XpressProblem

java.lang.Object
- com.dashoptimization.XPRSobject
- - com.dashoptimization.XPRSprob
  - - com.dashoptimization.objects.XpressProblem

All Implemented Interfaces:

java.lang.AutoCloseable
```
public class XpressProblem
extends XPRSprob
```
Optimizer interface that allows modeling by objects.

Nested Class Summary

Modifier and Type	Class	Description
`static class`	`XpressProblem.BranchObject`	Subclass of `XPRSbranchobject` that allows using `Variable` instances to specify bounds and constraints.
`static class`	`XpressProblem.CallbackAPI`	API for adding and removing callbacks to this problem.

Nested classes/interfaces inherited from class com.dashoptimization.XPRSprob
XPRSprob.AbstractUserFunction, XPRSprob.AttributeInfo, XPRSprob.Attributes, XPRSprob.BasisValue, XPRSprob.ControlInfo, XPRSprob.Controls, XPRSprob.GeneralConstraintInfo, XPRSprob.GlobalInfo, XPRSprob.IISData, XPRSprob.IISStatusInfo, XPRSprob.InfeasInfo, XPRSprob.MapDeltaFunction, XPRSprob.MapDeltaFunctor, XPRSprob.MapFunction, XPRSprob.MapFunctor, XPRSprob.MatrixInfo, XPRSprob.MatrixTriplets, XPRSprob.MIPEntityInfo, XPRSprob.MultiMapDeltaFunction, XPRSprob.MultiMapDeltaFunctor, XPRSprob.MultiMapFunction, XPRSprob.MultiMapFunctor, XPRSprob.PWLInfo, XPRSprob.RowInfo, XPRSprob.Solution, XPRSprob.SolVal, XPRSprob.StatusSolution, XPRSprob.VecMapDeltaFunction, XPRSprob.VecMapDeltaFunctor, XPRSprob.VecMapFunction, XPRSprob.VecMapFunctor

Field Summary

Modifier and Type	Field	Description
`XpressProblem.CallbackAPI`	`callbacks`	Property through which callbacks for this instance can be accessed.
`static Variable`	`NULL_VARIABLE`	Variable object that is used to indicate a constant term in expressions that are implemented as maps.

Fields inherited from class com.dashoptimization.XPRSprob
cannotAddPWLs, cannotAddSets, EQ, GEQ, LEQ

Constructor Summary

Constructor	Description
`XpressProblem()`	Create a new problem without internal name.
`XpressProblem(java.lang.String problemName)`	Create a new problem with internal name.
`XpressProblem(java.lang.String problemName, java.lang.String licensePath)`	Create a new problem with internal name and non-default license.

