Java Reference

Java Reference

CpSolverResponse.Builder

Detailed Description

The response returned by a solver trying to solve a CpModelProto.
TODO(user): support returning multiple solutions. Look at the Stubby
streaming API as we probably wants to get them as they are found.
Next id: 24

Protobuf type

operations_research.sat.CpSolverResponse

Definition at line 1260 of file CpSolverResponse.java.

Public Member Functions

.lang.Override Builder clear ()
 
.lang.Override com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()
 
.lang.Override com.google.ortools.sat.CpSolverResponse getDefaultInstanceForType ()
 
.lang.Override com.google.ortools.sat.CpSolverResponse build ()
 
.lang.Override com.google.ortools.sat.CpSolverResponse buildPartial ()
 
.lang.Override Builder clone ()
 
.lang.Override Builder setField (com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
 
.lang.Override Builder clearField (com.google.protobuf.Descriptors.FieldDescriptor field)
 
.lang.Override Builder clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)
 
.lang.Override Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value)
 
.lang.Override Builder addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
 
.lang.Override Builder mergeFrom (com.google.protobuf.Message other)
 
Builder mergeFrom (com.google.ortools.sat.CpSolverResponse other)
 
.lang.Override final boolean isInitialized ()
 
.lang.Override Builder mergeFrom (com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException
 
.lang.Override int getStatusValue ()
 
Builder setStatusValue (int value)
 
.lang.Override com.google.ortools.sat.CpSolverStatus getStatus ()
 
Builder setStatus (com.google.ortools.sat.CpSolverStatus value)
 
Builder clearStatus ()
 
java.util.List< java.lang.Long > getSolutionList ()
 
int getSolutionCount ()
 
long getSolution (int index)
 
Builder setSolution (int index, long value)
 
Builder addSolution (long value)
 
Builder addAllSolution (java.lang.Iterable<? extends java.lang.Long > values)
 
Builder clearSolution ()
 
.lang.Override double getObjectiveValue ()
 
Builder setObjectiveValue (double value)
 
Builder clearObjectiveValue ()
 
.lang.Override double getBestObjectiveBound ()
 
Builder setBestObjectiveBound (double value)
 
Builder clearBestObjectiveBound ()
 
java.util.List< java.lang.Long > getSolutionLowerBoundsList ()
 
int getSolutionLowerBoundsCount ()
 
long getSolutionLowerBounds (int index)
 
Builder setSolutionLowerBounds (int index, long value)
 
Builder addSolutionLowerBounds (long value)
 
Builder addAllSolutionLowerBounds (java.lang.Iterable<? extends java.lang.Long > values)
 
Builder clearSolutionLowerBounds ()
 
java.util.List< java.lang.Long > getSolutionUpperBoundsList ()
 repeated int64 solution_upper_bounds = 19; More...
 
int getSolutionUpperBoundsCount ()
 repeated int64 solution_upper_bounds = 19; More...
 
long getSolutionUpperBounds (int index)
 repeated int64 solution_upper_bounds = 19; More...
 
Builder setSolutionUpperBounds (int index, long value)
 repeated int64 solution_upper_bounds = 19; More...
 
Builder addSolutionUpperBounds (long value)
 repeated int64 solution_upper_bounds = 19; More...
 
Builder addAllSolutionUpperBounds (java.lang.Iterable<? extends java.lang.Long > values)
 repeated int64 solution_upper_bounds = 19; More...
 
Builder clearSolutionUpperBounds ()
 repeated int64 solution_upper_bounds = 19; More...
 
java.util.List< com.google.ortools.sat.IntegerVariableProtogetTightenedVariablesList ()
 
int getTightenedVariablesCount ()
 
com.google.ortools.sat.IntegerVariableProto getTightenedVariables (int index)
 
Builder setTightenedVariables (int index, com.google.ortools.sat.IntegerVariableProto value)
 
Builder setTightenedVariables (int index, com.google.ortools.sat.IntegerVariableProto.Builder builderForValue)
 
Builder addTightenedVariables (com.google.ortools.sat.IntegerVariableProto value)
 
Builder addTightenedVariables (int index, com.google.ortools.sat.IntegerVariableProto value)
 
Builder addTightenedVariables (com.google.ortools.sat.IntegerVariableProto.Builder builderForValue)
 
Builder addTightenedVariables (int index, com.google.ortools.sat.IntegerVariableProto.Builder builderForValue)
 
