CVXOPT

Python Software for Convex Optimization

CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language.

Current release

Version 1.1.5 includes:

• efficient Python classes for dense and sparse matrices (real and complex), with Python indexing and slicing and overloaded operations for matrix arithmetic
• an interface to most of the double-precision real and complex BLAS
• an interface to LAPACK routines for solving linear equations and least-squares problems, matrix factorizations (LU, Cholesky, LDL^T and QR), symmetric eigenvalue and singular value decomposition, and Schur factorization
• an interface to the fast Fourier transform routines from FFTW
• interfaces to the sparse LU and Cholesky solvers from UMFPACK and CHOLMOD
• routines for linear, second-order cone, and semidefinite programming problems
• routines for nonlinear convex optimization
• interfaces to the linear programming solver in GLPK, the semidefinite programming solver in DSDP5, and the linear, quadratic and second-order cone programming solvers in MOSEK
• a modeling tool for specifying convex piecewise-linear optimization problems.

Availability

A platform-independent source package and a binary Windows installer are available from the Download section. CVXOPT is also available precompiled for the major platforms:

• Debian Linux
• Ubuntu Linux
• Fedora Linux
• Python(x,y) for Microsoft Windows

Authors

CVXOPT is developed by Martin Andersen (martin.skovgaard.andersen@gmail.com), Joachim Dahl (dahl.joachim@gmail.com), and Lieven Vandenberghe (vandenbe@ee.ucla.edu).

CVXOPT was originally developed for use in our own work, and is being made available in the hope that it may be useful to others. We welcome feedback, bug reports, and suggestions for improvements, but can only offer very limited support.