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The CVXPY documentation is atcvxpy.org.

We are building a CVXPY community onDiscord. Join the conversation! For issues and long-form discussions, useGithub Issues andGithub Discussions.

Contents

• Installation
• Getting started
• Issues
• Community
• Contributing
• Team
• Citing

CVXPY is a Python-embedded modeling language for convex optimization problems. It allows you to 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 where the variable is constrained by lower and upper bounds:

importcvxpyascpimportnumpy# Problem data.m=30n=20numpy.random.seed(1)A=numpy.random.randn(m,n)b=numpy.random.randn(m)# Construct the problem.x=cp.Variable(n)objective=cp.Minimize(cp.sum_squares(A @x-b))constraints= [0<=x,x<=1]prob=cp.Problem(objective,constraints)# The optimal objective is returned by prob.solve().result=prob.solve()# The optimal value for x is stored in x.value.print(x.value)# The optimal Lagrange multiplier for a constraint# is stored in constraint.dual_value.print(constraints[0].dual_value)

With CVXPY, you can model

• convex optimization problems,
• mixed-integer convex optimization problems,
• geometric programs, and
• quasiconvex programs.

CVXPY is not a solver. It relies upon the open source solversClarabel,SCS,andOSQP. Additional solvers areavailable,but must be installed separately.

CVXPY began as a Stanford University research project. It is now developed bymany people, across many institutions and countries.

CVXPY is available on PyPI, and can be installed with

pip install cvxpy

CVXPY can also be installed with conda, using

conda install -c conda-forge cvxpy

CVXPY has the following dependencies:

• Python >= 3.9
• Clarabel >= 0.5.0
• OSQP >= 0.6.2
• SCS >= 3.2.4.post1
• NumPy >= 1.20.0
• SciPy >= 1.6.0

For detailed instructions, see theinstallationguide.

To get started with CVXPY, check out the following:

• official CVXPY tutorial
• example library
• API reference

We encourage you to report issues using theGithub tracker. We welcome all kinds of issues, especially those related to correctness, documentation, performance, and feature requests.

For basic usage questions (e.g., "Why isn't my problem DCP?"), please useStackOverflow instead.

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 onDiscord.
• To have longer, in-depth discussions with the CVXPY community, useGithub Discussions.
• To share feature requests and bug reports, useGithub Issues.

Please be respectful in your communications with the CVXPY community, and make sure to abide by ourcode of conduct.

We appreciate all contributions. You don't need to be an expert in convexoptimization to help out.

You should firstinstallCVXPY from source.Here are some simple ways to start contributing immediately:

• Read the CVXPY source code and improve the documentation, or address TODOs
• Enhance thewebsite documentation
• Browse theissue tracker, and look for issues tagged as "help wanted"
• Polish theexample library
• Add abenchmark

If you'd like to add a new example to our library, or implement a new feature,please get in touch with us first to make sure that your priorities align withours.

Contributions should be submitted aspull requests.A member of the CVXPY development team will review the pull request and guideyou through the contributing process.

Before starting work on your contribution, please read thecontributing guide.

CVXPY is a community project, built from the contributions of manyresearchers and engineers.

CVXPY is developed and maintained byStevenDiamond,AkshayAgrawal,Riley Murray,Philipp Schiele,Bartolomeo Stellato,andParth Nobel, with many others contributingsignificantly.A non-exhaustive list of people who have shaped CVXPY over theyears includes Stephen Boyd, Eric Chu, Robin Verschueren,Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, Chris Dembia, andWilliam Zhang.

For more information about the team and our processes, see ourgovernance document.

If you use CVXPY for academic work, we encourage you tocite our papers. If you use CVXPY in industry, we'd love to hear from you as well, on Discord or over email.