optlang provides a common interface to a series of optimization solvers (linear & non-linear) and relies on sympy for problem formulation (constraints, objectives, variables, etc.). Adding new solvers is easy: just sub-class the high-level interface and implement the necessary solver specific routines.
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.
Formulating and solving the problem is straightforward (example taken from GLPK documentation):
from optlang 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
- Gurobi interface (very efficient MILP solver)
- CPLEX interface (very efficient MILP solver)
- 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.