Quick start

This chapter presents the main components needed to build and optimize models using Python-MIP. A full description of the methods and their parameters can be found at Chapter 4.

The first step to enable Python-MIP in your Python code is to add:

from mip import *

When loaded, Python-MIP will display its installed version:

Using Python-MIP package version 1.6.2

Creating Models

The model class represents the optimization model. The code below creates an empty Mixed-Integer Linear Programming problem with default settings.

m = Model()

By default, the optimization sense is set to Minimize and the selected solver is set to CBC. If Gurobi is installed and configured, it will be used instead. You can change the model objective sense or force the selection of a specific solver engine using additional parameters for the constructor:

m = Model(sense=MAXIMIZE, solver_name=CBC) # use GRB for Gurobi

After creating the model, you should include your decision variables, objective function and constraints. These tasks will be discussed in the next sections.


Decision variables are added to the model using the add_var() method. Without parameters, a single variable with domain in \(\mathbb{R}^+\) is created and its reference is returned:

x = m.add_var()

By using Python list initialization syntax, you can easily create a vector of variables. Let’s say that your model will have n binary decision variables (n=10 in the example below) indicating if each one of 10 items is selected or not. The code below creates 10 binary variables y[0], …, y[n-1] and stores their references in a list.

n = 10
y = [ m.add_var(var_type=BINARY) for i in range(n) ]

Additional variable types are CONTINUOUS (default) and INTEGER. Some additional properties that can be specified for variables are their lower and upper bounds (lb and ub, respectively), and names (property name). Naming a variable is optional and it is particularly useful if you plan to save you model (see Saving, Loading and Checking Model Properties) in .LP or .MPS file formats, for instance. The following code creates an integer variable named zCost which is restricted to be in range \(\{-10,\ldots,10\}\). Note that the variable’s reference is stored in a Python variable named z.

z = m.add_var(name='zCost', var_type=INTEGER, lb=-10, ub=10)

You don’t need to store references for variables, even though it is usually easier to do so to write constraints. If you do not store these references, you can get them afterwards using the Model function var_by_name(). The following code retrieves the reference of a variable named zCost and sets its upper bound to 5:

vz = m.var_by_name('zCost')
vz.ub = 5


Constraints are linear expressions involving variables, a sense of ==, <= or >= for equal, less or equal and greater or equal, respectively, and a constant. The constraint \(x+y \leq 10\) can be easily included within model m:

m += x + y <= 10

Summation expressions can be implemented with the function xsum(). If for a knapsack problem with \(n\) items, each one with weight \(w_i\), we would like to include a constraint to select items with binary variables \(x_i\) respecting the knapsack capacity \(c\), then the following code could be used to include this constraint within the model m:

m += xsum(w[i]*x[i] for i in range(n)) <= c

Conditional inclusion of variables in the summation is also easy. Let’s say that only even indexed items are subjected to the capacity constraint:

m += xsum(w[i]*x[i] for i in range(n) if i%2 == 0) <= c

Finally, it may be useful to name constraints. To do so is straightforward: include the constraint’s name after the linear expression, separating it with a comma. An example is given below:

m += xsum(w[i]*x[i] for i in range(n) if i%2 == 0) <= c, 'even_sum'

As with variables, reference of constraints can be retrieved by their names. Model function constr_by_name() is responsible for this:

constraint = m.constr_by_name('even_sum')

Objective Function

By default a model is created with the Minimize sense. The following code alters the objective function to \(\sum_{i=0}^{n-1} c_ix_i\) by setting the objective attribute of our example model m:

m.objective = xsum(c[i]*x[i] for i in range(n))

To specify whether the goal is to Minimize or Maximize the objetive function, two useful functions were included: minimize() and maximize(). Below are two usage examples:

m.objective = minimize(xsum(c[i]*x[i] for i in range(n)))
m.objective = maximize(xsum(c[i]*x[i] for i in range(n)))

You can also change the optimization direction by setting the sense model property to MINIMIZE or MAXIMIZE.

