proxsuite-nlp is a C library, implementing a primal-dual augmented Lagrangian-type algorithm for nonlinear optimization on manifolds, as well as some modelling tools.

From our channel

conda install -c simple-robotics proxsuite-nlp

To build proxsuite-nlp from source the easiest way is to use Pixi.

Pixi is a cross-platform package management tool for developers that will install all required dependencies in .pixi directory. It's used by our CI agent so you have the guarantee to get the right dependencies.

Run the following command to install dependencies, configure, build and test the project:

pixi run test

The project will be built in the build directory. You can run pixi shell and build the project with cmake and ninja manually.

Clone this repo using

git clone [url-to-repo] --recursive

Create a build tree using CMake, build and install:

cd your/checkout/folder/
cmake -S . -B build
cmake --build build/ --config Release --target install

Dependencies

• CMake (with the JRL CMake modules)
• Eigen>=3.3.7
• fmtlib>=9.1.0, <11
• Boost>=1.71
• (optional) eigenpy>=3.2.0 | conda (Python bindings)
• (optional) pinocchio | conda
• a C -14 compliant compiler

Python dependencies:

• numpy
• matplotlib
• typed-argument-parser
• meshcat-python

• To build against a Conda environment, activate the environment and add export CMAKE_PREFIX_PATH=$CONDA_PREFIX before running CMake.

• To build the documentation:

  cd build/
  make doc

The following people have been involved in the development of proxsuite-nlp and are warmly thanked for their contributions:

• Wilson Jallet (LAAS-CNRS/Inria): main developer and manager of the project
• Sarah El Kazdadi (Inria): linear algebra modules developer
• Fabian Schramm (Inria): core developper
• Joris Vaillant (Inria): core developer
• Justin Carpentier (Inria): project coordinator
• Nicolas Mansard (LAAS-CNRS): project coordinator

The development of proxsuite-nlp is actively supported by the Willow team @INRIA and the Gepetto team @LAAS-CNRS.