Special Ordered Set (SOS) constraints of type 1 or 2. See https://en.wikipedia.org/wiki/Special_ordered_set As of 2019/04, only SCIP and Gurobi support this constraint type.
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static pb::MessageParser< MPSosConstraint > | Parser [get] |
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static pbr::MessageDescriptor | Descriptor [get] |
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global::OperationsResearch.MPSosConstraint.Types.Type | Type [get, set] |
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bool | HasType [get] |
| Gets whether the "type" field is set.
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pbc::RepeatedField< int > | VarIndex [get] |
| Variable index (w.r.t. the "variable" field of MPModelProto) of the variables in the SOS.
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pbc::RepeatedField< double > | Weight [get] |
| 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).
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Special Ordered Set (SOS) constraints of type 1 or 2. See https://en.wikipedia.org/wiki/Special_ordered_set As of 2019/04, only SCIP and Gurobi support this constraint type.
Definition at line 2243 of file LinearSolver.pb.cs.
◆ MPSosConstraint() [1/2]
OperationsResearch.MPSosConstraint.MPSosConstraint |
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◆ MPSosConstraint() [2/2]
OperationsResearch.MPSosConstraint.MPSosConstraint |
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MPSosConstraint | other | ) |
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◆ CalculateSize()
int OperationsResearch.MPSosConstraint.CalculateSize |
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◆ ClearType()
void OperationsResearch.MPSosConstraint.ClearType |
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◆ Clone()
◆ Equals() [1/2]
◆ Equals() [2/2]
override bool OperationsResearch.MPSosConstraint.Equals |
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object | other | ) |
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◆ GetHashCode()
override int OperationsResearch.MPSosConstraint.GetHashCode |
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◆ MergeFrom() [1/2]
◆ MergeFrom() [2/2]
void OperationsResearch.MPSosConstraint.MergeFrom |
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pb.CodedInputStream | input | ) |
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◆ ToString()
override string OperationsResearch.MPSosConstraint.ToString |
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◆ WriteTo()
void OperationsResearch.MPSosConstraint.WriteTo |
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pb.CodedOutputStream | output | ) |
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◆ TypeFieldNumber
const int OperationsResearch.MPSosConstraint.TypeFieldNumber = 1 |
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◆ VarIndexFieldNumber
const int OperationsResearch.MPSosConstraint.VarIndexFieldNumber = 2 |
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◆ WeightFieldNumber
const int OperationsResearch.MPSosConstraint.WeightFieldNumber = 3 |
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◆ Descriptor
pbr.MessageDescriptor OperationsResearch.MPSosConstraint.Descriptor |
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◆ HasType
bool OperationsResearch.MPSosConstraint.HasType |
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◆ Parser
pb.MessageParser<MPSosConstraint> OperationsResearch.MPSosConstraint.Parser |
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staticget |
◆ Type
global.OperationsResearch.MPSosConstraint.Types.Type OperationsResearch.MPSosConstraint.Type |
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◆ VarIndex
pbc.RepeatedField<int> OperationsResearch.MPSosConstraint.VarIndex |
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◆ Weight
pbc.RepeatedField<double> OperationsResearch.MPSosConstraint.Weight |
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get |
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).
Definition at line 2352 of file LinearSolver.pb.cs.
The documentation for this class was generated from the following file: