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# GRBModel

GRBModel

  Gurobi model object. Commonly used methods include `addVar` (adds a new decision variable to the model), `addConstr` (adds a new constraint to the model), `optimize` (optimizes the current model), and `get` (retrieves the value of an attribute).

  While the Java garbage collector will eventually collect an unused `GRBModel` object, the vast majority of the memory associated with a model is stored outside of the Java heap. As a result, the garbage collector can’t see this memory usage, and thus it can’t take this quantity into account when deciding whether collection is necessary. We recommend that you call `GRBModel.dispose` when you are done using a model.

  GRBModel GRBModel(GRBEnv env)

    Constructor for `GRBModel` that creates an empty model. You can then call `addVar` and `addConstr` to populate the model with variables and constraints.

    Arguments:

      env – Environment for new model.

    Return value:

      New model object. Model initially contains no variables or constraints.

    Example:

          // Create environment
          GRBEnv env = new GRBEnv();
          // Create model and attach it to environment
          GRBModel model = new GRBModel(env);

  GRBModel GRBModel(GRBEnv env, String filename)

    Constructor for `GRBModel` that reads a model from a file. Note that the type of the file is encoded in the file name suffix. Valid suffixes are `.mps`, `.rew`, `.lp`, `.rlp`, `.dua`, `.dlp`, `.ilp`, or `.opb`. The files can be compressed, so additional suffixes of `.zip`, `.gz`, `.bz2`, `.7z` or `.xz` are accepted.

    Arguments:

      - env – Environment for new model.

      - filename – Name of the file containing the model.

    Return value:

      New model object.

    Example:

          // Create environment
          GRBEnv env = new GRBEnv();
          // Create model from file myModel.mps and attach it to environment
          GRBModel model = new GRBModel(env, "myModel.mps");

  GRBModel GRBModel(GRBModel model)

    Constructor for `GRBModel` that creates a copy of an existing model. Note that due to the lazy update approach in Gurobi, you have to call `update` before copying it.

    Arguments:

      model – Model to copy.

    Return value:

      New model object. Model is a clone of the original.

    Example:

          // Create environment
          GRBEnv env = new GRBEnv();
          // Create model and attach it to environment
          GRBModel model = new GRBModel(env);
          // ...
          // Update model before copying
          model.update();
          // Copy model
          GRBModel copy = new GRBModel(model);

  GRBModel GRBModel(GRBModel model, GRBEnv targetenv)

    Copy an existing model to a different environment. Multiple threads can not work simultaneously within the same environment. Copies of models must therefore reside in different environments for multiple threads to operate on them simultaneously.

    Note that this method itself is not thread safe, so you should either call it from the main thread or protect access to it with a lock.

    Note that pending updates will not be applied to the model, so you should call `update` before copying if you would like those to be included in the copy.

    For Compute Server users, note that you can copy a model from a client to a Compute Server environment, but it is not possible to copy models from a Compute Server environment to another (client or Compute Server) environment.

    Arguments:

      - model – Model to copy.

      - targetenv – Environment to copy model into.

    Return value:

      New model object. Model is a clone of the original.

    Example:

          // Create environment
          GRBEnv env = new GRBEnv();
          // Create model and attach it to environment
          GRBModel model = new GRBModel(env);
          // ...
          // Update model before copying
          model.update();
          // Create another environment
          GRBEnv env2 = new GRBEnv();
          // Copy model and attach it to env2
          GRBModel copy = new GRBModel(model, env2);

  GRBConstr addConstr(GRBLinExpr lhsExpr, char sense, GRBLinExpr rhsExpr, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhsExpr – Left-hand side expression for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side expression for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          GRBVar z = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "z");
          // Create linear expression x + y
          GRBLinExpr lexpr = new GRBLinExpr();
          lexpr.addTerm(1.0, x);
          lexpr.addTerm(1.0, y);
          // Create linear expression 2*z
          GRBLinExpr rexpr = new GRBLinExpr();
          rexpr.addTerm(2.0, z);

          // Add linear constraint x + y = 2*z with name c1
          GRBConstr constr = model.addConstr(lexpr, GRB.EQUAL, rexpr, "c1");

  GRBConstr addConstr(GRBLinExpr lhsExpr, char sense, GRBVar rhsVar, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhsExpr – Left-hand side expression for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsVar – Right-hand side variable for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          GRBVar z = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "z");
          // Create linear expression x + y
          GRBLinExpr lexpr = new GRBLinExpr();
          lexpr.addTerm(1.0, x);
          lexpr.addTerm(1.0, y);

          // Add linear constraint x + y = z with name c1
          GRBConstr constr = model.addConstr(lexpr, GRB.EQUAL, z, "c1");

  GRBConstr addConstr(GRBLinExpr lhsExpr, char sense, double rhs, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhsExpr – Left-hand side expression for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhs – Right-hand side value for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expression x + y
          GRBLinExpr lexpr = new GRBLinExpr();
          lexpr.addTerm(1.0, x);
          lexpr.addTerm(1.0, y);

          // Add linear constraint x + y = 0 with name c1
          GRBConstr constr = model.addConstr(lexpr, GRB.EQUAL, 0.0, "c1");

  GRBConstr addConstr(GRBVar lhsVar, char sense, GRBLinExpr rhsExpr, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhsVar – Left-hand side variable for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side expression for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          GRBVar z = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "z");
          // Create linear expression x + y
          GRBLinExpr rexpr = new GRBLinExpr();
          rexpr.addTerm(1.0, x);
          rexpr.addTerm(1.0, y);

          // Add linear constraint z >= x + y with name c1
          GRBConstr constr = model.addConstr(z, GRB.GREATER_EQUAL, rexpr, "c1");

  GRBConstr addConstr(GRBVar lhsVar, char sense, GRBVar rhsVar, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhsVar – Left-hand side variable for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsVar – Right-hand side variable for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");

          // Add linear constraint x <= y with name c1
          GRBConstr constr = model.addConstr(x, GRB.LESS_EQUAL, y, "c1");

  GRBConstr addConstr(GRBVar lhsVar, char sense, double rhs, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhsVar – Left-hand side variable for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhs – Right-hand side value for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variable
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");

          // Add linear constraint x <= 2 with name c1
          GRBConstr constr = model.addConstr(x, GRB.LESS_EQUAL, 2.0, "c1");

  GRBConstr addConstr(double lhs, char sense, GRBVar rhsVar, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhs – Left-hand side value for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsVar – Right-hand side variable for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variable
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");

          // Add linear constraint 1 <= x with name c1
          GRBConstr constr = model.addConstr(1.0, GRB.LESS_EQUAL, x, "c1");

  GRBConstr addConstr(double lhs, char sense, GRBLinExpr rhsExpr, String name)

    Add a single linear constraint to a model.

    Arguments:

      - lhs – Left-hand side value for new linear constraint.

      - sense – Sense for new linear constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side expression for new linear constraint.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expression x + y
          GRBLinExpr rexpr = new GRBLinExpr();
          rexpr.addTerm(1.0, x);
          rexpr.addTerm(1.0, y);

          // Add linear constraint 1 >= x + y with name c1
          GRBConstr constr = model.addConstr(1.0, GRB.GREATER_EQUAL, rexpr, "c1");

  GRBConstr[] addConstrs(int count)

    Add `count` new linear constraints to a model. The new constraints are all of the form `0 <= 0`.

    We recommend that you build your model one constraint at a time (using `addConstr`), since it introduces no significant overhead and we find that it produces simpler code. Feel free to use these methods if you disagree, though.

    Arguments:

      count – Number of constraints to add.

    Return value:

      Array of new constraint objects.

    Example:

          // Add 5 empty constraints (0 <= 0)
          GRBConstr[] constrs = model.addConstrs(5)

  GRBConstr[] addConstrs(GRBLinExpr[] lhsExprs, char[] senses, double[] rhss, String[] names)

    Add new linear constraints to a model. The number of added constraints is determined by the length of the input arrays (which must be consistent across all arguments).

    We recommend that you build your model one constraint at a time (using `addConstr`), since it introduces no significant overhead and we find that it produces simpler code. Feel free to use these methods if you disagree, though.

    Arguments:

      - lhsExprs – Left-hand side expressions for the new linear constraints.

      - senses – Senses for new linear constraints (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhss – Right-hand side values for the new linear constraints.

      - names – Names for new constraints.

    Return value:

      Array of new constraint objects.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expressions x + y and x - 2.0 * y
          GRBLinExpr expr1 = new GRBLinExpr();
          expr1.addTerm(1.0, x);
          expr1.addTerm(1.0, y);
          GRBLinExpr expr2 = new GRBLinExpr();
          expr2.addTerm(1.0, x);
          expr2.addTerm(-2.0, y);

          GRBLinExpr[] lhsExprs = {expr1, expr2};
          char[] senses = {GRB.LESS_EQUAL, GRB.GREATER_EQUAL};
          double[] rhss = {1.0, 2.0};
          String[] names = {"c1", "c2"};

          // Add two linear constraints:
          // x + y <= 1.0 with name c1
          // x - 2*y >= 2.0 with name c2
          GRBConstr[] constrs = model.addConstrs(lhsExprs, senses, rhss, names);

  GRBConstr[] addConstrs(GRBLinExpr[] lhsExprs, char[] senses, double[] rhss, String[] names, int start, int len)

    Add new linear constraints to a model. This signature allows you to use arrays to hold the various constraint attributes (left-hand side, sense, etc.), without forcing you to add one constraint for each entry in the array. The `start` and `len` arguments allow you to specify which constraints to add.

    We recommend that you build your model one constraint at a time (using `addConstr`), since it introduces no significant overhead and we find that it produces simpler code. Feel free to use these methods if you disagree, though.

    Arguments:

      - lhsExprs – Left-hand side expressions for the new linear constraints.

      - senses – Senses for new linear constraints (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhss – Right-hand side values for the new linear constraints.

      - names – Names for new constraints.

      - start – The first constraint in the list to add.

      - len – The number of constraints to add.

    Return value:

      Array of new constraint objects.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expressions x + y and x - 2.0 * y
          GRBLinExpr expr1 = new GRBLinExpr();
          expr1.addTerm(1.0, x);
          expr1.addTerm(1.0, y);
          GRBLinExpr expr2 = new GRBLinExpr();
          expr2.addTerm(1.0, x);
          expr2.addTerm(-2.0, y);

          GRBLinExpr[] lhsExprs = {expr1, expr2};
          char[] senses = {GRB.LESS_EQUAL, GRB.GREATER_EQUAL};
          double[] rhss = {1.0, 2.0};
          String[] names = {"c1", "c2"};

          // Add two linear constraints:
          // x + y <= 1.0 with name c1
          // x - 2*y >= 2.0 with name c2
          GRBConstr[] constrs = model.addConstrs(lhsExprs, senses, rhss, names, 0, 2);

  GRBGenConstr addGenConstrMax(GRBVar resvar, GRBVar[] vars, double constant, String name)

    Add a new general constraint of type `GRB.GENCONSTR_MAX` to a model.

    A MAX constraint \(r = \max\{x_1,\ldots,x_n,c\}\) states that the resultant variable \(r\) should be equal to the maximum of the operand variables \(x_1,\ldots,x_n\) and the constant \(c\).

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - vars – Array of variables that are the operands of the new constraint.

      - constant – The additional constant operand of the new constraint.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create variables
          GRBVar resvar = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "resvar");
          double[] ub = {10.0, 10.0, 10.0};
          GRBVar[] x = model.addVars(null, ub, null, null, null);
          // Add constraint resvar = max{x[0], x[1], x[2], 2.0}
          GRBGenConstr gc = model.addGenConstrMax(resvar, x, 2.0, "maxconstr");

  GRBGenConstr addGenConstrMin(GRBVar resvar, GRBVar[] vars, double constant, String name)

    Add a new general constraint of type `GRB.GENCONSTR_MIN` to a model.

    A MIN constraint \(r = \min\{x_1,\ldots,x_n,c\}\) states that the resultant variable \(r\) should be equal to the minimum of the operand variables \(x_1,\ldots,x_n\) and the constant \(c\).

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - vars – Array of variables that are the operands of the new constraint.

      - constant – The additional constant operand of the new constraint.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create variables
          GRBVar resvar = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "resvar");
          double[] lb = {-10.0, -10.0, -10.0};
          double[] ub = {0.0, 0.0, 0.0};
          GRBVar[] x = model.addVars(lb, ub, null, null, null);
          // Add constraint resvar = min{x[0], x[1], x[2], -2.0}
          GRBGenConstr gc = model.addGenConstrMin(resvar, x, -2.0, "minconstr");

  GRBGenConstr addGenConstrAbs(GRBVar resvar, GRBVar argvar, String name)

    Add a new general constraint of type `GRB.GENCONSTR_ABS` to a model.

    An ABS constraint \(r = \mbox{abs}\{x\}\) states that the resultant variable \(r\) should be equal to the absolute value of the argument variable \(x\).

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - argvar – The argument variable of the new constraint.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create variables
          GRBVar resvar = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "resvar");
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          // Add constraint resvar = |x|
          GRBGenConstr gc = model.addGenConstrAbs(resvar, x, "absconstr");

  GRBGenConstr addGenConstrAnd(GRBVar resvar, GRBVar[] vars, String name)

    Add a new general constraint of type `GRB.GENCONSTR_AND` to a model.

    An AND constraint \(r = \mbox{and}\{x_1,\ldots,x_n\}\) states that the binary resultant variable \(r\) should be \(1\) if and only if all of the operand variables \(x_1,\ldots,x_n\) are equal to \(1\). If any of the operand variables is \(0\), then the resultant should be \(0\) as well.

    Note that all variables participating in such a constraint will be forced to be binary, independent of how they were created.

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - vars – Array of variables that are the operands of the new constraint.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create binary variables
          GRBVar resvar = model.addVar(0.0, 1.0, 0.0, GRB.BINARY, "resvar");
          GRBVar[] x = model.addVars(3, GRB.BINARY);
          // Add constraint resvar = AND{x[0], x[1], x[2]}
          GRBGenConstr gc = model.addGenConstrAnd(resvar, x, "andconstr");

  GRBGenConstr addGenConstrOr(GRBVar resvar, GRBVar[] vars, String name)

    Add a new general constraint of type `GRB.GENCONSTR_OR` to a model.

    An OR constraint \(r = \mbox{or}\{x_1,\ldots,x_n\}\) states that the binary resultant variable \(r\) should be \(1\) if and only if any of the operand variables \(x_1,\ldots,x_n\) is equal to \(1\). If all operand variables are \(0\), then the resultant should be \(0\) as well.

