public static final class CpModelProto.Builder extends com.google.protobuf.GeneratedMessage.Builder<CpModelProto.Builder> implements CpModelProtoOrBuilder
A constraint programming problem.Protobuf type
operations_research.sat.CpModelProto| Modifier and Type | Method and Description |
|---|---|
CpModelProto.Builder |
addAllAssumptions(java.lang.Iterable<? extends java.lang.Integer> values)
A list of literals.
|
CpModelProto.Builder |
addAllConstraints(java.lang.Iterable<? extends ConstraintProto> values)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
addAllSearchStrategy(java.lang.Iterable<? extends DecisionStrategyProto> values)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
addAllVariables(java.lang.Iterable<? extends IntegerVariableProto> values)
The associated Protos should be referred by their index in these fields.
|
CpModelProto.Builder |
addAssumptions(int value)
A list of literals.
|
CpModelProto.Builder |
addConstraints(ConstraintProto.Builder builderForValue)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
addConstraints(ConstraintProto value)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
addConstraints(int index,
ConstraintProto.Builder builderForValue)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
addConstraints(int index,
ConstraintProto value)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
ConstraintProto.Builder |
addConstraintsBuilder()
repeated .operations_research.sat.ConstraintProto constraints = 3; |
ConstraintProto.Builder |
addConstraintsBuilder(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
addSearchStrategy(DecisionStrategyProto.Builder builderForValue)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
addSearchStrategy(DecisionStrategyProto value)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
addSearchStrategy(int index,
DecisionStrategyProto.Builder builderForValue)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
addSearchStrategy(int index,
DecisionStrategyProto value)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
DecisionStrategyProto.Builder |
addSearchStrategyBuilder()
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
DecisionStrategyProto.Builder |
addSearchStrategyBuilder(int index)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
addVariables(IntegerVariableProto.Builder builderForValue)
The associated Protos should be referred by their index in these fields.
|
CpModelProto.Builder |
addVariables(IntegerVariableProto value)
The associated Protos should be referred by their index in these fields.
|
CpModelProto.Builder |
addVariables(int index,
IntegerVariableProto.Builder builderForValue)
The associated Protos should be referred by their index in these fields.
|
CpModelProto.Builder |
addVariables(int index,
IntegerVariableProto value)
The associated Protos should be referred by their index in these fields.
|
IntegerVariableProto.Builder |
addVariablesBuilder()
The associated Protos should be referred by their index in these fields.
|
IntegerVariableProto.Builder |
addVariablesBuilder(int index)
The associated Protos should be referred by their index in these fields.
|
CpModelProto |
build() |
CpModelProto |
buildPartial() |
CpModelProto.Builder |
clear() |
CpModelProto.Builder |
clearAssumptions()
A list of literals.
|
CpModelProto.Builder |
clearConstraints()
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
clearFloatingPointObjective()
Advanced usage.
|
CpModelProto.Builder |
clearName()
For debug/logging only.
|
CpModelProto.Builder |
clearObjective()
The objective to minimize.
|
CpModelProto.Builder |
clearSearchStrategy()
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
clearSolutionHint()
Solution hint.
|
CpModelProto.Builder |
clearSymmetry()
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
CpModelProto.Builder |
clearVariables()
The associated Protos should be referred by their index in these fields.
|
int |
getAssumptions(int index)
A list of literals.
|
int |
getAssumptionsCount()
A list of literals.
|
java.util.List<java.lang.Integer> |
getAssumptionsList()
A list of literals.
|
ConstraintProto |
getConstraints(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
ConstraintProto.Builder |
getConstraintsBuilder(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
java.util.List<ConstraintProto.Builder> |
getConstraintsBuilderList()
repeated .operations_research.sat.ConstraintProto constraints = 3; |
int |
getConstraintsCount()
repeated .operations_research.sat.ConstraintProto constraints = 3; |
java.util.List<ConstraintProto> |
getConstraintsList()
repeated .operations_research.sat.ConstraintProto constraints = 3; |
ConstraintProtoOrBuilder |
getConstraintsOrBuilder(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
java.util.List<? extends ConstraintProtoOrBuilder> |
getConstraintsOrBuilderList()
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto |
getDefaultInstanceForType() |
static com.google.protobuf.Descriptors.Descriptor |
getDescriptor() |
com.google.protobuf.Descriptors.Descriptor |
getDescriptorForType() |
FloatObjectiveProto |
getFloatingPointObjective()
Advanced usage.