Builder addAllTightenedVariables (java.lang.Iterable<? extends com.google.ortools.sat.IntegerVariableProto > values)
 
Builder clearTightenedVariables ()
 
Builder removeTightenedVariables (int index)
 
com.google.ortools.sat.IntegerVariableProto.Builder getTightenedVariablesBuilder (int index)
 
com.google.ortools.sat.IntegerVariableProtoOrBuilder getTightenedVariablesOrBuilder (int index)
 
java.util.List<? extends com.google.ortools.sat.IntegerVariableProtoOrBuildergetTightenedVariablesOrBuilderList ()
 
com.google.ortools.sat.IntegerVariableProto.Builder addTightenedVariablesBuilder ()
 
com.google.ortools.sat.IntegerVariableProto.Builder addTightenedVariablesBuilder (int index)
 
java.util.List< com.google.ortools.sat.IntegerVariableProto.BuildergetTightenedVariablesBuilderList ()
 
java.util.List< java.lang.Integer > getSufficientAssumptionsForInfeasibilityList ()
 
int getSufficientAssumptionsForInfeasibilityCount ()
 
int getSufficientAssumptionsForInfeasibility (int index)
 
Builder setSufficientAssumptionsForInfeasibility (int index, int value)
 
Builder addSufficientAssumptionsForInfeasibility (int value)
 
Builder addAllSufficientAssumptionsForInfeasibility (java.lang.Iterable<? extends java.lang.Integer > values)
 
Builder clearSufficientAssumptionsForInfeasibility ()
 
.lang.Override boolean getAllSolutionsWereFound ()
 
Builder setAllSolutionsWereFound (boolean value)
 
Builder clearAllSolutionsWereFound ()
 
.lang.Override long getNumBooleans ()
 
Builder setNumBooleans (long value)
 
Builder clearNumBooleans ()
 
.lang.Override long getNumConflicts ()
 int64 num_conflicts = 11; More...
 
Builder setNumConflicts (long value)
 int64 num_conflicts = 11; More...
 
Builder clearNumConflicts ()
 int64 num_conflicts = 11; More...
 
.lang.Override long getNumBranches ()
 int64 num_branches = 12; More...
 
Builder setNumBranches (long value)
 int64 num_branches = 12; More...
 
Builder clearNumBranches ()
 int64 num_branches = 12; More...
 
.lang.Override long getNumBinaryPropagations ()
 int64 num_binary_propagations = 13; More...
 
Builder setNumBinaryPropagations (long value)
 int64 num_binary_propagations = 13; More...
 
Builder clearNumBinaryPropagations ()
 int64 num_binary_propagations = 13; More...
 
.lang.Override long getNumIntegerPropagations ()
 int64 num_integer_propagations = 14; More...
 
Builder setNumIntegerPropagations (long value)
 int64 num_integer_propagations = 14; More...
 
Builder clearNumIntegerPropagations ()
 int64 num_integer_propagations = 14; More...
 
.lang.Override double getWallTime ()
 double wall_time = 15; More...
 
Builder setWallTime (double value)
 double wall_time = 15; More...
 
Builder clearWallTime ()
 double wall_time = 15; More...
 
.lang.Override double getUserTime ()
 double user_time = 16; More...
 
Builder setUserTime (double value)
 double user_time = 16; More...
 
Builder clearUserTime ()
 double user_time = 16; More...
 
.lang.Override double getDeterministicTime ()
 double deterministic_time = 17; More...
 
Builder setDeterministicTime (double value)
 double deterministic_time = 17; More...
 
Builder clearDeterministicTime ()
 double deterministic_time = 17; More...
 
.lang.Override double getPrimalIntegral ()
 double primal_integral = 22; More...
 
Builder setPrimalIntegral (double value)
 double primal_integral = 22; More...
 
Builder clearPrimalIntegral ()
 double primal_integral = 22; More...
 
java.lang.String getSolutionInfo ()
 
com.google.protobuf.ByteString getSolutionInfoBytes ()
 
Builder setSolutionInfo (java.lang.String value)
 
Builder clearSolutionInfo ()
 
Builder setSolutionInfoBytes (com.google.protobuf.ByteString value)
 
.lang.Override final Builder setUnknownFields (final com.google.protobuf.UnknownFieldSet unknownFields)
 
.lang.Override final Builder mergeUnknownFields (final com.google.protobuf.UnknownFieldSet unknownFields)
 

Static Public Member Functions

static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()
 

Protected Member Functions

.lang.Override com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable ()
 

Member Function Documentation

◆ addAllSolution()

Builder addAllSolution ( java.lang.Iterable<? extends java.lang.Long >  values)
inline
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
valuesThe solution to add.
Returns
This builder for chaining.

