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Convex optimization, for everyone.
Help us benchmark CVXPY solvers! Contribute problems and results to the Solver Benchmarks project. We are building a CVXPY community on Discord. Join the conversation!
CVXPY is an open source Python-embedded modeling language for convex optimization problems. It lets you express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.
For example, the following code solves a least-squares problem with box constraints:
This short script is a basic example of what CVXPY can do. In addition to convex programming, CVXPY also supports parametrized programming, geometric programming, mixed-integer convex programs, quasiconvex programming, and nonlinear programming.
For a guided tour of CVXPY, check out the tutorial. For applications to machine learning, control, finance, and more, browse the library of examples. For applications that involve nonlinear programming, visit the library of DNLP examples. For background on convex optimization, see the book Convex Optimization by Boyd and Vandenberghe.
CVXPY relies on the open source solvers Clarabel, OSQP, SCS, HIGHS. Additional solvers are supported, but must be installed separately.
The CVXPY community consists of researchers, data scientists, software engineers, and students from all over the world. We welcome you to join us!
To chat with the CVXPY community in real-time, join us on Discord.
To have longer, in-depth discussions with the CVXPY community, use Github discussions.
To share feature requests and bug reports, use the issue tracker.
CVXPY is a community project, built from the contributions of many researchers and engineers.
CVXPY is developed and maintained by Steven Diamond, Riley Murray, Philipp Schiele, Parth Nobel, and William Zhang. CVXPY’s emeritus maintainers are Bartolomeo Stellato and Akshay Agrawal. Many others have contributed significantly. A non-exhaustive list of people who have shaped CVXPY over the years includes Stephen Boyd, Eric Chu, Robin Verschueren, Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, Chris Dembia, and Daniel Cederberg.
We appreciate all contributions. To get involved, see our contributing guide and join us on Discord.
CVXPY 1.9 introduces Disciplined Nonlinear Programming (DNLP), a grammar for specifying nonlinear programs (NLP) which extends CVXPY beyond convex optimization. CVXPY currently supports four NLP solvers: IPOPT, KNITRO, UNO, and COPT. For a complete list of changes, see the changelog.