Method Summary

Modifier and Type	Method	Description
`<C extends Index> C`	`addConstraint(ConstraintDefinition<C> def)`	Add a single constraint to this problem.
`<C extends Index> java.util.stream.Stream<C>`	`addConstraints(int[] data, java.util.function.IntFunction<ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`<C extends Index> void`	`addConstraints(int count1, int count2, int count3, int count4, int count5, Function5<java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<C extends Index> void`	`addConstraints(int count1, int count2, int count3, int count4, Function4<java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<C extends Index> void`	`addConstraints(int count1, int count2, int count3, Function3<java.lang.Integer,java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<C extends Index> void`	`addConstraints(int count1, int count2, java.util.function.BiFunction<java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<C extends Index> java.util.stream.Stream<C>`	`addConstraints(int count, java.util.function.IntFunction<ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`<C extends Index> java.util.stream.Stream<C>`	`addConstraints(ConstraintDefinition<C>[] defs)`	Add multiple of the same type constraints to the problem.
`<K1,K2,K3,C extends Index> void`	`addConstraints(java.lang.Iterable<K1> iterable1, java.lang.Iterable<K2> iterable2, java.lang.Iterable<K3> iterable3, Function3<K1,K2,K3,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<K1,K2,K3,K4,C extends Index> void`	`addConstraints(java.lang.Iterable<K1> iterable1, java.lang.Iterable<K2> iterable2, java.lang.Iterable<K3> iterable3, java.lang.Iterable<K4> iterable4, Function4<K1,K2,K3,K4,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<K1,K2,K3,K4,K5,C extends Index> void`	`addConstraints(java.lang.Iterable<K1> iterable1, java.lang.Iterable<K2> iterable2, java.lang.Iterable<K3> iterable3, java.lang.Iterable<K4> iterable4, java.lang.Iterable<K5> iterable5, Function5<K1,K2,K3,K4,K5,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<K1,K2,C extends Index> void`	`addConstraints(java.lang.Iterable<K1> iterable1, java.lang.Iterable<K2> iterable2, java.util.function.BiFunction<K1,K2,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<C extends Index,T extends ConstraintDefinition<C>> java.util.stream.Stream<C>`	`addConstraints(java.lang.Iterable<T> defs)`	Add multiple constraints of the same type to the problem.
`<T,C extends Index> java.util.stream.Stream<C>`	`addConstraints(java.lang.Iterable<T> data, java.util.function.Function<T,ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`<T,C extends Index> java.util.stream.Stream<java.util.Map.Entry<T,C>>`	`addConstraints(java.util.Iterator<T> data, java.util.function.Function<T,ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`<C extends Index> java.util.stream.Stream<C>`	`addConstraints(java.util.stream.IntStream data, java.util.function.IntFunction<ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`<C extends Index,T extends ConstraintDefinition<C>> java.util.stream.Stream<C>`	`addConstraints(java.util.stream.Stream<T> defs)`	Add multiple constraints of the same type to the problem.
`<T,C extends Index> java.util.stream.Stream<C>`	`addConstraints(java.util.stream.Stream<T> data, java.util.function.Function<T,ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`<K1,K2,C extends Index> void`	`addConstraints(K1[] array1, K2[] array2, java.util.function.BiFunction<K1,K2,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<K1,K2,K3,C extends Index> void`	`addConstraints(K1[] array1, K2[] array2, K3[] array3, Function3<K1,K2,K3,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<K1,K2,K3,K4,C extends Index> void`	`addConstraints(K1[] array1, K2[] array2, K3[] array3, K4[] array4, Function4<K1,K2,K3,K4,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<K1,K2,K3,K4,K5,C extends Index> void`	`addConstraints(K1[] array1, K2[] array2, K3[] array3, K4[] array4, K5[] array5, Function5<K1,K2,K3,K4,K5,ConstraintDefinition<C>> makeConstraint)`	Add multiple constraints to this problem.
`<T,C extends Index> java.util.stream.Stream<C>`	`addConstraints(T[] data, java.util.function.Function<T,ConstraintDefinition<C>> defs)`	Add multiple constraints of the same type to the problem.
`int`	`addCut(int cuttype, Inequality.Definition cut)`	Add a cut at the current node.
`void`	`addMipSol(double[] values, Variable[] variables, java.lang.String name)`	Adds a new feasible, infeasible or partial MIP solution for the problem to the Optimizer.
`void`	`addObjective(Expression obj, int priority, double weight)`	Add a new objective function.
`Variable`	`addVariable()`	Add a single variable to this problem.
`Variable`	`addVariable(double lb, double ub, ColumnType type)`	Add a single variable to this problem.
`Variable`	`addVariable(double lb, double ub, ColumnType type, double limit, java.lang.String name)`	Add a single variable to this problem.
`Variable`	`addVariable(double lb, double ub, ColumnType type, java.lang.String name)`	Add a single variable to this problem.
`Variable`	`addVariable(ColumnType type)`	Add a single variable to this problem.
`Variable`	`addVariable(ColumnType type, java.lang.String name)`	Add a single variable to this problem.
`Variable`	`addVariable(java.lang.String name)`	Add a single variable to this problem.
`VariableBuilder.VariableArrayBuilder`	`addVariables(int dim)`	Create an 1 dimensional array of variables.
`VariableBuilder.VariableArray2Builder`	`addVariables(int dim1, int dim2)`	Create an 2 dimensional array of variables.
`VariableBuilder.VariableArray3Builder`	`addVariables(int dim1, int dim2, int dim3)`	Create an 3 dimensional array of variables.
`VariableBuilder.VariableArray4Builder`	`addVariables(int dim1, int dim2, int dim3, int dim4)`	Create an 4 dimensional array of variables.
`VariableBuilder.VariableArray5Builder`	`addVariables(int dim1, int dim2, int dim3, int dim4, int dim5)`	Create an 5 dimensional array of variables.
`<K1> VariableBuilder.VariableMapBuilder<K1>`	`addVariables(java.util.Collection<K1> coll1)`	Create an 1 dimensional map of variables.
`<K1,K2> VariableBuilder.VariableMap2Builder<K1,K2>`	`addVariables(java.util.Collection<K1> coll1, java.util.Collection<K2> coll2)`	Create an 2 dimensional map of variables.
`<K1,K2,K3> VariableBuilder.VariableMap3Builder<K1,K2,K3>`	`addVariables(java.util.Collection<K1> coll1, java.util.Collection<K2> coll2, java.util.Collection<K3> coll3)`	Create an 3 dimensional map of variables.
`<K1,K2,K3,K4> VariableBuilder.VariableMap4Builder<K1,K2,K3,K4>`	`addVariables(java.util.Collection<K1> coll1, java.util.Collection<K2> coll2, java.util.Collection<K3> coll3, java.util.Collection<K4> coll4)`	Create an 4 dimensional map of variables.
`<K1,K2,K3,K4,K5> VariableBuilder.VariableMap5Builder<K1,K2,K3,K4,K5>`	`addVariables(java.util.Collection<K1> coll1, java.util.Collection<K2> coll2, java.util.Collection<K3> coll3, java.util.Collection<K4> coll4, java.util.Collection<K5> coll5)`	Create an 5 dimensional map of variables.
`<K1> VariableBuilder.VariableMapBuilder<K1>`	`addVariables(java.util.stream.Stream<K1> strm)`	Create an 1 dimensional map of variables.
`<K1> VariableBuilder.VariableMapBuilder<K1>`	`addVariables(K1[] arr1)`	Create an 1 dimensional map of variables.
`<K1,K2> VariableBuilder.VariableMap2Builder<K1,K2>`	`addVariables(K1[] arr1, K2[] arr2)`	Create an 2 dimensional map of variables.
`<K1,K2,K3> VariableBuilder.VariableMap3Builder<K1,K2,K3>`	`addVariables(K1[] arr1, K2[] arr2, K3[] arr3)`	Create an 3 dimensional map of variables.
`<K1,K2,K3,K4> VariableBuilder.VariableMap4Builder<K1,K2,K3,K4>`	`addVariables(K1[] arr1, K2[] arr2, K3[] arr3, K4[] arr4)`	Create an 4 dimensional map of variables.
`<K1,K2,K3,K4,K5> VariableBuilder.VariableMap5Builder<K1,K2,K3,K4,K5>`	`addVariables(K1[] arr1, K2[] arr2, K3[] arr3, K4[] arr4, K5[] arr5)`	Create an 5 dimensional map of variables.
`void`	`bndSA(int len, Variable[] variables, double[] lblower, double[] lbupper, double[] ublower, double[] ubupper)`	Returns upper and lower sensitivity ranges for specified variables' lower and upper bounds.
`void`	`bndSA(Variable[] variables, double[] lblower, double[] lbupper, double[] ublower, double[] ubupper)`	Returns upper and lower sensitivity ranges for specified variables' lower and upper bounds.
`Variable[][]`	`buildVariables(VariableBuilder.Array2Builder builder)`	Create a variable array from a builder.
`<I> I`	`buildVariables(VariableBuilder.Array2Builder builder, java.util.function.Supplier<I> makeResult, Action4<I,java.lang.Integer,java.lang.Integer,Variable> addResult)`	Create a variable array from a builder.
`Variable[][][]`	`buildVariables(VariableBuilder.Array3Builder builder)`	Create a variable array from a builder.
`<I> I`	`buildVariables(VariableBuilder.Array3Builder builder, java.util.function.Supplier<I> makeResult, Action5<I,java.lang.Integer,java.lang.Integer,java.lang.Integer,Variable> addResult)`	Create a variable array from a builder.
`Variable[][][][]`	`buildVariables(VariableBuilder.Array4Builder builder)`	Create a variable array from a builder.
`<I> I`	`buildVariables(VariableBuilder.Array4Builder builder, java.util.function.Supplier<I> makeResult, Action6<I,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,Variable> addResult)`	Create a variable array from a builder.
`Variable[][][][][]`	`buildVariables(VariableBuilder.Array5Builder builder)`	Create a variable array from a builder.
`<I> I`	`buildVariables(VariableBuilder.Array5Builder builder, java.util.function.Supplier<I> makeResult, Action7<I,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,Variable> addResult)`	Create a variable array from a builder.
`Variable[]`	`buildVariables(VariableBuilder.ArrayBuilder builder)`	Create a variable array from a builder.
`<I> I`	`buildVariables(VariableBuilder.ArrayBuilder builder, java.util.function.Supplier<I> makeResult, Action3<I,java.lang.Integer,Variable> addResult)`	Create a variable array from a builder.
`<K1,K2> HashMap2<K1,K2,Variable>`	`buildVariables(VariableBuilder.Map2Builder<K1,K2> builder)`	Create a variable map from a builder.
`<I,K1,K2> I`	`buildVariables(VariableBuilder.Map2Builder<K1,K2> builder, java.util.function.Supplier<I> makeResult, Action4<I,K1,K2,Variable> addResult)`	Create a variable map from a builder.
`<K1,K2,K3> HashMap3<K1,K2,K3,Variable>`	`buildVariables(VariableBuilder.Map3Builder<K1,K2,K3> builder)`	Create a variable map from a builder.
`<I,K1,K2,K3> I`	`buildVariables(VariableBuilder.Map3Builder<K1,K2,K3> builder, java.util.function.Supplier<I> makeResult, Action5<I,K1,K2,K3,Variable> addResult)`	Create a variable map from a builder.
`<K1,K2,K3,K4> HashMap4<K1,K2,K3,K4,Variable>`	`buildVariables(VariableBuilder.Map4Builder<K1,K2,K3,K4> builder)`	Create a variable map from a builder.
`<I,K1,K2,K3,K4> I`	`buildVariables(VariableBuilder.Map4Builder<K1,K2,K3,K4> builder, java.util.function.Supplier<I> makeResult, Action6<I,K1,K2,K3,K4,Variable> addResult)`	Create a variable map from a builder.
`<K1,K2,K3,K4,K5> HashMap5<K1,K2,K3,K4,K5,Variable>`	`buildVariables(VariableBuilder.Map5Builder<K1,K2,K3,K4,K5> builder)`	Create a variable map from a builder.
`<I,K1,K2,K3,K4,K5> I`	`buildVariables(VariableBuilder.Map5Builder<K1,K2,K3,K4,K5> builder, java.util.function.Supplier<I> makeResult, Action7<I,K1,K2,K3,K4,K5,Variable> addResult)`	Create a variable map from a builder.
`<K1> java.util.HashMap<K1,Variable>`	`buildVariables(VariableBuilder.MapBuilder<K1> builder)`	Create a variable map from a builder.
`<I,K1> I`	`buildVariables(VariableBuilder.MapBuilder<K1> builder, java.util.function.Supplier<I> makeResult, Action3<I,K1,Variable> addResult)`	Create a variable map from a builder.
`void`	`chgBounds(Variable[] variables, byte[] bndType, double[] bndValue)`	Change bounds for multiple variables.
`XpressProblem`	`chgCoef(Inequality row, Variable variable, double coefficient)`	Changes the coefficient for `variable` in `row` in the linear matrix.
`XpressProblem`	`chgCoefs(Inequality[] row, Variable[] variable, double[] coefficient)`	Change coefficients in the linear matrix.
`void`	`chgColType(Variable[] variables, ColumnType[] colType)`	Change types for multiple variables.
`XpressProblem`	`chgObj(Variable[] variables, double[] coefficients)`	Change objective function coefficients.
`XpressProblem`	`chgObj(Variable variable, double coefficient)`	Change objective function coefficient.
`XpressProblem`	`chgObjN(int objidx, Variable[] variables, double[] coefficients)`	Change objective function coefficients.
`XpressProblem`	`chgObjN(int objidx, Variable variable, double coefficient)`	Change an objective function coefficient.
`void`	`chgRHS(Inequality[] rows, double[] newRHS)`	Change right-hand side for multiple rows.
`void`	`chgRHSrange(Inequality[] rows, double[] newRange)`	Change right-hand side ranges for multiple rows.
`void`	`chgRowType(Inequality[] rows, RowType[] rowType)`	Change types for multiple rows.
`XpressProblem.BranchObject`	`createBranchObject()`	Create a new branch object.
`void`	`delCols(int ncols, int[] colind)`	Delete columns from this problem.
`void`	`delGenCons(int nconstraints, int[] conind)`	Delete constraints from this problem.
`void`	`delGeneralConstraints(GeneralConstraint[] constraints)`	Delete general constraints.
`void`	`delIndicator(Inequality row)`	Delete a single indicator constraint.
`void`	`delInequalities(Inequality[] rows)`
`void`	`delPwlCons(int npwls, int[] pwlind)`	Delete piecewise linear constraints from this problem.
`void`	`delPwlConstraints(PWL[] pwls)`
`void`	`delRows(int nrows, int[] rowind)`	Delete rows from this problem.
`void`	`delSets(int nsets, int[] sosind)`	Delete sets from this problem.
`void`	`delSOS(SOS sos)`	Delete a single SOS from the problem.
`void`	`delSOS(SOS[] soss)`	Delete special ordered set constraints.
`void`	`delVariables(Variable[] vars)`
`GeneralConstraint`	`generalConstraintForIndex(int index)`	Map a general constraint index to a `GeneralConstraint` object.
`GeneralConstraint[]`	`generalConstraintForIndices(int first, int last)`	Map a range of general constraint indices to `GeneralConstraint` objects.
`double`	`getCoef(Inequality row, Variable variable)`	Query a single coefficient from the linear matrix.
`double`	`getDual(Inequality r)`	Get the dual for a single row.
`double[]`	`getDuals(Inequality[] rows)`	Get the duals for an array of rows.
`double[]`	`getDuals(java.util.Collection<Inequality> rows)`	Get the duals for a collection of rows.
`GeneralConstraint[]`	`getGeneralConstraints()`	Get all the general constraints currently defined in this problem.
`IIS`	`getIIS(int iis)`	Get the specified IIS.
`IndicatorObjects`	`getIndicator(Inequality row)`	Get indicator information for a single row.
`Inequality[]`	`getInequalities()`	Get all the inequalities currently defined in this problem.
`LinTermList`	`getInequalityLinear(Inequality row)`	Get the linear part of the left-hand side of a row.
`LinExpression`	`getInequalityLinear(Inequality row, java.util.function.Supplier<LinExpression> expression)`	Get the linear part of the left-hand side of a row.
`Expression`	`getLhsExpression(int row)`	Get the left-hand side of a row as an expression.
`double`	`getObj(Variable variable)`	Query an objective function coefficient.
`double`	`getObjN(int objidx, Variable variable)`	Query an objective function coefficient.
`double`	`getRedCost(Variable v)`	Get the reduced cost for a single variable.
`double[]`	`getRedCosts(Variable[] vars)`	Get the reduced costs for an array of variables.
`double[]`	`getRedCosts(java.util.Collection<Variable> vars)`	Get the reduced costs for a collection of variables.
`double`	`getSlack(Inequality r)`	Get the current slack for a single row.
`double[]`	`getSlacks(Inequality[] rows)`	Get the current slacks for an array of rows.
`double[]`	`getSlacks(java.util.Collection<Inequality> rows)`	Get the current slacks for a collection of rows.
`double`	`getSolution(Variable v)`	Get the current solution for a single variable.
`double[]`	`getSolution(Variable[] vars)`	Get the current solution for an array of variables.
`double[]`	`getSolution(java.util.Collection<Variable> vars)`	Get the current solution for a collection of variables.
`Variable[]`	`getVariables()`	Get all the variables currently defined in this problem.
`Inequality[]`	`inequalitiesForIndices(int first, int last)`
`Inequality`	`inequalityForIndex(int index)`
`boolean`	`isOriginal()`	Check whether this model instance is in the original state.
`void`	`loadDelayedRows(Inequality[] rows)`	Marks a set of rows as delayed rows.
`void`	`loadModelCuts(Inequality[] rows)`	Marks a set of rows as model cuts.
`void`	`nlpSetInitVal(Variable[] variables, double[] values)`	Set initial values of variables for a non-linear solve.
`void`	`nlpSetInitVal(java.util.Map<Variable,java.lang.Double> values)`	Set initial values of variables for a non-linear solve.
`void`	`objSA(int len, Variable[] variables, double[] lower, double[] upper)`	Returns upper and lower sensitivity ranges for specified objective function coefficients.
`void`	`objSA(Variable[] variables, double[] lower, double[] upper)`	Returns upper and lower sensitivity ranges for specified objective function coefficients.
`PWL`	`pwlForIndex(int index)`
`PWL[]`	`pwlsForIndices(int first, int last)`
`int`	`repairWeightedInfeas(java.util.Map<Inequality,java.lang.Double> lepref, java.util.Map<Inequality,java.lang.Double> gepref, java.util.Map<Variable,java.lang.Double> lbpref, java.util.Map<Variable,java.lang.Double> ubpref, char phase2, double delta, java.lang.String flags)`	Repair infeasibilities using weights.
`void`	`rhsSA(int len, Inequality[] rows, double[] lower, double[] upper)`	Returns upper and lower sensitivity ranges for specified right hand side (RHS) function coefficients.
`void`	`rhsSA(Inequality[] rows, double[] lower, double[] upper)`	Returns upper and lower sensitivity ranges for specified right hand side (RHS) function coefficients.
`void`	`setIndicator(Variable indicatorVariable, boolean indicatorValue, Inequality row)`	Add an indicator constraint to this model.
`void`	`setIndicators(int[] data, java.util.function.IntFunction<Variable> indicatorVariable, java.util.function.IntFunction<java.lang.Boolean> indicatorValue, java.util.function.IntFunction<Inequality> row)`	Add indicator constraints to this model.
`void`	`setIndicators(int count, java.util.function.Function<java.lang.Integer,Variable> indicatorVariable, java.util.function.Function<java.lang.Integer,java.lang.Boolean> indicatorValue, java.util.function.Function<java.lang.Integer,Inequality> row)`	Add indicator constraints to this model.
`void`	`setIndicators(Variable[] indicatorVariable, boolean[] indicatorValue, Inequality[] row)`	Add indicator constraints to this model.
`<T> void`	`setIndicators(java.lang.Iterable<T> data, java.util.function.Function<T,Variable> indicatorVariable, java.util.function.Function<T,java.lang.Boolean> indicatorValue, java.util.function.Function<T,Inequality> row)`	Add indicator constraints to this model.
`<T> void`	`setIndicators(java.util.Collection<T> data, java.util.function.Function<T,Variable> indicatorVariable, java.util.function.Function<T,java.lang.Boolean> indicatorValue, java.util.function.Function<T,Inequality> row)`	Add indicator constraints to this model.
`void`	`setIndicators(java.util.stream.IntStream data, java.util.function.IntFunction<Variable> indicatorVariable, java.util.function.IntFunction<java.lang.Boolean> indicatorValue, java.util.function.IntFunction<Inequality> row)`	Add indicator constraints to this model.
`<T> void`	`setIndicators(java.util.stream.Stream<T> data, java.util.function.Function<T,Variable> indicatorVariable, java.util.function.Function<T,java.lang.Boolean> indicatorValue, java.util.function.Function<T,Inequality> row)`	Add indicator constraints to this model.
`<T> void`	`setIndicators(T[] data, java.util.function.Function<T,Variable> indicatorVariable, java.util.function.Function<T,java.lang.Boolean> indicatorValue, java.util.function.Function<T,Inequality> row)`	Add indicator constraints to this model.
`void`	`setObjective(int idx, Expression obj, int priority, double weight, double abstol, double reltol)`	Set an objective function and its controls.
`void`	`setObjective(Expression obj)`	Set the objective function to a new expression.
`void`	`setObjective(Expression obj, XPRSenumerations.ObjSense sense)`	Set the objective function to a new expression.
`static <T extends java.lang.Comparable<T>> void`	`sortParallelArrays(T[] mvec, double[] dvec, int offset, int size)`	Sort parallel arrays according to index keys.
`static <T extends java.lang.Comparable<T>> void`	`sortParallelArrays(T[] key1, T[] key2, double[] dvec, int offset, int size)`	Sort parallel arrays according to integer pair keys.
`SOS`	`sosForIndex(int index)`
`SOS[]`	`sosForIndices(int first, int last)`
`Variable`	`variableForIndex(int index)`
`Variable[]`	`variablesForIndices(int first, int last)`

Methods inherited from class com.dashoptimization.XPRSprob
addAfterObjectiveListener, addAfterObjectiveListener, addAfterObjectiveListener, addAfterObjectiveListener, addBarIterationListener, addBarIterationListener, addBarIterationListener, addBarIterationListener, addBarLogListener, addBarLogListener, addBarLogListener, addBarLogListener, addBeforeObjectiveListener, addBeforeObjectiveListener, addBeforeObjectiveListener, addBeforeObjectiveListener, addBeforeSolveListener, addBeforeSolveListener, addBeforeSolveListener, addBeforeSolveListener, addChangeBranchObjectListener, addChangeBranchObjectListener, addChangeBranchObjectListener, addChangeBranchObjectListener, addCheckTimeListener, addCheckTimeListener, addCheckTimeListener, addCheckTimeListener, addChgBranchListener, addChgBranchListener, addChgBranchListener, addChgBranchListener, addChgNodeListener, addChgNodeListener, addChgNodeListener, addChgNodeListener, addCol, addCol, addCol, addCol, addCol, addCol, addCols, addCols, addColumn, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addColumns, addComputeRestartListener, addComputeRestartListener, addComputeRestartListener, addComputeRestartListener, addCut, addCut, addCutLogListener, addCutLogListener, addCutLogListener, addCutLogListener, addCutMgrListener, addCutMgrListener, addCutMgrListener, addCutMgrListener, addCuts, addCuts, addDestroyMTListener, addDestroyMTListener, addDestroyMTListener, addDestroyMTListener, addGapNotifyListener, addGapNotifyListener, addGapNotifyListener, addGapNotifyListener, addGenCons, addGenCons, addInfNodeListener, addInfNodeListener, addInfNodeListener, addInfNodeListener, addIntSolListener, addIntSolListener, addIntSolListener, addIntSolListener, addLpLogListener, addLpLogListener, addLpLogListener, addLpLogListener, addMessageListener, addMessageListener, addMessageListener, addMessageListener, addMipLogListener, addMipLogListener, addMipLogListener, addMipLogListener, addMipSol, addMipSol, addMipSol, addMipThreadListener, addMipThreadListener, addMipThreadListener, addMipThreadListener, addMsgHandlerListener, addMsgHandlerListener, addMsgHandlerListener, addMsgHandlerListener, addMsJobEndListener, addMsJobEndListener, addMsJobEndListener, addMsJobEndListener, addMsJobStartListener, addMsJobStartListener, addMsJobStartListener, addMsJobStartListener, addMsWinnerListener, addMsWinnerListener, addMsWinnerListener, addMsWinnerListener, addNames, addNames, addNewnodeListener, addNewnodeListener, addNewnodeListener, addNewnodeListener, addNlpCoefEvalErrorListener, addNlpCoefEvalErrorListener, addNlpCoefEvalErrorListener, addNlpCoefEvalErrorListener, addNodeCutoffListener, addNodeCutoffListener, addNodeCutoffListener, addNodeCutoffListener, addNodeLPSolvedListener, addNodeLPSolvedListener, addNodeLPSolvedListener, addNodeLPSolvedListener, addObj, addOptNodeListener, addOptNodeListener, addOptNodeListener, addOptNodeListener, addPreIntsolListener, addPreIntsolListener, addPreIntsolListener, addPreIntsolListener, addPreNodeListener, addPreNodeListener, addPreNodeListener, addPreNodeListener, addPresolveListener, addPresolveListener, addPresolveListener, addPresolveListener, addPwlCons, addPwlCons, addQMatrix, addQMatrix, addRow, addRow, addRow, addRow, addRow, addRows, addRows, addRows, addRows, addSet, addSetNames, addSets, addSets, addSets, addSets, addSlpCascadeEndListener, addSlpCascadeEndListener, addSlpCascadeEndListener, addSlpCascadeEndListener, addSlpCascadeStartListener, addSlpCascadeStartListener, addSlpCascadeStartListener, addSlpCascadeStartListener, addSlpCascadeVarFailListener, addSlpCascadeVarFailListener, addSlpCascadeVarFailListener, addSlpCascadeVarFailListener, addSlpCascadeVarListener, addSlpCascadeVarListener, addSlpCascadeVarListener, addSlpCascadeVarListener, addSlpConstructListener, addSlpConstructListener, addSlpConstructListener, addSlpConstructListener, addSlpDrColListener, addSlpDrColListener, addSlpDrColListener, addSlpDrColListener, addSlpIntSolListener, addSlpIntSolListener, addSlpIntSolListener, addSlpIntSolListener, addSlpIterEndListener, addSlpIterEndListener, addSlpIterEndListener, addSlpIterEndListener, addSlpIterStartListener, addSlpIterStartListener, addSlpIterStartListener, addSlpIterStartListener, addSlpIterVarListener, addSlpIterVarListener, addSlpIterVarListener, addSlpIterVarListener, addSlpPreUpdateLinearizationListener, addSlpPreUpdateLinearizationListener, addSlpPreUpdateLinearizationListener, addSlpPreUpdateLinearizationListener, addUserSolNotifyListener, addUserSolNotifyListener, addUserSolNotifyListener, addUserSolNotifyListener, alter, attributes, basisCondition, basisStability, basisStability, basisStability, binVar, binVar, binVarArray, binVarArray, binVarMap, binVarMap, bndsa, btran, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, buildColumns, calcObjective, calcObjective, calcObjN, calcObjN, calcReducedCosts, calcSlacks, calcSolInfo, calcSolInfo, chgBounds, chgBounds, chgCoef, chgColType, chgColType, chgGlbLimit, chgGlbLimit, chgLB, chgMCoef, chgMCoef, chgMCoef, chgMQObj, chgMQObj, chgMQObj, chgObj, chgObj, chgObjN, chgObjSense, chgObjSense, chgQObj, chgQRowCoeff, chgRHS, chgRHS, chgRHSrange, chgRHSrange, chgRowType, chgRowType, chgUB, clearIIS, clearObjective, clearRowFlags, close, controls, contVar, contVar, contVar, contVar, contVarArray, contVarArray, contVarArray, contVarArray, contVarArray, contVarMap, contVarMap, copyCallBacks, copyControls, copyProb, createBranchObject, createBranchObjectFromGlobal, crossoverLpSol, crossoverLpSol, delCPCuts, delCPCuts, delCPCuts, delCPCuts, delCPCuts, delCuts, delCuts, delCuts, delCuts, delCuts, delCuts, delIndicator, delIndicators, delObj, delQMatrix, destroyProb, dumpControls, estimateRowDualRanges, firstIIS, firstIIS, fixGlobals, fixMIPEntities, ftran, getAttribInfo, getAttribInfo, getBarNumStability, getBasis, getBasisVal, getBasisVal, getCoef, getCoef, getColBasisVal, getCols, getCols, getCols, getCols, getCols, getColType, getColType, getColType, getColumnName, getColumnNames, getControlInfo, getControlInfo, getCPCutList, getCPCutList, getCPCutList, getCPCutList, getCPCuts, getCPCuts, getCPCuts, getCutList, getCutList, getCutList, getCutList, getCutList, getCutMap, getCutSlack, getCutSlack, getDblAttrib, getDblAttrib, getDblControl, getDblControl, getDirs, getDirs, getDirs, getDiscreteCols, getDual, getDual, getDualRay, getDualRay, getDuals, getDuals, getDuals, getDuals, getDuals, getGenCons, getGenCons, getGenCons, getGenConsName, getGenConsNames, getGlobal, getGlobal, getGlobalEntities, getGlobalSets, getIISData, getIISData, getIndex, getIndex, getIndicator, getIndicators, getIndicators, getInfeas, getInfeas, getIntAttrib, getIntAttrib, getIntControl, getIntControl, getLastBarSol, getLastBarSol, getLastBarSol, getLastBarSolDjs, getLastBarSolDuals, getLastBarSolSlack, getLastBarSolX, getLastError, getLastError, getLB, getLB, getLB, getLongAttrib, getLongAttrib, getLongControl, getLongControl, getLpSol, getLpSol, getLpSol, getLpSolDjs, getLpSolDuals, getLpSolSlack, getLpSolVal, getLpSolVal, getLpSolX, getMessageStatus, getMessageStatus, getMIPEntities, getMIPEntities, getMIPEntities, getMIPEntities, getMIPEntities, getMipSol, getMipSol, getMipSol, getMipSolSlack, getMipSolVal, getMipSolVal, getMipSolX, getMQObj, getMQObj, getMQObj, getMQObj, getMQObj, getName, getName, getNameListObject, getNames, getNames, getNames, getNames, getNlpsol, getObj, getObj, getObj, getObjDblAttrib, getObjDblAttrib, getObjDblControl, getObjDblControl, getObjIntAttrib, getObjIntAttrib, getObjIntAttrib, getObjIntControl, getObjIntControl, getObjLongAttrib, getObjN, getPivotOrder, getPivots, getPresolveBasis, getPresolveMap, getPresolveSol, getPresolveSol, getPresolveSol, getPresolveSolDjs, getPresolveSolDuals, getPresolveSolSlack, getPresolveSolX, getPrimalRay, getPrimalRay, getProbName, getProbName, getPwlCons, getPwlCons, getPwlCons, getPWLName, getPWLNames, getQObj, getQObj, getQRowCoeff, getQRowCoeff, getQRowIndices, getQRowQMatrix, getQRowQMatrix, getQRowQMatrixTriplets, getQRowQMatrixTriplets, getQRowQMatrixTriplets, getQRows, getQRows, getQRows, getRedCost, getRedCost, getRedCosts, getRedCosts, getRedCosts, getRedCosts, getRedCosts, getRHS, getRHS, getRHS, getRHSrange, getRHSrange, getRHSrange, getRowBasisVal, getRowFlags, getRowFlags, getRowFlags, getRowName, getRowNames, getRows, getRows, getRows, getRows, getRows, getRowType, getRowType, getRowType, getScale, getScaledInfeas, getScaledInfeas, getSetName, getSetNames, getSets, getSlack, getSlack, getSlacks, getSlacks, getSlacks, getSlacks, getSlacks, getSol, getSol, getSol, getSolDjs, getSolDuals, getSolSlack, getSolution, getSolution, getSolution, getSolution, getSolution, getSolution, getSolution, getSolX, getStrAttrib, getStrAttrib, getStrControl, getStrControl, getStringControl, getStrStringAttrib, getUB, getUB, getUB, getUnbVec, getUnbVec, iISAll, IISAll, iISIsolations, IISIsolations, iISStatus, IISStatus, IISStatus, interrupt, interrupt, interrupt, intVar, intVar, intVar, intVar, intVarArray, intVarArray, intVarArray, intVarArray, intVarArray, intVarMap, intVarMap, loadBasis, loadBranchDirs, loadBranchDirs, loadCuts, loadCuts, loadCuts, loadCuts, loadDelayedRows, loadDirs, loadGlobal, loadGlobal, loadLp, loadLp, loadLp, loadLp, loadLPSol, loadLPSol, loadMIP, loadMIP, loadMipSol, loadMipSol, loadMIQCQP, loadMIQCQP, loadMIQP, loadMIQP, loadModelCuts, loadPresolveBasis, loadPresolveDirs, loadQCQP, loadQCQP, loadQCQPGlobal, loadQCQPGlobal, loadQGlobal, loadQGlobal, loadQP, loadQP, loadSecureVecs, lpOptimize, lpOptimize, maxim, maxim, minim, minim, mipOptimize, mipOptimize, msAddCustomPreset, msAddJob, msAddPreset, msClear, nextIIS, nextIIS, nlpAddFormulas, nlpAddUserFunction, nlpAddUserFunction, nlpAddUserFunction, nlpAddUserFunction, nlpAddUserFunction, nlpAddUserFunction, nlpCalcSlacks, nlpChgFormula, nlpChgFormulaStr, nlpChgFormulaString, nlpCurrentIV, nlpDelFormulas, nlpDelUserFunction, nlpEvaluateFormula, nlpEvaluateFormula, nlpGetFormula, nlpGetFormulaRows, nlpGetFormulaStr, nlpGetFormulaString, nlpImportLibFunc, nlpLoadFormulas, nlpOptimize, nlpPostsolveProb, nlpPrintEvalInfo, nlpSetFunctionError, nlpSetInitVal, nlpSetInitVal, nlpValidate, nlpValidateKKT, nlpValidateRow, nlpValidateVector, objSA, optimize, optimize, optimize, pivot, postSolve, postSolveSol, presolveRow, presolveRow, presolveRow, readBasis, readBasis, readBasis, readBinSol, readBinSol, readBinSol, readDirs, readDirs, readProb, readProb, readSlxSol, readSlxSol, readSlxSol, refineMipSol, refineMipSol, removeAfterObjectiveListener, removeAfterObjectiveListener, removeAfterObjectiveListener, removeAfterObjectiveListeners, removeBarIterationListener, removeBarIterationListener, removeBarIterationListener, removeBarIterationListeners, removeBarLogListener, removeBarLogListener, removeBarLogListener, removeBarLogListeners, removeBeforeObjectiveListener, removeBeforeObjectiveListener, removeBeforeObjectiveListener, removeBeforeObjectiveListeners, removeBeforeSolveListener, removeBeforeSolveListener, removeBeforeSolveListener, removeBeforeSolveListeners, removeChangeBranchObjectListener, removeChangeBranchObjectListener, removeChangeBranchObjectListener, removeChangeBranchObjectListeners, removeCheckTimeListener, removeCheckTimeListener, removeCheckTimeListener, removeCheckTimeListeners, removeChgBranchListener, removeChgBranchListener, removeChgBranchListener, removeChgBranchListeners, removeChgNodeListener, removeChgNodeListener, removeChgNodeListener, removeChgNodeListeners, removeComputeRestartListener, removeComputeRestartListener, removeComputeRestartListener, removeComputeRestartListeners, removeCutLogListener, removeCutLogListener, removeCutLogListener, removeCutLogListeners, removeCutMgrListener, removeCutMgrListener, removeCutMgrListener, removeCutMgrListeners, removeDestroyMTListener, removeDestroyMTListener, removeDestroyMTListener, removeDestroyMTListeners, removeGapNotifyListener, removeGapNotifyListener, removeGapNotifyListener, removeGapNotifyListeners, removeInfNodeListener, removeInfNodeListener, removeInfNodeListener, removeInfNodeListeners, removeIntSolListener, removeIntSolListener, removeIntSolListener, removeIntSolListeners, removeLpLogListener, removeLpLogListener, removeLpLogListener, removeLpLogListeners, removeMessageListener, removeMessageListener, removeMessageListener, removeMessageListeners, removeMipLogListener, removeMipLogListener, removeMipLogListener, removeMipLogListeners, removeMipThreadListener, removeMipThreadListener, removeMipThreadListener, removeMipThreadListeners, removeMsgHandlerListener, removeMsgHandlerListener, removeMsgHandlerListener, removeMsgHandlerListeners, removeMsJobEndListener, removeMsJobEndListener, removeMsJobEndListener, removeMsJobEndListeners, removeMsJobStartListener, removeMsJobStartListener, removeMsJobStartListener, removeMsJobStartListeners, removeMsWinnerListener, removeMsWinnerListener, removeMsWinnerListener, removeMsWinnerListeners, removeNewnodeListener, removeNewnodeListener, removeNewnodeListener, removeNewnodeListeners, removeNlpCoefEvalErrorListener, removeNlpCoefEvalErrorListener, removeNlpCoefEvalErrorListener, removeNlpCoefEvalErrorListeners, removeNodeCutoffListener, removeNodeCutoffListener, removeNodeCutoffListener, removeNodeCutoffListeners, removeNodeLPSolvedListener, removeNodeLPSolvedListener, removeNodeLPSolvedListener, removeNodeLPSolvedListeners, removeOptNodeListener, removeOptNodeListener, removeOptNodeListener, removeOptNodeListeners, removePreIntsolListener, removePreIntsolListener, removePreIntsolListener, removePreIntsolListeners, removePreNodeListener, removePreNodeListener, removePreNodeListener, removePreNodeListeners, removePresolveListener, removePresolveListener, removePresolveListener, removePresolveListeners, removeSlpCascadeEndListener, removeSlpCascadeEndListener, removeSlpCascadeEndListener, removeSlpCascadeEndListeners, removeSlpCascadeStartListener, removeSlpCascadeStartListener, removeSlpCascadeStartListener, removeSlpCascadeStartListeners, removeSlpCascadeVarFailListener, removeSlpCascadeVarFailListener, removeSlpCascadeVarFailListener, removeSlpCascadeVarFailListeners, removeSlpCascadeVarListener, removeSlpCascadeVarListener, removeSlpCascadeVarListener, removeSlpCascadeVarListeners, removeSlpConstructListener, removeSlpConstructListener, removeSlpConstructListener, removeSlpConstructListeners, removeSlpDrColListener, removeSlpDrColListener, removeSlpDrColListener, removeSlpDrColListeners, removeSlpIntSolListener, removeSlpIntSolListener, removeSlpIntSolListener, removeSlpIntSolListeners, removeSlpIterEndListener, removeSlpIterEndListener, removeSlpIterEndListener, removeSlpIterEndListeners, removeSlpIterStartListener, removeSlpIterStartListener, removeSlpIterStartListener, removeSlpIterStartListeners, removeSlpIterVarListener, removeSlpIterVarListener, removeSlpIterVarListener, removeSlpIterVarListeners, removeSlpPreUpdateLinearizationListener, removeSlpPreUpdateLinearizationListener, removeSlpPreUpdateLinearizationListener, removeSlpPreUpdateLinearizationListeners, removeUserSolNotifyListener, removeUserSolNotifyListener, removeUserSolNotifyListener, removeUserSolNotifyListeners, repairInfeas, repairInfeas, repairWeightedInfeas, repairWeightedInfeas, repairWeightedInfeasBounds, repairWeightedInfeasBounds, restore, restore, restore, rhsSA, save, saveAs, scale, setDblControl, setDefaultControl, setDefaults, setIndicator, setIndicators, setIntControl, setLogFile, setLongControl, setMessageStatus, setObjDblControl, setObjective, setObjective, setObjIntControl, setProbname, setStrControl, slpAddCoefs, slpCascadeOrder, slpCascadeSol, slpChgCascadeNLimit, slpChgCCoef, slpChgCoef, slpChgCoefStr, slpChgDeltaType, slpChgRowStatus, slpChgRowStatus, slpChgRowWt, slpConstruct, slpDelCoefs, slpDelCoefs, slpEvaluateCoef, slpEvaluateCoef, slpFixPenalties, slpGetCCoef, slpGetCoefFormula, slpGetCoefs, slpGetCoefStr, slpGetRowStatus, slpGetRowWT, slpGetRowWT, slpLoadCoefs, slpReInitialize, slpSetDetRow, slpSetDetRow, slpUnConstruct, slpUpdateLinearization, sparseBTran, sparseFTran, storeCuts, storeCuts, storeCuts, strongBranch, strongBranchCB, tune, tuneProbSetFile, tunerReadMethod, tunerWriteMethod, unloadProb, varArray, varArray, varArray, varArray, varArray, varMap, varMap, writeBasis, writeBasis, writeBasis, writeBinSol, writeBinSol, writeBinSol, writeDirs, writeDirs, writeIIS, writeIIS, writeProb, writeProb, writeProb, writePrtSol, writePrtSol, writePrtSol, writeSlxSol, writeSlxSol, writeSlxSol, writeSol, writeSol, writeSol