    Note that all variables participating in such a constraint will be forced to be binary, independent of how they were created.

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - vars – Array of variables that are the operands of the new constraint.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create binary variables
          GRBVar resvar = model.addVar(0.0, 1.0, 0.0, GRB.BINARY, "resvar");
          GRBVar[] x = model.addVars(3, GRB.BINARY);
          // Add constraint resvar = OR{x[0], x[1], x[2]}
          GRBGenConstr gc = model.addGenConstrOr(resvar, x, "orconstr");

  GRBGenConstr addGenConstrNorm(GRBVar resvar, GRBVar[] vars, double which, String name)

    Add a new general constraint of type `GRB.GENCONSTR_NORM` to a model.

    A NORM constraint \(r = \mbox{norm}\{x_1,\ldots,x_n\}\) states that the resultant variable \(r\) should be equal to the vector norm of the argument vector \(x_1,\ldots,x_n\).

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - vars – Array of variables that are the operands of the new constraint. Note that this array may not contain duplicates.

      - which – Which norm to use. Options are 0, 1, 2, and GRB.INFINITY.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create variables
          GRBVar resvar = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "resvar");
          double[] lb = {-10.0, -10.0, -10.0};
          double[] ub = {10.0, 10.0, 10.0};
          GRBVar[] x = model.addVars(lb, ub, null, null, null);
          // Add constraint resvar = (x_0^2 + x_1^2 + x_2^2)^(0.5)
          GRBGenConstr gc = model.addGenConstrNorm(resvar, x, 2, "2normconstr");

  GRBGenConstr addGenConstrNL(GRBVar resvar, int[] opcode, double[] data, int[] parent, String name)

    Add a new general constraint of type `GRB.GENCONSTR_NL` to a model.

    A NL constraint \(r = f(x)\) states that the resultant variable \(r\) should be equal to the function value \(f(x)\) of the given function \(f\), provided as an expression tree described in Nonlinear Constraints.

    Arguments:

      - resvar – The resultant variable of the new constraint.

      - opcode – An array containing the operation codes for the nodes.

      - data – An array containing the auxiliary data for each node.

      - parent – An array providing the parent index of the nodes

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

        // Add nonlinear constraint x0 = sin(2.5 * x1) + x2 to the model

        GRBVar x0 = model.addVar(0.0, 1.0, 0.0, GRB.CONTINUOUS, "x0");
        GRBVar x1 = model.addVar(0.0, 1.0, 0.0, GRB.CONTINUOUS, "x1");
        GRBVar x2 = model.addVar(0.0, 1.0, 0.0, GRB.CONTINUOUS, "x2");

        int[]    opcode = {GRB.OPCODE_PLUS, GRB.OPCODE_SIN,
                           GRB.OPCODE_MULTIPLY, GRB.OPCODE_CONSTANT,
                           GRB.OPCODE_VARIABLE, GRB.OPCODE_VARIABLE};
        double[] data   = {-1.0, -1.0, -1.0, 2.5, 1.0, 2.0};
        int[]    parent = {-1, 0, 1, 2, 2, 0};

        GRBGenConstr nlgc = model.addGenConstrNL(x0, opcode, data, parent, "nlconstr");

  GRBGenConstr addGenConstrIndicator(GRBVar binvar, int binval, GRBLinExpr expr, char sense, double rhs, String name)

    Add a new general constraint of type `GRB.GENCONSTR_INDICATOR` to a model.

    An INDICATOR constraint \(z = f \rightarrow a^Tx \leq b\) states that if the binary indicator variable \(z\) is equal to \(f\), where \(f \in \{0,1\}\), then the linear constraint \(a^Tx \leq b\) should hold. On the other hand, if \(z = 1-f\) , the linear constraint may be violated. The sense of the linear constraint can also be specified to be \(=\) or \(\geq\) .

    Note that the indicator variable \(z\) of a constraint will be forced to be binary, independent of how it was created.

    Arguments:

      - binvar – The binary indicator variable.

      - binval – The value for the binary indicator variable that would force the linear constraint to be satisfied (\(0\) or \(1\)).

      - expr – Left-hand side expression for the linear constraint triggered by the indicator.

      - sense – Sense for the linear constraint. Options are `GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`.

      - rhs – Right-hand side value for the linear constraint.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create binary indicator variable
          GRBVar z = model.addVar(0.0, 1.0, 0.0, GRB.BINARY, "resvar");
          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expression x + y
          GRBLinExpr lexpr1 = new GRBLinExpr();
          lexpr1.addTerm(1.0, x);
          lexpr1.addTerm(1.0, y);
          // Add constraint if z = 1 then x + y <= 2
          GRBGenConstr gc = model.addGenConstrIndicator(z, 1, lexpr, GRB.LESS_EQUAL, 2.0, "indicatorconstr");

  GRBGenConstr addGenConstrPWL(GRBVar xvar, GRBVar yvar, double[] xpts, double[] ypts, String name)

    Add a new general constraint of type `GRB.GENCONSTR_PWL` to a model.

    A piecewise-linear (PWL) constraint states that the relationship \(y = f(x)\) must hold between variables \(x\) and \(y\), where \(f\) is a piecewise-linear function. The breakpoints for \(f\) are provided as arguments. Refer to the description of piecewise-linear objectives for details of how piecewise-linear functions are defined.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - xpts – The \(x\) values for the points that define the piecewise-linear function. Must be in non-decreasing order.

      - ypts – The \(y\) values for the points that define the piecewise-linear function.

      - name – Name for the new general constraint.

    Return value:

      New general constraint.

    Example:

          // Create variables
          GRBVar x = model.addVar(-1.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create point pairs for the PWL function
          double[] xpts = {-1, 0, 0, 0, 1};
          double[] ypts = {2, 1, 0, 1, 2};
          // Add pwl constraint y = PWL(x)
          GRBGenConstr gc = model.addGenConstrPWL(x, y, xpts, ypts, "pwlconstr");

  GRBGenConstr addGenConstrPoly(GRBVar xvar, GRBVar yvar, double[] p, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_POLY` to a model.

    A polynomial function constraint states that the relationship \(y = p_0 x^d + p_1 x^{d-1} + ... + p_{d-1} x + p_{d}\) should hold between variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - p – The coefficients for the polynomial function (starting with the coefficient for the highest power).

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-5.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create array holding coefficients
          double[] p = {3.0, 2.0, 0.0, 1.0};
          // Add constraint y = 3.0 * x^3 + 2.0 * x^2 + 1.0
          GRBGenConstr gc = model.addGenConstrPoly(x, y, p, "polyconstr", "");

  GRBGenConstr addGenConstrExp(GRBVar xvar, GRBVar yvar, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_EXP` to a model.

    A natural exponential function constraint states that the relationship \(y = \exp(x)\) should hold for variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-5.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = exp(x)
          GRBGenConstr gc = model.addGenConstrExp(x, y, "expconstr", "");

  GRBGenConstr addGenConstrExpA(GRBVar xvar, GRBVar yvar, double a, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_EXPA` to a model.

    An exponential function constraint states that the relationship \(y = a^x\) should hold for variables \(x\) and \(y\), where \(a > 0\)

    is the (constant) base.

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - a – The base of the function, \(a > 0\) .

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(1.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = 2.0^x
          GRBGenConstr gc = model.addGenConstrExpA(x, y, 2.0, "exp2constr", "");

  GRBGenConstr addGenConstrLog(GRBVar xvar, GRBVar yvar, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_LOG` to a model.

    A natural logarithmic function constraint states that the relationship \(y = \log(x)\) should hold for variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(1.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = log(x)
          GRBGenConstr gc = model.addGenConstrLog(x, y, "logconstr", "");

  GRBGenConstr addGenConstrLogA(GRBVar xvar, GRBVar yvar, double a, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_LOGA` to a model.

    A logarithmic function constraint states that the relationship \(y = \log_a(x)\) should hold for variables \(x\) and \(y\), where \(a > 0\)

    is the (constant) base.

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - a – The base of the function, \(a > 0\) .

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(1.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = log_2(x)
          GRBGenConstr gc = model.addGenConstrLogA(x, y, 2.0, "log2constr", "");

  GRBGenConstr addGenConstrLogistic(GRBVar xvar, GRBVar yvar, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_LOGISTIC` to a model.

    A logistic function constraint states that the relationship \(y = \frac{1}{1 + e^{-x}}\) should hold for variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-5.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = logistic(x) = 1.0 / (1.0 + exp(-x))
          GRBGenConstr gc = model.addGenConstrLogistic(x, y, "logisticconstr", "");

  GRBGenConstr addGenConstrPow(GRBVar xvar, GRBVar yvar, double a, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_POW` to a model.

    A power function constraint states that the relationship \(y = x^a\) should hold for variables \(x\) and \(y\), where \(a\) is the (constant) exponent.

    If the exponent \(a\) is negative, the lower bound on \(x\) must be strictly positive. If the exponent isn’t an integer, the lower bound on \(x\) must be non-negative.

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - a – The exponent of the function.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-5.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = x^3
          GRBGenConstr gc = model.addGenConstrPow(x, y, 3, "pow3constr", "");

  GRBGenConstr addGenConstrSin(GRBVar xvar, GRBVar yvar, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_SIN` to a model.

    A sine function constraint states that the relationship \(y = \sin(x)\) should hold for variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-5.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = sin(x)
          GRBGenConstr gc = model.addGenConstrSin(x, y, "sinconstr", "");

  GRBGenConstr addGenConstrCos(GRBVar xvar, GRBVar yvar, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_COS` to a model.

    A cosine function constraint states that the relationship \(y = \cos(x)\) should hold for variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-5.0, 5.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = cos(x)
          GRBGenConstr gc = model.addGenConstrCos(x, y, "cosconstr", "");

  GRBGenConstr addGenConstrTan(GRBVar xvar, GRBVar yvar, String name, String options)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Add a new general constraint of type `GRB.GENCONSTR_TAN` to a model.

    A tangent function constraint states that the relationship \(y = \tan(x)\) should hold for variables \(x\) and \(y\).

    A piecewise-linear approximation of the function is added to the model. The details of the approximation are controlled using the following four attributes (or using the parameters with the same names): FuncPieces, FuncPieceError, FuncPieceLength, and FuncPieceRatio. Alternatively, the function can be treated as a nonlinear constraint by setting the attribute FuncNonlinear. For details, consult the General Constraint discussion.

    Arguments:

      - xvar – The \(x\) variable.

      - yvar – The \(y\) variable.

      - name – Name for the new general constraint.

      - options – A string that can be used to set the attributes that control the piecewise-linear approximation of this function constraint. To assign a value to an attribute, follow the attribute name with an equal sign and the desired value (with no spaces). Assignments for different attributes should be separated by spaces (e.g. “FuncPieces=-1 FuncPieceError=0.001”).

    Return value:

      New general constraint.

    Example:

          // Create argument variable
          GRBVar x = model.addVar(-1.0, 1.0, 0.0, GRB.CONTINUOUS, "x");
          // Create resultant variable
          GRBVar y = model.addVar(-GRB.INFINITY, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Add constraint y = tan(x)
          GRBGenConstr gc = model.addGenConstrTan(x, y, "tanconstr", "");

  GRBQConstr addQConstr(GRBQuadExpr lhsExpr, char sense, GRBQuadExpr rhsExpr, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhsExpr – Left-hand side quadratic expression for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side quadratic expression for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create quadratic expression x^2 + x*y + y
          GRBQuadExpr lhsExpr = new GRBQuadExpr();
          lhsExpr.addTerm(1.0, x, x);
          lhsExpr.addTerm(1.0, x, y);
          lhsExpr.addTerm(1.0, y);
          // Create quadratic expression y^2
          GRBQuadExpr rhsExpr = new GRBQuadExpr();
          rhsExpr.addTerm(1.0, y, y);

          // Add quadratic constraint x^2 + x*y + y = y^2 with name c1
          GRBQConstr constr = model.addQConstr(lhsExpr, GRB.EQUAL, rhsExpr, "c1");

  GRBQConstr addQConstr(GRBQuadExpr lhsExpr, char sense, GRBVar rhsVar, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhsExpr – Left-hand side quadratic expression for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsVar – Right-hand side variable for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create quadratic expression x^2 + x*y + y
          GRBQuadExpr lhsExpr = new GRBQuadExpr();
          lhsExpr.addTerm(1.0, x, x);
          lhsExpr.addTerm(1.0, x, y);
          lhsExpr.addTerm(1.0, y);

          // Add quadratic constraint x^2 + x*y + y = y with name c1
          GRBQConstr constr = model.addQConstr(lhsExpr, GRB.EQUAL, y, "c1");

  GRBQConstr addQConstr(GRBQuadExpr lhsExpr, char sense, GRBLinExpr rhsExpr, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhsExpr – Left-hand side quadratic expression for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side linear expression for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create quadratic expression x^2 + x*y + y
          GRBQuadExpr lhsExpr = new GRBQuadExpr();
          lhsExpr.addTerm(1.0, x, x);
          lhsExpr.addTerm(1.0, x, y);
          lhsExpr.addTerm(1.0, y);
          // Create linear expression 2*x - y
          GRBLinExpr rhsExpr = new GRBLinExpr();
          rhsExpr.addTerm(2.0, x);
          rhsExpr.addTerm(-1.0, y);

          // Add quadratic constraint x^2 + x*y + y = 2*x - y with name c1
          GRBQConstr constr = model.addQConstr(lhsExpr, GRB.EQUAL, rhsExpr, "c1");

  GRBQConstr addQConstr(GRBQuadExpr lhsExpr, char sense, double rhs, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhsExpr – Left-hand side quadratic expression for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhs – Right-hand side value for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create quadratic expression x^2 + x*y + y
          GRBQuadExpr lhsExpr = new GRBQuadExpr();
          lhsExpr.addTerm(1.0, x, x);
          lhsExpr.addTerm(1.0, x, y);
          lhsExpr.addTerm(1.0, y);

          // Add quadratic constraint x^2 + x*y + y = 0 with name c1
          GRBQConstr constr = model.addQConstr(lhsExpr, GRB.EQUAL, 0, "c1");

  GRBQConstr addQConstr(GRBLinExpr lhsExpr, char sense, GRBQuadExpr rhsExpr, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhsExpr – Left-hand side linear expression for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side quadratic expression for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

           // Create variables
           GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
           GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
           // Create linear expression 2*x - y
           GRBLinExpr lhsExpr = new GRBLinExpr();
           lhsExpr.addTerm(2.0, x);
           lhsExpr.addTerm(-1.0, y);
          // Create quadratic expression x^2 + x*y + y
           GRBQuadExpr rhsExpr = new GRBQuadExpr();
           rhsExpr.addTerm(1.0, x, x);
           rhsExpr.addTerm(1.0, x, y);
           rhsExpr.addTerm(1.0, y);

           // Add quadratic constraint 2*x - y = x^2 + x*y + y with name c1
           GRBQConstr constr = model.addQConstr(lhsExpr, GRB.EQUAL, rhsExpr, "c1");

  GRBQConstr addQConstr(GRBVar lhsVar, char sense, GRBQuadExpr rhsExpr, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhsVar – Left-hand side variable for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side quadratic expression for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create quadratic expression x^2 + x*y + y
          GRBQuadExpr rhsExpr = new GRBQuadExpr();
          rhsExpr.addTerm(1.0, x, x);
          rhsExpr.addTerm(1.0, x, y);
          rhsExpr.addTerm(1.0, y);

          // Add quadratic constraint x <= x^2 + x*y + y with name c1
          GRBQConstr constr = model.addQConstr(x, GRB.LESS_EQUAL, rhsExpr, "c1");

  GRBQConstr addQConstr(double lhs, char sense, GRBQuadExpr rhsExpr, String name)

    Add a quadratic constraint to a model.

