Google OR-Tools v9.11
a fast and portable software suite for combinatorial optimization
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Public Member Functions | |
boolean | hasType () |
com.google.ortools.linearsolver.MPSosConstraint.Type | getType () |
java.util.List< java.lang.Integer > | getVarIndexList () |
int | getVarIndexCount () |
int | getVarIndex (int index) |
java.util.List< java.lang.Double > | getWeightList () |
int | getWeightCount () |
double | getWeight (int index) |
Definition at line 7 of file MPSosConstraintOrBuilder.java.
com.google.ortools.linearsolver.MPSosConstraint.Type com.google.ortools.linearsolver.MPSosConstraintOrBuilder.getType | ( | ) |
optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
int com.google.ortools.linearsolver.MPSosConstraintOrBuilder.getVarIndex | ( | int | index | ) |
Variable index (w.r.t. the "variable" field of MPModelProto) of the variables in the SOS.
repeated int32 var_index = 2;
index | The index of the element to return. |
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
int com.google.ortools.linearsolver.MPSosConstraintOrBuilder.getVarIndexCount | ( | ) |
Variable index (w.r.t. the "variable" field of MPModelProto) of the variables in the SOS.
repeated int32 var_index = 2;
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
java.util.List< java.lang.Integer > com.google.ortools.linearsolver.MPSosConstraintOrBuilder.getVarIndexList | ( | ) |
Variable index (w.r.t. the "variable" field of MPModelProto) of the variables in the SOS.
repeated int32 var_index = 2;
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
double com.google.ortools.linearsolver.MPSosConstraintOrBuilder.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;
index | The index of the element to return. |
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
int com.google.ortools.linearsolver.MPSosConstraintOrBuilder.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;
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
java.util.List< java.lang.Double > com.google.ortools.linearsolver.MPSosConstraintOrBuilder.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;
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.
boolean com.google.ortools.linearsolver.MPSosConstraintOrBuilder.hasType | ( | ) |
optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
Implemented in com.google.ortools.linearsolver.MPSosConstraint.Builder, and com.google.ortools.linearsolver.MPSosConstraint.