Methods inherited from class com.dashoptimization.XPRSobject
addMsgHandlerListener, addMsgHandlerListener, destroy, isDestroyed

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
 - NULL_VARIABLE
```
public static final Variable NULL_VARIABLE
```
 Variable object that is used to indicate a constant term in expressions that are implemented as maps.
 
 Since:
 
 43.00
 - callbacks
```
public final XpressProblem.CallbackAPI callbacks
```
 Property through which callbacks for this instance can be accessed.
- Constructor Detail
 - XpressProblem
```
public XpressProblem()
```
 Create a new problem without internal name. If not yet done then Xpress is initialized with the license found at the default license location. If Xpress is initialized this way then it will be automatically de-initialized once the newly created problem instance is closed.
 
 Since:
 
 43.00
 - XpressProblem
```
public XpressProblem(java.lang.String problemName)
```
 Create a new problem with internal name. If not yet done then Xpress is initialized with the license found at the default license location. If Xpress is initialized this way then it will be automatically de-initialized once the newly created problem instance is closed.
 
 Parameters:
 
 problemName - Internal name of new problem, can be null.
 
 Since:
 
 43.00
 - XpressProblem
```
public XpressProblem(java.lang.String problemName,
 java.lang.String licensePath)
```
 Create a new problem with internal name and non-default license. If not yet done then Xpress is initialized with the license found at licensePath. If Xpress is initialized this way then it will be automatically de-initialized once the newly created problem instance is closed.
 
 Parameters:
 
 problemName - Internal name of new problem, can be null.
 
 licensePath - Path to license location. Can be null or the empty string to indicate that the license is in the default location.
 
 Since:
 
 43.00
- Method Detail
 - chgObj
```
public XpressProblem chgObj(Variable variable,
 double coefficient)
```
 Change objective function coefficient. This changes linear coefficient in the first/primary objective function. In order to delete a coefficient from the obejctive set it to 0 (zero),
 Used in these examples:
 
 GlobalObjectiveParametrics
 
 GoalProg
 Parameters:
 
 variable - Variable for which to change coefficient.
 
 coefficient - New coefficient for variables.
 
 Returns:
 
 Always returns this instance.
 
 Since:
 
 43.00
 - chgObj
```
public XpressProblem chgObj(Variable[] variables,
 double[] coefficients)
```
 Change objective function coefficients. This changes linear coefficients in the first/primary objective function. In order to delete a coefficient from the obejctive set it to 0 (zero),
 Used in these examples:
 
 GlobalObjectiveParametrics
 
 GoalProg
 Parameters:
 
 variables - Variables for which to change coefficients.
 
 coefficients - New coefficients for variables.
 
 Returns:
 
 Always returns this instance.
 
 Since:
 
 43.00
 - chgObjN
```
public XpressProblem chgObjN(int objidx,
 Variable variable,
 double coefficient)
```
 Change an objective function coefficient. This changes linear coefficient in the objective function objidx. In order to delete a coefficient from the obejctive set it to 0 (zero),
 Used in these examples:
 
 GoalProg
 Parameters:
 
 objidx - Index of objective function to change,
 
 variable - Variable for which to change coefficient.
 
 coefficient - New coefficient for variable.
 
 Returns:
 
 Always returns this instance.
 
 Since:
 
 43.00
 - chgObjN
```
public XpressProblem chgObjN(int objidx,
 Variable[] variables,
 double[] coefficients)
```
 Change objective function coefficients. This changes linear coefficients in the objective function objidx. In order to delete a coefficient from the obejctive set it to 0 (zero),
 Used in these examples:
 
 GoalProg
 Parameters:
 
 objidx - Index of objective function to change,
 
 variables - Variables for which to change coefficients.
 
 coefficients - New coefficients for variables.
 
 Returns:
 
 Always returns this instance.
 
 Since:
 
 43.00
 - chgCoef
```
public XpressProblem chgCoef(Inequality row,
 Variable variable,
 double coefficient)
```
 Changes the coefficient for variable in row in the linear matrix.
 
 Parameters:
 
 row - Inequality in which to change coefficient.
 
 variable - Variable for which to change coefficient.
 
 coefficient - The new coefficient.
 
 Returns:
 
 Always returns this instance.
 
 Since:
 
 43.00
 - chgCoefs
```
public XpressProblem chgCoefs(Inequality[] row,
 Variable[] variable,
 double[] coefficient)
```
 Change coefficients in the linear matrix. The function changes the coefficients as indicated by the triplets given by row, variable, coefficient.
 
 Parameters:
 
 row - Inequality in which to change coefficient.
 
 variable - Variable for which to change coefficient.
 
 coefficient - The new coefficient.
 
 Returns:
 
 Always returns this instance.
 
 Since:
 
 43.00
 - repairWeightedInfeas
```
public int repairWeightedInfeas(java.util.Map<Inequality,java.lang.Double> lepref,
 java.util.Map<Inequality,java.lang.Double> gepref,
 java.util.Map<Variable,java.lang.Double> lbpref,
 java.util.Map<Variable,java.lang.Double> ubpref,
 char phase2,
 double delta,
 java.lang.String flags)
```
 Repair infeasibilities using weights. By relaxing a set of selected constraints and bounds of an infeasible problem, it attempts to identify a 'solution' that violates the selected set of constraints and bounds minimally, while satisfying all other constraints and bounds. Among such solution candidates, it selects one that is optimal regarding the original objective function.
 Used in these examples:
 
 Repair
 Parameters:
 
 lepref - Preferences for relaxing the less-than-or-equal direction of rows. Inequalities without preference will not be relaxed. Can be null.
 
 gepref - Preferences for relaxing the greater-than-or-equal direction of rows. Inequalities without preference will not be relaxed. Can be null.
 
 lbpref - Preferences for relaxing the lower bounds of variables. Variables without preference will not be relaxed. Can be null.
 
 ubpref - Preferences for relaxing the upper bounds of variables. Variables without preference will not be relaxed. Can be null.
 
 phase2 - Controls the second phase of optimization.
 
 delta - Relaxation multiplier in second phase - 1.
 
 flags - Flags passed to the optimizer.
 
 Returns:
 
 Result status.
 
 Since:
 
 43.00
 - inequalityForIndex
```
public Inequality inequalityForIndex(int index)
```
 Since:
 
 43.00
 - inequalitiesForIndices
```
public Inequality[] inequalitiesForIndices(int first,
 int last)
```
 Since:
 
 43.00
 - getInequalities
```
public Inequality[] getInequalities()
```
 Get all the inequalities currently defined in this problem.
 
 Since:
 
 43.00
 - delRows
```
public void delRows(int nrows,
 int[] rowind)
```
 Delete rows from this problem. Note that this may be expensive if you use com.dashoptimization.objects.Inequality instances to formulate your problem since indices of remaining rows will have to be updated after rows were deleted from the underlying model. If you have to delete multiple rows then it is best to collect all rows to be deleted in an array and then delete all of them with a single call to this function.
 
 Overrides:
 
 delRows in class XPRSprob
 
 Parameters:
 
 nrows - Number of rows to delete.
 
 rowind - Indices of rows to delete.
 
 Since:
 
 43.00
 - delInequalities
```
public void delInequalities(Inequality[] rows)
```
 Since:
 
 43.00
 - getSlacks
```
public double[] getSlacks(Inequality[] rows)
```
 Get the current slacks for an array of rows. The values in the returned array are in 1-to-1 correspondence with the rows in rows.
 
 Since:
 
 43.00
 - getSlacks
```
public double[] getSlacks(java.util.Collection<Inequality> rows)
```
 Get the current slacks for a collection of rows. The values in the returned array are in 1-to-1 correspondence with the rows in rows.
 
 Since:
 
 43.00
 - getSlack
```
public double getSlack(Inequality r)
```
 Get the current slack for a single row.
 
 Since:
 
 43.00
 - getDuals
```
public double[] getDuals(Inequality[] rows)
```
 Get the duals for an array of rows. The values in the returned array are in 1-to-1 correspondence with the rows in rows.
 
 Since:
 
 43.00
 - getDuals
```
public double[] getDuals(java.util.Collection<Inequality> rows)
```
 Get the duals for a collection of rows. The values in the returned array are in 1-to-1 correspondence with the row in row.
 
 Since:
 
 43.00
 - getDual
```
public double getDual(Inequality r)
```
 Get the dual for a single row.
 
 Since:
 
 43.00
 - rhsSA
```
public void rhsSA(int len,
 Inequality[] rows,
 double[] lower,
 double[] upper)
```
 Returns upper and lower sensitivity ranges for specified right hand side (RHS) function coefficients. If the RHS coefficients are varied within these ranges the current basis remains optimal and the reduced costs remain valid.
 
 Parameters:
 
 len - Sensitity analysis is only performed for the first len elements in rows.
 
 rows - The rows for which to perform sensitivity analysis.
 
 lower - Array of length at least len that receives the sensitivity lower bounds.
 
 upper - Array of length at least len that receives the sensitivity upper bounds.
 
 Since:
 
 43.00
 - rhsSA
```
public void rhsSA(Inequality[] rows,
 double[] lower,
 double[] upper)
```
 Returns upper and lower sensitivity ranges for specified right hand side (RHS) function coefficients. If the RHS coefficients are varied within these ranges the current basis remains optimal and the reduced costs remain valid.
 
 Parameters:
 
 rows - The rows for which to perform sensitivity analysis.
 
 lower - Array at least as long as rows that receives the sensitivity lower bounds.
 
 upper - Array at least as long as rows that receives the sensitivity upper bounds.
 
