---
id: proxsuite
version: "0.7.3"
license: BSD-2-Clause
license_treatment: permissive
maintenance: active
---

# proxsuite — Quadratic Programming Solver for Robotics and beyond.

License: permissive · Maintenance: active · Downloads: 102.8K/mo

## What it is and what it does

ProxSuite is a collection of numerical solvers for quadratic programs (QPs), linear programs (LPs), and related optimization problems, built on primal-dual proximal algorithms. It targets robotics applications but is domain-agnostic and can solve dense, sparse, or matrix-free problems. The package is a C++ template library with Python bindings, offering warm-starting, batch solving, handling of nonconvex QPs, and infeasibility detection.

The core solver, ProxQP, handles standard QP form with equality and inequality constraints. It is integrated into CVXPY, CasADi, and TSID, and is actively maintained by research groups at Inria and École Normale Supérieure de Paris. Installation is straightforward via pip, conda, or brew on Linux, macOS, and Windows; runtime dependencies are numpy and scipy.

Use it for:

- Solve quadratic programs in robotics applications, such as inverse dynamics with contacts via TSID.
- Use as a backend solver within CVXPY or CasADi for convex optimization modeling.
- Handle large-scale or sparse QPs where specialized matrix factorization backends improve performance.
- Solve batches of QPs in parallel for parameter-sweep or sensitivity analysis workflows.
- Detect and handle infeasible QPs or find the closest feasible solution when problems are ill-posed.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

ProxSuite is a numerically robust quadratic programming solver that handles dense, sparse, and matrix-free problems with specialized algorithms for robotics and general optimization.

Yes, if you need a robust, actively maintained QP solver with good performance on hard problems and integration into major optimization ecosystems. The permissive BSD-2-Clause license, medium install friction, and zero known vulnerabilities make it a safe choice. Install with caution only if your platform is not among the prebuilt wheel targets (Linux x86_64/aarch64, macOS Intel/ARM, Windows).

## Install

pip install proxsuite
uv add proxsuite
poetry add proxsuite

## Installing proxsuite

Before you install: Medium install friction due to compiled C++ backend with prebuilt wheels for Python 3.9–3.14 on Linux, macOS (Intel and ARM), and Windows. Active maintenance with last release 95 days ago; no known vulnerabilities.

License in practice: BSD-2-Clause permissive license allows commercial and private use with minimal restrictions; attribution required.

Quickstart:

pip install proxsuite

import proxsuite
import numpy as np

H = np.eye(2)
g = np.array([1., 2.])
qp = proxsuite.proxqp.dense.QP(H, g)
qp.solve()

Requires Python 3.9 or later; prebuilt wheels available for common platforms, but compilation from source may be needed on unsupported architectures.

Verify before relying:

- Whether the package supports Julia bindings as described in the excerpt, or if those are separate.
- Performance characteristics on specific problem sizes or sparsity patterns beyond the benchmarks mentioned.
- Availability and maturity of the QPLayer differentiable QP layer feature.

## Package facts

- License: BSD-2-Clause (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 102.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

quadratic programming solver, QP solver for robotics, convex optimization, proximal algorithm solver, constrained optimization, linear programming solver, numerical optimization, optimization, quadratic-programming, robotics

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