    Important

    Gurobi can handle both convex and non-convex quadratic constraints. The differences between them can be both important and subtle. Refer to this discussion for additional information.

    Arguments:

      - lhs – Left-hand side value for new quadratic constraint.

      - sense – Sense for new quadratic constraint (`GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`).

      - rhsExpr – Right-hand side quadratic expression for new quadratic constraint.

      - name – Name for new constraint.

    Return value:

      New quadratic constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(-2.0, 2.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create quadratic expression x^2 + x*y + y
          GRBQuadExpr rhsExpr = new GRBQuadExpr();
          rhsExpr.addTerm(1.0, x, x);
          rhsExpr.addTerm(1.0, x, y);
          rhsExpr.addTerm(1.0, y);

          // Add quadratic constraint 0 <= x^2 + x*y + y with name c1
          GRBQConstr constr = model.addQConstr(0.0, GRB.LESS_EQUAL, rhsExpr, "c1");

  GRBConstr addRange(GRBLinExpr expr, double lower, double upper, String name)

    Add a single range constraint to a model. A range constraint states that the value of the input expression must be between the specified `lower` and `upper` bounds in any solution.

    Note that range constraints are stored internally as equality constraints. We add an extra variable to the model to capture the range information. Thus, the Sense attribute on a range constraint will always be `GRB.EQUAL`. In particular introducing a range constraint

    \[L \leq a^T x \leq U\]

    is equivalent to adding a slack variable \(s\) and the following constraints

    \[\begin{split}\begin{array}{rl} a^T x + s & = U \\ 0 \leq s & \leq U - L. \end{array}\end{split}\]
    Arguments:

      - expr – Linear expression for new range constraint.

      - lower – Lower bound for linear expression.

      - upper – Upper bound for linear expression.

      - name – Name for new constraint.

    Return value:

      New constraint object.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, 10.0, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(-10.0, 3.0, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expression x + y
          GRBLinExpr lexpr = new GRBLinExpr();
          lexpr.addTerm(1.0, x);
          lexpr.addTerm(1.0, y);
          // Add range constraints -3 <= x + y <= 2 with name c1
          GRBConstr constr = model.addRange(lexpr, -3.0, 2.0, "c1");

  GRBConstr[] addRanges(GRBLinExpr[] exprs, double[] lower, double[] upper, String[] names)

    Add new range constraints to a model. A range constraint states that the value of the input expression must be between the specified `lower` and `upper` bounds in any solution.

    Arguments:

      - exprs – Linear expressions for the new range constraints.

      - lower – Lower bounds for linear expressions.

      - upper – Upper bounds for linear expressions.

      - names – Names for new range constraints.

    Return value:

      Array of new constraint objects.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create linear expressions x + y and x - 2.0 * y
          GRBLinExpr lexpr1 = new GRBLinExpr();
          lexpr1.addTerm(1.0, x);
          lexpr1.addTerm(1.0, y);
          GRBLinExpr lexpr2 = new GRBLinExpr();
          lexpr2.addTerm(1.0, x);
          lexpr2.addTerm(-2.0, y);

          GRBLinExpr[] lexprs = {lexpr1, lexpr2};
          double[] lower = {-3.0, 2.0};
          double[] upper = {-1.0, 5.0};
          String[] names = {"c1", "c2"};
          // Add constraints -3.0 <= x + y <= 2.0 and -1.0 <= x - 2.0 * y <= 5.0
          GRBConstr[] constrs = model.addRanges(lexprs, lower, upper, names);

  GRBSOS addSOS(GRBVar[] vars, double[] weights, int type)

    Add an SOS constraint to the model. Please refer to the SOS Constraints section in the Reference Manual for additional details.

    Arguments:

      - vars – Array of variables that participate in the SOS constraint.

      - weights – Weights for the variables in the SOS constraint.

      - type – SOS type (can be `GRB.SOS_TYPE1` or `GRB.SOS_TYPE2`).

    Return value:

      New SOS constraint.

    Example:

          // Create variables
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");
          GRBVar y = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "y");
          // Create helper arrays
          GRBVar[] vars = {x, y};
          double[] weights = {1.0, 2.0};
          // Add SOS1 constraint over x and y
          GRBSOS constr = model.addSOS(vars, weights, GRB.SOS_TYPE1);

  GRBVar addVar(double lb, double ub, double obj, char type, String name)

    Add a single decision variable to a model; non-zero entries will be added later.

    Arguments:

      - lb – Lower bound for new variable.

      - ub – Upper bound for new variable.

      - obj – Objective coefficient for new variable.

      - type – Variable type for new variable (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`).

      - name – Name for new variable.

    Return value:

      New variable object.

    Example:

          // Create variable
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, "x");

  GRBVar addVar(double lb, double ub, double obj, char type, GRBConstr[] constrs, double[] coeffs, String name)

    Add a single decision variable and the associated non-zero coefficients to a model.

    Arguments:

      - lb – Lower bound for new variable.

      - ub – Upper bound for new variable.

      - obj – Objective coefficient for new variable.

      - type – Variable type for new variable (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`).

      - constrs – Array of constraints in which the variable participates.

      - coeffs – Array of coefficients for each constraint in which the variable participates. The lengths of the `constrs` and `coeffs` arrays must be identical.

      - name – Name for new variable.

    Return value:

      New variable object.

    Example:

          // Add 3 trivial linear constraints to model
          GRBConstr[] constrs = model.addConstrs(3);
          // Constraint coefficients for variable x
          double[] coeffs = {1.0, 2.0, 3.0};
          // Add variable x with coeffs 1.0, 2.0, 3.0 to the newly created trivial constraints
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, constrs, coeffs, "x");

  GRBVar addVar(double lb, double ub, double obj, char type, GRBColumn col, String name)

    Add a single decision variable to a model. This signature allows you to specify the set of constraints to which the new variable belongs using a `GRBColumn` object.

    Arguments:

      - lb – Lower bound for new variable.

      - ub – Upper bound for new variable.

      - obj – Objective coefficient for new variable.

      - type – Variable type for new variable (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`).

      - col – GRBColumn object for specifying a set of constraints to which new variable belongs.

      - name – Name for new variable.

    Return value:

      New variable object.

    Example:

          // Add 3 trivial linear constraints to model
          GRBConstr[] constrs = model.addConstrs(3);
          // Constraint coefficients for variable x
          double[] coeffs = {1.0, 2.0, 3.0};
          // Create and fill GRBColumn object
          GRBColumn col = new GRBColumn();
          col.addTerms(coeffs, constrs);
          // Add variable x with coefs 1.0, 2.0, 3.0 to the newly created trivial constraints
          GRBVar x = model.addVar(0.0, GRB.INFINITY, 0.0, GRB.CONTINUOUS, col, "x");

  GRBVar[] addVars(int count, char type)

    Add `count` new decision variables to a model. All associated attributes take their default values, except the variable `type`, which is specified as an argument.

    Arguments:

      - count – Number of variables to add.

      - type – Variable type for new variables (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`).

    Return value:

      Array of new variable objects.

    Example:

          // Add 3 binary variables
          GRBVar[] x = model.addVars(3, GRB.BINARY);

  GRBVar[] addVars(double[] lb, double[] ub, double[] obj, char[] type, String[] names)

    Add new decision variables to a model. The number of added variables is determined by the length of the input arrays (which must be consistent across all arguments).

    Arguments:

      - lb – Lower bounds for new variables. Can be `null`, in which case the variables get lower bounds of 0.0.

      - ub – Upper bounds for new variables. Can be `null`, in which case the variables get infinite upper bounds.

      - obj – Objective coefficients for new variables. Can be `null`, in which case the variables get objective coefficients of 0.0.

      - type – Variable types for new variables (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`). Can be `null`, in which case the variables are assumed to be continuous.

      - names – Names for new variables. Can be `null`, in which case all variables are given default names.

    Return value:

      Array of new variable objects.

    Example:

          // Create 3 variables with default lower bound and default type
          double[] ub    = {1, 1, 2};
          double[] obj   = {-2, -1, -1};
          String[] names = {"x0", "x1", "x2"};
          GRBVar[] x = model.addVars(null, ub, obj, null, names);

  GRBVar[] addVars(double[] lb, double[] ub, double[] obj, char[] type, String[] names, int start, int len)

    Add new decision variables to a model. This signature allows you to use arrays to hold the various variable attributes (lower bound, upper bound, etc.), without forcing you to add a variable for each entry in the array. The `start` and `len` arguments allow you to specify which variables to add.

    Arguments:

      - lb – Lower bounds for new variables. Can be `null`, in which case the variables get lower bounds of 0.0.

      - ub – Upper bounds for new variables. Can be `null`, in which case the variables get infinite upper bounds.

      - obj – Objective coefficients for new variables. Can be `null`, in which case the variables get objective coefficients of 0.0.

      - type – Variable types for new variables (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`). Can be `null`, in which case the variables are assumed to be continuous.

      - names – Names for new variables. Can be `null`, in which case all variables are given default names.

      - start – The first variable in the list to add.

      - len – The number of variables to add.

    Return value:

      Array of new variable objects.

    Example:

          // Create 3 variables with default lower bound and default type
          double[] ub    = {1, 1, 2};
          double[] obj   = {-2, -1, -1};
          String[] names = {"x0", "x1", "x2"};
          // Add only the first two variables
          GRBVar[] x = model.addVars(null, ub, obj, null, names, 0, 2);

  GRBVar[] addVars(double[] lb, double[] ub, double[] obj, char[] type, String[] names, GRBColumn[] cols)

    Add new decision variables to a model. This signature allows you to specify the list of constraints to which each new variable belongs using an array of `GRBColumn` objects.

    Arguments:

      - lb – Lower bounds for new variables. Can be `null`, in which case the variables get lower bounds of 0.0.

      - ub – Upper bounds for new variables. Can be `null`, in which case the variables get infinite upper bounds.

      - obj – Objective coefficients for new variables. Can be `null`, in which case the variables get objective coefficients of 0.0.

      - type – Variable types for new variables (`GRB.CONTINUOUS`, `GRB.BINARY`, `GRB.INTEGER`, `GRB.SEMICONT`, or `GRB.SEMIINT`). Can be `null`, in which case the variables are assumed to be continuous.

      - names – Names for new variables. Can be `null`, in which case all variables are given default names.

      - cols – GRBColumn objects for specifying a set of constraints to which each new column belongs.

    Return value:

      Array of new variable objects.

    Example:

          // Create 3 variables with default lower bound and default type
          double[] ub    = {1, 1, 2};
          double[] obj   = {-2, -1, -1};
          String[] names = {"x0", "x1", "x2"};
          // Create an array of previously created GRBColumn objects
          GRBColumn[] col = {col1, col2, col3};
          GRBVar[] x = model.addVars(null, ub, obj, null, names, col);

  void chgCoeff(GRBConstr constr, GRBVar var, double newval)

    Change one coefficient in the model. The desired change is captured using a `GRBVar` object, a `GRBConstr` object, and a desired coefficient for the specified variable in the specified constraint. If you make multiple changes to the same coefficient, the last one will be applied.

    Note that, due to our lazy update approach, the change won’t actually take effect until you update the model (using `GRBModel.update`), optimize the model (using `GRBModel.optimize`), or write the model to disk (using `GRBModel.write`).

    Arguments:

      - constr – Constraint for coefficient to be changed.

      - var – Variable for coefficient to be changed.

      - newval – Desired new value for coefficient.

    Example:

          // Change coefficient of variable x in constraint c1
          model.chgCoeff(c1, x, 1.0);

  void chgCoeffs(GRBConstr[] constrs, GRBVar[] vars, double[] newvals)

    Change a list of coefficients in the model. Each desired change is captured using a `GRBVar` object, a `GRBConstr` object, and a desired coefficient for the specified variable in the specified constraint. The entries in the input arrays each correspond to a single desired coefficient change. The lengths of the input arrays must all be the same. If you make multiple changes to the same coefficient, the last one will be applied.

    Note that, due to our lazy update approach, the change won’t actually take effect until you update the model (using `GRBModel.update`), optimize the model (using `GRBModel.optimize`), or write the model to disk (using `GRBModel.write`).

    Arguments:

      - constrs – Constraints for coefficients to be changed.

      - vars – Variables for coefficients to be changed.

      - newvals – Desired new values for coefficients.

    Example:

          // Create arrays using previously created constraints and variables
          GRBConstr[] constrs = {c1, c2, c3};
          GRBVar[] vars = {x, y, z};
          double[] vals = {1.0, 2.0, 3.0};
          // Change coefficients of variables x, y, z in constraints c1, c2, and c3, respectively
          model.chgCoeffs(constrs, vars, vals);

  void computeIIS()

    Compute an Irreducible Inconsistent Subsystem (IIS).

    An IIS is a subset of the constraints and variable bounds with the following properties:

    - It is still infeasible, and

    - If a single constraint or bound is removed, the subsystem becomes feasible.

    Note that an infeasible model may have multiple IISs. The one returned by Gurobi is not necessarily the smallest one; there may exist others with fewer constraints or bounds.

