsForInfeasibilityCount

      int getSufficientAssumptionsForInfeasibilityCount()
       A subset of the model "assumptions" field. This will only be filled if the
 status is INFEASIBLE. This subset of assumption will be enough to still get
 an infeasible problem.

 This is related to what is called the irreducible inconsistent subsystem or
 IIS. Except one is only concerned by the provided assumptions. There is
 also no guarantee that we return an irreducible (aka minimal subset).
 However, this is based on SAT explanation and there is a good chance it is
 not too large.

 If you really want a minimal subset, a possible way to get one is by
 changing your model to minimize the number of assumptions at false, but
 this is likely an harder problem to solve.

 Important: Currently, this is minimized only in single-thread and if the
 problem is not an optimization problem, otherwise, it will always include
 all the assumptions.

 TODO(user): Allows for returning multiple core at once.
      repeated int32 sufficient_assumptions_for_infeasibility = 23;
      Returns:
      The count of sufficientAssumptionsForInfeasibility.

    • getSufficientAssumptionsForInfeasibility

      int getSufficientAssumptionsForInfeasibility(int index)
       A subset of the model "assumptions" field. This will only be filled if the
 status is INFEASIBLE. This subset of assumption will be enough to still get
 an infeasible problem.

 This is related to what is called the irreducible inconsistent subsystem or
 IIS. Except one is only concerned by the provided assumptions. There is
 also no guarantee that we return an irreducible (aka minimal subset).
 However, this is based on SAT explanation and there is a good chance it is
 not too large.

 If you really want a minimal subset, a possible way to get one is by
 changing your model to minimize the number of assumptions at false, but
 this is likely an harder problem to solve.

 Important: Currently, this is minimized only in single-thread and if the
 problem is not an optimization problem, otherwise, it will always include
 all the assumptions.

 TODO(user): Allows for returning multiple core at once.
      repeated int32 sufficient_assumptions_for_infeasibility = 23;
      Parameters:
      index - The index of the element to return.
      Returns:
      The sufficientAssumptionsForInfeasibility at the given index.

    • hasIntegerObjective

      boolean hasIntegerObjective()
       Contains the integer objective optimized internally. This is only filled if
 the problem had a floating point objective, and on the final response, not
 the ones given to callbacks.
      .operations_research.sat.CpObjectiveProto integer_objective = 28;
      Returns:
      Whether the integerObjective field is set.

    • getIntegerObjective

      CpObjectiveProto getIntegerObjective()
       Contains the integer objective optimized internally. This is only filled if
 the problem had a floating point objective, and on the final response, not
 the ones given to callbacks.
      .operations_research.sat.CpObjectiveProto integer_objective = 28;
      Returns:
      The integerObjective.

    • getIntegerObjectiveOrBuilder

      CpObjectiveProtoOrBuilder getIntegerObjectiveOrBuilder()
       Contains the integer objective optimized internally. This is only filled if
 the problem had a floating point objective, and on the final response, not
 the ones given to callbacks.
      .operations_research.sat.CpObjectiveProto integer_objective = 28;

    • getInnerObjectiveLowerBound

      long getInnerObjectiveLowerBound()
       Advanced usage.

 A lower bound on the integer expression of the objective. This is either a
 bound on the expression in the returned integer_objective or on the integer
 expression of the original objective if the problem already has an integer
 objective.

 TODO(user): This should be renamed integer_objective_lower_bound.
      int64 inner_objective_lower_bound = 29;
      Returns:
      The innerObjectiveLowerBound.

    • getNumIntegers

      long getNumIntegers()
       Some statistics about the solve.

 Important: in multithread, this correspond the statistics of the first
 subsolver. Which is usually the one with the user defined parameters. Or
 the default-search if none are specified.
      int64 num_integers = 30;
      Returns:
      The numIntegers.

    • getNumBooleans

      long getNumBooleans()
      int64 num_booleans = 10;
      Returns:
      The numBooleans.

    • getNumFixedBooleans

      long getNumFixedBooleans()
      int64 num_fixed_booleans = 31;
      Returns:
      The numFixedBooleans.

    • getNumConflicts

      long getNumConflicts()
      int64 num_conflicts = 11;
      Returns:
      The numConflicts.

    • getNumBranches

      long getNumBranches()
      int64 num_branches = 12;
      Returns:
      The numBranches.

    • getNumBinaryPropagations

      long getNumBinaryPropagations()
      int64 num_binary_propagations = 13;
      Returns:
      The numBinaryPropagations.

    • getNumIntegerPropagations

      long getNumIntegerPropagations()
      int64 num_integer_propagations = 14;
      Returns:
      The numIntegerPropagations.

    • getNumRestarts

      long getNumRestarts()
      int64 num_restarts = 24;
      Returns:
      The numRestarts.

    • getNumLpIterations

      long getNumLpIterations()
      int64 num_lp_iterations = 25;
      Returns:
      The numLpIterations.

    • getWallTime

      double getWallTime()
       The time counted from the beginning of the Solve() call.
      double wall_time = 15;
      Returns:
      The wallTime.

    • getUserTime

      double getUserTime()
      double user_time = 16;
      Returns:
      The userTime.

    • getDeterministicTime

      double getDeterministicTime()
      double deterministic_time = 17;
      Returns:
      The deterministicTime.

    • getGapIntegral

      double getGapIntegral()
       The integral of log(1 + absolute_objective_gap) over time.
      double gap_integral = 22;
      Returns:
      The gapIntegral.

    • getSolutionInfo

      String getSolutionInfo()
       Additional information about how the solution was found. It also stores
 model or parameters errors that caused the model to be invalid.
      string solution_info = 20;
      Returns:
      The solutionInfo.

    • getSolutionInfoBytes

      com.google.protobuf.ByteString getSolutionInfoBytes()
       Additional information about how the solution was found. It also stores
 model or parameters errors that caused the model to be invalid.
      string solution_info = 20;
      Returns:
      The bytes for solutionInfo.

    • getSolveLog

      String getSolveLog()
       The solve log will be filled if the parameter log_to_response is set to
 true.
      string solve_log = 26;
      Returns:
      The solveLog.

    • getSolveLogBytes

      com.google.protobuf.ByteString getSolveLogBytes()
       The solve log will be filled if the parameter log_to_response is set to
 true.
      string solve_log = 26;
      Returns:
      The bytes for solveLog.