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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: ortools/linear_solver/linear_solver.proto
package com.google.ortools.linearsolver;
/**
*
* 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.
*
*
* Protobuf type {@code operations_research.MPSosConstraint}
*/
public final class MPSosConstraint extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:operations_research.MPSosConstraint)
MPSosConstraintOrBuilder {
private static final long serialVersionUID = 0L;
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public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return com.google.ortools.linearsolver.LinearSolver.internal_static_operations_research_MPSosConstraint_descriptor;
}
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/**
* Protobuf enum {@code operations_research.MPSosConstraint.Type}
*/
public enum Type
implements com.google.protobuf.ProtocolMessageEnum {
/**
*
* At most one variable in `var_index` must be non-zero.
*
*
* SOS1_DEFAULT = 0;
*/
SOS1_DEFAULT(0),
/**
*
* At most two consecutive variables from `var_index` can be non-zero (i.e.
* for some i, var_index[i] and var_index[i+1]). See
* http://www.eudoxus.com/lp-training/5/5-6-special-ordered-sets-of-type-2
*
*
* SOS2 = 1;
*/
SOS2(1),
;
/**
*
* At most one variable in `var_index` must be non-zero.
*
*
* SOS1_DEFAULT = 0;
*/
public static final int SOS1_DEFAULT_VALUE = 0;
/**
*
* At most two consecutive variables from `var_index` can be non-zero (i.e.
* for some i, var_index[i] and var_index[i+1]). See
* http://www.eudoxus.com/lp-training/5/5-6-special-ordered-sets-of-type-2
*
*
* SOS2 = 1;
*/
public static final int SOS2_VALUE = 1;
public final int getNumber() {
return value;
}
/**
* @param value The numeric wire value of the corresponding enum entry.
* @return The enum associated with the given numeric wire value.
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static Type valueOf(int value) {
return forNumber(value);
}
/**
* @param value The numeric wire value of the corresponding enum entry.
* @return The enum associated with the given numeric wire value.
*/
public static Type forNumber(int value) {
switch (value) {
case 0: return SOS1_DEFAULT;
case 1: return SOS2;
default: return null;
}
}
public static com.google.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
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}
private static final com.google.protobuf.Internal.EnumLiteMap<
Type> internalValueMap =
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}
public final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return com.google.ortools.linearsolver.MPSosConstraint.getDescriptor().getEnumTypes().get(0);
}
private static final Type[] VALUES = values();
public static Type valueOf(
com.google.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
return VALUES[desc.getIndex()];
}
private final int value;
private Type(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:operations_research.MPSosConstraint.Type)
}
private int bitField0_;
public static final int TYPE_FIELD_NUMBER = 1;
private int type_;
/**
* optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
* @return Whether the type field is set.
*/
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/**
* optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
* @return The type.
*/
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}
public static final int VAR_INDEX_FIELD_NUMBER = 2;
private com.google.protobuf.Internal.IntList varIndex_;
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @return A list containing the varIndex.
*/
@java.lang.Override
public java.util.List
getVarIndexList() {
return varIndex_;
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @return The count of varIndex.
*/
public int getVarIndexCount() {
return varIndex_.size();
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @param index The index of the element to return.
* @return The varIndex at the given index.
*/
public int getVarIndex(int index) {
return varIndex_.getInt(index);
}
public static final int WEIGHT_FIELD_NUMBER = 3;
private com.google.protobuf.Internal.DoubleList weight_;
/**
*
* 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;
* @return A list containing the weight.
*/
@java.lang.Override
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return weight_;
}
/**
*
* 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;
* @return The count of weight.
*/
public int getWeightCount() {
return weight_.size();
}
/**
*
* 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;
* @param index The index of the element to return.
* @return The weight at the given index.
*/
public double getWeight(int index) {
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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.
*
*
* Protobuf type {@code operations_research.MPSosConstraint}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:operations_research.MPSosConstraint)
com.google.ortools.linearsolver.MPSosConstraintOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return com.google.ortools.linearsolver.LinearSolver.internal_static_operations_research_MPSosConstraint_descriptor;
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/**
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*/
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* @return The type.
*/
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}
/**
* optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
* @param value The type to set.
* @return This builder for chaining.