Definition at line 1771 of file CpSolverResponse.java.

◆ addAllSolutionLowerBounds()

Builder addAllSolutionLowerBounds ( java.lang.Iterable<? extends java.lang.Long >  values)
inline
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
valuesThe solutionLowerBounds to add.
Returns
This builder for chaining.

Definition at line 2012 of file CpSolverResponse.java.

◆ addAllSolutionUpperBounds()

Builder addAllSolutionUpperBounds ( java.lang.Iterable<? extends java.lang.Long >  values)
inline

repeated int64 solution_upper_bounds = 19;

Parameters
valuesThe solutionUpperBounds to add.
Returns
This builder for chaining.

Definition at line 2100 of file CpSolverResponse.java.

◆ addAllSufficientAssumptionsForInfeasibility()

Builder addAllSufficientAssumptionsForInfeasibility ( java.lang.Iterable<? extends java.lang.Integer >  values)
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Parameters
valuesThe sufficientAssumptionsForInfeasibility to add.
Returns
This builder for chaining.

Definition at line 2737 of file CpSolverResponse.java.

◆ addAllTightenedVariables()

Builder addAllTightenedVariables ( java.lang.Iterable<? extends com.google.ortools.sat.IntegerVariableProto values)
inline
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;

Definition at line 2386 of file CpSolverResponse.java.

◆ addRepeatedField()

.lang.Override Builder addRepeatedField ( com.google.protobuf.Descriptors.FieldDescriptor  field,
java.lang.Object  value 
)
inline

Definition at line 1439 of file CpSolverResponse.java.

◆ addSolution()

Builder addSolution ( long  value)
inline
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
valueThe solution to add.
Returns
This builder for chaining.

Definition at line 1753 of file CpSolverResponse.java.

◆ addSolutionLowerBounds()

Builder addSolutionLowerBounds ( long  value)
inline
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
valueThe solutionLowerBounds to add.
Returns
This builder for chaining.

Definition at line 1992 of file CpSolverResponse.java.

◆ addSolutionUpperBounds()

Builder addSolutionUpperBounds ( long  value)
inline

repeated int64 solution_upper_bounds = 19;

Parameters
valueThe solutionUpperBounds to add.
Returns
This builder for chaining.

Definition at line 2089 of file CpSolverResponse.java.

◆ addSufficientAssumptionsForInfeasibility()

Builder addSufficientAssumptionsForInfeasibility ( int  value)
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Parameters
valueThe sufficientAssumptionsForInfeasibility to add.
Returns
This builder for chaining.

Definition at line 2712 of file CpSolverResponse.java.

◆ addTightenedVariables() [1/4]

Builder addTightenedVariables ( com.google.ortools.sat.IntegerVariableProto  value)
inline
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;

Definition at line 2273 of file CpSolverResponse.java.

◆ addTightenedVariables() [2/4]

Builder addTightenedVariables ( com.google.ortools.sat.IntegerVariableProto.Builder  builderForValue)
inline
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;

Definition at line 2332 of file CpSolverResponse.java.

◆ addTightenedVariables() [3/4]

Builder addTightenedVariables ( int  index,
com.google.ortools.sat.IntegerVariableProto  value 
)
inline
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;

Definition at line 2302 of file CpSolverResponse.java.

◆ addTightenedVariables() [4/4]

Builder addTightenedVariables ( int  index,
com.google.ortools.sat.IntegerVariableProto.Builder  builderForValue 
)
inline
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;

Definition at line 2359 of file CpSolverResponse.java.

◆ addTightenedVariablesBuilder() [1/2]

com.google.ortools.sat.IntegerVariableProto.Builder addTightenedVariablesBuilder ( )
inline
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;

Definition at line 2533 of file CpSolverResponse.java.

◆ addTightenedVariablesBuilder() [2/2]

com.google.ortools.sat.IntegerVariableProto.Builder addTightenedVariablesBuilder ( int  index)
inline
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;

Definition at line 2553 of file CpSolverResponse.java.

◆ build()

.lang.Override com.google.ortools.sat.CpSolverResponse build ( )
inline

Definition at line 1353 of file CpSolverResponse.java.

