optlang is a Python package for solving mathematical optimization problems, i.e. maximizing or minimizing an objective function over a set of variables subject to a number of constraints. Optlang provides a common interface to a series of optimization tools, so different solver backends can be changed in a transparent way.

Optlang takes advantage of the symbolic math library sympy to allow objective functions and constraints to be easily formulated from symbolic expressions of variables (see examples).

Install using pip

pip install optlang

Local installations like

python setup.py install

might fail installing the dependencies (unresolved issue with easy_install). Running

pip install -r requirements.txt

beforehand should fix this issue.

The documentation for optlang is provided at readthedocs.org.

• sympy >= 0.7.5
• six >= 1.9.0

And at least one of the following

• swiglpk >= 0.1.0
• cplex
• gurobipy
• scipy

Formulating and solving the problem is straightforward (example taken from GLPK documentation):

from optlang.glpk_interface import Model, Variable, Constraint, Objective

x1 = Variable('x1', lb=0)
x2 = Variable('x2', lb=0)
x3 = Variable('x3', lb=0)

c1 = Constraint(x1 + x2 + x3, ub=100)
c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)
c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)

obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')

model = Model(name='Simple model')
model.objective = obj
model.add([c1, c2, c3])

status = model.optimize()

print "status:", model.status
print "objective value:", model.objective.value
for var_name, var in model.variables.iteritems():
    print var_name, "=", var.primal

The example will produce the following output:

status: optimal
objective value: 733.333333333
x2 = 66.6666666667
x3 = 0.0
x1 = 33.3333333333

• Mosek interface (provides academic licenses)
• GAMS output (support non-linear problem formulation)
• DEAP (support for heuristic optimization)
• Interface to NEOS optimization server (for testing purposes and solver evaluation)
• Automatically handle fractional and absolute value problems when dealing with LP/MILP/QP solvers (like GLPK, CPLEX etc.)

The optlang trello board also provides a good overview of the project's roadmap.

• Models should always be serializable to common problem formulation languages (CPLEX, GAMS, etc.)
• Models should be picklable
• Common solver configuration interface (presolver, MILP gap, etc.)