cvxpy · PyPI

2023-08-31 20:34| 来源: 网络整理| 查看: 265

CVXPY

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The CVXPY documentation is at cvxpy.org.

We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussions, use Github Issues and Github 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:

import cvxpy as cp import numpy # Problem data. m = 30 n = 20 numpy.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 = 0.4.1 ECOS >= 2 SCS >= 1.1.6 NumPy >= 1.15 SciPy >= 1.1.0

For detailed instructions, see the installation guide.

Getting started

To get started with CVXPY, check out the following:

official CVXPY tutorial example library API reference Issues

We encourage you to report issues using the Github 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 use StackOverflow instead.

Community

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 Github Issues.

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

Contributing

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

You should first install CVXPY from source. Here are some simple ways to start contributing immediately:

Read the CVXPY source code and improve the documentation, or address TODOs Enhance the website documentation Browse the issue tracker, and look for issues tagged as "help wanted" Polish the example library Add a benchmark

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 with ours.

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

Before starting work on your contribution, please read the contributing guide.

Team

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

CVXPY is developed and maintained by Steven Diamond, Akshay Agrawal, Riley Murray, Philipp Schiele, and Bartolomeo Stellato, with many others contributing significantly. A non-exhaustive list of people who have shaped CVXPY over the years includes Stephen Boyd, Eric Chu, Robin Verschueren, Michael Sommerauer, Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, Chris Dembia, and Philipp Schiele.

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

Citing

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

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