    IIS results are returned in a number of attributes: IISConstr, IISLB, IISUB, IISSOS, IISQConstr, and IISGenConstr. Each indicates whether the corresponding model element is a member of the computed IIS.

    Note that for models with general function constraints, piecewise-linear approximation of the constraints may cause unreliable IIS results.

    The IIS log provides information about the progress of the algorithm, including a guess at the eventual IIS size.

    Termination parameters such as TimeLimit, WorkLimit, MemLimit, and SoftMemLimit are considered when computing an IIS. If an IIS computation is interrupted before completion or stops due to a termination parameter, Gurobi will return the smallest infeasible subsystem found to that point. The model attribute IISMinimal can be used to check whether the computed IIS is minimal.

    The IISConstrForce, IISLBForce, IISUBForce, IISSOSForce, IISQConstrForce, and IISGenConstrForce attributes allow you mark model elements to either include or exclude from the computed IIS. Setting the attribute to 1 forces the corresponding element into the IIS, setting it to 0 forces it out of the IIS, and setting it to -1 allows the algorithm to decide.

    To give an example of when these attributes might be useful, consider the case where an initial model is known to be feasible, but it becomes infeasible after adding constraints or tightening bounds. If you are only interested in knowing which of the changes caused the infeasibility, you can force the unmodified bounds and constraints into the IIS. That allows the IIS algorithm to focus exclusively on the new constraints, which will often be substantially faster.

    Note that setting any of the `Force` attributes to 0 may make the resulting subsystem feasible, which would then make it impossible to construct an IIS. Trying anyway will result in a IIS_NOT_INFEASIBLE error. Similarly, setting this attribute to 1 may result in an IIS that is not irreducible. More precisely, the system would only be irreducible with respect to the model elements that have force values of -1 or 0.

    This method populates the IISConstr, IISQConstr, and IISGenConstr constraint attributes, the IISSOS, SOS attribute, and the IISLB and IISUB variable attributes. You can also obtain information about the results of the IIS computation by writing a `.ilp` format file (see `GRBModel.write`). This file contains only the IIS from the original model.

    Use the IISMethod parameter to adjust the behavior of the IIS algorithm.

    Note that this method can be used to compute IISs for both continuous and MIP models.

    Example:

          // Compute IIS for infeasible model
          if (model.get(GRB.IntAttr.Status) == GRB.Status.INFEASIBLE) {
             model.computeIIS();
          }

  void convertToFixed()

    Turn the MIP model into a continuous one, in place. A solution (e.g. obtained through a call to the `optimize` method) or a MIP start must be available in the MIP model. If no solution is available, the MIP start specified with StartNumber is used.

    In the model, each integer variable is fixed to the value that variable takes in the MIP solution or MIP start. In addition, continuous variables may be fixed to satisfy SOS or general constraints. The result is that the model has neither integrality constraints, SOS constraints, nor general constraints any more.

    Note

    While the fixed problem is always a continuous model, it may contain a non-convex quadratic objective or non-convex quadratic constraints. As a result, it may still be solved using the MIP algorithm.

    Note

    An error is raised if the converted model contains more than one objective or scenario, or if the model contains concurrent environments or tune environments.

    Convert the current model to a fixed model.

    Example:

          model.convertToFixed();

  void discardConcurrentEnvs()

    Discard concurrent environments for a model.

    The concurrent environments created by `getConcurrentEnv` will be used by every subsequent call to the concurrent optimizer until the concurrent environments are discarded.

    Use `getMultiobjEnv` to create a multi-objective environment.

    Example:

          GRBEnv env0 = model.getConcurrentEnv(0);
          GRBEnv env1 = model.getConcurrentEnv(1);

          env0.set(GRB.IntParam.Method, 0);
          env1.set(GRB.IntParam.Method, 1);

          model.optimize();
          model.discardConcurrentEnvs();

  void discardMultiobjEnvs()

    Discard all multi-objective environments associated with the model, thus restoring multi objective optimization to its default behavior.

    Please refer to the discussion of Multiple Objectives for information on how to specify multiple objective functions and control the trade-off between them.

    Use `getMultiobjEnv` to create a multi-objective environment.

    Example:

          GRBEnv env0 = model.getMultiobjEnv(0);
          GRBEnv env1 = model.getMultiobjEnv(1);

          env0.set(GRB.IntParam.Method, 0);
          env1.set(GRB.IntParam.Method, 1);

          model.optimize();
          model.discardMultiobjEnvs();

  void dispose()

    Release the resources associated with a `GRBModel` object. While the Java garbage collector will eventually reclaim these resources, we recommend that you call the `dispose` method when you are done using a model.

    You should not attempt to use a `GRBModel` object after calling `dispose` on it.

    Example:

          GRBEnv env = new GRBEnv();
          GRBModel model = new GRBModel(env);
          // ...
          model.optimize();
          model.dispose();
          env.dispose();

  double feasRelax(int relaxobjtype, boolean minrelax, GRBVar[] vars, double[] lbpen, double[] ubpen, GRBConstr[] constrs, double[] rhspen)

    Modifies the `GRBModel` object to create a feasibility relaxation. Note that you need to call `optimize` on the result to compute the actual relaxed solution.

    The feasibility relaxation is a model that, when solved, minimizes the amount by which the solution violates the bounds and linear constraints of the original model. This method provides a number of options for specifying the relaxation.

    If you specify `relaxobjtype=0`, the objective of the feasibility relaxation is to minimize the sum of the weighted magnitudes of the bound and constraint violations. The `lbpen`, `ubpen`, and `rhspen` arguments specify the cost per unit violation in the lower bounds, upper bounds, and linear constraints, respectively.

    If you specify `relaxobjtype=1`, the objective of the feasibility relaxation is to minimize the weighted sum of the squares of the bound and constraint violations. The `lbpen`, `ubpen`, and `rhspen` arguments specify the coefficients on the squares of the lower bound, upper bound, and linear constraint violations, respectively.

    If you specify `relaxobjtype=2`, the objective of the feasibility relaxation is to minimize the weighted count of bound and constraint violations. The `lbpen`, `ubpen`, and `rhspen` arguments specify the cost of violating a lower bound, upper bound, and linear constraint, respectively.

    To give an example, if a constraint with `rhspen` value `p` is violated by 2.0, it would contribute `2*p` to the feasibility relaxation objective for `relaxobjtype=0`, it would contribute `2*2*p` for `relaxobjtype=1`, and it would contribute `p` for `relaxobjtype=2`.

    The `minrelax` argument is a boolean that controls the type of feasibility relaxation that is created. If `minrelax=false`, optimizing the returned model gives a solution that minimizes the cost of the violation. If `minrelax=true`, optimizing the returned model finds a solution that minimizes the original objective, but only from among those solutions that minimize the cost of the violation. Note that `feasRelax` must solve an optimization problem to find the minimum possible relaxation when `minrelax=true`, which can be quite expensive.

    There are two signatures for this method. The more complex one takes a list of variables and constraints, as well as penalties associated with relaxing the corresponding lower bounds, upper bounds, and constraints. If a variable or constraint is not included in one of these lists, the associated bounds or constraints may not be violated. The simpler signature takes a pair of boolean arguments, `vrelax` and `crelax`, that indicate whether variable bounds and/or constraints can be violated. If `vrelax`/`crelax` is `true`, then every bound/constraint is allowed to be violated, respectively, and the associated cost is 1.0.

    For an example of how this routine transforms a model, and more details about the variables and constraints created, please see this section.

    Note that this is a destructive method: it modifies the model on which it is invoked. If you don’t want to modify your original model, use the `GRBModel constructor` to create a copy before invoking this method.

    Create a feasibility relaxation model.

    Arguments:

      - relaxobjtype – The cost function used when finding the minimum cost relaxation.

      - minrelax – The type of feasibility relaxation to perform.

      - vars – Variables whose bounds are allowed to be violated.

      - lbpen – Penalty for violating a variable lower bound. One entry for each variable in argument `vars`.

      - ubpen – Penalty for violating a variable upper bound. One entry for each variable in argument `vars`.

      - constrs – Linear constraints that are allowed to be violated.

      - rhspen – Penalty for violating a linear constraint. One entry for each constraint in argument `constrs`.

    Return value:

      Zero if `minrelax` is false. If `minrelax` is true, the return value is the objective value for the relaxation performed. If the value is less than 0, it indicates that the method failed to create the feasibility relaxation.

    Example:

          // Compute feasibility relaxation for infeasible model
          if (model.get(GRB.IntAttr.Status) == GRB.Status.INFEASIBLE) {
             GRBVar[] vars = model.getVars();
             double[] ubpen = new double[model.get(GRB.IntAttr.NumVars)];
             for (int i = 0; i < model.get(GRB.IntAttr.NumVars); i++) {
                ubpen[i] = 1.0;
             }
             model.feasRelax(0, false, vars, null, ubpen, null, null);
             model.optimize();
          }

  double feasRelax(int relaxobjtype, boolean minrelax, boolean vrelax, boolean crelax)

    Modifies the `GRBModel` object to create a feasibility relaxation. Note that you need to call `optimize` on the result to compute the actual relaxed solution.

    The feasibility relaxation is a model that, when solved, minimizes the amount by which the solution violates the bounds and linear constraints of the original model. This method provides a number of options for specifying the relaxation.

    If you specify `relaxobjtype=0`, the objective of the feasibility relaxation is to minimize the sum of the weighted magnitudes of the bound and constraint violations. The `lbpen`, `ubpen`, and `rhspen` arguments specify the cost per unit violation in the lower bounds, upper bounds, and linear constraints, respectively.

    If you specify `relaxobjtype=1`, the objective of the feasibility relaxation is to minimize the weighted sum of the squares of the bound and constraint violations. The `lbpen`, `ubpen`, and `rhspen` arguments specify the coefficients on the squares of the lower bound, upper bound, and linear constraint violations, respectively.

    If you specify `relaxobjtype=2`, the objective of the feasibility relaxation is to minimize the weighted count of bound and constraint violations. The `lbpen`, `ubpen`, and `rhspen` arguments specify the cost of violating a lower bound, upper bound, and linear constraint, respectively.

    To give an example, if a constraint with `rhspen` value `p` is violated by 2.0, it would contribute `2*p` to the feasibility relaxation objective for `relaxobjtype=0`, it would contribute `2*2*p` for `relaxobjtype=1`, and it would contribute `p` for `relaxobjtype=2`.

    The `minrelax` argument is a boolean that controls the type of feasibility relaxation that is created. If `minrelax=false`, optimizing the returned model gives a solution that minimizes the cost of the violation. If `minrelax=true`, optimizing the returned model finds a solution that minimizes the original objective, but only from among those solutions that minimize the cost of the violation. Note that `feasRelax` must solve an optimization problem to find the minimum possible relaxation when `minrelax=true`, which can be quite expensive.

    There are two signatures for this method. The more complex one takes a list of variables and constraints, as well as penalties associated with relaxing the corresponding lower bounds, upper bounds, and constraints. If a variable or constraint is not included in one of these lists, the associated bounds or constraints may not be violated. The simpler signature takes a pair of boolean arguments, `vrelax` and `crelax`, that indicate whether variable bounds and/or constraints can be violated. If `vrelax`/`crelax` is `true`, then every bound/constraint is allowed to be violated, respectively, and the associated cost is 1.0.

    For an example of how this routine transforms a model, and more details about the variables and constraints created, please see this section.

    Note that this is a destructive method: it modifies the model on which it is invoked. If you don’t want to modify your original model, use the `GRBModel constructor` to create a copy before invoking this method.

    Simplified method for creating a feasibility relaxation model.

    Arguments:

      - relaxobjtype – The cost function used when finding the minimum cost relaxation.

      - minrelax – The type of feasibility relaxation to perform.

      - vrelax – Indicates whether variable bounds can be relaxed (with a cost of 1.0 for any violations.

      - crelax – Indicates whether linear constraints can be relaxed (with a cost of 1.0 for any violations.

    Return value:

      Zero if `minrelax` is false. If `minrelax` is true, the return value is the objective value for the relaxation performed. If the value is less than 0, it indicates that the method failed to create the feasibility relaxation.

    Example:

          // Compute feasibility relaxation for infeasible model
          if (model.get(GRB.IntAttr.Status) == GRB.Status.INFEASIBLE) {
             model.feasRelax(1, false, false, true);
             model.optimize();
          }

  GRBModel fixedModel()

    Create the fixed model associated with a MIP model. A solution (e.g. obtained through a call to the `optimize` method) or a MIP start must be available in the MIP model. If no solution is available, the MIP start specified with StartNumber is used.

    In the model, each integer variable is fixed to the value that variable takes in the MIP solution or MIP start. In addition, continuous variables may be fixed to satisfy SOS or general constraints. The result is that the model has neither integrality constraints, SOS constraints, nor general constraints any more.

    Note

    While the fixed problem is always a continuous model, it may contain a non-convex quadratic objective or non-convex quadratic constraints. As a result, it may still be solved using the MIP algorithm.

    Note

    On a multi-objective model, all but the first objectives are ignored. All scenarios are ignored as well, if any.

    Return value:

      Fixed model associated with calling object.

    Example:

          GRBModel fixedModel = model.fixedModel();

  double get(GRB.DoubleParam param)
  int get(GRB.IntParam param)
  String get(GRB.StringParam param)

    Query the value of a double, integer, or string-valued parameter.

    Arguments:

      param – The parameter being queried.

    Return value:

      The current value of the requested parameter.

    Example:

          // Get value of TimeLimit parameter
          double timelimit = model.get(GRB.DoubleParam.TimeLimit);
          // Get value of PumpPasses parameter
          int pumppasses = model.get(GRB.IntParam.PumpPasses);
          // Get value of LogFile parameter
          String logfile = model.get(GRB.StringParam.LogFile);

  double get(GRB.DoubleAttr attr)
  int get(GRB.IntAttr attr)
  String get(GRB.StringAttr attr)

    Query the value of a double, integer, or string-valued model attribute.

    Arguments:

      attr – The attribute being queried.

    Return value:

      The current value of the requested attribute.

    Example:

          // Get value ObjVal attribute
          double val = model.get(GRB.DoubleAttr.ObjVal);
          // Get number of variables in the model
          int numvars = model.get(GRB.IntAttr.NumVars);
          // Get ModelName attribute
          String modelname = model.get(GRB.StringAttr.ModelName);

  char[] get(GRB.CharAttr attr, GRBVar[] objs)
  char[] get(GRB.CharAttr attr, GRBConstr[] objs)
  char[] get(GRB.CharAttr attr, GRBQConstr[] objs)
  double[] get(GRB.DoubleAttr attr, GRBVar[] objs)
  double[] get(GRB.DoubleAttr attr, GRBConstr[] objs)
  double[] get(GRB.DoubleAttr attr, GRBQConstr[] objs)
  double[] get(GRB.DoubleAttr attr, GRBGenConstr[] objs)
  int[] get(GRB.IntAttr attr, GRBVar[] objs)
  int[] get(GRB.IntAttr attr, GRBConstr[] objs)
  int[] get(GRB.IntAttr attr, GRBQConstr[] objs)
  int[] get(GRB.IntAttr attr, GRBGenConstr[] objs)
  String[] get(GRB.StringAttr attr, GRBVar[] objs)
  String[] get(GRB.StringAttr attr, GRBConstr[] objs)
  String[] get(GRB.StringAttr attr, GRBQConstr[] objs)
  String[] get(GRB.StringAttr attr, GRBGenConstr[] objs)

    Query an attribute for an array of modeling objects.

    Arguments:

      - attr – The attribute being queried.

      - objs – The modeling objects, i.e., variables or constraints, whose attribute values are being queried.

    Return value:

      The current values of the requested attribute for each input object.

    Example:

          // Get sense attribute values for all linear constraints
          GRBConstr[] constrs = model.getConstrs();
          char[] sense = model.get(GRB.CharAttr.Sense, constrs);
          // Get lower bound attribute values of all variables
          GRBVar[] vars = model.getVars();
          double[] lb = model.get(GRB.DoubleAttr.LB, vars);
          // Get IISQConstr attribute values of all quadratic constraints
          GRBQConstr[] qconstrs = model.getQConstrs();
          int[] iisconstrs = model.get(GRB.IntAttr.IISQConstr, qconstrs);
          // Get constraint name values of all quadratic constraints
          String[] qconstrnames = model.get(GRB.StringAttr.QCName, qconstrs);

  char[] get(GRB.CharAttr attr, GRBVar[] objs, int start, int len)
  char[] get(GRB.CharAttr attr, GRBConstr[] objs, int start, int len)
  char[] get(GRB.CharAttr attr, GRBQConstr[] objs, int start, int len)
  double[] get(GRB.DoubleAttr attr, GRBVar[] objs, int start, int len)
  double[] get(GRB.DoubleAttr attr, GRBConstr[] objs, int start, int len)
  double[] get(GRB.DoubleAttr attr, GRBQConstr[] objs, int start, int len)
  double[] get(GRB.DoubleAttr attr, GRBGenConstr[] objs, int start, int len)
  int[] get(GRB.IntAttr attr, GRBVar[] objs, int start, int len)
  int[] get(GRB.IntAttr attr, GRBConstr[] objs, int start, int len)
  int[] get(GRB.IntAttr attr, GRBQConstr[] objs, int start, int len)
  int[] get(GRB.IntAttr attr, GRBGenConstr[] objs, int start, int len)
  String[] get(GRB.StringAttr attr, GRBVar[] objs, int start, int len)
  String[] get(GRB.StringAttr attr, GRBConstr[] objs, int start, int len)
  String[] get(GRB.StringAttr attr, GRBQConstr[] objs, int start, int len)
  String[] get(GRB.StringAttr attr, GRBGenConstr[] objs, int start, int len)

    Query an attribute for a sub-array of modeling objects.

    Arguments:

      - attr – The attribute being queried.

      - objs – A one-dimensional array of modeling objects, i.e., variables or constraints, whose attribute values are being queried.

      - start – The index of the first object of interest in the list.

      - len – The number of objects.

    Return value:

      The current values of the requested attribute for each input object.

    Example:

          GRBVar[] vars = model.getVars();
          // Get variable type attribute values for variables indexed from 0 to 2
          char[] vtype = model.get(GRB.CharAttr.VType, vars, 0, 3);
          // Get lower bound attribute values for variables indexed from 0 to 2
          double[] lb = model.get(GRB.DoubleAttr.LB, vars, 0, 3);
          // Get GenConstrType attribute values for general constraints indexed from 0 to 2
          GRBGenConstr[] genconstrs = model.getGenConstrs();
          int[] types = model.get(GRB.IntAttr.GenConstrType, genconstrs, 0, 3);
          // Get general constraint name attribute values for constraints indexed from 0 to 2
          String[] genconstrnames = model.get(GRB.StringAttr.GenConstrName, genconstrs, 0, 3);

  char[][] get(GRB.CharAttr attr, GRBVar[][] objs)
  char[][] get(GRB.CharAttr attr, GRBConstr[][] objs)
  char[][] get(GRB.CharAttr attr, GRBQConstr[][] objs)
  double[][] get(GRB.DoubleAttr attr, GRBVar[][] objs)
  double[][] get(GRB.DoubleAttr attr, GRBConstr[][] objs)
  double[][] get(GRB.DoubleAttr attr, GRBQConstr[][] objs)
  double[][] get(GRB.DoubleAttr attr, GRBGenConstr[][] objs)
  int[][] get(GRB.IntAttr attr, GRBVar[][] objs)
  int[][] get(GRB.IntAttr attr, GRBConstr[][] objs)
  int[][] get(GRB.IntAttr attr, GRBQConstr[][] objs)
  int[][] get(GRB.IntAttr attr, GRBGenConstr[][] objs)
  String[][] get(GRB.StringAttr attr, GRBVar[][] objs)
  String[][] get(GRB.StringAttr attr, GRBConstr[][] objs)
  String[][] get(GRB.StringAttr attr, GRBQConstr[][] objs)
  String[][] get(GRB.StringAttr attr, GRBGenConstr[][] objs)

    Query a attribute for a two-dimensional array of modeling objects.

    Arguments:

      - attr – The attribute being queried.

      - objs – A two-dimensional array of modeling objects, i.e., variables or constraints, whose attribute values are being queried.

    Return value:

      The current values of the requested attribute for each input object.

    Example:

          GRBVar[][] vars = new GRBVar[10][10];
          // ...
          // Get variable type attribute values a 2d array of variables
          char[][] vtype = model.get(GRB.CharAttr.VType, vars);
          // Get lower bound attribute values for a 2d array of variables
          double[][] lb = model.get(GRB.DoubleAttr.LB, vars);
          // Get vbasis attribute values for a 2d array of variables
          int[][] vbasis = model.get(GRB.IntAttr.VBasis, vars);
          // Get variable names for a 2d array of variables
          String[][] varnames = model.get(GRB.StringAttr.VarName, vars);

  char[][][] get(GRB.CharAttr attr, GRBVar[][][] objs)
  char[][][] get(GRB.CharAttr attr, GRBConstr[][][] objs)
  char[][][] get(GRB.CharAttr attr, GRBQConstr[][][] objs)
  double[][][] get(GRB.DoubleAttr attr, GRBVar[][][] objs)
  double[][][] get(GRB.DoubleAttr attr, GRBConstr[][][] objs)
  double[][][] get(GRB.DoubleAttr attr, GRBQConstr[][][] objs)
  double[][][] get(GRB.DoubleAttr attr, GRBGenConstr[][][] objs)
  int[][][] get(GRB.IntAttr attr, GRBVar[][][] objs)
  int[][][] get(GRB.IntAttr attr, GRBConstr[][][] objs)
  int[][][] get(GRB.IntAttr attr, GRBQConstr[][][] objs)
  int[][][] get(GRB.IntAttr attr, GRBGenConstr[][][] objs)
  String[][][] get(GRB.StringAttr attr, GRBVar[][][] objs)
  String[][][] get(GRB.StringAttr attr, GRBConstr[][][] objs)
  String[][][] get(GRB.StringAttr attr, GRBQConstr[][][] objs)
  String[][][] get(GRB.StringAttr attr, GRBGenConstr[][][] objs)

    Query a attribute for a three-dimensional array of modeling objects.

    Arguments:

      - attr – The attribute being queried.

      - objs – A three-dimensional array of modeling objects, i.e., variables or constraints, whose attribute values are being queried.

    Return value:

      The current values of the requested attribute for each input object.

    Example:

          GRBConstr[][][] constrs = new GRBConstr[10][10][10];
          // ...
          // Get sense attribute values for a 3d array of linear constraints
          char[][][] sense = model.get(GRB.CharAttr.Sense, constrs);
          // Get RHS attribute values for a 3d array of constraints
          double[][][] rhs = model.get(GRB.DoubleAttr.RHS, constrs);
          // Get CBasis attribute values for a 3d array of constraints
          int[][][] cbasis = model.get(GRB.IntAttr.CBasis, constrs);
          // Get constraint name attribute values
          String[][][] constrnames = model.get(GRB.StringAttr.ConstrName, constrs);

  double getCoeff(GRBConstr constr, GRBVar var)

    Query the coefficient of variable `var` in linear constraint `constr` (note that the result can be zero).

    Arguments:

      - constr – The requested constraint.

      - var – The requested variable.

    Return value:

      The current value of the requested coefficient.

    Example:

          // Get coefficient of variable x in constraint c1
          double coeff = model.getCoeff(c1, x);

  GRBColumn getCol(GRBVar var)

    Retrieve the list of constraints in which a variable participates, and the associated coefficients. The result is returned as a `GRBColumn` object.

    Arguments:

      var – The variable of interest.

    Return value:

      A `GRBColumn` object that captures the set of constraints in which the variable participates.

    Example:

          // Get column of coefficient and constraint pairs for variable x
          GRBColumn col = model.getCol(x)

  GRBEnv getConcurrentEnv(int num)

    Create/retrieve a concurrent environment for a model.

    This method provides fine-grained control over the concurrent optimizer. By creating your own concurrent environments and setting appropriate parameters on these environments (e.g., the Method parameter), you can control exactly which strategies the concurrent optimizer employs. For example, if you create two concurrent environments, and set Method to primal simplex for one and dual simplex for the other, subsequent concurrent optimizer runs will use the two simplex algorithms rather than the default choices.

    Note that you must create contiguously numbered concurrent environments, starting with `num=0`. For example, if you want three concurrent environments, they must be numbered 0, 1, and 2.

    Once you create concurrent environments, they will be used for every subsequent concurrent optimization on that model. Use `discardConcurrentEnvs` to revert back to default concurrent optimizer behavior.

    Arguments:

      num – The concurrent environment number.

    Return value:

      The concurrent environment for the model.

    Example:

          GRBEnv env0 = model.getConcurrentEnv(0);
          GRBEnv env1 = model.getConcurrentEnv(1);

          env0.set(GRB.IntParam.Method, 0); // primal simplex
          env1.set(GRB.IntParam.Method, 1); // dual simplex

          model.optimize();
          model.discardConcurrentEnvs();

  GRBConstr getConstrByName(String name)

    Retrieve a linear constraint from its name. If multiple linear constraints have the same name, this method chooses one arbitrarily. Returns null if no constraint has that name.

    Arguments:

      name – The name of the desired linear constraint.

    Return value:

      The requested linear constraint.

    Example:

          // Retrieve linear constraint named "constr1"
          GRBConstr c1 = model.getConstrByName("constr1");

    Note

    Retrieving constraint objects by name is not recommended in general. When adding constraints to a model, you should keep track of the returned objects in your own data structures in order to retrieve them efficiently for model building and extracting attribute values.

  GRBConstr[] getConstrs()

    Retrieve an array of all linear constraints in the model.

    Return value:

      All linear constraints in the model.

    Example:

          // Retrieve all linear constraints
          GRBConstr[] constrs = model.getConstrs();

  void getGenConstrMax(GRBGenConstr genc, GRBVar[] resvar, GRBVar[] vars, int[] len, double[] constant)

    Retrieve the data associated with a general constraint of type MAX. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `vars` argument. The routine returns the total number of operand variables in the specified general constraint in `len`. That allows you to make certain that the `vars` array is of sufficient size to hold the result of the second call.

    See also `addGenConstrMax` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Store the resultant variable of the constraint at `resvar[0]`.

      - vars – Array to store the operand variables of the constraint.

      - len – Store the number of operand variables of the constraint at `len[0]`.

      - constant – Store the additional constant operand of the constraint at `constant[0]`.

    Example:

          GRBVar[] resvar = new GRBVar[1];
          GRBVar[] vars;
          int[]    len = new int[1];
          double[] constant = new double[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_MAX) {
            model.getGenConstrMax(genc, resvar, null, len, null);
            // Initialize correct array size
            vars = new GRBVar[len[0]];
            model.getGenConstrMax(genc, resvar, vars, len, constant);
          }

  void getGenConstrMin(GRBGenConstr genc, GRBVar[] resvar, GRBVar[] vars, int[] len, double[] constant)

    Retrieve the data associated with a general constraint of type MIN. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `vars` argument. The routine returns the total number of operand variables in the specified general constraint in `len`. That allows you to make certain that the `vars` array is of sufficient size to hold the result of the second call.

    See also `addGenConstrMin` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Store the resultant variable of the constraint at `resvar[0]`.

      - vars – Array to store the operand variables of the constraint.

      - len – Store the number of operand variables of the constraint at `len[0]`.

      - constant – Store the additional constant operand of the constraint at `constant[0]`.

    Example:

          GRBVar[] resvar = new GRBVar[1];
          GRBVar[] vars;
          int[]    len = new int[1];
          double[] constant = new double[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_MIN) {
            model.getGenConstrMin(genc, resvar, null, len, null);
            // Initialize correct array size
            vars = new GRBVar[len[0]];
            model.getGenConstrMin(genc, resvar, vars, len, constant);
          }

  void getGenConstrAbs(GRBGenConstr genc, GRBVar[] resvar, GRBVar[] argvar)

    Retrieve the data associated with a general constraint of type ABS. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrAbs` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Store the resultant variable of the constraint at `resvar[0]`.

      - argvar – Store the argument variable of the constraint at `argvar[0]`.

    Example:

          GRBVar[] resvar = new GRBVar[1];
          GRBVar[] argvar = new GRBVar[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_ABS) {
            model.getGenConstrAbs(genc, resvar, argvar);
          }

  void getGenConstrAnd(GRBGenConstr genc, GRBVar[] resvar, GRBVar[] vars, int[] len)

    Retrieve the data associated with a general constraint of type AND. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `vars` argument. The routine returns the total number of operand variables in the specified general constraint in `len`. That allows you to make certain that the `vars` array is of sufficient size to hold the result of the second call.