◆ buildPartial()

.lang.Override com.google.ortools.sat.CpSolverResponse buildPartial ( )
inline

Definition at line 1362 of file CpSolverResponse.java.

◆ clear()

.lang.Override Builder clear ( )
inline

Definition at line 1294 of file CpSolverResponse.java.

◆ clearAllSolutionsWereFound()

Builder clearAllSolutionsWereFound ( )
inline
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
This builder for chaining.

Definition at line 2815 of file CpSolverResponse.java.

◆ clearBestObjectiveBound()

Builder clearBestObjectiveBound ( )
inline
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
This builder for chaining.

Definition at line 1891 of file CpSolverResponse.java.

◆ clearDeterministicTime()

Builder clearDeterministicTime ( )
inline

double deterministic_time = 17;

Returns
This builder for chaining.

Definition at line 3075 of file CpSolverResponse.java.

◆ clearField()

.lang.Override Builder clearField ( com.google.protobuf.Descriptors.FieldDescriptor  field)
inline

Definition at line 1423 of file CpSolverResponse.java.

◆ clearNumBinaryPropagations()

Builder clearNumBinaryPropagations ( )
inline

int64 num_binary_propagations = 13;

Returns
This builder for chaining.

Definition at line 2951 of file CpSolverResponse.java.

◆ clearNumBooleans()

Builder clearNumBooleans ( )
inline
Some statistics about the solve.

int64 num_booleans = 10;

Returns
This builder for chaining.

Definition at line 2858 of file CpSolverResponse.java.

◆ clearNumBranches()

Builder clearNumBranches ( )
inline

int64 num_branches = 12;

Returns
This builder for chaining.

Definition at line 2920 of file CpSolverResponse.java.

◆ clearNumConflicts()

Builder clearNumConflicts ( )
inline

int64 num_conflicts = 11;

Returns
This builder for chaining.

Definition at line 2889 of file CpSolverResponse.java.

◆ clearNumIntegerPropagations()

Builder clearNumIntegerPropagations ( )
inline

int64 num_integer_propagations = 14;

Returns
This builder for chaining.

Definition at line 2982 of file CpSolverResponse.java.

◆ clearObjectiveValue()

Builder clearObjectiveValue ( )
inline
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
This builder for chaining.

Definition at line 1842 of file CpSolverResponse.java.

◆ clearOneof()

.lang.Override Builder clearOneof ( com.google.protobuf.Descriptors.OneofDescriptor  oneof)
inline

Definition at line 1428 of file CpSolverResponse.java.

◆ clearPrimalIntegral()

Builder clearPrimalIntegral ( )
inline

double primal_integral = 22;

Returns
This builder for chaining.

Definition at line 3106 of file CpSolverResponse.java.

◆ clearSolution()

Builder clearSolution ( )
inline
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
This builder for chaining.

Definition at line 1790 of file CpSolverResponse.java.

◆ clearSolutionInfo()

Builder clearSolutionInfo ( )
inline
Additional information about how the solution was found.

string solution_info = 20;

Returns
This builder for chaining.

Definition at line 3182 of file CpSolverResponse.java.

◆ clearSolutionLowerBounds()

Builder clearSolutionLowerBounds ( )
inline
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
This builder for chaining.

Definition at line 2033 of file CpSolverResponse.java.

◆ clearSolutionUpperBounds()

Builder clearSolutionUpperBounds ( )
inline

repeated int64 solution_upper_bounds = 19;

Returns
This builder for chaining.

Definition at line 2112 of file CpSolverResponse.java.

◆ clearStatus()

Builder clearStatus ( )
inline
The status of the solve.

.operations_research.sat.CpSolverStatus status = 1;

Returns
This builder for chaining.

Definition at line 1662 of file CpSolverResponse.java.

◆ clearSufficientAssumptionsForInfeasibility()

Builder clearSufficientAssumptionsForInfeasibility ( )
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Returns
This builder for chaining.

Definition at line 2763 of file CpSolverResponse.java.

◆ clearTightenedVariables()

Builder clearTightenedVariables ( )
inline
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;

Definition at line 2414 of file CpSolverResponse.java.

◆ clearUserTime()

Builder clearUserTime ( )
inline

double user_time = 16;

Returns
This builder for chaining.

Definition at line 3044 of file CpSolverResponse.java.

◆ clearWallTime()

Builder clearWallTime ( )
inline

double wall_time = 15;

Returns
This builder for chaining.

Definition at line 3013 of file CpSolverResponse.java.

