Package com.google.ortools.sat

Interface CpSolverResponseOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
CpSolverResponse, CpSolverResponse.Builder
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

      java.util.List<java.lang.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.

    • getSolutionLowerBoundsList

      java.util.List<java.lang.Long> getSolutionLowerBoundsList()
       Advanced usage.
 If the problem has some variables that are not fixed at the end of the
 search (because of a particular search strategy in the CpModelProto) then
 this will be used instead of filling the solution above. The two fields
 will then contains the lower and upper bounds of each variable as they were
 when the best "solution" was found.
      repeated int64 solution_lower_bounds = 18;
      Returns:
      A list containing the solutionLowerBounds.

    • getSolutionLowerBoundsCount

      int getSolutionLowerBoundsCount()
       Advanced usage.
 If the problem has some variables that are not fixed at the end of the
 search (because of a particular search strategy in the CpModelProto) then
 this will be used instead of filling the solution above. The two fields
 will then contains the lower and upper bounds of each variable as they were
 when the best "solution" was found.
      repeated int64 solution_lower_bounds = 18;
      Returns:
      The count of solutionLowerBounds.

    • getSolutionLowerBounds

      long getSolutionLowerBounds​(int index)
       Advanced usage.
 If the problem has some variables that are not fixed at the end of the
 search (because of a particular search strategy in the CpModelProto) then
 this will be used instead of filling the solution above. The two fields
 will then contains the lower and upper bounds of each variable as they were
 when the best "solution" was found.
      repeated int64 solution_lower_bounds = 18;
      Parameters:
      index - The index of the element to return.
      Returns:
      The solutionLowerBounds at the given index.

    • getSolutionUpperBoundsList

      java.util.List<java.lang.Long> getSolutionUpperBoundsList()
      repeated int64 solution_upper_bounds = 19;
      Returns:
      A list containing the solutionUpperBounds.

    • getSolutionUpperBoundsCount

      int getSolutionUpperBoundsCount()
      repeated int64 solution_upper_bounds = 19;
      Returns:
      The count of solutionUpperBounds.

    • getSolutionUpperBounds

      long getSolutionUpperBounds​(int index)
      repeated int64 solution_upper_bounds = 19;
      Parameters:
      index - The index of the element to return.
      Returns:
      The solutionUpperBounds at the given index.

    • getTightenedVariablesList

      java.util.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.
 If the problem is a feasibility problem, then these bounds will be valid
 for any feasible solution. If the problem is an optimization problem, then
 these bounds will only be valid for any OPTIMAL solutions, it can exclude
 sub-optimal feasible ones.
      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.
 If the problem is a feasibility problem, then these bounds will be valid
 for any feasible solution. If the problem is an optimization problem, then
 these bounds will only be valid for any OPTIMAL solutions, it can exclude
 sub-optimal feasible ones.
      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.
 If the problem is a feasibility problem, then these bounds will be valid
 for any feasible solution. If the problem is an optimization problem, then
 these bounds will only be valid for any OPTIMAL solutions, it can exclude
 sub-optimal feasible ones.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getTightenedVariablesOrBuilderList

      java.util.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.
 If the problem is a feasibility problem, then these bounds will be valid
 for any feasible solution. If the problem is an optimization problem, then
 these bounds will only be valid for any OPTIMAL solutions, it can exclude
 sub-optimal feasible ones.
      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.
 If the problem is a feasibility problem, then these bounds will be valid
 for any feasible solution. If the problem is an optimization problem, then
 these bounds will only be valid for any OPTIMAL solutions, it can exclude
 sub-optimal feasible ones.
      repeated .operations_research.sat.IntegerVariableProto tightened_variables = 21;

    • getSufficientAssumptionsForInfeasibilityList

      java.util.List<java.lang.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.
 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.
 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.
 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.

    • getAllSolutionsWereFound

      boolean getAllSolutionsWereFound()
       This will be true iff the solver was asked to find all solutions to a
 satisfiability problem (or all optimal solutions to an optimization
 problem), and it was successful in doing so.
 TODO(user): Remove as we also use the OPTIMAL vs FEASIBLE status for that.
      bool all_solutions_were_found = 5;
      Returns:
      The allSolutionsWereFound.

    • getNumBooleans

      long getNumBooleans()
       Some statistics about the solve.
      int64 num_booleans = 10;
      Returns:
      The numBooleans.

    • 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()
      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.

    • getPrimalIntegral

      double getPrimalIntegral()
      double primal_integral = 22;
      Returns:
      The primalIntegral.

    • getSolutionInfo

      java.lang.String getSolutionInfo()
       Additional information about how the solution was found.
      string solution_info = 20;
      Returns:
      The solutionInfo.

    • getSolutionInfoBytes

      com.google.protobuf.ByteString getSolutionInfoBytes()
       Additional information about how the solution was found.
      string solution_info = 20;
      Returns:
      The bytes for solutionInfo.

    • getSolveLog

      java.lang.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.