|
FloatObjectiveProto.Builder |
getFloatingPointObjectiveBuilder()
Advanced usage.
|
FloatObjectiveProtoOrBuilder |
getFloatingPointObjectiveOrBuilder()
Advanced usage.
|
java.lang.String |
getName()
For debug/logging only.
|
com.google.protobuf.ByteString |
getNameBytes()
For debug/logging only.
|
CpObjectiveProto |
getObjective()
The objective to minimize.
|
CpObjectiveProto.Builder |
getObjectiveBuilder()
The objective to minimize.
|
CpObjectiveProtoOrBuilder |
getObjectiveOrBuilder()
The objective to minimize.
|
DecisionStrategyProto |
getSearchStrategy(int index)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
DecisionStrategyProto.Builder |
getSearchStrategyBuilder(int index)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
java.util.List<DecisionStrategyProto.Builder> |
getSearchStrategyBuilderList()
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
int |
getSearchStrategyCount()
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
java.util.List<DecisionStrategyProto> |
getSearchStrategyList()
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
DecisionStrategyProtoOrBuilder |
getSearchStrategyOrBuilder(int index)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
java.util.List<? extends DecisionStrategyProtoOrBuilder> |
getSearchStrategyOrBuilderList()
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
PartialVariableAssignment |
getSolutionHint()
Solution hint.
|
PartialVariableAssignment.Builder |
getSolutionHintBuilder()
Solution hint.
|
PartialVariableAssignmentOrBuilder |
getSolutionHintOrBuilder()
Solution hint.
|
SymmetryProto |
getSymmetry()
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
SymmetryProto.Builder |
getSymmetryBuilder()
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
SymmetryProtoOrBuilder |
getSymmetryOrBuilder()
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
IntegerVariableProto |
getVariables(int index)
The associated Protos should be referred by their index in these fields.
|
IntegerVariableProto.Builder |
getVariablesBuilder(int index)
The associated Protos should be referred by their index in these fields.
|
java.util.List<IntegerVariableProto.Builder> |
getVariablesBuilderList()
The associated Protos should be referred by their index in these fields.
|
int |
getVariablesCount()
The associated Protos should be referred by their index in these fields.
|
java.util.List<IntegerVariableProto> |
getVariablesList()
The associated Protos should be referred by their index in these fields.
|
IntegerVariableProtoOrBuilder |
getVariablesOrBuilder(int index)
The associated Protos should be referred by their index in these fields.
|
java.util.List<? extends IntegerVariableProtoOrBuilder> |
getVariablesOrBuilderList()
The associated Protos should be referred by their index in these fields.
|
boolean |
hasFloatingPointObjective()
Advanced usage.
|
boolean |
hasObjective()
The objective to minimize.
|
boolean |
hasSolutionHint()
Solution hint.
|
boolean |
hasSymmetry()
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable |
internalGetFieldAccessorTable() |
boolean |
isInitialized() |
CpModelProto.Builder |
mergeFloatingPointObjective(FloatObjectiveProto value)
Advanced usage.
|
CpModelProto.Builder |
mergeFrom(com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry) |
CpModelProto.Builder |
mergeFrom(CpModelProto other) |
CpModelProto.Builder |
mergeFrom(com.google.protobuf.Message other) |
CpModelProto.Builder |
mergeObjective(CpObjectiveProto value)
The objective to minimize.
|
CpModelProto.Builder |
mergeSolutionHint(PartialVariableAssignment value)
Solution hint.
|
CpModelProto.Builder |
mergeSymmetry(SymmetryProto value)
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
CpModelProto.Builder |
removeConstraints(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
removeSearchStrategy(int index)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
removeVariables(int index)
The associated Protos should be referred by their index in these fields.
|
CpModelProto.Builder |
setAssumptions(int index,
int value)
A list of literals.