◆ clone()

.lang.Override Builder clone ( )
inline

Definition at line 1413 of file CpSolverResponse.java.

◆ getAllSolutionsWereFound()

.lang.Override boolean getAllSolutionsWereFound ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 2783 of file CpSolverResponse.java.

◆ getBestObjectiveBound()

.lang.Override double getBestObjectiveBound ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 1861 of file CpSolverResponse.java.

◆ getDefaultInstanceForType()

.lang.Override com.google.ortools.sat.CpSolverResponse getDefaultInstanceForType ( )
inline

Definition at line 1348 of file CpSolverResponse.java.

◆ getDescriptor()

static final com.google.protobuf.Descriptors.Descriptor getDescriptor ( )
inlinestatic

Definition at line 1265 of file CpSolverResponse.java.

◆ getDescriptorForType()

.lang.Override com.google.protobuf.Descriptors.Descriptor getDescriptorForType ( )
inline

Definition at line 1343 of file CpSolverResponse.java.

◆ getDeterministicTime()

.lang.Override double getDeterministicTime ( )
inline

double deterministic_time = 17;

Returns
The deterministicTime.

Implements CpSolverResponseOrBuilder.

Definition at line 3057 of file CpSolverResponse.java.

◆ getNumBinaryPropagations()

.lang.Override long getNumBinaryPropagations ( )
inline

int64 num_binary_propagations = 13;

Returns
The numBinaryPropagations.

Implements CpSolverResponseOrBuilder.

Definition at line 2933 of file CpSolverResponse.java.

◆ getNumBooleans()

.lang.Override long getNumBooleans ( )
inline
Some statistics about the solve.

int64 num_booleans = 10;

Returns
The numBooleans.

Implements CpSolverResponseOrBuilder.

Definition at line 2832 of file CpSolverResponse.java.

◆ getNumBranches()

.lang.Override long getNumBranches ( )
inline

int64 num_branches = 12;

Returns
The numBranches.

Implements CpSolverResponseOrBuilder.

Definition at line 2902 of file CpSolverResponse.java.

◆ getNumConflicts()

.lang.Override long getNumConflicts ( )
inline

int64 num_conflicts = 11;

Returns
The numConflicts.

Implements CpSolverResponseOrBuilder.

Definition at line 2871 of file CpSolverResponse.java.

◆ getNumIntegerPropagations()

.lang.Override long getNumIntegerPropagations ( )
inline

int64 num_integer_propagations = 14;

Returns
The numIntegerPropagations.

Implements CpSolverResponseOrBuilder.

Definition at line 2964 of file CpSolverResponse.java.

◆ getObjectiveValue()

.lang.Override double getObjectiveValue ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 1810 of file CpSolverResponse.java.

◆ getPrimalIntegral()

.lang.Override double getPrimalIntegral ( )
inline

double primal_integral = 22;

Returns
The primalIntegral.

Implements CpSolverResponseOrBuilder.

Definition at line 3088 of file CpSolverResponse.java.

◆ getSolution()

long getSolution ( int  index)
inline
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
indexThe index of the element to return.
Returns
The solution at the given index.

Implements CpSolverResponseOrBuilder.

Definition at line 1718 of file CpSolverResponse.java.

◆ getSolutionCount()

int getSolutionCount ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 1703 of file CpSolverResponse.java.

◆ getSolutionInfo()

java.lang.String getSolutionInfo ( )
inline
Additional information about how the solution was found.

string solution_info = 20;

Returns
The solutionInfo.

Implements CpSolverResponseOrBuilder.

Definition at line 3122 of file CpSolverResponse.java.

◆ getSolutionInfoBytes()

com.google.protobuf.ByteString getSolutionInfoBytes ( )
inline
Additional information about how the solution was found.

string solution_info = 20;

Returns
The bytes for solutionInfo.

Implements CpSolverResponseOrBuilder.

Definition at line 3143 of file CpSolverResponse.java.

◆ getSolutionList()

java.util.List<java.lang.Long> getSolutionList ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 1688 of file CpSolverResponse.java.

◆ getSolutionLowerBounds()

long getSolutionLowerBounds ( int  index)
inline
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
indexThe index of the element to return.
Returns
The solutionLowerBounds at the given index.

Implements CpSolverResponseOrBuilder.

Definition at line 1953 of file CpSolverResponse.java.

◆ getSolutionLowerBoundsCount()

int getSolutionLowerBoundsCount ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 1936 of file CpSolverResponse.java.

