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.

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.

Quick install with :

   brew install proxsuite

This approach is available on Linux and Mac OS X.

Alternative approaches

Installation from source is presented here.

Compiling a first example program

For the fastest performance, use the following command to enable vectorization when compiling the simple example.

g++ -O3 -march=native -DNDEBUG -std=gnu++17 -DPROXSUITE_VECTORIZE examples/first_example_dense.cpp -o first_example_dense $(pkg-config --cflags proxsuite)

Using ProxSuite with CMake

If you want to use ProxSuite with CMake, the following tiny example should help you:

```cmake cmake_minimum_required(VERSION 3.10)

project(Example CXX) find_package(proxsuite REQUIRED) set(CMAKE_CXX_STANDARD 17) # set(CMAKE_CXX_STANDARD 14) will work too

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