Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).
Sympy based mathematical programming language
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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's object-oriented API takes advantage of the symbolic math library
sympy <http://sympy.org/en/index.html>__ to allow objective functions
and constraints to be easily formulated from symbolic expressions of
variables (see examples).
Show us some love by staring this repo if you find optlang useful!
Also, please use the GitHub issue tracker <https://github.com/biosustain/optlang/issues>_
to let us know about bugs or feature requests, or our gitter channel <https://gitter.im/biosustain/optlang>_ if you have problems or questions regarding optlang.
Installation
Install using pip
::
pip install optlang
This will also install `swiglpk <https://github.com/biosustain/swiglpk>`_, an interface to the open source (mixed integer) LP solver `GLPK <https://www.gnu.org/software/glpk/>`_.
Quadratic programming (and MIQP) is supported through additional optional solvers (see below).
Dependencies
The following dependencies are needed.
sympy >= 1.0.0 <http://sympy.org/en/index.html>__swiglpk >= 1.4.3 <https://pypi.python.org/pypi/swiglpk>__The following are optional dependencies that allow other solvers to be used.
cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>__ (LP, MILP, QP, MIQP)gurobipy <http://www.gurobi.com>__ (LP, MILP, QP, MIQP)scipy <http://www.scipy.org>__ (LP)osqp <https://osqp.org/>__ (LP, QP)Example
Formulating and solving the problem is straightforward (example taken
from `GLPK documentation <http://www.gnu.org/software/glpk>`__):
.. code-block:: python
from optlang import Model, Variable, Constraint, Objective
# All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.
x1 = Variable('x1', lb=0)
x2 = Variable('x2', lb=0)
x3 = Variable('x3', lb=0)
# A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).
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)
# An objective can be formulated
obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')
# Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.
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)
print("----------")
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
Using a particular solver
-------------------------
If you have more than one solver installed, it's also possible to specify which one to use, by importing directly from the
respective solver interface, e.g. :code:`from optlang.glpk_interface import Model, Variable, Constraint, Objective`
Documentation
Documentation for optlang is provided at
readthedocs.org <http://optlang.readthedocs.org/en/latest/>__.
Citation
Please cite |JOSS| if you use optlang in a scientific publication. In case you would like to reference a specific version of of optlang you can also include the respective Zenodo DOI (|DOI| points to the latest version).
Contributing
Please read <CONTRIBUTING.md>__.
Funding
The development of optlang was partly support by the Novo Nordisk Foundation.
Future outlook
Mosek <http://www.mosek.com/>__ interface (provides academic
licenses)GAMS <http://www.gams.com/>__ output (support non-linear problem
formulation)DEAP <https://code.google.com/p/deap/>__ (support for heuristic
optimization)NEOS <http://www.neos-server.org/neos/>__ optimization
server (for testing purposes and solver evaluation)CPLEX <http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>__
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FAQs
Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).
We found that optlang demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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