◆ getSolutionLowerBoundsList()

java.util.List<java.lang.Long> getSolutionLowerBoundsList ( )
inline
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.

Implements CpSolverResponseOrBuilder.

Definition at line 1919 of file CpSolverResponse.java.

◆ getSolutionUpperBounds()

long getSolutionUpperBounds ( int  index)
inline

repeated int64 solution_upper_bounds = 19;

Parameters
indexThe index of the element to return.
Returns
The solutionUpperBounds at the given index.

Implements CpSolverResponseOrBuilder.

Definition at line 2068 of file CpSolverResponse.java.

◆ getSolutionUpperBoundsCount()

int getSolutionUpperBoundsCount ( )
inline

repeated int64 solution_upper_bounds = 19;

Returns
The count of solutionUpperBounds.

Implements CpSolverResponseOrBuilder.

Definition at line 2060 of file CpSolverResponse.java.

◆ getSolutionUpperBoundsList()

java.util.List<java.lang.Long> getSolutionUpperBoundsList ( )
inline

repeated int64 solution_upper_bounds = 19;

Returns
A list containing the solutionUpperBounds.

Implements CpSolverResponseOrBuilder.

Definition at line 2052 of file CpSolverResponse.java.

◆ getStatus()

.lang.Override com.google.ortools.sat.CpSolverStatus getStatus ( )
inline
The status of the solve.

.operations_research.sat.CpSolverStatus status = 1;

Returns
The status.

Implements CpSolverResponseOrBuilder.

Definition at line 1631 of file CpSolverResponse.java.

◆ getStatusValue()

.lang.Override int getStatusValue ( )
inline
The status of the solve.

.operations_research.sat.CpSolverStatus status = 1;

Returns
The enum numeric value on the wire for status.

Implements CpSolverResponseOrBuilder.

Definition at line 1604 of file CpSolverResponse.java.

◆ getSufficientAssumptionsForInfeasibility()

int getSufficientAssumptionsForInfeasibility ( int  index)
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Parameters
indexThe index of the element to return.
Returns
The sufficientAssumptionsForInfeasibility at the given index.

Implements CpSolverResponseOrBuilder.

Definition at line 2663 of file CpSolverResponse.java.

◆ getSufficientAssumptionsForInfeasibilityCount()

int getSufficientAssumptionsForInfeasibilityCount ( )
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Returns
The count of sufficientAssumptionsForInfeasibility.

Implements CpSolverResponseOrBuilder.

Definition at line 2641 of file CpSolverResponse.java.

◆ getSufficientAssumptionsForInfeasibilityList()

java.util.List<java.lang.Integer> getSufficientAssumptionsForInfeasibilityList ( )
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Returns
A list containing the sufficientAssumptionsForInfeasibility.

Implements CpSolverResponseOrBuilder.

Definition at line 2619 of file CpSolverResponse.java.

◆ getTightenedVariables()

com.google.ortools.sat.IntegerVariableProto getTightenedVariables ( int  index)
inline
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;

Implements CpSolverResponseOrBuilder.

Definition at line 2193 of file CpSolverResponse.java.

◆ getTightenedVariablesBuilder()

com.google.ortools.sat.IntegerVariableProto.Builder getTightenedVariablesBuilder ( int  index)
inline
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;

Definition at line 2466 of file CpSolverResponse.java.

◆ getTightenedVariablesBuilderList()

java.util.List<com.google.ortools.sat.IntegerVariableProto.Builder> getTightenedVariablesBuilderList ( )
inline
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;

Definition at line 2575 of file CpSolverResponse.java.

◆ getTightenedVariablesCount()

int getTightenedVariablesCount ( )
inline
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;

Implements CpSolverResponseOrBuilder.

Definition at line 2170 of file CpSolverResponse.java.

◆ getTightenedVariablesList()

java.util.List<com.google.ortools.sat.IntegerVariableProto> getTightenedVariablesList ( )
inline
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;

Implements CpSolverResponseOrBuilder.

Definition at line 2147 of file CpSolverResponse.java.

◆ getTightenedVariablesOrBuilder()

com.google.ortools.sat.IntegerVariableProtoOrBuilder getTightenedVariablesOrBuilder ( int  index)
inline
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;

Implements CpSolverResponseOrBuilder.

Definition at line 2486 of file CpSolverResponse.java.

