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A Python interface to conic optimization solvers.

Project description

PICOS is a user friendly Python API to several conic and integer programming solvers, designed to be used by both application developers and researchers as well as instructors teaching courses on mathematical optimization. It allows you to enter an optimization problem as a high level model, with painless support for (complex) vector and matrix variables and multidimensional algebra. Your model will be transformed to the standard form understood by an appropriate solver that is available at runtime. This makes your application portable as users have the choice between several commercial and open source solvers.


PICOS supports the following solvers and problem types. To use a solver, you need to separately install it along with the Python interface listed here.




CPLEX included Yes Yes     Yes non-free
CVXOPT native Yes Yes Yes GP   GPL-3
ECOS ecos-python Yes Yes   Yes Yes GPL-3
GLPK swiglpk Yes       Yes GPL-3
Gurobi gurobipy Yes Yes     Yes non-free
MOSEK included Yes Yes Yes   Yes non-free
OSQP native Yes QP       Apache-2.0
SCIP PySCIPOpt Yes Yes     Yes ZIB/MIT
SMCP native     Yes     GPL-3


This is what it looks like to solve a multidimensional mixed integer program with PICOS:

>>> import picos as pc
>>> P = pc.Problem()
>>> x = pc.IntegerVariable("x", 2)
>>> P += 2*x <= 11
>>> P.maximize = pc.sum(x)
>>> P.solve(solver="glpk")  # Optional: Use GLPK as backend.
<feasible primal solution (claimed optimal) from glpk>
>>> P.value
>>> print(x)
[ 5.00e+00]
[ 5.00e+00]

You can head to our quick examples or the tutorial for more.


As of release 2.2, PICOS requires Python 3.4 or later.

Via pip

If you are using pip you can run pip install picos to get the latest version.

Via Anaconda

If you are using Anaconda you can run conda install -c picos picos to get the latest version.

Via your system’s package manager

Distribution Latest major version Latest version
Arch Linux python-picos python-picos-git

If you are packaging PICOS for additional platforms, please let us know.

From source

The PICOS source code can be found on GitLab. There are only two dependencies:


The full documentation can be browsed online or downloaded in PDF form.




For an up-to-date list of all code contributors, please refer to the contributors page. Should a reference from before 2019 be unclear, see also the old contributors page on GitHub.


The preferred way to cite PICOS in your research is our JOSS paper:

  author  = {Guillaume Sagnol and Maximilian Stahlberg},
  journal = {Journal of Open Source Software},
  title   = {{PICOS}: A {Python} interface to conic optimization solvers},
  year    = {2022},
  issn    = {2475-9066},
  month   = feb,
  number  = {70},
  pages   = {3915},
  volume  = {7},
  doi     = {10.21105/joss.03915},

If citing a specific version of PICOS is necessary, then we offer also source deposits on Zenodo.


PICOS is free and open source software and available to you under the terms of the GNU GPL v3.

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