    See also `addGenConstrAnd` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Store the resultant variable of the constraint at `resvar[0]`.

      - vars – Array to store the operand variables of the constraint.

      - len – Store the number of operand variables of the constraint at `len[0]`.

    Example:

          GRBVar[] resvar = new GRBVar[1];
          GRBVar[] vars;
          int[]    len = new int[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_AND) {
            model.getGenConstrAnd(genc, resvar, null, len);
            // Initialize correct array size
            vars = new GRBVar[len[0]];
            model.getGenConstrAnd(genc, resvar, vars, len);
          }

  void getGenConstrOr(GRBGenConstr genc, GRBVar[] resvar, GRBVar[] vars, int[] len)

    Retrieve the data associated with a general constraint of type OR. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `vars` argument. The routine returns the total number of operand variables in the specified general constraint in `len`. That allows you to make certain that the `vars` array is of sufficient size to hold the result of the second call.

    See also `addGenConstrOr` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Store the resultant variable of the constraint at `resvar[0]`.

      - vars – Array to store the operand variables of the constraint.

      - len – Store the number of operand variables of the constraint at `len[0]`.

    Example:

          GRBVar[] resvar = new GRBVar[1];
          GRBVar[] vars;
          int[]    len = new int[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_OR) {
            model.getGenConstrOr(genc, resvar, null, len);
            // Initialize correct array size
            vars = new GRBVar[len[0]];
            model.getGenConstrOr(genc, resvar, vars, len);
          }

  void getGenConstrNorm(GRBGenConstr genc, GRBVar[] resvar, GRBVar[] vars, int[] len, double[] which)

    Retrieve the data associated with a general constraint of type NORM. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `vars` argument. The routine returns the total number of operand variables in the specified general constraint in `len`. That allows you to make certain that the `vars` array is of sufficient size to hold the result of the second call.

    See also `addGenConstrNorm` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Store the resultant variable of the constraint at `resvar[0]`.

      - vars – Array to store the operand variables of the constraint.

      - len – Store the number of operand variables of the constraint at `len[0]`.

      - which – Store the norm type (possible values are 0, 1, 2, or GRB.INFINITY).

    Example:

          GRBVar[] resvar = new GRBVar[1];
          GRBVar[] vars;
          int[]    len = new int[1];
          double[] which = new double[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_NORM) {
            model.getGenConstrNorm(genc, resvar, null, len, null);
            // Initialize correct array size
            vars = new GRBVar[len[0]];
            model.getGenConstrNorm(genc, resvar, vars, len, which);
          }

  void getGenConstrNL(GRBGenConstr genc, GRBVar[] resvar, int[] nnodes, int[] opcode, double[] data, int[] parent)

    Retrieve the data associated with a general constraint of type NL. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `opcode`, `data` and `parent` arguments. The routine returns the total number of nodes that form the expression tree for the specified general constraint in `nnodes`. That allows you to make certain that the `opcode`, `data` and `parent` arrays are of sufficient size to hold the result of the second call.

    See also `addGenConstrNL` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - resvar – Stores the resultant variable of the constraint at `resvar[0]`.

      - nnodes – Stores the number of nodes required to describe the nonlinear expression at `nnodes[0]`.

      - opcode – Stores the array containing the operation codes for the nodes.

      - data – Stores the array containing auxiliary data for each node.

      - parent – Stores the array providing the parent index of each node.

    Example:

        GRBVar   resvar = new GRBVar[1];
        int      nnodes = new int[1];
        int[]    opcode;
        double[] data;
        int[]    parent;
        // Retrieve general constraint type
        int type = genc.get(GRB.IntAttr.GenConstrType);

        if (type == GRB.GENCONSTR_NL) {
          // Get number of nodes
          model.getGenConstrNL(genc, resvar, nnodes, null, null, null);
          // Initialize correct array sizes
          opcode = new int[nnodes[0]];
          data   = new double[nnodes[0]];
          parent = new int[nnodes[0]];
          // Get array data
          model.getGenConstrNL(genc, resvar, nnodes, opcode, data, parent);
        }

  void getGenConstrIndicator(GRBGenConstr genc, GRBVar[] binvar, int[] binval, GRBLinExpr[] expr, char[] sense, double[] rhs)

    Retrieve the data associated with a general constraint of type INDICATOR. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrIndicator` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - binvar – Store the binary indicator variable of the constraint at `binvar[0]`.

      - binval – Store the value that the indicator variable has to take in order to trigger the linear constraint at `binval[0]`.

      - expr – Create a `GRBLinExpr` object to store the left-hand side expression of the linear constraint that is triggered by the indicator at `expr[0]`.

      - sense – Store the sense for the linear constraint at `sense[0]`. Options are `GRB.LESS_EQUAL`, `GRB.EQUAL`, or `GRB.GREATER_EQUAL`.

      - rhs – Store the right-hand side value for the linear constraint at `rhs[0]`.

    Example:

          GRBVar[]     binvar = new GRBVar[1];
          int[]        binval = new int[1];
          GRBLinExpr[] expr   = new GRBLinExpr[1];
          char[]       sense  = new char[1];
          double[]     rhs    = new double[1];
          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_INDICATOR) {
            model.getGenConstrIndicator(genc, binvar, binval, expr, sense, rhs);
          }

  void getGenConstrPWL(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar, int[] npts, double[] xpts, double[] ypts)

    Retrieve the data associated with a general constraint of type PWL. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `xpts` and `ypts` arguments. The routine returns the length for the `xpts` and `ypts` arrays in `npts`. That allows you to make certain that the `xpts` and `ypts` arrays are of sufficient size to hold the result of the second call.

    See also `addGenConstrPWL` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

      - npts – Store the number of points that define the piecewise-linear function.

      - xpts – The \(x\) values for the points that define the piecewise-linear function.

      - ypts – The \(y\) values for the points that define the piecewise-linear function.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];
          int[]    npts = new int[1];
          double[] xpts, ypts;

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_PWL) {
            model.getGenConstrPWL(genc, xvar, yvar, npts, null, null);
            // Initialize correct array sizes
            xpts = new double[npts[0]];
            ypts = new double[npts[0]];
            model.getGenConstrPWL(genc, xvar, yvar, npts, xpts, ypts);
          }

  void getGenConstrPoly(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar, int[] plen, double[] p)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type POLY. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    Typical usage is to call this routine twice. In the first call, you specify the requested general constraint, with a `null` value for the `p` argument. The routine returns the length of the `p` array in `plen`. That allows you to make certain that the `p` array is of sufficient size to hold the result of the second call.

    See also `addGenConstrPoly` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

      - plen – Store the array length for p. If \(x^d\) is the highest power term, then \(d+1\) will be returned.

      - p – The coefficients for polynomial function.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];
          int[]    plen = new int[1];
          double[] p;

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_POLY) {
            model.getGenConstrPoly(genc, xvar, yvar, plen, null);
            // Initialize correct array sizes
            p = new double[plen[0]];
            model.getGenConstrPoly(genc, xvar, yvar, plen, p);
          }

  void getGenConstrExp(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type EXP. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrExp` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_EXP) {
            model.getGenConstrExp(genc, xvar, yvar);
          }

  void getGenConstrExpA(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar, double[] a)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type EXPA. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrExpA` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

      - a – Store the base of the function.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];
          double[] a    = new double[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_EXPA) {
            model.getGenConstrExpA(genc, xvar, yvar, a);
          }

  void getGenConstrLog(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type LOG. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrLog` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_LOG) {
            model.getGenConstrLog(genc, xvar, yvar);
          }

  void getGenConstrLogA(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar, double[] a)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type LOGA. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrLogA` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

      - a – Store the base of the function.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];
          double[] a    = new double[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_LOGA) {
            model.getGenConstrLogA(genc, xvar, yvar, a);
          }

  void getGenConstrLogistic(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type LOGISTIC. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrLogistic` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_LOGISTIC) {
            model.getGenConstrLogistic(genc, xvar, yvar);
          }

  void getGenConstrPow(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar, double[] a)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type POW. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrPow` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

      - a – Store the exponent of the function.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];
          double[] a    = new double[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_POW) {
            model.getGenConstrPow(genc, xvar, yvar, a);
          }

  void getGenConstrSin(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type SIN. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrSin` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_SIN) {
            model.getGenConstrSin(genc, xvar, yvar);
          }

  void getGenConstrCos(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type COS. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrCos` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_COS) {
            model.getGenConstrCos(genc, xvar, yvar);
          }

  void getGenConstrTan(GRBGenConstr genc, GRBVar[] xvar, GRBVar[] yvar)

    Deprecated since version 13.0: Using function constraints is deprecated since version 13.0. Please use nonlinear constraints instead.

    Retrieve the data associated with a general constraint of type TAN. Calling this method for a general constraint of a different type leads to an exception. You can query the GenConstrType attribute to determine the type of the general constraint.

    See also `addGenConstrTan` for a description of the semantics of this general constraint type.

    Any of the following arguments can be `null`.

    Arguments:

      - genc – The general constraint object.

      - xvar – Store the \(x\) variable.

      - yvar – Store the \(y\) variable.

    Example:

          GRBVar[] xvar = new GRBVar[1];
          GRBVar[] yvar = new GRBVar[1];

          // Retrieve general constraint type
          int type = genc.get(GRB.IntAttr.GenConstrType);

          if (type == GRB.GENCONSTR_TAN) {
            model.getGenConstrTan(genc, xvar, yvar);
          }

  GRBGenConstr[] getGenConstrs()

    Retrieve an array of all general constraints in the model.

    Return value:

      All general constraints in the model.

    Example:

          GRBGenConstr[] constrs = model.getGenConstrs();

  String getJSONSolution()

    After a call to `optimize`, this method returns the resulting solution and related model attributes as a JSON string. Please refer to the JSON solution format section for details.

    Return value:

      A JSON string.

    Example:

          String solution = model.getJSONSolution();

  GRBEnv getMultiobjEnv(int index)

    Create/retrieve a multi-objective environment for the optimization pass with the given index. This environment enables fine-grained control over the multi-objective optimization process. Specifically, by changing parameters on this environment, you modify the behavior of the optimization that occurs during the corresponding pass of the multi-objective optimization.

    Each multi-objective environment starts with a copy of the current model environment.

    Please refer to the discussion of Multiple Objectives for information on how to specify multiple objective functions and control the trade-off between them.

    Please refer to the discussion on Combining Blended and Hierarchical Objectives for information on the optimization passes to solve multi-objective models.

    Use `discardMultiobjEnvs` to discard multi-objective environments and return to standard behavior.

    Arguments:

      index – The optimization pass index, starting from 0.

    Return value:

      The multi-objective environment for that optimization pass when solving the model.

    Example:

          GRBEnv env0 = model.getMultiobjEnv(0);
          GRBEnv env1 = model.getMultiobjEnv(1);

          env0.set(GRB.IntParam.Method, 0);
          env1.set(GRB.IntParam.Method, 1);

          model.optimize();
          model.discardMultiobjEnvs();

  GRBExpr getObjective()

    Retrieve the optimization objective.

    Note that the constant and linear portions of the objective can also be retrieved using the ObjCon and Obj attributes.

    Return value:

      The model objective.

    Example:

          GRBExpr obj = model.getObjective();
          double objVal = obj.getValue();

  GRBLinExpr getObjective(int index)

    Retrieve an alternative optimization objective. Alternative objectives will always be linear. You can also use this routine to retrieve the primary objective (using `index` = 0), but you will get an exception if the primary objective contains quadratic terms.

    Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

    Note that alternative objectives can also be retrieved using the ObjNCon and ObjN attributes.

    Arguments:

      index – The index for the requested alternative objective.

    Return value:

      The requested alternative objective.

    Example:

          // Get objective with index 1
          GRBLinExpr obj = model.getObjective(1);
          double objVal = obj.getValue();

  int getPWLObj(GRBVar var, double[] x, double[] y)

    Retrieve the piecewise-linear objective function for a variable. The return value gives the number of points that define the function, and the \(x\) and \(y\) arguments give the coordinates of the points, respectively. The \(x\) and \(y\) arguments must be large enough to hold the result. Call this method with `null` values for \(x\) and \(y\) if you just want the number of points.

    Refer to this discussion for additional information on what the values in \(x\) and \(y\) mean.

    Arguments:

      - var – The variable whose objective function is being retrieved.

      - x – The \(x\) values for the points that define the piecewise-linear function. These will always be in non-decreasing order.

      - y – The \(y\) values for the points that define the piecewise-linear function.

    Return value:

      The number of points that define the piecewise-linear objective function.

    Example:

          int npts = model.getPWLObj(var, null, null);
          double[] x = new double[npts];
          double[] y = new double[npts];
          // Get PWL objective
          model.getPWLObj(var, x, y);

  GRBQuadExpr getQCRow(GRBQConstr qc)

    Retrieve the left-hand side expression from a quadratic constraint. The result is returned as a `GRBQuadExpr` object.

    Arguments:

      qc – The quadratic constraint of interest.

    Return value:

      A `GRBQuadExpr` object that captures the left-hand side of the quadratic constraint.

    Example:

          GRBQConstr[] constrs = model.getQConstrs();
          // Get left-hand side of first quadratic constraint
          GRBQuadExpr qexpr = model.getQCRow(constrs[0]);

  GRBQConstr[] getQConstrs()

    Retrieve an array of all quadratic constraints in the model.

    Return value:

      All quadratic constraints in the model.

    Example:

          // Get all quadratic constraints
          GRBQConstr[] constrs = model.getQConstrs();

  GRBLinExpr getRow(GRBConstr constr)

    Retrieve a list of variables that participate in a constraint, and the associated coefficients. The result is returned as a `GRBLinExpr` object.

    Arguments:

      constr – The constraint of interest. A `GRBConstr` object, typically obtained from `addConstr` or `getConstrs`.

    Return value:

      A `GRBLinExpr` object that captures the set of variables that participate in the constraint.

    Example:

          GRBConstr[] constrs = model.getConstrs();
          // Get left-hand side of first linear constraint
          GRBLinExpr lexpr = model.getRow(constrs[0]);

  int getSOS(GRBSOS sos, GRBVar[] vars, double[] weights, int[] type)

    Retrieve the list of variables that participate in an SOS constraint, and the associated coefficients. The return value is the length of this list. Note that the argument arrays must be long enough to accommodate the result. Call the method with `null` array arguments to determine the appropriate array lengths.

    Arguments:

      - sos – The SOS set of interest.

      - vars – A list of variables that participate in `sos`. Can be `null`.

