Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).

Project Description

Sympy based mathematical programming language

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 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 to let us know about bugs or feature requests, or our gitter channel if you have problems or questions regarding optlang.

Installation

Install using pip

pip install optlang

This will also install swiglpk, an interface to the open source (mixed integer) LP solver GLPK. Quadratic programming (and MIQP) is supported through additional optional solvers (see below).

Dependencies

The following dependencies are needed.

The following are optional dependencies that allow other solvers to be used.

  • cplex (LP, MILP, QP, MIQP)
  • gurobipy (LP, MILP (QP and MIQP support will be added in the future))
  • scipy (LP)

Example

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

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

Documentation

Documentation for optlang is provided at readthedocs.org.

Citation

Please cite 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 ( points to the latest version).

Contributing

Please read CONTRIBUTING.md.

Future outlook

  • 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.

Release History

Release History

This version
History Node

1.2.3

History Node

1.2.2

History Node

1.2.1

History Node

1.2.0

History Node

1.1.5

History Node

1.1.4

History Node

1.1.3

History Node

1.1.2

History Node

1.1.1

History Node

1.0.5

History Node

1.0.4

History Node

1.0.3

History Node

1.0.2

History Node

1.0.1

History Node

1.0.0

History Node

0.6.4

History Node

0.6.3

History Node

0.6.2

History Node

0.6.1

History Node

0.6.0

History Node

0.6.0b1

History Node

0.5.0

History Node

0.4.2

History Node

0.4.1

History Node

0.4.0

History Node

0.3.3

History Node

0.3.2

History Node

0.3.1

History Node

0.3.0

History Node

0.2.21

History Node

0.2.19

History Node

0.2.18

History Node

0.2.17

History Node

0.2.16

History Node

0.2.15

History Node

0.2.14

History Node

0.2.13

History Node

0.2.9

History Node

0.2.8

History Node

0.2.7

History Node

0.2.6

History Node

0.2.5

History Node

0.2.4

History Node

0.2.3

History Node

0.2.1

History Node

v0.2.1

History Node

0.1.2

History Node

0.1.0

History Node

0.0.4

History Node

0.0.3

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
optlang-1.2.3.tar.gz (99.0 kB) Copy SHA256 Checksum SHA256 Source Sep 19, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting