Try our new documentation site (beta).

Build a model

Examples: diet, facility, mip1, piecewise, qcp, qp, sos, sudoku, workforce1, workforce2, workforce3, workforce4

Several of the Gurobi examples build models from scratch. We start by focusing on two, mip1 and sos, which build very simple models to illustrate the basic process.

Typically, the first step in building a model is to create an empty model. This is done using the GRBnewmodel function in C:

  error = GRBnewmodel(env, &model, "mip1", 0, NULL, NULL, NULL, NULL);
You can optionally create a set of variables when you create the model, as well as specifying bounds, objective coefficients, and names for these variables. These examples add new variables separately.

In C++, C#, and Java, you create a new model using the GRBModel constructor. In Java, this looks like:

  GRBModel model = new GRBModel(env);
In Python, the class is called Model, and its constructor is similar to the GRBModel constructor for C++ and Java.

Once the model has been created, the typical next step is to add variables. In C, you use the GRBaddvars function to add one or more variables:

  error = GRBaddvars(model, 3, 0, NULL, NULL, NULL, obj, NULL, NULL, vtype, NULL);
In C++, Java, and Python, you use the addVar method on the Model object (AddVar in C#). In Java, this looks like:
  GRBVar x = model.addVar(0.0, 1.0, -1.0, GRB.BINARY, "x");
The new variable's lower bound, upper bound, objective coefficient, type, and name are specified as arguments. In C++ and Python, you can omit these arguments and use default values; see the Gurobi Reference Manual for details.

After adding variables to the model, the next step is to call the update function (GRBupdatemodel() in C, model.update() in C++, Java, and Python, model.Update() in C#). Model modifications are performed in a lazy fashion in the Gurobi optimizer -- they don't affect the model until the next update or optimize call. You cannot utilize the new variables (e.g., in constraints) until you call the update function.

The next step is to add constraints to the model. Linear constraints are added through the GRBaddconstr function in C:

  error = GRBaddconstr(model, 3, ind, val, GRB_LESS_EQUAL, 4.0, "c0");
To add a linear constraint in C, you must specify a list of variable indices and coefficients for the left-hand side, a sense for the constraint (e.g., GRB_LESS_EQUAL), and a right-hand side constant. You can also give the constraint a name; if you omit the name, Gurobi will assign a default name for the constraint.

In C++, C#, Java, and Python, you build a linear constraint by first building linear expressions for the left- and right-hand sides. In Java, which doesn't support operator overloading, you build an expression as follows:

  GRBLinExpr expr = new GRBLinExpr();
  expr.addTerm(1.0, x); expr.addTerm(2.0, y); expr.addTerm(3.0, z);
You then use the addConstr method on the Model object to add a constraint using these linear expressions for the left- and right-hand sides:
  model.addConstr(expr, GRB_LESS_EQUAL, 4.0, "c0");

For C++, C#, and Python, the standard mathematical operators such as +, *, <= have been overloaded so that the linear expression resembles a traditional mathematical expression. In C++:

  model.addConstr(x + 2 * y + 3 * z <= 4, "c0");

Adding an special ordered set (SOS) constraint is similar. In C, you add one or more SOS constraint using the GRBaddsos function:

  error = GRBaddsos(model, 1, 2, sostype, sosbeg, sosind, soswt);
For each SOS constraint, you must specify a list of members and a weight for each member.

In C++, C#, Java, and Python, you use the addSOS method on the Model object:

  model.addSOS(sosv1, soswt1, GRB.SOS_TYPE1);

Once the model has been built, the typical next step is to optimize it (using GRBoptimize in C, model.optimize in C++, Java, and Python, or model.Optimize in C#). You can then query the X attribute on the variables to retrieve the solution (and the VarName attribute to retrieve the variable name for each variable). In C, the X attribute is retrieved as follows:

  error = GRBgetdblattrarray(model, GRB_DBL_ATTR_X, 0, 3, sol);

In C++:

  cout << x.get(GRB_StringAttr_VarName) << " "
       << x.get(GRB_DoubleAttr_X) << endl;
  cout << y.get(GRB_StringAttr_VarName) << " "
       << y.get(GRB_DoubleAttr_X) << endl;
  cout << z.get(GRB_StringAttr_VarName) << " "
       << z.get(GRB_DoubleAttr_X) << endl;

In Java:

  System.out.println(x.get(GRB.StringAttr.VarName) +
                     " " + x.get(GRB.DoubleAttr.X));
  System.out.println(y.get(GRB.StringAttr.VarName) +
                     " " + y.get(GRB.DoubleAttr.X));
  System.out.println(z.get(GRB.StringAttr.VarName) +
                     " " + z.get(GRB.DoubleAttr.X));

In C#:

  Console.WriteLine(x.Get(GRB.StringAttr.VarName) +
                    " " + x.Get(GRB.DoubleAttr.X));
  Console.WriteLine(y.Get(GRB.StringAttr.VarName) +
                    " " + y.Get(GRB.DoubleAttr.X));
  Console.WriteLine(z.Get(GRB.StringAttr.VarName) +
                    " " + z.Get(GRB.DoubleAttr.X));

In Python:

  for v in m.getVars():
    print v.varName, v.x

When querying or modifying attribute values for an array of constraints or variables, it is generally more efficient to perform the action on the whole array at once. This is quite natural in the C interface, where most of the attribute routines take array arguments. In the C++, C#, and Java interface, you can use the get and set methods on the Model object to work directly with arrays of attribute values. In the sudoku Java example, this is done as follows:

  double[][][] x = model.get(GRB.DoubleAttr.X, vars);
Note that the Python interface doesn't have array query routines. Python is an interpreted language, and overheads associated with queries on individual attributes are a small contributor to overall runtime.

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.