      - weights – The SOS weights for each participating variable. Can be `null`.

      - type – The type of the SOS set (either `GRB.SOS_TYPE1` or `GRB.SOS_TYPE2`) is returned in `type[0]`.

    Return value:

      The number of entries placed in the output arrays. Note that you should consult the return value to determine the length of the result; the arrays sizes won’t necessarily match the result size.

    Example:

          GRBSOS[] constrs = model.getSOSs();
          // Get variables in first SOS constraint
          int len = model.getSOS(constrs[0], null, null, null);
          GRBVar[] vars = new GRBVar[len];
          double[] weights = new double[len];
          int[] type = new int[len];
          model.getSOS(constrs[0], vars, weights, type);

  GRBSOS[] getSOSs()

    Retrieve an array of all SOS constraints in the model.

    Return value:

      All SOS constraints in the model.

    Example:

          // Get all SOS constraints
          GRBSOS[] constrs = model.getSOSs();

  void getTuneResult(int i)

    Use this method to retrieve the results of a previous `tune` call. Calling this method with argument `n` causes tuned parameter set `n` to be copied into the model. Parameter sets are stored in order of decreasing quality, with parameter set 0 being the best. The number of available sets is stored in attribute TuneResultCount.

    Once you have retrieved a tuning result, you can call `optimize` to use these parameter settings to optimize the model, or `write` to write the changed parameters to a `.prm` file.

    Please refer to the Parameter Tuning section in the Reference Manual for details on the tuning tool.

    Arguments:

      i – The index of the tuning result to retrieve. The best result is available as index 0. The number of stored results is available in attribute TuneResultCount.

    Example:

          model.tune();
          model.getTuneResult(0);

  GRBVar getVarByName(String name)

    Retrieve a variable from its name. If multiple variables have the same name, this method chooses one arbitrarily. Returns null if no variable has that name.

    Arguments:

      name – The name of the desired variable.

    Return value:

      The requested variable.

    Example:

          // Get variable with name "var1"
          GRBVar v = model.getVarByName("var1");

    Note

    Retrieving variable objects by name is not recommended in general. When adding variables to a model, you should keep track of the returned objects in your own data structures in order to retrieve them efficiently for model building and extracting attribute values.

  GRBVar[] getVars()

    Retrieve an array of all variables in the model.

    Return value:

      All variables in the model.

    Example:

          // Get all variables
          GRBVar[] vars = model.getVars();

  void optimize()

    Optimize a model. The algorithm used for the optimization depends on the model type (simplex or barrier for a continuous model; branch-and-cut for a MIP model). Upon successful completion, this method will populate the solution related attributes of the model. See the Attributes section for more information on attributes. The algorithm will terminate early if it reaches any of the limits set by termination parameters.

    Please consult this section in the Reference Manual for a discussion of some of the practical issues associated with solving a precisely defined mathematical model using finite-precision floating-point arithmetic.

    Note that this method will process all pending model modifications.

    Example:

          model.optimize();

  void optimizeasync()

    Optimize a model asynchronously. This method returns immediately. Your program can perform other computations while optimization proceeds in the background. To check the state of the asynchronous optimization, query the Status attribute for the model. A value of GRB.Status.INPROGRESS indicates that the optimization has not yet completed. When you are done with your foreground tasks, you must call `sync` to sync your foreground program with the asynchronous optimization task.

    Note that the set of Gurobi calls that you are allowed to make while optimization is running in the background is severely limited. Specifically, you can only perform attribute queries, and only for a few attributes (listed below). Any other calls on the running model, or on any other models that were built within the same Gurobi environment, will raise an exception with error code GRB.Error.OPTIMIZATION_IN_PROGRESS.

    Note that there are no such restrictions on models built in other environments. Thus, for example, you could create multiple environments, and then have a single foreground program launch multiple simultaneous asynchronous optimizations, each in its own environment.

    As already noted, you are allowed to query the value of the Status attribute while an asynchronous optimization is in progress. The other attributes that can be queried are: ObjVal, ObjBound, IterCount, NodeCount, BarIterCount, NLBarIterCount, and NLBarIterCount. In each case, the returned value reflects progress in the optimization to that point. Any attempt to query the value of an attribute not on this list will raise an exception with error code GRB.Error.OPTIMIZATION_IN_PROGRESS.

    Example:

          // Start asynchronous optimization
          model.optimizeasync();

          // Poll the status while optimization is in progress
          while (model.get(GRB.IntAttr.Status) == GRB.INPROGRESS) {
              Thread.sleep(100);  // 100 ms sleep
              System.out.println("ObjVal=" + model.get(GRB.DoubleAttr.ObjVal) +
                                 " ObjBound=" + model.get(GRB.DoubleAttr.ObjBound));
          }

          // Synchronize model
          model.sync();

          // Query solution and process results
          // ...

  String optimizeBatch()

    Submit a new batch request to the Cluster Manager. Returns the BatchID (a string), which uniquely identifies the job in the Cluster Manager and can be used to query the status of this request (from this program or from any other). Once the request has completed, the BatchID can also be used to retrieve the associated solution. To submit a batch request, you must tag at least one element of the model by setting one of the VTag, CTag or QCTag attributes. For more details on batch optimization, please refer to the Batch Optimization section.

    Note that this routine will process all pending model modifications.

    Return value:

      A unique string identifier for the batch request.

    Example:

          String batchID = model.optimizeBatch();

  GRBModel presolve()

    Perform presolve on a model.

    Please note that the presolved model computed by this function may be different from the presolved model computed when optimizing the model.

    Return value:

      Presolved version of original model.

    Example:

          GRBModel presolved = model.presolve();

  void read(String filename)

    This method is the general entry point for importing data from a file into a model. It can be used to read basis files for continuous models, start vectors for MIP models, variable hints for MIP models, branching priorities for MIP models, or parameter settings. The type of data read is determined by the file suffix. File formats are described in the File Format section.

    Note that reading a file does not process all pending model modifications. These modifications can be processed by calling `GRBModel.update`.

    Note also that this is not the method to use if you want to read a new model from a file. For that, use the `GRBModel constructor`. One variant of the constructor takes the name of the file that contains the new model as its argument.

    Arguments:

      - filename – Name of the file to read. The suffix on the file must be either `.bas` (for an LP basis), `.mst` or `.sol` (for a MIP start), `.hnt` (for MIP hints), `.ord` (for a priority order), `.attr` (for a collection of attribute settings), or `.prm` (for a parameter file). The suffix may optionally be followed by `.zip`, `.gz`, `.bz2`, `.7z` or `.xz`.

      - filename – Name of the file to read.

    Example:

          model.read("myModel.mps");

  void remove(GRBConstr obj)
  void remove(GRBGenConstr obj)
  void remove(GRBQConstr obj)
  void remove(GRBSOS obj)
  void remove(GRBVar obj)

    Remove a model object, i.e., variable or constraint, from the model. Note that, due to our lazy update approach, the change won’t actually take effect until you update the model (using `GRBModel.update`), optimize the model (using `GRBModel.optimize`), or write the model to disk (using `GRBModel.write`).

    Arguments:

      constr – The object to remove.

    Example:

          GRBConstr[] constrs = model.getConstrs();
          // Remove first linear constraint
          model.remove(constrs[0]);
          GRBVar[] vars = model.getVars();
          // Remove first variable
          model.remove(vars[0]);

  void reset()

    Reset the model to an unsolved state, discarding any previously computed solution information.

    Example:

          model.reset();

  void reset(int clearall)

    Reset the model to an unsolved state, discarding any previously computed solution information.

    Arguments:

      clearall – A value of 1 discards additional information that affects the solution process but not the actual model (currently MIP starts, variable hints, branching priorities, lazy flags, and partition information). Pass 0 to just discard the solution.

    Example:

          model.reset(1);

  void resetParams()

    Reset all parameters to their default values.

    Example:

          model.resetParams();

  void setCallback(GRBCallback cb)

    Set the callback object for a model. The `callback()` method on this object will be called periodically from the Gurobi solver. You will have the opportunity to obtain more detailed information about the state of the optimization from this callback. See the documentation for `GRBCallback` for additional information.

    Note that a model can only have a single callback method, so this call will replace an existing callback.

    Arguments:

      cb – New callback object. To disable a previously set callback, call this method with a `null` argument.

    Example:

          class MyCallback extends GRBCallback {
              // ...
          }

          MyCallback cb = new MyCallback();
          model.setCallback(cb);

  void setCallback(GRBCallback cb, int wheres)

    Set the callback object for a model. This function additionally allows for specifying for which `where` flags the callback should be invoked. The `callback()` method on this object will be called periodically from the Gurobi solver. You will have the opportunity to obtain more detailed information about the state of the optimization from this callback. See the documentation for `GRBCallback` for additional information.

    Note that a model can only have a single callback method, so this call will replace an existing callback.

    Arguments:

      - cb – New callback object. To disable a previously set callback, call this method with a `null` argument.

      - wheres – A bit vector defining for which `where` flags the callback should be invoked. It should contain value 1 in the n-th position if you want the callback to be invoked for the `where` flag of value n. See the possible values at Callback Codes, and the example below.

    Note

    Even if the `POLLING` bit is not set in the wheres bit vector, the callback may still be invoked with `where` set to that value.

    Example:

          class MyCallback extends GRBCallback {
              // ...
          }

          MyCallback cb = new MyCallback();
          // Call callback only if the ``where`` flag equals POLLING or MESSAGE
          model.setCallback(cb, (1<<GRB.CB.POLLING)|(1<<GRB.CB.MESSAGE));

  void set(GRB.DoubleParam param, double newval)
  void set(GRB.IntParam param, int newval)
  void set(GRB.StringParam param, String newval)

    Set the value of a double, int or string parameter.

    The difference between setting a parameter on a model and setting it on an environment (i.e., through `GRBEnv.set`) is that the former modifies the parameter for a single model, while the latter modifies the parameter for every model that is subsequently built using that environment (and leaves the parameter unchanged for models that were previously built using that environment).

    Arguments:

      - param – The parameter being modified.

      - newval – The desired new value for the parameter.

    Example:

          model.set(GRB.DoubleParam.TimeLimit, 10.0);
          model.set(GRB.IntParam.PumpPasses, 10);
          model.set(GRB.StringParam.LogFile, "myLog.log");

  void set(String param, String newval)

    Set the value of any parameter using strings alone.

    The difference between setting a parameter on a model and setting it on an environment (i.e., through `GRBEnv.set`) is that the former modifies the parameter for a single model, while the latter modifies the parameter for every model that is subsequently built using that environment (and leaves the parameter unchanged for models that were previously built using that environment).

    Arguments:

      - param – The name of the parameter being modified.

      - newval – The desired new value for the parameter.

    Example:

          // Set the TimeLimit parameter via strings
          model.set("TimeLimit", "10.0");

  void set(GRB.DoubleAttr attr, double newval)
  void set(GRB.IntAttr attr, int newval)
  void set(GRB.StringAttr attr, String newval)

    Set the value of a double, int, or string-valued model attribute.

    Arguments:

      - attr – The attribute being modified.

      - newval – The desired new value for the attribute.

    Example:

          // Adjust objective constant
          model.set(GRB.DoubleAttr.ObjCon, 2.0);
          // Set the model's sense
          model.set(GRB.IntAttr.ModelSense, -1);
          // Set model name
          model.set(GRB.StringAttr.ModelName, "myModel");

  void set(GRB.CharAttr attr, GRBVar[] objs, char[] newvals)
  void set(GRB.CharAttr attr, GRBConstr[] objs, char[] newvals)
  void set(GRB.CharAttr attr, GRBQConstr[] objs, char[] newvals)
  void set(GRB.DoubleAttr attr, GRBVar[] objs, double[] newvals)
  void set(GRB.DoubleAttr attr, GRBConstr[] objs, double[] newvals)
  void set(GRB.DoubleAttr attr, GRBQConstr[] objs, double[] newvals)
  void set(GRB.DoubleAttr attr, GRBGenConstr[] objs, double[] newvals)
  void set(GRB.IntAttr attr, GRBVar[] objs, int[] newvals)
  void set(GRB.IntAttr attr, GRBConstr[] objs, int[] newvals)
  void set(GRB.IntAttr attr, GRBQConstr[] objs, int[] newvals)
  void set(GRB.IntAttr attr, GRBGenConstr[] objs, int[] newvals)
  void set(GRB.StringAttr attr, GRBVar[] objs, String[] newvals)
  void set(GRB.StringAttr attr, GRBConstr[] objs, String[] newvals)
  void set(GRB.StringAttr attr, GRBQConstr[] objs, String[] newvals)
  void set(GRB.StringAttr attr, GRBGenConstr[] objs, String[] newvals)

    Set an attribute for an array of modeling objects.

    Arguments:

      - attr – The attribute being modified.

      - objs – The modeling objects, i.e., variables or constraints, whose attribute values are being modified.

      - newvals – The desired new values for the attribute for each input object.

    Example:

          // Add 3 binary variables
          GRBVar[] x = model.addVars(3, GRB.BINARY);
          // Change the type of the 3 variables to CONTINUOUS
          char[] newtypes = {'C', 'C', 'C'};
          model.set(GRB.CharAttr.VType, x, newtypes);
          // Change the lower bound of the 3 variables
          double[] newlbounds = {0.1, 0.2, 0.3};
          model.Set(GRB.DoubleAttr.LB, x, newlbounds);
          // Add 3 constraints
          GRBConstr[] constrs = model.addConstrs(3);
          // Set the CBasis of the constraints
          int[] newcbasis = {0, 0, 0};
          model.set(GRB.IntAttr.CBasis, constrs, newcbasis);
          // Set the names of the constraints
          String[] newnames = {"c1", "c2", "c3"};
          model.set(GRB.StringAttr.ConstrName, constrs, newnames);

  void set(GRB.CharAttr attr, GRBVar[] objs, char[] newvals, int start, int len)
  void set(GRB.CharAttr attr, GRBConstr[] objs, char[] newvals, int start, int len)
  void set(GRB.CharAttr attr, GRBQConstr[] objs, char[] newvals, int start, int len)
  void set(GRB.DoubleAttr attr, GRBVar[] objs, double[] newvals, int start, int len)
  void set(GRB.DoubleAttr attr, GRBConstr[] objs, double[] newvals, int start, int len)
  void set(GRB.DoubleAttr attr, GRBQConstr[] objs, double[] newvals, int start, int len)
  void set(GRB.DoubleAttr attr, GRBGenConstr[] objs, double[] newvals, int start, int len)
  void set(GRB.IntAttr attr, GRBVar[] objs, int[] newvals, int start, int len)
  void set(GRB.IntAttr attr, GRBConstr[] objs, int[] newvals, int start, int len)
  void set(GRB.IntAttr attr, GRBQConstr[] objs, int[] newvals, int start, int len)
  void set(GRB.IntAttr attr, GRBGenConstr[] objs, int[] newvals, int start, int len)
  void set(GRB.StringAttr attr, GRBVar[] objs, String[] newvals, int start, int len)
  void set(GRB.StringAttr attr, GRBConstr[] objs, String[] newvals, int start, int len)
  void set(GRB.StringAttr attr, GRBQConstr[] objs, String[] newvals, int start, int len)
  void set(GRB.StringAttr attr, GRBGenConstr[] objs, String[] newvals, int start, int len)

    Set an attribute for a sub-array of modeling objects.