 Since:
 
 43.00
 - chgRHS
```
public void chgRHS(Inequality[] rows,
 double[] newRHS)
```
 Change right-hand side for multiple rows.
 Used in these examples:
 
 GlobalRHSParametrics
 
 Repair
 Parameters:
 
 rows - Inequalities for which to change right-hand sides.
 
 newRHS - New right-hand side values.
 
 Since:
 
 43.00
 - chgRowType
```
public void chgRowType(Inequality[] rows,
 RowType[] rowType)
```
 Change types for multiple rows.
 Used in these examples:
 
 Repair
 Parameters:
 
 rows - Inequalities for which to change types.
 
 rowType - New row types.
 
 Since:
 
 43.00
 - chgRHSrange
```
public void chgRHSrange(Inequality[] rows,
 double[] newRange)
```
 Change right-hand side ranges for multiple rows.
 Used in these examples:
 
 Repair
 Parameters:
 
 rows - Inequalities for which to right-hand side ranges.
 
 newRange - New right-hand side range values.
 
 Since:
 
 43.00
 - loadDelayedRows
```
public void loadDelayedRows(Inequality[] rows)
```
 Marks a set of rows as delayed rows.
 
 Parameters:
 
 rows - Inequalities to mark as delayed.
 
 Since:
 
 43.00
 - loadModelCuts
```
public void loadModelCuts(Inequality[] rows)
```
 Marks a set of rows as model cuts.
 
 Parameters:
 
 rows - Inequalities to mark as model cuts.
 
 Since:
 
 43.00
 - addConstraints
```
public <T,C extends Index> java.util.stream.Stream<java.util.Map.Entry<T,C>> addConstraints(java.util.Iterator<T> data,
 java.util.function.Function<T,ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified functions for each element in data and adds a constraint as specified by the return values of the functions.
 
 Type Parameters:
 
 T - Data type.
 
 C - Constraint type.
 
 Parameters:
 
 data - Data to generate constraints.
 
 defs - Function to produce the constraint definitions.
 
 Returns:
 
 The created constraint indexed by data.
 
 Since:
 
 43.00
 - addConstraint
```
public <C extends Index> C addConstraint(ConstraintDefinition<C> def)
```
 Add a single constraint to this problem.
 
 Type Parameters:
 
 C - Constraint type.
 
 Parameters:
 
 def - Definition of the to add.
 
 Returns:
 
 The new constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index,T extends ConstraintDefinition<C>> java.util.stream.Stream<C> addConstraints(java.util.stream.Stream<T> defs)
```
 Add multiple constraints of the same type to the problem.
 
 Type Parameters:
 
 C - Constraint type.
 
 T - Definition type.
 
 Parameters:
 
 defs - The constraints to add.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index,T extends ConstraintDefinition<C>> java.util.stream.Stream<C> addConstraints(java.lang.Iterable<T> defs)
```
 Add multiple constraints of the same type to the problem.
 
 Type Parameters:
 
 C - Constraint type.
 
 T - Definition type.
 
 Parameters:
 
 defs - The constraints to add.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> java.util.stream.Stream<C> addConstraints(ConstraintDefinition<C>[] defs)
```
 Add multiple of the same type constraints to the problem.
 
 Type Parameters:
 
 C - Constraint type.
 
 Parameters:
 
 defs - The constraints to add.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> java.util.stream.Stream<C> addConstraints(int count,
 java.util.function.IntFunction<ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified function for each element in [0, ..., count[ and adds a constraint as specified by the return value of the function.
 
 Type Parameters:
 
 C - Constraint type.
 
 Parameters:
 
 count - Number of constraints to add.
 
 defs - Function to produce the constraints to add.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <T,C extends Index> java.util.stream.Stream<C> addConstraints(java.util.stream.Stream<T> data,
 java.util.function.Function<T,ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified function for each element in data and adds a constraint as specified by the return value of the function.
 
 Type Parameters:
 
 T - Data type.
 
 C - Constraint type.
 
 Parameters:
 
 data - Data to generate constraints.
 
 defs - Function to generate a constraint for each data element.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <T,C extends Index> java.util.stream.Stream<C> addConstraints(T[] data,
 java.util.function.Function<T,ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified function for each element in data and adds a constraint as specified by the return value of the function.
 
 Type Parameters:
 
 T - Data type
 
 C - Constraint type.
 
 Parameters:
 
 data - Data to generate constraints.
 
 defs - Function to generate a constraint for each data element.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <T,C extends Index> java.util.stream.Stream<C> addConstraints(java.lang.Iterable<T> data,
 java.util.function.Function<T,ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified function for each element in data and adds a constraint as specified by the return value of the function.
 
 Type Parameters:
 
 T - Data type
 
 C - Constraint type.
 
 Parameters:
 
 data - Data for the constraints.
 
 defs - Function to generate a constraint for each data element.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> java.util.stream.Stream<C> addConstraints(int[] data,
 java.util.function.IntFunction<ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified function for each element in data and adds a constraint as specified by the return value of the function.
 
 Type Parameters:
 
 C - Constraint type.
 
 Parameters:
 
 data - Data to generate constraints.
 