Saving, Loading and Checking Model Properties

Model methods write() and read() can be used to save and load, respectively, MIP models. Supported file formats for models are the LP file format, which is more readable and suitable for debugging, and the MPS file format, which is recommended for extended compatibility, since it is an older and more widely adopted format. When calling the write() method, the file name extension (.lp or .mps) is used to define the file format. Therefore, to save a model m using the lp file format to the file model.lp we can use:


Likewise, we can read a model, which results in creating variables and constraints from the LP or MPS file read. Once a model is read, all its attributes become available, like the number of variables, constraints and non-zeros in the constraint matrix:

print('model has {} vars, {} constraints and {} nzs'.format(m.num_cols, m.num_rows, m.num_nz))

Optimizing and Querying Optimization Results

MIP solvers execute a Branch-&-Cut (BC) algorithm that in finite time will provide the optimal solution. This time may be, in many cases, too large for your needs. Fortunately, even when the complete tree search is too expensive, results are often available in the beginning of the search. Sometimes a feasible solution is produced when the first tree nodes are processed and a lot of additional effort is spent improving the dual bound, which is a valid estimate for the cost of the optimal solution. When this estimate, the lower bound for minimization, matches exactly the cost of the best solution found, the upper bound, the search is concluded. For practical applications, usually a truncated search is executed. The optimize() method, that executes the optimization of a formulation, accepts optionally processing limits as parameters. The following code executes the branch-&-cut algorithm to solve a model m for up to 300 seconds.

m.max_gap = 0.05
status = m.optimize(max_seconds=300)
if status == OptimizationStatus.OPTIMAL:
    print('optimal solution cost {} found'.format(m.objective_value))
elif status == OptimizationStatus.FEASIBLE:
    print('sol.cost {} found, best possible: {}'.format(m.objective_value, m.objective_bound))
elif status == OptimizationStatus.NO_SOLUTION_FOUND:
    print('no feasible solution found, lower bound is: {}'.format(m.objective_bound))
if status == OptimizationStatus.OPTIMAL or status == OptimizationStatus.FEASIBLE:
    for v in m.vars:
       if abs(v.x) > 1e-6: # only printing non-zeros
          print('{} : {}'.format(v.name, v.x))

Additional processing limits may be used: max_nodes restricts the maximum number of explored nodes in the search tree and max_solutions finishes the BC algorithm after a number of feasible solutions are obtained. It is also wise to specify how tight the bounds should be to conclude the search. The model attribute max_mip_gap specifies the allowable percentage deviation of the upper bound from the lower bound for concluding the search. In our example, whenever the distance of the lower and upper bounds is less or equal 5% (see line 1), the search can be finished.

The optimize() method returns the status (OptimizationStatus) of the BC search: OPTIMAL if the search was concluded and the optimal solution was found; FEASIBLE if a feasible solution was found but there was no time to prove whether this solution was optimal or not; NO_SOLUTION_FOUND if in the truncated search no solution was found; INFEASIBLE or INT_INFEASIBLE if no feasible solution exists for the model; UNBOUNDED if there are missing constraints or ERROR if some error occurred during optimization. In the example above, if a feasible solution is available (line 8), variables which have value different from zero are printed. Observe also that even when no feasible solution is available the dual bound (lower bound in the case of minimization) is available (line 8): if a truncated execution was performed, i.e., the solver stopped due to the time limit, you can check this dual bound which is an estimate of the quality of the solution found checking the gap property.

During the tree search, it is often the case that many different feasible solutions are found. The solver engine stores this solutions in a solution pool. The following code prints all routes found while optimizing the Traveling Salesman Problem.

for k in range(model.num_solutions):
    print('route {} with length {}'.format(k, model.objective_values[k]))
    for (i, j) in product(range(n), range(n)):
        if x[i][j].xi(k) >= 0.98:
            print('\tarc ({},{})'.format(i,j))

Performance Tuning

Tree search algorithms of MIP solvers deliver a set of improved feasible solutions and lower bounds. Depending on your application you will be more interested in the quick production of feasible solutions than in improved lower bounds that may require expensive computations, even if in the long term these computations prove worthy to prove the optimality of the solution found. The model property emphasis provides three different settings:

  1. default setting:

    tries to balance between the search of improved feasible solutions and improved lower bounds;

  2. feasibility:

    focus on finding improved feasible solutions in the first moments of the search process, activates heuristics;

  3. optimality:

    activates procedures that produce improved lower bounds, focusing in pruning the search tree even if the production of the first feasible solutions is delayed.

Changing this setting to 1 or 2 triggers the activation/deactivation of several algorithms that are processed at each node of the search tree that impact the solver performance. Even though in average these settings change the solver performance as described previously, depending on your formulation the impact of these changes may be very different and it is usually worth to check the solver behavior with these different settings in your application.

Another parameter that may be worth tuning is the cuts attribute, that controls how much computational effort should be spent in generating cutting planes.