    Arguments:

      - attr – The attribute being modified.

      - objs – A one-dimensional array of modeling objects, i.e., variables or constraints, whose attribute values are being modified.

      - newvals – The desired new values for the attribute for each input object. This array must have the same length as the objs array, even if not all elements will be used.

      - start – The index of the first object of interest in the list.

      - len – The number of objects for which the attribute should be changed, counting forward from start.

    Example:

          // Add 3 binary variables
          GRBVar[] x = model.addVars(3, GRB.BINARY);
          // Change the type of the first 2 variables to CONTINUOUS
          // Length has to be the same as length of array x
          char[] newtypes = {'C', 'C', ' '};
          model.set(GRB.CharAttr.VType, x, newtypes, 0, 2);
          // Change the lower bound of the first 2 variables
          double[] newlbounds = {0.1, 0.2, 0.0};
          model.Set(GRB.DoubleAttr.LB, x, newlbounds, 0, 2);
          // Add 3 general constraints
          GRBGenConstr[] genconstrs = model.addGenConstrs(3);
          // Set the names of the first 2 general constraints
          String[] newvals = {"gc1", "gc2", "gc3"};
          model.set(GRB.StringAttr.GenConstrName, genconstrs, newvals, 0, 2);

    Deprecated since version 12.0.2: This method is deprecated, please use the `set(attr, vars, newvals)` function instead.

  void set(GRB.CharAttr attr, GRBVar[][] objs, char[][] newvals)
  void set(GRB.CharAttr attr, GRBConstr[][] objs, char[][] newvals)
  void set(GRB.CharAttr attr, GRBQConstr[][] objs, char[][] newvals)
  void set(GRB.DoubleAttr attr, GRBVar[][] objs, double[][] newvals)
  void set(GRB.DoubleAttr attr, GRBConstr[][] objs, double[][] newvals)
  void set(GRB.DoubleAttr attr, GRBQConstr[][] objs, double[][] newvals)
  void set(GRB.DoubleAttr attr, GRBGenConstr[][] objs, double[][] newvals)
  void set(GRB.IntAttr attr, GRBVar[][] objs, int[][] newvals)
  void set(GRB.IntAttr attr, GRBConstr[][] objs, int[][] newvals)
  void set(GRB.IntAttr attr, GRBQConstr[][] objs, int[][] newvals)
  void set(GRB.IntAttr attr, GRBGenConstr[][] objs, int[][] newvals)
  void set(GRB.StringAttr attr, GRBVar[][] objs, String[][] newvals)
  void set(GRB.StringAttr attr, GRBConstr[][] objs, String[][] newvals)
  void set(GRB.StringAttr attr, GRBQConstr[][] objs, String[][] newvals)
  void set(GRB.StringAttr attr, GRBGenConstr[][] objs, String[][] newvals)

    Set an attribute for a two-dimensional array of modeling objects.

    Arguments:

      - attr – The attribute being modified.

      - objs – A two-dimensional array of modeling objects, i.e., variables or constraints, whose attribute values are being modified.

      - newvals – The desired new values for the attribute for each input object.

    Example:

          // Change the sense of a 2x2 array of constraints
          GRBConstr[][] constrs = new GRBConstr[2][2];
          // ...
          char[][] newsenses = {{'<', '='}, {'<', '='}};
          model.set(GRB.CharAttr.Sense, constrs, newsenses);
          // Change the RHS
          double[][] newrhs = {{1.0, 2.0}, {3.0, 4.0}};
          model.set(GRB.DoubleAttr.RHS, constrs, newrhs);
          // Set the CBasis
          int[][] newcbasis = {{0, -1}, {-1, 0}};
          model.set(GRB.IntAttr.CBasis, constrs, newcbasis);
          // Set the names
          String[][] newnames = {{"c1", "c2"}, {"c3", "c4"}};
          model.set(GRB.StringAttr.ConstrName, constrs, newnames);

  void set(GRB.CharAttr attr, GRBVar[][][] objs, char[][][] newvals)
  void set(GRB.CharAttr attr, GRBConstr[][][] objs, char[][][] newvals)
  void set(GRB.CharAttr attr, GRBQConstr[][][] objs, char[][][] newvals)
  void set(GRB.DoubleAttr attr, GRBVar[][][] objs, double[][][] newvals)
  void set(GRB.DoubleAttr attr, GRBConstr[][][] objs, double[][][] newvals)
  void set(GRB.DoubleAttr attr, GRBQConstr[][][] objs, double[][][] newvals)
  void set(GRB.DoubleAttr attr, GRBGenConstr[][][] objs, double[][][] newvals)
  void set(GRB.IntAttr attr, GRBVar[][][] objs, int[][][] newvals)
  void set(GRB.IntAttr attr, GRBConstr[][][] objs, int[][][] newvals)
  void set(GRB.IntAttr attr, GRBQConstr[][][] objs, int[][][] newvals)
  void set(GRB.IntAttr attr, GRBGenConstr[][][] objs, int[][][] newvals)
  void set(GRB.StringAttr attr, GRBVar[][][] objs, String[][][] newvals)
  void set(GRB.StringAttr attr, GRBConstr[][][] objs, String[][][] newvals)
  void set(GRB.StringAttr attr, GRBQConstr[][][] objs, String[][][] newvals)
  void set(GRB.StringAttr attr, GRBGenConstr[][][] objs, String[][][] newvals)

    Set an attribute for a three-dimensional array of modeling objects.

    Arguments:

      - attr – The attribute being modified.

      - objs – A three-dimensional array of modeling objects, i.e., variables or constraints, whose attribute values are being modified.

      - newvals – The desired new values for the attribute for each input object.

    Example:

          // Change the sense of a 2x2x2 array of constraints
          GRBConstr[][][] constrs = new GRBConstr[2][2][2];
          // ...
          char[][][] newsenses = {{{'<', '='}, {'<', '='}},{{'<', '='}, {'<', '='}}};
          model.set(GRB.CharAttr.Sense, constrs, newsenses);
          // Change the RHS
          double[][][] newrhs = {{{1.0, 2.0}, {3.0, 4.0}}, {{5.0, 6.0}, {7.0, 8.0}}};
          model.set(GRB.DoubleAttr.RHS, constrs, newrhs);
          // Set the CBasis
          int[][][] newcbasis = {{{0, -1}, {-1, 0}}, {{0, -1}, {-1, 0}}};
          model.set(GRB.IntAttr.CBasis, constrs, newcbasis);
          // Set the names
          String[][][] newnames = {{{"c1", "c2"}, {"c3", "c4"}}, {{"c5", "c6"}, {"c7", "c8"}}};
          model.set(GRB.StringAttr.ConstrName, constrs, newnames);

  void setLogCallback(java.util.function.Consumer<String> logCallback)

    Sets a logging callback function to query all output posted by the model object. Can be set after a model was created.

    Arguments:

      logCallback – The logging callback function.

    Example:

          // Set a simple logging callback that prints logs
          model.setLogCallback(log -> System.out.println("Log: " + log));

  void setObjective(GRBExpr expr, int sense)

    Set the model objective equal to a linear expression (for multi-objective optimization, see `setObjectiveN`).

    Note that you can also modify the linear portion of a model objective using the Obj variable attribute. If you wish to mix and match these two approaches, please note that this method replaces the entire existing objective, while the Obj attribute can be used to modify individual linear terms.

    Arguments:

      - expr – New model objective.

      - sense – New optimization sense (`GRB.MINIMIZE` for minimization, `GRB.MAXIMIZE` for maximization).

    Example:

          // Set objective and sense
          model.setObjective(expression, GRB.MINIMIZE);

  void setObjective(GRBExpr expr)

    Set the model objective equal to quadratic expression (for multi-objective optimization, see `setObjectiveN`). The sense of the objective is determined by the value of the ModelSense attribute.

    Note that this method replaces the entire existing objective, while the Obj attribute can be used to modify individual linear terms.

    Arguments:

      expr – New model objective.

    Example:

          // Set objective without specifying sense (use default)
          model.setObjective(expr);

  void setObjectiveN(GRBLinExpr expr, int index, int priority, double weight, double abstol, double reltol, String name)

    Set an alternative optimization objective equal to a linear expression.

    Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

    Note that you can also modify an alternative objective using the ObjN variable attribute. If you wish to mix and match these two approaches, please note that this method replaces the entire existing objective, while the ObjN attribute can be used to modify individual terms.

    Arguments:

      - expr – New alternative objective.

      - index – Index for new objective. If you use an index of 0, this routine will change the primary optimization objective.

      - priority – Priority for the alternative objective. This initializes the ObjNPriority attribute for this objective.

      - weight – Weight for the alternative objective. This initializes the ObjNWeight attribute for this objective.

      - abstol – Absolute tolerance for the alternative objective. This initializes the ObjNAbsTol attribute for this objective.

      - reltol – Relative tolerance for the alternative objective. This initializes the ObjNRelTol attribute for this objective.

      - name – Name of the alternative objective. This initializes the ObjNName attribute for this objective.

    Example:

          // Primary objective: expr1
          model.setObjectiveN(expr1, 0, 2, 1.0, 2.0, 0.1, "PrimaryObj");
          // Secondary objective: expr2
          model.setObjectiveN(expr2, 1, 1, 1.0, 0.0, 0.0, "SecondaryObj");

  void setPWLObj(GRBVar var, double[] x, double[] y)

    Set a piecewise-linear objective function for a variable.

    The arguments to this method specify a list of points that define a piecewise-linear objective function for a single variable. Specifically, the \(x\) and \(y\) arguments give coordinates for the vertices of the function.

    For additional details on piecewise-linear objective functions, refer to this discussion.

    Set the piecewise-linear objective function for a variable.

    Arguments:

      - var – The variable whose objective function is being set.

      - x – The \(x\) values for the points that define the piecewise-linear function. Must be in non-decreasing order.

      - y – The \(y\) values for the points that define the piecewise-linear function.

    Example:

          GRBVar v = model.addVar(0.0, 1.0, 0.0, GRB.CONTINUOUS, "var");
          double[] xVals = {0.0, 0.5, 1.0};
          double[] yVals = {0.5, 0.25, 0.75};
          model.setPWLObj(v, xVals, yVals);

  GRBModel singleScenarioModel()

    Capture a single scenario from a multi-scenario model. Use the ScenarioNumber parameter to indicate which scenario to capture.

    The model on which this method is invoked must be a multi-scenario model, and the result will be a single-scenario model.

    Return value:

      Model for a single scenario.

    Example:

          model.set(GRB.IntAttr.NumScenarios, 1);
          model.update();
          GRBModel singleScenario = model.singleScenarioModel();

  void sync()

    Wait for a previous asynchronous optimization call to complete.

    Calling `optimizeasync` returns control to the calling routine immediately. The caller can perform other computations while optimization proceeds, and can check on the progress of the optimization by querying various model attributes. The `sync` call forces the calling program to wait until the asynchronous optimization call completes. You must call `sync` before the corresponding model object is deleted.

    The `sync` call throws an exception if the optimization itself ran into any problems. In other words, exceptions thrown by this method are those that `optimize` itself would have thrown, had the original method not been asynchronous.

    Note that you need to call `sync` even if you know that the asynchronous optimization has already completed.

    Example:

          model.optimizeasync();
          // ...
          model.sync();

  void terminate()

    Generate a request to terminate the current optimization. This method can be called at any time during an optimization (from a callback, from another thread, from an interrupt handler, etc.). Note that, in general, the request won’t be acted upon immediately.

    When the optimization stops, the Status attribute will be equal to `GRB_INTERRUPTED`.

    Example:

          model.terminate();

  void tune()

    Perform an automated search for parameter settings that improve performance. Upon completion, this method stores the best parameter sets it found. The number of stored parameter sets can be determined by querying the value of the TuneResultCount attribute. The actual settings can be retrieved using `getTuneResult`.

    Please refer to the Parameter Tuning section in the Reference Manual for details on the tuning tool.

    Example:

          model.tune();
          // Get best tuning result
          model.getTuneResult(0);

  void update()

    Process any pending model modifications.

    Example:

          model.update();

  void write(String filename)

    This method is the general entry point for writing optimization data to a file. It can be used to write optimization models, solution vectors, basis vectors, start vectors, or parameter settings. The type of data written is determined by the file suffix. File formats are described in the File Format section.

    Note that writing a model to a file will process all pending model modifications. This is also true when writing other model information such as solutions, bases, etc.

    Note also that when you write a Gurobi parameter file (PRM), both integer or double parameters not at their default value will be saved, but no string parameter will be saved into the file.

    Finally, note that when IgnoreNames=1, the names of variables and constraints are replaced with default names when writing a file.

    Arguments:

      filename – The name of the file to be written. The file type is encoded in the file name suffix. Valid suffixes are `.mps`, `.rew`, `.lp`, or `.rlp` for writing the model itself, `.dua` or `.dlp` for writing the dualized model (only pure LP), `.ilp` for writing just the IIS associated with an infeasible model (see `GRBModel.computeIIS` for further information), `.sol` for writing the solution selected by the SolutionNumber parameter, `.mst` for writing a start vector, `.hnt` for writing a hint file, `.bas` for writing an LP basis, `.prm` for writing modified parameter settings, `.attr` for writing model attributes, or `.json` for writing solution information in JSON format. If your system has compression utilities installed (e.g., `7z` or `zip` for Windows, and `gzip`, `bzip2`, or `unzip` for Linux or macOS), then the files can be compressed, so additional suffixes of `.zip`, `.gz`, `.bz2`, `.7z` or `.xz` are accepted.

    Example:

          model.write("myModel.lp");

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