PICOS 2.3.1

A Python interface to conic optimization solvers.

PICOS is a user friendly Python API to several conic and integer programming solvers, very much like YALMIP or CVX under MATLAB.

PICOS allows you to enter a mathematical 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.

Features

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.

Solver	Python interface	LP	SOCP, QCQP	SDP	EXP	MIP	License
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	included	Yes	Yes			Yes	non-free
MOSEK	included	Yes	Yes	Yes	WIP	Yes	non-free
OSQP	native	Yes	QP				Apache-2.0
SCIP	PySCIPOpt	Yes	Yes			Yes	ZIB/MIT
SMCP	native			Yes			GPL-3

Example

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
10.0
>>> print(x)
[ 5.00e+00]
[ 5.00e+00]

You can head to the tutorial for more examples.

Installation

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 release	Latest version
Arch Linux	python-picos	python-picos-git

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

From source

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

• NumPy

• CVXOPT

Documentation

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

Credits

Developers

• Guillaume Sagnol has started work on PICOS in 2012.

• Maximilian Stahlberg is extending and co-maintaining PICOS since 2017.

Contributors

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

License

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