 defs - Function to generate a constraint for each data item.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> java.util.stream.Stream<C> addConstraints(java.util.stream.IntStream data,
 java.util.function.IntFunction<ConstraintDefinition<C>> defs)
```
 Add multiple constraints of the same type to the problem. Calls the specified function for each element in data and adds a constraint as specified by the return value of the function.
 
 Type Parameters:
 
 C - Constraint type.
 
 Parameters:
 
 data - Data to generate constraints.
 
 defs - Function to generate a constraint for each data element.
 
 Returns:
 
 The new constraints.
 
 Since:
 
 43.00
 - variableForIndex
```
public Variable variableForIndex(int index)
```
 Since:
 
 43.00
 - variablesForIndices
```
public Variable[] variablesForIndices(int first,
 int last)
```
 Since:
 
 43.00
 - delCols
```
public void delCols(int ncols,
 int[] colind)
```
 Delete columns from this problem. Note that this may be expensive if you use Variable instances to formulate your problem since indices of remaining variables will have to be updated after columns were deleted from the underlying model. If you have to delete multiple variables then it is best to collect all variables to be deleted in an array and then delete all of them with a single call to this function.
 
 Overrides:
 
 delCols in class XPRSprob
 
 Parameters:
 
 ncols - Number of columns to delete.
 
 colind - Indices of columns to delete.
 
 Since:
 
 43.00
 - delVariables
```
public void delVariables(Variable[] vars)
```
 Since:
 
 43.00
 - getSolution
```
public double[] getSolution(Variable[] vars)
```
 Get the current solution for an array of variables. The values in the returned array are in 1-to-1 correspondence with the variables in vars.
 Used in these examples:
 
 Polygon
 
 PolygonMap
 
 PolygonMapDelta
 
 PolygonMultiMap
 
 PolygonMultiMapDelta
 
 PolygonVecMap
 
 PolygonVecMapDelta
 Since:
 
 43.00
 - getSolution
```
public double[] getSolution(java.util.Collection<Variable> vars)
```
 Get the current solution for a collection of variables. The values in the returned array are in 1-to-1 correspondence with the variables in vars.
 Used in these examples:
 
 Polygon
 
 PolygonMap
 
 PolygonMapDelta
 
 PolygonMultiMap
 
 PolygonMultiMapDelta
 
 PolygonVecMap
 
 PolygonVecMapDelta
 Since:
 
 43.00
 - getSolution
```
public double getSolution(Variable v)
```
 Get the current solution for a single variable.
 Used in these examples:
 
 Polygon
 
 PolygonMap
 
 PolygonMapDelta
 
 PolygonMultiMap
 
 PolygonMultiMapDelta
 
 PolygonVecMap
 
 PolygonVecMapDelta
 Since:
 
 43.00
 - getRedCosts
```
public double[] getRedCosts(Variable[] vars)
```
 Get the reduced costs for an array of variables. The values in the returned array are in 1-to-1 correspondence with the variables in vars.
 
 Since:
 
 43.00
 - getRedCosts
```
public double[] getRedCosts(java.util.Collection<Variable> vars)
```
 Get the reduced costs for a collection of variables. The values in the returned array are in 1-to-1 correspondence with the variables in vars.
 
 Since:
 
 43.00
 - getRedCost
```
public double getRedCost(Variable v)
```
 Get the reduced cost for a single variable.
 
 Since:
 
 43.00
 - objSA
```
public void objSA(int len,
 Variable[] variables,
 double[] lower,
 double[] upper)
```
 Returns upper and lower sensitivity ranges for specified objective function coefficients. If the objective coefficients are varied within these ranges the current basis remains optimal and the reduced costs remain valid.
 
 Parameters:
 
 len - Sensitivity analysis is performed only on the first len variables in variables.
 
 variables - Variables on which to perform sensitivity analysis.
 
 lower - Array of length at least len that receives the sensitivity analysis lower bounds.
 
 upper - Array of length at least len that receives the sensitivity analysis upper bounds.
 
 Since:
 
 43.00
 - objSA
```
public void objSA(Variable[] variables,
 double[] lower,
 double[] upper)
```
 Returns upper and lower sensitivity ranges for specified objective function coefficients. If the objective coefficients are varied within these ranges the current basis remains optimal and the reduced costs remain valid.
 
 Parameters:
 
 variables - Variables on which to perform sensitivity analysis.
 
 lower - Array at least as long as variables that receives the sensitivity analysis lower bounds.
 
 upper - Array at least as long as variables that receives the sensitivity analysis upper bounds.
 
 Since:
 
 43.00
 - bndSA
```
public void bndSA(int len,
 Variable[] variables,
 double[] lblower,
 double[] lbupper,
 double[] ublower,
 double[] ubupper)
```
 Returns upper and lower sensitivity ranges for specified variables' lower and upper bounds. If the bounds are varied within these ranges the current basis remains optimal and feasible.
 
 Parameters:
 
 len - Sensitivity analysis is performed only on the first len variables in variables.
 
 variables - Variables on which to perform sensitivity analysis.
 
 lblower - Array of length at least len that receives the sensitivity analysis lower bounds for the variables' lower bound constraints.
 
 lbupper - Array of length at least len that receives the sensitivity analysis upper bounds for the variables' lower bound constraints.
 
 ublower - Array of length at least len that receives the sensitivity analysis lower bounds for the variables' upper bound constraints.
 
 ubupper - Array of length at least len that receives the sensitivity analysis upper bounds for the variables' upper bound constraints.
 
 Since:
 
 43.00
 - bndSA
```
public void bndSA(Variable[] variables,
 double[] lblower,
 double[] lbupper,
 double[] ublower,
 double[] ubupper)
```
 Returns upper and lower sensitivity ranges for specified variables' lower and upper bounds. If the bounds are varied within these ranges the current basis remains optimal and feasible.
 
 Parameters:
 
 variables - Variables on which to perform sensitivity analysis.
 
 lblower - Array at least as long as variables that receives the sensitivity analysis lower bounds for the variables' lower bound constraints.
 
 lbupper - Array at least as long as variables that receives the sensitivity analysis upper bounds for the variables' lower bound constraints.
 
 ublower - Array at least as long as variables that receives the sensitivity analysis lower bounds for the variables' upper bound constraints.
 
 ubupper - Array at least as long as variables that receives the sensitivity analysis upper bounds for the variables' upper bound constraints.
 
 Since:
 
 43.00
 - addMipSol
```
public void addMipSol(double[] values,
 Variable[] variables,
 java.lang.String name)
```
 Adds a new feasible, infeasible or partial MIP solution for the problem to the Optimizer.
 Used in these examples:
 
 AddMipSol
 
 TSP
 Parameters:
 
 values - Values for variables.
 
 variables - Variables in the solution.
 
 name - Name for solution, can be null.
 
 Since:
 
 43.00
 - getVariables
```
public Variable[] getVariables()
```
 Get all the variables currently defined in this problem.
 
 Since:
 
 43.00
 - chgBounds
```
public void chgBounds(Variable[] variables,
 byte[] bndType,
 double[] bndValue)
```
 Change bounds for multiple variables.
 Used in these examples:
 
 AddMipSol
 
 FixBV
 
 RoundInt
 Parameters:
 
 variables - Variables for which to change bounds.
 
 bndType - Type of bounds to change.
 
 bndValue - New bounds.
 
 Since:
 
 43.00
 - chgColType
```
public void chgColType(Variable[] variables,
 ColumnType[] colType)
```
 Change types for multiple variables.
 Used in these examples:
 
 Trimloss
 Parameters:
 
 variables - Variables for which to change types.
 
 colType - New column types.
 
 Since:
 
 43.00
 - getInequalityLinear
```
public LinTermList getInequalityLinear(Inequality row)
```
 Get the linear part of the left-hand side of a row.
 
 Parameters:
 
 row - The row for which the linear part is queried.
 
 Returns:
 
 The linear part of the row. This is guaranteed to not have any duplicates.
 
 Since:
 
 43.00
 - getInequalityLinear
```
public LinExpression getInequalityLinear(Inequality row,
 java.util.function.Supplier<LinExpression> expression)
```
 Get the linear part of the left-hand side of a row. The function adds the linear part of row to the expression provided by expression. If expression is null then the function allocates a new com.dashoptimization.objects.LinTermList for it. If the function allocates a new com.dashoptimization.objects.LinTermList then it also returns that new object. In that case the list is guaranteed to not have any duplicates.
 
 Parameters:
 
 row - The row for which the linear part is queried.
 
 expression - Creates the expression that is returned by this function. Can be null in which case a new com.dashoptimization.objects.LinTermList is created.
 
 Returns:
 
 An expression that represent the linear part of the given row.
 
 Since:
 
 43.00
 - getCoef
```
public double getCoef(Inequality row,
 Variable variable)
```
 Query a single coefficient from the linear matrix. Retrieves the coefficient of variable in row in the linear matrix. Note that the coefficient will be 0 (zero) in case the variable has no coefficient in that row.
 
 Parameters:
 
 row - The row in which to query the coefficient.
 
 variable - The variable for which to query the coefficient.
 
 Returns:
 
 The queried coefficient.
 
 Since:
 
 43.00
 - getObj
```
public double getObj(Variable variable)
```
 Query an objective function coefficient. Retrieves the linear objective coefficient for the specified variable from the first/primary objective.
 Used in these examples:
 
 Knapsack
 Parameters:
 
 variable - The variable for which to query the objective.
 
 Returns:
 
 The objective coefficient for variable.
 
 Since:
 
 43.00
 - getObjN
```
public double getObjN(int objidx,
 Variable variable)
```
 Query an objective function coefficient. Retrieves the linear objective coefficient for the specified variable from objective objidx.
 
 Parameters:
 
 objidx - objects.Index of objective function to query.
 
 variable - The variable for which to query the objective.
 
 Returns:
 
 The objective coefficient for variable.
 
 Since:
 
 43.00
 - pwlForIndex
```
public PWL pwlForIndex(int index)
```
 Since:
 
 43.00
 - pwlsForIndices
```
public PWL[] pwlsForIndices(int first,
 int last)
```
 Since:
 
 43.00
 - delPwlCons
```
public void delPwlCons(int npwls,
 int[] pwlind)
```
 Delete piecewise linear constraints from this problem. Note that this may be expensive if you use PWL instances to formulate your problem since indices of remaining variables will have to be updated after columns were deleted from the underlying model. If you have to delete multiple variables then it is best to collect all variables to be deleted in an array and then delete all of them with a single call to this function.
 
 Overrides:
 
 delPwlCons in class XPRSprob
 
 Parameters:
 
 npwls - Number of constraints to delete.
 
 pwlind - Indices of constraints to delete.
 
 Since:
 
 43.00
 - delPwlConstraints
```
public void delPwlConstraints(PWL[] pwls)
```
 Since:
 
 43.00
 - getGeneralConstraints
```
public GeneralConstraint[] getGeneralConstraints()
```
 Get all the general constraints currently defined in this problem.
 
 Returns:
 
 All general constraints in this problem.
 
 Since:
 
 43.00
 - generalConstraintForIndex
```
public GeneralConstraint generalConstraintForIndex(int index)
```
 Map a general constraint index to a GeneralConstraint object.
 
 Parameters:
 
 index - The index to map.
 
 Returns:
 
 The GeneralConstraint object for index.
 
 Since:
 
 43.00
 - generalConstraintForIndices
```
public GeneralConstraint[] generalConstraintForIndices(int first,
 int last)
```
 Map a range of general constraint indices to GeneralConstraint objects.
 
 Parameters:
 
 first - First index in range (inclusive).
 
 last - Last index in range (inclusive).
 
 Returns:
 
 The GeneralConstraint objects in the specified range.
 
 Since:
 
 43.00
 - delGenCons
```
public void delGenCons(int nconstraints,
 int[] conind)
```
 Delete constraints from this problem. Note that this may be expensive if you use GeneralConstraint instances to formulate your problem since indices of remaining constraints will have to be updated after constraints were deleted from the underlying model. If you have to delete multiple constraints then it is best to collect all constraints to be deleted in an array and then delete all of them with a single call to this function.
 
 Overrides:
 
 delGenCons in class XPRSprob
 
 Parameters:
 
 nconstraints - Number of constraints to delete.
 
 conind - Indices of constraints to delete.
 
 Since:
 
 43.00
 - delGeneralConstraints
```
public void delGeneralConstraints(GeneralConstraint[] constraints)
```
 Delete general constraints.
 
 Parameters:
 
 constraints - The constraints to be deleted.
 
 Since:
 
 43.00
 - getIIS
```
public IIS getIIS(int iis)
```
 Get the specified IIS.
 
 Parameters:
 
 iis - The index of the IIS for which data is queried.
 
 Returns:
 
 The IIS specified by iis.
 
 Since:
 
 43.00
 - isOriginal
```
public boolean isOriginal()
```
 Check whether this model instance is in the original state. A model is not in the original state if it is a callback problem or its problem state indicates being presolved.
 Used in these examples:
 
 MostViolated
 Since:
 
 43.00
 - addCut
```
public int addCut(int cuttype,
 Inequality.Definition cut)
```
 Add a cut at the current node. It is assumed that the left-hand side ( lhs) is formulated in terms of the original model. So you can use Variable instances to formulate it. When using plain variable indices, be sure these indices are for the original model. The function will automatically transform (presolve) the cut to the presolved space. If presolving the cut fails then the cut is not added and the reason for failure is returned. Failing to presolve (and thus adding) a cut is not a problem in every context. That is why this does not raise an exception but instead leaves it to the caller to decide whether this is an error or not.
 Used in these examples:
 
 TSP
 Parameters:
 
 cuttype - User defined cut type.
 
 cut - The inequality to add as cut.
 
 Returns:
 
 0 (zero) if the cut was successfully added. Otherwise the status code from presolveRow that indicates why the cut could not be presolved. It is up to the caller to decide whether not being able to presolve the cut is a problem or not.
 
 Since:
 
 43.00
 - createBranchObject
```
public XpressProblem.BranchObject createBranchObject()
```
 Create a new branch object. This is different from the super class's createBranchObject() function in two aspects: - it returns an instance of BranchObject, not XPRSbranchobject. The format is a subclass of the latter and has functions to add bounds and constraints that reference Variable objects. - the returned object always operates in the original space.
 Used in these examples:
 
 MostViolated
 Since:
 
 43.00
 - setObjective
```
public void setObjective(Expression obj)
```
 Set the objective function to a new expression.
 Used in these examples:
 
 TSP
 Parameters:
 
 obj - The new objective
 
 Since:
 
 43.00
 - setObjective
```
public void setObjective(Expression obj,
 XPRSenumerations.ObjSense sense)
```
 Set the objective function to a new expression.
 Used in these examples:
 
 TSP
 Parameters:
 
 obj - The new objective
 
 sense - The new objective sense (minimize or maximize).
 
 Since:
 
 43.00
 - addObjective
```
public void addObjective(Expression obj,
 int priority,
 double weight)
```
 Add a new objective function. This turns the problem into a multi-objective problem.
 
 Parameters:
 
 obj - The new objective
 
 priority - Priority for new objective.
 
 weight - Weight for new objective.
 
 Since:
 
 43.00
 - setObjective
```
public void setObjective(int idx,
 Expression obj,
 int priority,
 double weight,
 double abstol,
 double reltol)
```
 Set an objective function and its controls. In case of error: - if the function created new objectives then all newly created objectives will be removed, - if an existing objective was modified then that objective function will be cleared and all controls will be unchanged.
 Used in these examples:
 
 TSP
 Parameters:
 
 idx - Index of objective to set.
 
 obj - New objective for idx.
 
 priority - New objective priority.
 
 weight - New objective weight.
 
 abstol - New absolute tolerance for objective idx.
 
 reltol - New relative tolerance for objective idx.
 
 Since:
 
 43.00
 - nlpSetInitVal
```
public void nlpSetInitVal(java.util.Map<Variable,java.lang.Double> values)
```
 Set initial values of variables for a non-linear solve.
 
 Parameters:
 
 values - Values to be set. For each variable/value pair in this map the corresponding initial value is set.
 
 Since:
 
 43.00
 - nlpSetInitVal
```
public void nlpSetInitVal(Variable[] variables,
 double[] values)
```
 Set initial values of variables for a non-linear solve.
 
 Parameters:
 
 variables - Variables for which to set values.
 
 values - Values for variables. This must have the same length as variables. The initial value for variables[i] is values[i].
 
 Since:
 
 43.00
 - getLhsExpression
```
public Expression getLhsExpression(int row)
```
 Get the left-hand side of a row as an expression.
 
 Parameters:
 
 row - Index of row to fetch.
 
 Returns:
 
 The left-hand side of row row.
 
 Since:
 
 43.00
 - sosForIndex
```
public SOS sosForIndex(int index)
```
 Since:
 
 43.00
 - sosForIndices
```
public SOS[] sosForIndices(int first,
 int last)
```
 Since:
 
 43.00
 - delSets
```
public void delSets(int nsets,
 int[] sosind)
```
 Delete sets from this problem. Note that this may be expensive if you use SOS instances to formulate your problem since indices of remaining sets will have to be updated after sets were deleted from the underlying model. If you have to delete multiple sets then it is best to collect all sets to be deleted in an array and then delete all of them with a single call to this function.
 
 Overrides:
 
 delSets in class XPRSprob
 
 Parameters:
 
 nsets - Number of sets to delete.
 
 sosind - Indices of sets to delete.
 
 Since:
 
 43.00
 - delSOS
```
public void delSOS(SOS[] soss)
```
 Delete special ordered set constraints. Removes all the specified special ordered set constraints from this problem.
 
 Parameters:
 
 soss - SOS to be deleted.
 
 Since:
 
 43.00
 - delSOS
```
public void delSOS(SOS sos)
```
 Delete a single SOS from the problem.
 
 Parameters:
 
 sos - SOS to delete.
 
 Since:
 
 43.00
 - delIndicator
```
public void delIndicator(Inequality row)
```
 Delete a single indicator constraint. This only deletes the "indicator property from the specified row. Neither the associated variable nor the row are deleted.
 
 Parameters:
 
 row - Row from which to delete the indicator constraint.
 
 Since:
 
 43.00
 - getIndicator
```
public IndicatorObjects getIndicator(Inequality row)
```
 Get indicator information for a single row.
 
 Parameters:
 
 row - The row to query.
 
 Returns:
 
 Indicator information for row. This will be null if row is not an indicator row.
 
 Since:
 
 43.00
 - setIndicator
```
public void setIndicator(Variable indicatorVariable,
 boolean indicatorValue,
 Inequality row)
```
 Add an indicator constraint to this model. Both variable and row must exist.
 
 Parameters:
 
 indicatorVariable - Indicator variable.
 
 indicatorValue - Whether row becomes active if indicatorVariable is non-zero ( indicatorValue is true) or if indicatorVariable is zero ( indicatorValue is false).
 
 row - The implied row for the indicator constraint.
 
 Since:
 
 43.00
 - setIndicators
```
public void setIndicators(Variable[] indicatorVariable,
 boolean[] indicatorValue,
 Inequality[] row)
```
 Add indicator constraints to this model. The provided arrays must all have length at least count and for any i in 0.. count-1 an indicator constraint is constructed from indicatorVariable[i], indicatorValue[i] and row[i]. If indicatorValue[i] is true then the constraints states that if indicatorVariable[i] is 1 (one), then row[i] must be satisfied (and if indicator is 0 (zero) then row[i] is ignored). Otherwise, row[i] must be satisfied if indicator[i] is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Parameters:
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether rows become active if indicator variables are non-zero ( indicatorValue[.] is true) or if indicator variables are zero ( indicatorValue is false). If this is null then rows become active if indicator variables are non-zero.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public <T> void setIndicators(java.lang.Iterable<T> data,
 java.util.function.Function<T,Variable> indicatorVariable,
 java.util.function.Function<T,java.lang.Boolean> indicatorValue,
 java.util.function.Function<T,Inequality> row)
```
 Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Type Parameters:
 
 T - Data type.
 
 Parameters:
 
 data - Data for which constraints are created.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public <T> void setIndicators(T[] data,
 java.util.function.Function<T,Variable> indicatorVariable,
 java.util.function.Function<T,java.lang.Boolean> indicatorValue,
 java.util.function.Function<T,Inequality> row)
```
 Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Type Parameters:
 
 T - Data type.
 
 Parameters:
 
 data - Data for which constraints are created.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public <T> void setIndicators(java.util.Collection<T> data,
 java.util.function.Function<T,Variable> indicatorVariable,
 java.util.function.Function<T,java.lang.Boolean> indicatorValue,
 java.util.function.Function<T,Inequality> row)
```
 Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Type Parameters:
 
 T - Data type.
 