|
CpModelProto.Builder |
setConstraints(int index,
ConstraintProto.Builder builderForValue)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
setConstraints(int index,
ConstraintProto value)
repeated .operations_research.sat.ConstraintProto constraints = 3; |
CpModelProto.Builder |
setFloatingPointObjective(FloatObjectiveProto.Builder builderForValue)
Advanced usage.
|
CpModelProto.Builder |
setFloatingPointObjective(FloatObjectiveProto value)
Advanced usage.
|
CpModelProto.Builder |
setName(java.lang.String value)
For debug/logging only.
|
CpModelProto.Builder |
setNameBytes(com.google.protobuf.ByteString value)
For debug/logging only.
|
CpModelProto.Builder |
setObjective(CpObjectiveProto.Builder builderForValue)
The objective to minimize.
|
CpModelProto.Builder |
setObjective(CpObjectiveProto value)
The objective to minimize.
|
CpModelProto.Builder |
setSearchStrategy(int index,
DecisionStrategyProto.Builder builderForValue)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
setSearchStrategy(int index,
DecisionStrategyProto value)
Defines the strategy that the solver should follow when the
search_branching parameter is set to FIXED_SEARCH.
|
CpModelProto.Builder |
setSolutionHint(PartialVariableAssignment.Builder builderForValue)
Solution hint.
|
CpModelProto.Builder |
setSolutionHint(PartialVariableAssignment value)
Solution hint.
|
CpModelProto.Builder |
setSymmetry(SymmetryProto.Builder builderForValue)
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
CpModelProto.Builder |
setSymmetry(SymmetryProto value)
For now, this is not meant to be filled by a client writing a model, but
by our preprocessing step.
|
CpModelProto.Builder |
setVariables(int index,
IntegerVariableProto.Builder builderForValue)
The associated Protos should be referred by their index in these fields.
|
CpModelProto.Builder |
setVariables(int index,
IntegerVariableProto value)
The associated Protos should be referred by their index in these fields.
|
addRepeatedField, clearField, clearOneof, clone, getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownFields, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setField, setRepeatedField, setUnknownFields, setUnknownFieldSetBuilder, setUnknownFieldsProto3findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toStringaddAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageExceptionequals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitpublic static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<CpModelProto.Builder>public CpModelProto.Builder clear()
clear in interface com.google.protobuf.Message.Builderclear in interface com.google.protobuf.MessageLite.Builderclear in class com.google.protobuf.GeneratedMessage.Builder<CpModelProto.Builder>public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
getDescriptorForType in interface com.google.protobuf.Message.BuildergetDescriptorForType in interface com.google.protobuf.MessageOrBuildergetDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<CpModelProto.Builder>public CpModelProto getDefaultInstanceForType()
getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuildergetDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilderpublic CpModelProto build()
build in interface com.google.protobuf.Message.Builderbuild in interface com.google.protobuf.MessageLite.Builderpublic CpModelProto buildPartial()
buildPartial in interface com.google.protobuf.Message.BuilderbuildPartial in interface com.google.protobuf.MessageLite.Builderpublic CpModelProto.Builder mergeFrom(com.google.protobuf.Message other)
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<CpModelProto.Builder>public CpModelProto.Builder mergeFrom(CpModelProto other)
public final boolean isInitialized()
isInitialized in interface com.google.protobuf.MessageLiteOrBuilderisInitialized in class com.google.protobuf.GeneratedMessage.Builder<CpModelProto.Builder>public CpModelProto.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in interface com.google.protobuf.MessageLite.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<CpModelProto.Builder>java.io.IOExceptionpublic java.lang.String getName()
For debug/logging only. Can be empty.
string name = 1;getName in interface CpModelProtoOrBuilderpublic com.google.protobuf.ByteString getNameBytes()
For debug/logging only. Can be empty.
string name = 1;getNameBytes in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setName(java.lang.String value)
For debug/logging only. Can be empty.
string name = 1;value - The name to set.public CpModelProto.Builder clearName()
For debug/logging only. Can be empty.
string name = 1;public CpModelProto.Builder setNameBytes(com.google.protobuf.ByteString value)
For debug/logging only. Can be empty.
