Package 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

      All Methods Instance Methods Abstract Methods 
      Modifier and Type Method 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.MessageLiteOrBuilder

        isInitialized

      • Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

    • 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.