◆ getTightenedVariablesOrBuilderList()

java.util.List<? extends com.google.ortools.sat.IntegerVariableProtoOrBuilder> getTightenedVariablesOrBuilderList ( )
inline
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;

Implements CpSolverResponseOrBuilder.

Definition at line 2510 of file CpSolverResponse.java.

◆ getUserTime()

.lang.Override double getUserTime ( )
inline

double user_time = 16;

Returns
The userTime.

Implements CpSolverResponseOrBuilder.

Definition at line 3026 of file CpSolverResponse.java.

◆ getWallTime()

.lang.Override double getWallTime ( )
inline

double wall_time = 15;

Returns
The wallTime.

Implements CpSolverResponseOrBuilder.

Definition at line 2995 of file CpSolverResponse.java.

◆ internalGetFieldAccessorTable()

.lang.Override com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable ( )
inlineprotected

Definition at line 1271 of file CpSolverResponse.java.

◆ isInitialized()

.lang.Override final boolean isInitialized ( )
inline

Definition at line 1571 of file CpSolverResponse.java.

◆ mergeFrom() [1/3]

Builder mergeFrom ( com.google.ortools.sat.CpSolverResponse  other)
inline

Definition at line 1454 of file CpSolverResponse.java.

◆ mergeFrom() [2/3]

.lang.Override Builder mergeFrom ( com.google.protobuf.CodedInputStream  input,
com.google.protobuf.ExtensionRegistryLite  extensionRegistry 
) throws java.io.IOException
inline

Definition at line 1576 of file CpSolverResponse.java.

◆ mergeFrom() [3/3]

.lang.Override Builder mergeFrom ( com.google.protobuf.Message  other)
inline

Definition at line 1445 of file CpSolverResponse.java.

◆ mergeUnknownFields()

.lang.Override final Builder mergeUnknownFields ( final com.google.protobuf.UnknownFieldSet  unknownFields)
inline

Definition at line 3215 of file CpSolverResponse.java.

◆ removeTightenedVariables()

Builder removeTightenedVariables ( int  index)
inline
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;

Definition at line 2440 of file CpSolverResponse.java.

◆ setAllSolutionsWereFound()

Builder setAllSolutionsWereFound ( boolean  value)
inline
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;

Parameters
valueThe allSolutionsWereFound to set.
Returns
This builder for chaining.

Definition at line 2798 of file CpSolverResponse.java.

◆ setBestObjectiveBound()

Builder setBestObjectiveBound ( double  value)
inline
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;

Parameters
valueThe bestObjectiveBound to set.
Returns
This builder for chaining.

Definition at line 1875 of file CpSolverResponse.java.

◆ setDeterministicTime()

Builder setDeterministicTime ( double  value)
inline

double deterministic_time = 17;

Parameters
valueThe deterministicTime to set.
Returns
This builder for chaining.

Definition at line 3065 of file CpSolverResponse.java.

◆ setField()

.lang.Override Builder setField ( com.google.protobuf.Descriptors.FieldDescriptor  field,
java.lang.Object  value 
)
inline

Definition at line 1417 of file CpSolverResponse.java.

◆ setNumBinaryPropagations()

Builder setNumBinaryPropagations ( long  value)
inline

int64 num_binary_propagations = 13;

Parameters
valueThe numBinaryPropagations to set.
Returns
This builder for chaining.

Definition at line 2941 of file CpSolverResponse.java.

◆ setNumBooleans()

Builder setNumBooleans ( long  value)
inline
Some statistics about the solve.

int64 num_booleans = 10;

Parameters
valueThe numBooleans to set.
Returns
This builder for chaining.

Definition at line 2844 of file CpSolverResponse.java.

◆ setNumBranches()

Builder setNumBranches ( long  value)
inline

int64 num_branches = 12;

Parameters
valueThe numBranches to set.
Returns
This builder for chaining.

Definition at line 2910 of file CpSolverResponse.java.

◆ setNumConflicts()

Builder setNumConflicts ( long  value)
inline

int64 num_conflicts = 11;

Parameters
valueThe numConflicts to set.
Returns
This builder for chaining.

Definition at line 2879 of file CpSolverResponse.java.

◆ setNumIntegerPropagations()

Builder setNumIntegerPropagations ( long  value)
inline

int64 num_integer_propagations = 14;

Parameters
valueThe numIntegerPropagations to set.
Returns
This builder for chaining.

Definition at line 2972 of file CpSolverResponse.java.