string name = 1;value - The bytes for name to set.public java.util.List<IntegerVariableProto> getVariablesList()
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;getVariablesList in interface CpModelProtoOrBuilderpublic int getVariablesCount()
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;getVariablesCount in interface CpModelProtoOrBuilderpublic IntegerVariableProto getVariables(int index)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;getVariables in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setVariables(int index, IntegerVariableProto value)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder setVariables(int index, IntegerVariableProto.Builder builderForValue)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder addVariables(IntegerVariableProto value)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder addVariables(int index, IntegerVariableProto value)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder addVariables(IntegerVariableProto.Builder builderForValue)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder addVariables(int index, IntegerVariableProto.Builder builderForValue)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder addAllVariables(java.lang.Iterable<? extends IntegerVariableProto> values)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder clearVariables()
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public CpModelProto.Builder removeVariables(int index)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public IntegerVariableProto.Builder getVariablesBuilder(int index)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public IntegerVariableProtoOrBuilder getVariablesOrBuilder(int index)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;getVariablesOrBuilder in interface CpModelProtoOrBuilderpublic 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;getVariablesOrBuilderList in interface CpModelProtoOrBuilderpublic IntegerVariableProto.Builder addVariablesBuilder()
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public IntegerVariableProto.Builder addVariablesBuilder(int index)
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public java.util.List<IntegerVariableProto.Builder> getVariablesBuilderList()
The associated Protos should be referred by their index in these fields.
repeated .operations_research.sat.IntegerVariableProto variables = 2;public java.util.List<ConstraintProto> getConstraintsList()
repeated .operations_research.sat.ConstraintProto constraints = 3;getConstraintsList in interface CpModelProtoOrBuilderpublic int getConstraintsCount()
repeated .operations_research.sat.ConstraintProto constraints = 3;getConstraintsCount in interface CpModelProtoOrBuilderpublic ConstraintProto getConstraints(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3;getConstraints in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setConstraints(int index, ConstraintProto value)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder setConstraints(int index, ConstraintProto.Builder builderForValue)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder addConstraints(ConstraintProto value)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder addConstraints(int index, ConstraintProto value)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder addConstraints(ConstraintProto.Builder builderForValue)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder addConstraints(int index, ConstraintProto.Builder builderForValue)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder addAllConstraints(java.lang.Iterable<? extends ConstraintProto> values)
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder clearConstraints()
repeated .operations_research.sat.ConstraintProto constraints = 3;public CpModelProto.Builder removeConstraints(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3;public ConstraintProto.Builder getConstraintsBuilder(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3;public ConstraintProtoOrBuilder getConstraintsOrBuilder(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3;getConstraintsOrBuilder in interface CpModelProtoOrBuilderpublic java.util.List<? extends ConstraintProtoOrBuilder> getConstraintsOrBuilderList()
repeated .operations_research.sat.ConstraintProto constraints = 3;getConstraintsOrBuilderList in interface CpModelProtoOrBuilderpublic ConstraintProto.Builder addConstraintsBuilder()
repeated .operations_research.sat.ConstraintProto constraints = 3;public ConstraintProto.Builder addConstraintsBuilder(int index)
repeated .operations_research.sat.ConstraintProto constraints = 3;public java.util.List<ConstraintProto.Builder> getConstraintsBuilderList()
repeated .operations_research.sat.ConstraintProto constraints = 3;public boolean hasObjective()
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;hasObjective in interface CpModelProtoOrBuilderpublic CpObjectiveProto getObjective()
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;getObjective in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setObjective(CpObjectiveProto value)
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;public CpModelProto.Builder setObjective(CpObjectiveProto.Builder builderForValue)
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;public CpModelProto.Builder mergeObjective(CpObjectiveProto value)
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;public CpModelProto.Builder clearObjective()
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;public CpObjectiveProto.Builder getObjectiveBuilder()
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;public CpObjectiveProtoOrBuilder getObjectiveOrBuilder()
The objective to minimize. Can be empty for pure decision problems.
