com.google.ortools.linearsolver

Interface MPSosConstraintOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

MPSosConstraint, MPSosConstraint.Builder

public interface MPSosConstraintOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
MPSosConstraint.Type	getType() optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
int	getVarIndex(int index) Variable index (w.r.t.
int	getVarIndexCount() Variable index (w.r.t.
java.util.List<java.lang.Integer>	getVarIndexList() Variable index (w.r.t.
double	getWeight(int index) Optional: SOS weights.
int	getWeightCount() Optional: SOS weights.
java.util.List<java.lang.Double>	getWeightList() Optional: SOS weights.
boolean	hasType() optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

hasType

boolean hasType()

optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];

Returns:

Whether the type field is set.

getType

MPSosConstraint.Type getType()

optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];

Returns:

The type.

getVarIndexList

java.util.List<java.lang.Integer> getVarIndexList()

 Variable index (w.r.t. the "variable" field of MPModelProto) of the
 variables in the SOS.
 

repeated int32 var_index = 2;

Returns:

A list containing the varIndex.

getVarIndexCount

int getVarIndexCount()

 Variable index (w.r.t. the "variable" field of MPModelProto) of the
 variables in the SOS.
 

repeated int32 var_index = 2;

Returns:

The count of varIndex.

getVarIndex

int getVarIndex(int index)

 Variable index (w.r.t. the "variable" field of MPModelProto) of the
 variables in the SOS.
 

repeated int32 var_index = 2;

Parameters:

index - The index of the element to return.

Returns:

The varIndex at the given index.

getWeightList

java.util.List<java.lang.Double> getWeightList()

 Optional: SOS weights. If non-empty, must be of the same size as
 "var_index", and strictly increasing. If empty and required by the
 underlying solver, the 1..n sequence will be given as weights.
 SUBTLE: The weights can help the solver make branch-and-bound decisions
 that fit the underlying optimization model: after each LP relaxation, it
 will compute the "average weight" of the SOS variables, weighted by value
 (this is confusing: here we're using the values as weights), and the binary
 branch decision will be: is the non-zero variable above or below that?
 (weights are strictly monotonous, so the "cutoff" average weight
 corresponds to a "cutoff" index in the var_index sequence).
 

repeated double weight = 3;

Returns:

A list containing the weight.

getWeightCount

int getWeightCount()

 Optional: SOS weights. If non-empty, must be of the same size as
 "var_index", and strictly increasing. If empty and required by the
 underlying solver, the 1..n sequence will be given as weights.
 SUBTLE: The weights can help the solver make branch-and-bound decisions
 that fit the underlying optimization model: after each LP relaxation, it
 will compute the "average weight" of the SOS variables, weighted by value
 (this is confusing: here we're using the values as weights), and the binary
 branch decision will be: is the non-zero variable above or below that?
 (weights are strictly monotonous, so the "cutoff" average weight
 corresponds to a "cutoff" index in the var_index sequence).
 

repeated double weight = 3;

Returns:

The count of weight.

getWeight

double getWeight(int index)

 Optional: SOS weights. If non-empty, must be of the same size as
 "var_index", and strictly increasing. If empty and required by the
 underlying solver, the 1..n sequence will be given as weights.
 SUBTLE: The weights can help the solver make branch-and-bound decisions
 that fit the underlying optimization model: after each LP relaxation, it
 will compute the "average weight" of the SOS variables, weighted by value
 (this is confusing: here we're using the values as weights), and the binary
 branch decision will be: is the non-zero variable above or below that?
 (weights are strictly monotonous, so the "cutoff" average weight
 corresponds to a "cutoff" index in the var_index sequence).
 

repeated double weight = 3;

Parameters:

index - The index of the element to return.

Returns:

The weight at the given index.