Important

This repository is archived. Development on nonlinear solvers for control (on Lie groups for robotics) continues on the aligator repository.

At time of archiving, the dense nonlinear solver in this repository is not ported over to aligator. This might change in the future if there is demand for it (and especially for a sparse Lie group NLP solver).

proxsuite-nlp: a package for nonlinear optimization on manifolds [ARCHIVED]

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.

Installation

From Conda

From our channel

conda install -c simple-robotics proxsuite-nlp

From source with Pixi

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.

From source

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

Notes

• 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

Credits

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

Acknowledgments

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