 Parameters:
 
 data - Data for which constraints are created.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public <T> void setIndicators(java.util.stream.Stream<T> data,
 java.util.function.Function<T,Variable> indicatorVariable,
 java.util.function.Function<T,java.lang.Boolean> indicatorValue,
 java.util.function.Function<T,Inequality> row)
```
 Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Type Parameters:
 
 T - Data type.
 
 Parameters:
 
 data - Data for which constraints are created.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public void setIndicators(int count,
 java.util.function.Function<java.lang.Integer,Variable> indicatorVariable,
 java.util.function.Function<java.lang.Integer,java.lang.Boolean> indicatorValue,
 java.util.function.Function<java.lang.Integer,Inequality> row)
```
 Add indicator constraints to this model. The function iterates through [0, ... ,count[ and calls the provided functions for each in this interval. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Parameters:
 
 count - How many constraint to add.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public void setIndicators(int[] data,
 java.util.function.IntFunction<Variable> indicatorVariable,
 java.util.function.IntFunction<java.lang.Boolean> indicatorValue,
 java.util.function.IntFunction<Inequality> row)
```
 Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Parameters:
 
 data - Data for which constraints are created.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - setIndicators
```
public void setIndicators(java.util.stream.IntStream data,
 java.util.function.IntFunction<Variable> indicatorVariable,
 java.util.function.IntFunction<java.lang.Boolean> indicatorValue,
 java.util.function.IntFunction<Inequality> row)
```
 Add indicator constraints to this model. The function iterates through data and calls the provided functions for each element found. The return values of the functions are used to construct an indicator constraint. For a fixed t, if indicatorValue(t) returns true then the created constraint states that if indicatorVariable(t) is 1 (one), then row(t) must be satisfied and is ignored otherwise. Otherwise, row(t) must be satisfied if indicatorVariable(t) is 0 (zero) and is ignored otherwise. All referenced variables and rows must exist.
 
 Parameters:
 
 data - Data for which constraints are created.
 
 indicatorVariable - Indicator variables.
 
 indicatorValue - Whether indicator variables should be non-zero or zero for rows to become active. If this is null then none of the variables must be 1 (one) for rows to become active.
 
 row - The implied rows for the indicator constraints.
 
 Since:
 
 43.00
 - sortParallelArrays
```
public static <T extends java.lang.Comparable<T>> void sortParallelArrays(T[] mvec,
 double[] dvec,
 int offset,
 int size)
```
 Sort parallel arrays according to index keys.
 
 Since:
 
 43.00
 - sortParallelArrays
```
public static <T extends java.lang.Comparable<T>> void sortParallelArrays(T[] key1,
 T[] key2,
 double[] dvec,
 int offset,
 int size)
```
 Sort parallel arrays according to integer pair keys.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> void addConstraints(int count1,
 int count2,
 java.util.function.BiFunction<java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of [0,count1[, ..., [0,countN[ and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 C - The type of constraints.
 
 Parameters:
 
 count1 - Length of dimension 1.
 
 count2 - Length of dimension 2.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,C extends Index> void addConstraints(java.lang.Iterable<K1> iterable1,
 java.lang.Iterable<K2> iterable2,
 java.util.function.BiFunction<K1,K2,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the iterables and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 C - The type of constraints.
 
 Parameters:
 
 iterable1 - Keys for dimension 1.
 
 iterable2 - Keys for dimension 2.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,C extends Index> void addConstraints(K1[] array1,
 K2[] array2,
 java.util.function.BiFunction<K1,K2,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the arrays and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 C - The type of constraints.
 
 Parameters:
 
 array1 - Data for dimension 1.
 
 array2 - Data for dimension 2.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> void addConstraints(int count1,
 int count2,
 int count3,
 Function3<java.lang.Integer,java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of [0,count1[, ..., [0,countN[ and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 C - The type of constraints.
 
 Parameters:
 
 count1 - Length of dimension 1.
 
 count2 - Length of dimension 2.
 
 count3 - Length of dimension 3.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,K3,C extends Index> void addConstraints(java.lang.Iterable<K1> iterable1,
 java.lang.Iterable<K2> iterable2,
 java.lang.Iterable<K3> iterable3,
 Function3<K1,K2,K3,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the iterables and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 K3 - Data for dimension 3.
 
 C - The type of constraints.
 
 Parameters:
 
 iterable1 - Keys for dimension 1.
 
 iterable2 - Keys for dimension 2.
 
 iterable3 - Keys for dimension 3.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,K3,C extends Index> void addConstraints(K1[] array1,
 K2[] array2,
 K3[] array3,
 Function3<K1,K2,K3,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the arrays and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 K3 - Data for dimension 3.
 
 C - The type of constraints.
 
 Parameters:
 
 array1 - Data for dimension 1.
 
 array2 - Data for dimension 2.
 
 array3 - Data for dimension 3.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> void addConstraints(int count1,
 int count2,
 int count3,
 int count4,
 Function4<java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of [0,count1[, ..., [0,countN[ and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 C - The type of constraints.
 
 Parameters:
 
 count1 - Length of dimension 1.
 
 count2 - Length of dimension 2.
 
 count3 - Length of dimension 3.
 
 count4 - Length of dimension 4.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,K3,K4,C extends Index> void addConstraints(java.lang.Iterable<K1> iterable1,
 java.lang.Iterable<K2> iterable2,
 java.lang.Iterable<K3> iterable3,
 java.lang.Iterable<K4> iterable4,
 Function4<K1,K2,K3,K4,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the iterables and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 K3 - Data for dimension 3.
 
 K4 - Data for dimension 4.
 
 C - The type of constraints.
 
 Parameters:
 
 iterable1 - Keys for dimension 1.
 
 iterable2 - Keys for dimension 2.
 
 iterable3 - Keys for dimension 3.
 
 iterable4 - Keys for dimension 4.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,K3,K4,C extends Index> void addConstraints(K1[] array1,
 K2[] array2,
 K3[] array3,
 K4[] array4,
 Function4<K1,K2,K3,K4,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the arrays and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 K3 - Data for dimension 3.
 
 K4 - Data for dimension 4.
 
 C - The type of constraints.
 
 Parameters:
 
 array1 - Data for dimension 1.
 
 array2 - Data for dimension 2.
 
 array3 - Data for dimension 3.
 
 array4 - Data for dimension 4.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <C extends Index> void addConstraints(int count1,
 int count2,
 int count3,
 int count4,
 int count5,
 Function5<java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of [0,count1[, ..., [0,countN[ and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 C - The type of constraints.
 
 Parameters:
 
 count1 - Length of dimension 1.
 
 count2 - Length of dimension 2.
 
 count3 - Length of dimension 3.
 
 count4 - Length of dimension 4.
 
 count5 - Length of dimension 5.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,K3,K4,K5,C extends Index> void addConstraints(java.lang.Iterable<K1> iterable1,
 java.lang.Iterable<K2> iterable2,
 java.lang.Iterable<K3> iterable3,
 java.lang.Iterable<K4> iterable4,
 java.lang.Iterable<K5> iterable5,
 Function5<K1,K2,K3,K4,K5,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the iterables and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 K3 - Data for dimension 3.
 
 K4 - Data for dimension 4.
 
 K5 - Data for dimension 5.
 
 C - The type of constraints.
 
 Parameters:
 
 iterable1 - Keys for dimension 1.
 
 iterable2 - Keys for dimension 2.
 
 iterable3 - Keys for dimension 3.
 
 iterable4 - Keys for dimension 4.
 
 iterable5 - Keys for dimension 5.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - addConstraints
```
public <K1,K2,K3,K4,K5,C extends Index> void addConstraints(K1[] array1,
 K2[] array2,
 K3[] array3,
 K4[] array4,
 K5[] array5,
 Function5<K1,K2,K3,K4,K5,ConstraintDefinition<C>> makeConstraint)
```
 Add multiple constraints to this problem. Calls makeConstraint for each element in the cartesian product of the arrays and adds the constraint that is produced by this function call. A deep copy is made of each constraint, thus modifying the constraint later will not alter the model.
 
 Type Parameters:
 
 K1 - Data for dimension 1.
 
 K2 - Data for dimension 2.
 
 K3 - Data for dimension 3.
 
 K4 - Data for dimension 4.
 
 K5 - Data for dimension 5.
 
 C - The type of constraints.
 
 Parameters:
 
 array1 - Data for dimension 1.
 
 array2 - Data for dimension 2.
 
 array3 - Data for dimension 3.
 
 array4 - Data for dimension 4.
 
 array5 - Data for dimension 5.
 
 makeConstraint - Function to generate a constraint.
 
 Since:
 
 43.00
 - buildVariables
```
public Variable[] buildVariables(VariableBuilder.ArrayBuilder builder)
```
 Create a variable array from a builder.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created variables.
 
 Since:
 
 43.00
 - buildVariables
```
public  I buildVariables(VariableBuilder.ArrayBuilder builder,
 java.util.function.Supplier makeResult,
 Action3<I,java.lang.Integer,Variable> addResult)
```
 Create a variable array from a builder. This is a parametrized version of buildVariables(VariableBuilder.ArrayBuilder).
 
 Type Parameters:
 
 I - Type for object that stores the created variables.
 
 Parameters:
 
 builder - Builder to create variables.
 
 makeResult - Function to create the (empty) result.
 
 addResult - Function to add a variable to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - buildVariables
```
public <K1> java.util.HashMap<K1,Variable> buildVariables(VariableBuilder.MapBuilder<K1> builder)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created Variables.
 
 Since:
 
 43.00
 - buildVariables
```
public <I,K1> I buildVariables(VariableBuilder.MapBuilder<K1> builder,
 java.util.function.Supplier makeResult,
 Action3<I,K1,Variable> addResult)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 I - Type of result
 
 K1 - Data type for dimension 1.
 
 Parameters:
 
 builder - The builder to create variables.
 
 makeResult - Function to create an (empty) result.
 
 addResult - Function to add an element to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - addVariables
```
public VariableBuilder.VariableArrayBuilder addVariables(int dim)
```
 Create an 1 dimensional array of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toArray() function.
 // Create a multi-dimensional array of binary variables Variable [] = prob.addColumns(dim) .withType(com.dashoptimization.objects.ColumnType.Binary) .toArray();
 See VariableBuilder.VariableArrayBuilder for details of how to modify the specification in the builder.
 Parameters:
 
 dim - Dimension.
 
 Returns:
 
 A builder that will create the variables.
 
 Since:
 
 43.00
 - addVariables
```
public <K1> VariableBuilder.VariableMapBuilder<K1> addVariables(java.util.Collection<K1> coll1)
```
 Create an 1 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns java.util.HashMap<K1 ,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMapBuilder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 Parameters:
 
 coll1 - Data for dimension 1.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariables
```
public <K1> VariableBuilder.VariableMapBuilder<K1> addVariables(K1[] arr1)
```
 Create an 1 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns java.util.HashMap<K1 ,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMapBuilder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 Parameters:
 
 arr1 - Data for the builder.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariables
```
public <K1> VariableBuilder.VariableMapBuilder<K1> addVariables(java.util.stream.Stream<K1> strm)
```
 Create an 1 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns java.util.HashMap<K1 ,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMapBuilder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable(double lb,
 double ub,
 ColumnType type,
 java.lang.String name)
```
 Add a single variable to this problem.
 
 Parameters:
 
 lb - Lower bound for new variable.
 
 ub - Upper bound for new variable.
 
 type - Type for new variable.
 
 name - Name for new variable, can be null.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable(double lb,
 double ub,
 ColumnType type,
 double limit,
 java.lang.String name)
```
 Add a single variable to this problem.
 
 Parameters:
 
 lb - Lower bound for new variable.
 
 ub - Upper bound for new variable.
 
 type - Type for new variable.
 
 limit - Global limit for the variable. This is ignored unless the variable is semi-continuous, semi-integer or partial integer.
 
 name - Name for new variable, can be null.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable(double lb,
 double ub,
 ColumnType type)
```
 Add a single variable to this problem.
 
 Parameters:
 
 lb - Lower bound for new variable.
 
 ub - Upper bound for new variable.
 
 type - Type for new variable.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable(java.lang.String name)
```
 Add a single variable to this problem. The variable will have default type (continuous) and bounds (0 and infinity).
 
 Parameters:
 
 name - Name for new variable.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable(ColumnType type)
```
 Add a single variable to this problem. The variable will have default bounds and no name.
 
 Parameters:
 
 type - Type for new variable.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable(ColumnType type,
 java.lang.String name)
```
 Add a single variable to this problem. The variable will have default bounds.
 
 Parameters:
 
 type - Type for new variable.
 
 name - Name for new variable.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - addVariable
```
public Variable addVariable()
```
 Add a single variable to this problem. This variable will be continuous, have default bounds (0 and infinity) and will not have a name.
 
 Returns:
 
 The newly created variable.
 
 Since:
 
 43.00
 - buildVariables
```
public Variable[][] buildVariables(VariableBuilder.Array2Builder builder)
```
 Create a variable array from a builder.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created variables.
 
 Since:
 
 43.00
 - buildVariables
```
public  I buildVariables(VariableBuilder.Array2Builder builder,
 java.util.function.Supplier makeResult,
 Action4<I,java.lang.Integer,java.lang.Integer,Variable> addResult)
```
 Create a variable array from a builder. This is a parametrized version of buildVariables(VariableBuilder.Array2Builder).
 
 Type Parameters:
 
 I - Type for object that stores the created variables.
 
 Parameters:
 
 builder - Builder to create variables.
 
 makeResult - Function to create the (empty) result.
 
 addResult - Function to add a variable to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - buildVariables
```
public <K1,K2> HashMap2<K1,K2,Variable> buildVariables(VariableBuilder.Map2Builder<K1,K2> builder)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created Variables.
 
