ProxSuite

ProxSuite is a collection of open-source, numerically robust, precise, and efficient numerical solvers (e.g., LPs, QPs, etc.) rooted in revisited primal-dual proximal algorithms. Through ProxSuite, we aim to offer the community scalable optimizers that deal with dense, sparse, or matrix-free problems. While the first targeted application is Robotics, ProxSuite can be used in other contexts without limits.

ProxSuite is actively developed and supported by the Willow and Sierra research groups, joint research teams between Inria, École Normale Supérieure de Paris and Centre National de la Recherche Scientifique localized in France.

ProxSuite is already integrated into:

• CVXPY modeling language for convex optimization problems,
• CasADi’s symbolic framework for numerical optimization in general and optimal control. ProxQP is available in CasADi as a plugin to solve quadratic programs,
• TSID: robotic software for efficient robot inverse dynamics with contacts and based on Pinocchio.

We are ready to integrate ProxSuite within other optimization ecosystems.

Table of contents

• ProxSuite
  • ProxSuite main features
  • Documentation
  • Getting started
    • Quick install with :
    • Quick install with :
    • Quick install with :
    • Alternative approaches
    • Compiling a first example program
    • Using ProxSuite with CMake
  • ProxQP
    • Citing ProxQP
    • Numerical benchmarks
  • QPLayer
    • Citing QPLayer
  • Installation procedure
  • Contribution
  • Core-dev team
  • Credits

ProxSuite main features

Proxsuite is fast:

• C++ template library,
• cache-friendly.

Proxsuite is versatile, offering through a unified API advanced algorithms specialized for efficiently exploiting problem structures:

• dense, sparse, and matrix-free matrix factorization backends,
• advanced warm-starting options (e.g., equality-constrained initial guess, warm-start or cold-start options from previous results),

with dedicated features for

• handling more efficiently box constraints, linear programs, QP with diagonal Hessian, or with far more constraints than primal variables,
• solving nonconvex QPs,
• solving batches of QPs in parallel,
• solving the closest feasible QP if the QP appears to be primal infeasible,
• differentiating feasible and infeasible QPs.

Proxsuite is flexible:

• header only,
• C++ 14/17/20 compliant,
• Python and Julia bindings for easy code prototyping without sacrificing performance.

Proxsuite is extensible. Proxsuite is reliable and extensively tested, showing the best performances on the hardest problems of the literature. Proxsuite is supported and tested on Windows, Mac OS X, Unix, and Linux.

Documentation

The online ProxSuite documentation of the last release is available here.

Getting started

ProxSuite is distributed to many well-known package managers.

Quick install with :

   pip install proxsuite

This approach is available on Linux, Windows and Mac OS X.

Quick install with :

   conda install proxsuite -c conda-forge

This approach is available on Linux, Windows and Mac OS X.

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Contributing Guidelines

Thank you for your interest in contributing to proxsuite. Whether it’s a bug report, a new feature, a fix, or documentation, we value every contribution.

Read this document before opening an issue or a pull request.

All communication on this project must follow the Code of Conduct.

Table of contents

• Contributing Guidelines
  • Reporting bugs and feature requests
  • Asking questions
  • Contributing via pull requests
    • Choosing an issue
    • Set up the development environment
    • Pull request content
    • Keeping the pull request up-to-date
    • Running tests
    • Code style
    • Changelog
  • AI-assisted contributions
    • Responsibility
    • Disclosure
    • Communication
    • Translation
    • AI agent
  • Licensing

Reporting bugs and feature requests

Use the GitHub issue tracker to report bugs or suggest features.

Before opening an issue, check existing open and closed issues to avoid duplicates.

Use the appropriate template and give as much detail as possible. If you don’t use the template, maintainers may close your issue without explanation.

Asking questions

Ask questions in the discussions section. It separates development topics from community questions. Questions posted in the issue tracker will be moved to the discussions section.

Contributing via pull requests

Choosing an issue

Every external contributor pull request needs an associated issue. Open an issue first. Core developers will review it.

An issue is ready for a pull request when:

• It has the ready label.
• It is not assigned.
• It does not have the core developers label.

Issues with the core developers label are reserved for core developers.

If an issue meets these criteria, claim it with a short comment.

Set up the development environment

The easiest way to set up a development environment is to use pixi, as described in the build documentation.

See the CI workflows for examples with other package managers.

Pull request content

To create a pull request, follow the GitHub guides on forking a repository and creating a pull request.

In your pull request:

• Use a descriptive title and follow the pull request template.
• If the pull request is not ready for review, keep it as a draft.
• Keep it to a single self-contained change. Don’t mix unrelated fixes.
• Keep backward compatibility. Don’t break the API.
• Write tests that cover your changes.
• Add an entry to the changelog.
• Make sure code style checks pass (pixi run lint or pre-commit run --all-files).
• Make sure the CI is green. Ask for help if you’re stuck on a CI issue.
• Check all the appropriate items in the pull request template checklist.

Keeping the pull request up-to-date

You must rebase your work on the upstream devel branch.

git pull --rebase origin devel

Don’t omit the --rebase argument or a merge commit will be created. Using merge commits to update your pull request is discouraged as it creates a non-linear git history.

Running tests

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