The CVXPY documentation is at cvxpy.org.
Join the CVXPY mailing list, and use the issue tracker and StackOverflow for the best support.
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 <= 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)CVXPY is not a solver. It relies upon the open source solvers ECOS, SCS, and OSQP. Additional solvers are available, but must be installed separately.
CVXPY began as a Stanford University research project. It is now developed by many 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 >= 3.5
- multiprocess
- OSQP
- ECOS >= 2
- SCS >= 1.1.3
- NumPy >= 1.15
- SciPy >= 1.1.0
For detailed instructions, see the installation guide.
To get started with CVXPY, check out the following:
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.
To communicate with the CVXPY developer community, create a Github issue or use the CVXPY mailing list. Please be respectful in your communications with the CVXPY community, and make sure to abide by our code of conduct.
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.
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; feel free to reach out to the developers directly.
CVXPY is a community project, built from the contributions of many researchers and engineers.
CVXPY is developed and maintained by Steven Diamond, Akshay Agrawal, and Riley Murray, 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, Bartolomeo Stellato, Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, and Chris Dembia.