 Since:
 
 43.00
 - buildVariables
```
public <I,K1,K2> I buildVariables(VariableBuilder.Map2Builder<K1,K2> builder,
 java.util.function.Supplier makeResult,
 Action4<I,K1,K2,Variable> addResult)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 I - Type of result
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 Parameters:
 
 builder - The builder to create variables.
 
 makeResult - Function to create an (empty) result.
 
 addResult - Function to add an element to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - addVariables
```
public VariableBuilder.VariableArray2Builder addVariables(int dim1,
 int dim2)
```
 Create an 2 dimensional array of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toArray() function.
 // Create a multi-dimensional array of binary variables Variable [] [] = prob.addColumns(dim1 ,dim2) .withType(com.dashoptimization.objects.ColumnType.Binary) .toArray();
 See VariableBuilder.VariableArray2Builder for details of how to modify the specification in the builder.
 Parameters:
 
 dim1 - Dimension 1.
 
 dim2 - Dimension 2.
 
 Returns:
 
 A builder that will create the variables.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2> VariableBuilder.VariableMap2Builder<K1,K2> addVariables(java.util.Collection<K1> coll1,
 java.util.Collection<K2> coll2)
```
 Create an 2 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap2<K1 ,K2,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap2Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 Parameters:
 
 coll1 - Data for dimension 1.
 
 coll2 - Data for dimension 2.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2> VariableBuilder.VariableMap2Builder<K1,K2> addVariables(K1[] arr1,
 K2[] arr2)
```
 Create an 2 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap2<K1 ,K2,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap2Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 Parameters:
 
 arr1 - Data for the builder.
 
 arr2 - Data for the builder.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - buildVariables
```
public Variable[][][] buildVariables(VariableBuilder.Array3Builder builder)
```
 Create a variable array from a builder.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created variables.
 
 Since:
 
 43.00
 - buildVariables
```
public  I buildVariables(VariableBuilder.Array3Builder builder,
 java.util.function.Supplier makeResult,
 Action5<I,java.lang.Integer,java.lang.Integer,java.lang.Integer,Variable> addResult)
```
 Create a variable array from a builder. This is a parametrized version of buildVariables(VariableBuilder.Array3Builder).
 
 Type Parameters:
 
 I - Type for object that stores the created variables.
 
 Parameters:
 
 builder - Builder to create variables.
 
 makeResult - Function to create the (empty) result.
 
 addResult - Function to add a variable to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - buildVariables
```
public <K1,K2,K3> HashMap3<K1,K2,K3,Variable> buildVariables(VariableBuilder.Map3Builder<K1,K2,K3> builder)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created Variables.
 
 Since:
 
 43.00
 - buildVariables
```
public <I,K1,K2,K3> I buildVariables(VariableBuilder.Map3Builder<K1,K2,K3> builder,
 java.util.function.Supplier makeResult,
 Action5<I,K1,K2,K3,Variable> addResult)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 I - Type of result
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 Parameters:
 
 builder - The builder to create variables.
 
 makeResult - Function to create an (empty) result.
 
 addResult - Function to add an element to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - addVariables
```
public VariableBuilder.VariableArray3Builder addVariables(int dim1,
 int dim2,
 int dim3)
```
 Create an 3 dimensional array of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toArray() function.
 // Create a multi-dimensional array of binary variables Variable [] [] [] = prob.addColumns(dim1 ,dim2 ,dim3) .withType(com.dashoptimization.objects.ColumnType.Binary) .toArray();
 See VariableBuilder.VariableArray3Builder for details of how to modify the specification in the builder.
 Parameters:
 
 dim1 - Dimension 1.
 
 dim2 - Dimension 2.
 
 dim3 - Dimension 3.
 
 Returns:
 
 A builder that will create the variables.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2,K3> VariableBuilder.VariableMap3Builder<K1,K2,K3> addVariables(java.util.Collection<K1> coll1,
 java.util.Collection<K2> coll2,
 java.util.Collection<K3> coll3)
```
 Create an 3 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap3<K1 ,K2 ,K3,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2 ,coll3) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap3Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 Parameters:
 
 coll1 - Data for dimension 1.
 
 coll2 - Data for dimension 2.
 
 coll3 - Data for dimension 3.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2,K3> VariableBuilder.VariableMap3Builder<K1,K2,K3> addVariables(K1[] arr1,
 K2[] arr2,
 K3[] arr3)
```
 Create an 3 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap3<K1 ,K2 ,K3,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2 ,coll3) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap3Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 Parameters:
 
 arr1 - Data for the builder.
 
 arr2 - Data for the builder.
 
 arr3 - Data for the builder.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - buildVariables
```
public Variable[][][][] buildVariables(VariableBuilder.Array4Builder builder)
```
 Create a variable array from a builder.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created variables.
 
 Since:
 
 43.00
 - buildVariables
```
public  I buildVariables(VariableBuilder.Array4Builder builder,
 java.util.function.Supplier makeResult,
 Action6<I,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,Variable> addResult)
```
 Create a variable array from a builder. This is a parametrized version of buildVariables(VariableBuilder.Array4Builder).
 
 Type Parameters:
 
 I - Type for object that stores the created variables.
 
 Parameters:
 
 builder - Builder to create variables.
 
 makeResult - Function to create the (empty) result.
 
 addResult - Function to add a variable to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - buildVariables
```
public <K1,K2,K3,K4> HashMap4<K1,K2,K3,K4,Variable> buildVariables(VariableBuilder.Map4Builder<K1,K2,K3,K4> builder)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created Variables.
 
 Since:
 
 43.00
 - buildVariables
```
public <I,K1,K2,K3,K4> I buildVariables(VariableBuilder.Map4Builder<K1,K2,K3,K4> builder,
 java.util.function.Supplier makeResult,
 Action6<I,K1,K2,K3,K4,Variable> addResult)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 I - Type of result
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 Parameters:
 
 builder - The builder to create variables.
 
 makeResult - Function to create an (empty) result.
 
 addResult - Function to add an element to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - addVariables
```
public VariableBuilder.VariableArray4Builder addVariables(int dim1,
 int dim2,
 int dim3,
 int dim4)
```
 Create an 4 dimensional array of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toArray() function.
 // Create a multi-dimensional array of binary variables Variable [] [] [] [] = prob.addColumns(dim1 ,dim2 ,dim3 ,dim4) .withType(com.dashoptimization.objects.ColumnType.Binary) .toArray();
 See VariableBuilder.VariableArray4Builder for details of how to modify the specification in the builder.
 Parameters:
 
 dim1 - Dimension 1.
 
 dim2 - Dimension 2.
 
 dim3 - Dimension 3.
 
 dim4 - Dimension 4.
 
 Returns:
 
 A builder that will create the variables.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2,K3,K4> VariableBuilder.VariableMap4Builder<K1,K2,K3,K4> addVariables(java.util.Collection<K1> coll1,
 java.util.Collection<K2> coll2,
 java.util.Collection<K3> coll3,
 java.util.Collection<K4> coll4)
```
 Create an 4 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap4<K1 ,K2 ,K3 ,K4,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2 ,coll3 ,coll4) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap4Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 Parameters:
 
 coll1 - Data for dimension 1.
 
 coll2 - Data for dimension 2.
 
 coll3 - Data for dimension 3.
 
 coll4 - Data for dimension 4.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2,K3,K4> VariableBuilder.VariableMap4Builder<K1,K2,K3,K4> addVariables(K1[] arr1,
 K2[] arr2,
 K3[] arr3,
 K4[] arr4)
```
 Create an 4 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap4<K1 ,K2 ,K3 ,K4,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2 ,coll3 ,coll4) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap4Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 Parameters:
 
 arr1 - Data for the builder.
 
 arr2 - Data for the builder.
 
 arr3 - Data for the builder.
 
 arr4 - Data for the builder.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - buildVariables
```
public Variable[][][][][] buildVariables(VariableBuilder.Array5Builder builder)
```
 Create a variable array from a builder.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created variables.
 
 Since:
 
 43.00
 - buildVariables
```
public  I buildVariables(VariableBuilder.Array5Builder builder,
 java.util.function.Supplier makeResult,
 Action7<I,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,java.lang.Integer,Variable> addResult)
```
 Create a variable array from a builder. This is a parametrized version of buildVariables(VariableBuilder.Array5Builder).
 
 Type Parameters:
 
 I - Type for object that stores the created variables.
 
 Parameters:
 
 builder - Builder to create variables.
 
 makeResult - Function to create the (empty) result.
 
 addResult - Function to add a variable to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - buildVariables
```
public <K1,K2,K3,K4,K5> HashMap5<K1,K2,K3,K4,K5,Variable> buildVariables(VariableBuilder.Map5Builder<K1,K2,K3,K4,K5> builder)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 K5 - Data type for dimension 5.
 
 Parameters:
 
 builder - The builder to create variables.
 
 Returns:
 
 Created Variables.
 
 Since:
 
 43.00
 - buildVariables
```
public <I,K1,K2,K3,K4,K5> I buildVariables(VariableBuilder.Map5Builder<K1,K2,K3,K4,K5> builder,
 java.util.function.Supplier makeResult,
 Action7<I,K1,K2,K3,K4,K5,Variable> addResult)
```
 Create a variable map from a builder.
 
 Type Parameters:
 
 I - Type of result
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 K5 - Data type for dimension 5.
 
 Parameters:
 
 builder - The builder to create variables.
 
 makeResult - Function to create an (empty) result.
 
 addResult - Function to add an element to the result.
 
 Returns:
 
 Result as given by makeResult.
 
 Since:
 
 43.00
 - addVariables
```
public VariableBuilder.VariableArray5Builder addVariables(int dim1,
 int dim2,
 int dim3,
 int dim4,
 int dim5)
```
 Create an 5 dimensional array of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toArray() function.
 // Create a multi-dimensional array of binary variables Variable [] [] [] [] [] = prob.addColumns(dim1 ,dim2 ,dim3 ,dim4 ,dim5) .withType(com.dashoptimization.objects.ColumnType.Binary) .toArray();
 See VariableBuilder.VariableArray5Builder for details of how to modify the specification in the builder.
 Parameters:
 
 dim1 - Dimension 1.
 
 dim2 - Dimension 2.
 
 dim3 - Dimension 3.
 
 dim4 - Dimension 4.
 
 dim5 - Dimension 5.
 
 Returns:
 
 A builder that will create the variables.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2,K3,K4,K5> VariableBuilder.VariableMap5Builder<K1,K2,K3,K4,K5> addVariables(java.util.Collection<K1> coll1,
 java.util.Collection<K2> coll2,
 java.util.Collection<K3> coll3,
 java.util.Collection<K4> coll4,
 java.util.Collection<K5> coll5)
```
 Create an 5 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap5<K1 ,K2 ,K3 ,K4 ,K5,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2 ,coll3 ,coll4 ,coll5) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap5Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 K5 - Data type for dimension 5.
 
 Parameters:
 
 coll1 - Data for dimension 1.
 
 coll2 - Data for dimension 2.
 
 coll3 - Data for dimension 3.
 
 coll4 - Data for dimension 4.
 
 coll5 - Data for dimension 5.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00
 - addVariables
```
public <K1,K2,K3,K4,K5> VariableBuilder.VariableMap5Builder<K1,K2,K3,K4,K5> addVariables(K1[] arr1,
 K2[] arr2,
 K3[] arr3,
 K4[] arr4,
 K5[] arr5)
```
 Create an 5 dimensional map of variables. This function returns a builder that generates variables according to a specification. The specification can be modified. In order to actually create the variables, you have to call the returned builder's toMap() function.
 // Create a multi-dimensional array of binary columns com.dashoptimization.maps.HashMap5<K1 ,K2 ,K3 ,K4 ,K5,com.dashoptimization.objects.Variable> x = prob.addVariables(coll1 ,coll2 ,coll3 ,coll4 ,coll5) .withType(com.dashoptimization.objects.ColumnType.Binary) .toMap();
 See VariableBuilder.VariableMap5Builder for details of how to modify the specification in the builder.
 Type Parameters:
 
 K1 - Data type for dimension 1.
 
 K2 - Data type for dimension 2.
 
 K3 - Data type for dimension 3.
 
 K4 - Data type for dimension 4.
 
 K5 - Data type for dimension 5.
 
 Parameters:
 
 arr1 - Data for the builder.
 
 arr2 - Data for the builder.
 
 arr3 - Data for the builder.
 
 arr4 - Data for the builder.
 
 arr5 - Data for the builder.
 
 Returns:
 
 A builder that will create the columns.
 
 Since:
 
 43.00

© 2001-2024 Fair Isaac Corporation. All rights reserved. This documentation is the property of Fair Isaac Corporation (“FICO”). Receipt or possession of this documentation does not convey rights to disclose, reproduce, make derivative works, use, or allow others to use it except solely for internal evaluation purposes to determine whether to purchase a license to the software described in this documentation, or as otherwise set forth in a written software license agreement between you and FICO (or a FICO affiliate). Use of this documentation and the software described in it must conform strictly to the foregoing permitted uses, and no other use is permitted.

Contents

Index

Glossary

Search Results

XpressProblem

Nested Class Summary

Nested classes/interfaces inherited from class com.dashoptimization.XPRSprob

Field Summary

Fields inherited from class com.dashoptimization.XPRSprob

Constructor Summary

Method Summary

Methods inherited from class com.dashoptimization.XPRSprob

Methods inherited from class com.dashoptimization.XPRSobject

Methods inherited from class java.lang.Object

Field Detail

NULL_VARIABLE

callbacks

Constructor Detail

XpressProblem

XpressProblem

XpressProblem

Method Detail

chgObj

chgObj

chgObjN

chgObjN

chgCoef

chgCoefs

repairWeightedInfeas

inequalityForIndex

inequalitiesForIndices

getInequalities

delRows

delInequalities

getSlacks

getSlacks

getSlack

getDuals

getDuals

getDual

rhsSA

rhsSA

chgRHS

chgRowType

chgRHSrange

loadDelayedRows

loadModelCuts

addConstraints

addConstraint

addConstraints

addConstraints

addConstraints

addConstraints

addConstraints

addConstraints

addConstraints

addConstraints

addConstraints

variableForIndex

variablesForIndices

delCols

delVariables

getSolution

getSolution

getSolution

getRedCosts

getRedCosts

getRedCost

objSA

objSA

bndSA

bndSA

addMipSol

getVariables

chgBounds

chgColType

getInequalityLinear

getInequalityLinear

getCoef

getObj