*/
public Builder setType(com.google.ortools.linearsolver.MPSosConstraint.Type value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000001;
type_ = value.getNumber();
onChanged();
return this;
}
/**
* optional .operations_research.MPSosConstraint.Type type = 1 [default = SOS1_DEFAULT];
* @return This builder for chaining.
*/
public Builder clearType() {
bitField0_ = (bitField0_ & ~0x00000001);
type_ = 0;
onChanged();
return this;
}
private com.google.protobuf.Internal.IntList varIndex_ = emptyIntList();
private void ensureVarIndexIsMutable() {
if (!((bitField0_ & 0x00000002) != 0)) {
varIndex_ = mutableCopy(varIndex_);
bitField0_ |= 0x00000002;
}
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @return A list containing the varIndex.
*/
public java.util.List
getVarIndexList() {
return ((bitField0_ & 0x00000002) != 0) ?
java.util.Collections.unmodifiableList(varIndex_) : varIndex_;
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @return The count of varIndex.
*/
public int getVarIndexCount() {
return varIndex_.size();
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @param index The index of the element to return.
* @return The varIndex at the given index.
*/
public int getVarIndex(int index) {
return varIndex_.getInt(index);
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @param index The index to set the value at.
* @param value The varIndex to set.
* @return This builder for chaining.
*/
public Builder setVarIndex(
int index, int value) {
ensureVarIndexIsMutable();
varIndex_.setInt(index, value);
onChanged();
return this;
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @param value The varIndex to add.
* @return This builder for chaining.
*/
public Builder addVarIndex(int value) {
ensureVarIndexIsMutable();
varIndex_.addInt(value);
onChanged();
return this;
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @param values The varIndex to add.
* @return This builder for chaining.
*/
public Builder addAllVarIndex(
java.lang.Iterable values) {
ensureVarIndexIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, varIndex_);
onChanged();
return this;
}
/**
*
* Variable index (w.r.t. the "variable" field of MPModelProto) of the
* variables in the SOS.
*
*
* repeated int32 var_index = 2;
* @return This builder for chaining.
*/
public Builder clearVarIndex() {
varIndex_ = emptyIntList();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
return this;
}
private com.google.protobuf.Internal.DoubleList weight_ = emptyDoubleList();
private void ensureWeightIsMutable() {
if (!((bitField0_ & 0x00000004) != 0)) {
weight_ = mutableCopy(weight_);
bitField0_ |= 0x00000004;
}
}
/**
*
* 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;
* @return A list containing the weight.
*/
public java.util.List
getWeightList() {
return ((bitField0_ & 0x00000004) != 0) ?
java.util.Collections.unmodifiableList(weight_) : weight_;
}
/**
*
* 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;
* @return The count of weight.
*/
public int getWeightCount() {
return weight_.size();
}
/**
*
* 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;
* @param index The index of the element to return.
* @return The weight at the given index.
*/
public double getWeight(int index) {
return weight_.getDouble(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;
* @param index The index to set the value at.
* @param value The weight to set.
* @return This builder for chaining.
*/
public Builder setWeight(
int index, double value) {
ensureWeightIsMutable();
weight_.setDouble(index, value);
onChanged();
return this;
}
/**
*
* 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;
* @param value The weight to add.
* @return This builder for chaining.
*/
public Builder addWeight(double value) {
ensureWeightIsMutable();
weight_.addDouble(value);
onChanged();
return this;
}
/**
*
* 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;
* @param values The weight to add.
* @return This builder for chaining.
*/
public Builder addAllWeight(
java.lang.Iterable values) {
ensureWeightIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, weight_);
onChanged();
return this;
}
/**
*
* 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;
* @return This builder for chaining.
*/
public Builder clearWeight() {
weight_ = emptyDoubleList();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:operations_research.MPSosConstraint)
}
// @@protoc_insertion_point(class_scope:operations_research.MPSosConstraint)
private static final com.google.ortools.linearsolver.MPSosConstraint DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.ortools.linearsolver.MPSosConstraint();
}
public static com.google.ortools.linearsolver.MPSosConstraint getDefaultInstance() {
return DEFAULT_INSTANCE;
}
@java.lang.Deprecated public static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
@java.lang.Override
public MPSosConstraint parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new MPSosConstraint(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.ortools.linearsolver.MPSosConstraint getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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