.operations_research.sat.CpObjectiveProto objective = 4;getObjectiveOrBuilder in interface CpModelProtoOrBuilderpublic 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;hasFloatingPointObjective in interface CpModelProtoOrBuilderpublic 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;getFloatingPointObjective in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setFloatingPointObjective(FloatObjectiveProto value)
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;public CpModelProto.Builder setFloatingPointObjective(FloatObjectiveProto.Builder builderForValue)
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;public CpModelProto.Builder mergeFloatingPointObjective(FloatObjectiveProto value)
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;public CpModelProto.Builder clearFloatingPointObjective()
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;public FloatObjectiveProto.Builder getFloatingPointObjectiveBuilder()
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;public 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;getFloatingPointObjectiveOrBuilder in interface CpModelProtoOrBuilderpublic 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;getSearchStrategyList in interface CpModelProtoOrBuilderpublic 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;getSearchStrategyCount in interface CpModelProtoOrBuilderpublic 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;getSearchStrategy in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setSearchStrategy(int index, DecisionStrategyProto value)
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;public CpModelProto.Builder setSearchStrategy(int index, DecisionStrategyProto.Builder builderForValue)
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;public CpModelProto.Builder addSearchStrategy(DecisionStrategyProto value)
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;public CpModelProto.Builder addSearchStrategy(int index, DecisionStrategyProto value)
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;public CpModelProto.Builder addSearchStrategy(DecisionStrategyProto.Builder builderForValue)
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;public CpModelProto.Builder addSearchStrategy(int index, DecisionStrategyProto.Builder builderForValue)
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;public CpModelProto.Builder addAllSearchStrategy(java.lang.Iterable<? extends DecisionStrategyProto> values)
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;public CpModelProto.Builder clearSearchStrategy()
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;public CpModelProto.Builder removeSearchStrategy(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;public DecisionStrategyProto.Builder getSearchStrategyBuilder(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;public 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;getSearchStrategyOrBuilder in interface CpModelProtoOrBuilderpublic 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;getSearchStrategyOrBuilderList in interface CpModelProtoOrBuilderpublic DecisionStrategyProto.Builder addSearchStrategyBuilder()
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;public DecisionStrategyProto.Builder addSearchStrategyBuilder(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;public java.util.List<DecisionStrategyProto.Builder> getSearchStrategyBuilderList()
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;public 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;hasSolutionHint in interface CpModelProtoOrBuilderpublic 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;getSolutionHint in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setSolutionHint(PartialVariableAssignment value)
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;public CpModelProto.Builder setSolutionHint(PartialVariableAssignment.Builder builderForValue)
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;public CpModelProto.Builder mergeSolutionHint(PartialVariableAssignment value)
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;public CpModelProto.Builder clearSolutionHint()
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;public PartialVariableAssignment.Builder getSolutionHintBuilder()
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;public 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;getSolutionHintOrBuilder in interface CpModelProtoOrBuilderpublic 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;getAssumptionsList in interface CpModelProtoOrBuilderpublic 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;getAssumptionsCount in interface CpModelProtoOrBuilderpublic 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;getAssumptions in interface CpModelProtoOrBuilderindex - The index of the element to return.public CpModelProto.Builder setAssumptions(int index, int value)
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;index - The index to set the value at.value - The assumptions to set.public CpModelProto.Builder addAssumptions(int value)
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;value - The assumptions to add.public CpModelProto.Builder addAllAssumptions(java.lang.Iterable<? extends java.lang.Integer> values)
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;values - The assumptions to add.public CpModelProto.Builder clearAssumptions()
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;public 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;hasSymmetry in interface CpModelProtoOrBuilderpublic 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;getSymmetry in interface CpModelProtoOrBuilderpublic CpModelProto.Builder setSymmetry(SymmetryProto value)
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;public CpModelProto.Builder setSymmetry(SymmetryProto.Builder builderForValue)
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;public CpModelProto.Builder mergeSymmetry(SymmetryProto value)
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;public CpModelProto.Builder clearSymmetry()
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;public SymmetryProto.Builder getSymmetryBuilder()
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;public 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;getSymmetryOrBuilder in interface CpModelProtoOrBuilderCopyright © 2025. All rights reserved.