◆ setObjectiveValue()

Builder setObjectiveValue ( double  value)
inline
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;

Parameters
valueThe objectiveValue to set.
Returns
This builder for chaining.

Definition at line 1825 of file CpSolverResponse.java.

◆ setPrimalIntegral()

Builder setPrimalIntegral ( double  value)
inline

double primal_integral = 22;

Parameters
valueThe primalIntegral to set.
Returns
This builder for chaining.

Definition at line 3096 of file CpSolverResponse.java.

◆ setRepeatedField()

.lang.Override Builder setRepeatedField ( com.google.protobuf.Descriptors.FieldDescriptor  field,
int  index,
java.lang.Object  value 
)
inline

Definition at line 1433 of file CpSolverResponse.java.

◆ setSolution()

Builder setSolution ( int  index,
long  value 
)
inline
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
indexThe index to set the value at.
valueThe solution to set.
Returns
This builder for chaining.

Definition at line 1734 of file CpSolverResponse.java.

◆ setSolutionInfo()

Builder setSolutionInfo ( java.lang.String  value)
inline
Additional information about how the solution was found.

string solution_info = 20;

Parameters
valueThe solutionInfo to set.
Returns
This builder for chaining.

Definition at line 3164 of file CpSolverResponse.java.

◆ setSolutionInfoBytes()

Builder setSolutionInfoBytes ( com.google.protobuf.ByteString  value)
inline
Additional information about how the solution was found.

string solution_info = 20;

Parameters
valueThe bytes for solutionInfo to set.
Returns
This builder for chaining.

Definition at line 3197 of file CpSolverResponse.java.

◆ setSolutionLowerBounds()

Builder setSolutionLowerBounds ( int  index,
long  value 
)
inline
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
indexThe index to set the value at.
valueThe solutionLowerBounds to set.
Returns
This builder for chaining.

Definition at line 1971 of file CpSolverResponse.java.

◆ setSolutionUpperBounds()

Builder setSolutionUpperBounds ( int  index,
long  value 
)
inline

repeated int64 solution_upper_bounds = 19;

Parameters
indexThe index to set the value at.
valueThe solutionUpperBounds to set.
Returns
This builder for chaining.

Definition at line 2077 of file CpSolverResponse.java.

◆ setStatus()

Builder setStatus ( com.google.ortools.sat.CpSolverStatus  value)
inline
The status of the solve.

.operations_research.sat.CpSolverStatus status = 1;

Parameters
valueThe status to set.
Returns
This builder for chaining.

Definition at line 1645 of file CpSolverResponse.java.

◆ setStatusValue()

Builder setStatusValue ( int  value)
inline
The status of the solve.

.operations_research.sat.CpSolverStatus status = 1;

Parameters
valueThe enum numeric value on the wire for status to set.
Returns
This builder for chaining.

Definition at line 1616 of file CpSolverResponse.java.

◆ setSufficientAssumptionsForInfeasibility()

Builder setSufficientAssumptionsForInfeasibility ( int  index,
int  value 
)
inline
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.

repeated int32 sufficient_assumptions_for_infeasibility = 23;

Parameters
indexThe index to set the value at.
valueThe sufficientAssumptionsForInfeasibility to set.
Returns
This builder for chaining.

Definition at line 2686 of file CpSolverResponse.java.

◆ setTightenedVariables() [1/2]

Builder setTightenedVariables ( int  index,
com.google.ortools.sat.IntegerVariableProto  value 
)
inline
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;

Definition at line 2216 of file CpSolverResponse.java.

◆ setTightenedVariables() [2/2]

Builder setTightenedVariables ( int  index,
com.google.ortools.sat.IntegerVariableProto.Builder  builderForValue 
)
inline
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;

Definition at line 2246 of file CpSolverResponse.java.

◆ setUnknownFields()

.lang.Override final Builder setUnknownFields ( final com.google.protobuf.UnknownFieldSet  unknownFields)
inline

Definition at line 3209 of file CpSolverResponse.java.

◆ setUserTime()

Builder setUserTime ( double  value)
inline

double user_time = 16;

Parameters
valueThe userTime to set.
Returns
This builder for chaining.

Definition at line 3034 of file CpSolverResponse.java.

◆ setWallTime()

Builder setWallTime ( double  value)
inline

double wall_time = 15;

Parameters
valueThe wallTime to set.
Returns
This builder for chaining.

Definition at line 3003 of file CpSolverResponse.java.


The documentation for this class was generated from the following file: