Python interface for CUDA implementation of OSQP

Python wrapper for CUDA implementation of OSQP <https://osqp.org/>__.

The OSQP (Operator Splitting Quadratic Program) solver is a numerical optimization package for solving problems in the form

::

minimize        0.5 x' P x + q' x

subject to      l <= A x <= u

where x in R^n is the optimization variable. The objective function is defined by a positive semidefinite matrix P in S^n_+ and vector q in R^n. The linear constraints are defined by matrix A in R^{m x n} and vectors l in R^m U {-inf}^m, u in R^m U {+inf}^m.

Installation

You need to install the following:

• NVIDIA CUDA Toolkit <https://developer.nvidia.com/cuda-downloads>_

• CMake <https://cmake.org/>_

• GCC compiler <https://gcc.gnu.org/> (Linux) or Build Tools for Visual Studio 2017 <https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2017> (Windows)

Make sure the environment variable CUDA_PATH is set to the CUDA Toolkit install directory.

Then run the following commands in your terminal:

::

git clone --recurse-submodules https://github.com/oxfordcontrol/cuosqp cd cuosqp python setup.py install

Documentation

The interface is documented here <https://osqp.org/docs/interfaces/python.html>__.

Citing

If you use cuosqp for research, please cite our accompanying paper <https://doi.org/10.1016/j.jpdc.2020.05.021>__:

::

@article{cuosqp, author = {Schubiger, M. and Banjac, G. and Lygeros, J.}, title = {{GPU} acceleration of {ADMM} for large-scale quadratic programming}, journal = {Journal of Parallel and Distributed Computing}, year = {2020}, volume = {144}, pages = {55--67}, doi = {10.1016/j.jpdc.2020.05.021}, }