Proximal Augmented Lagrangian method for Quadratic Programs

| QPALM is a numerical optimization package that finds stationary points of (possibly nonconvex) quadratic programs, that is

.. math::

    \begin{aligned}
        & \underset{x}{\textbf{minimize}}
        & & \tfrac12 x^\top Q x + q^\top x\\
        & \textbf{subject to}
        & & b_\mathrm{min} \le Ax \le b_\mathrm{max} \\
    \end{aligned}

Documentation

| The documentation can be found at: <https://kul-optec.github.io/QPALM/Doxygen>_
| Examples are included as well: <https://kul-optec.github.io/QPALM/Doxygen/examples.html>_

Installation

Python ^^^^^^ | The QPALM Python interface is available from PyPI <https://pypi.org/project/qpalm>_, you can install it using:

.. code-block:: sh

python3 -m pip install qpalm

Julia, Matlab, C/C++/Fortran ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | Installation instructions for the Julia, Matlab, C, C++ and Fortran interfaces, as well as instructions for building QPALM from source, can be found on GitHub <https://github.com/kul-optec/QPALM/>_.

Supported platforms

| QPALM is written in C, with interfaces for C++, Python, Julia, Matlab and Fortran. The code itself is portable across all major platforms. Binaries are available for Linux on x86-64, AArch64, ARMv7 and ARMv6, for macOS on x86-64 and ARM64, and for Windows on x86-64.

Benchmarks

| Check out the papers below for detailed benchmark tests comparing QPALM with state-of-the-art solvers.

• QPALM: A Newton-type Proximal Augmented Lagrangian Method for Quadratic Programs <https://arxiv.org/abs/1911.02934>_.
• QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs <https://arxiv.org/abs/2010.02653>_.

Citing

| If you use QPALM in your research, please cite the following paper:

.. code-block:: bib

@inproceedings{hermans2019qpalm,
    author      = {Hermans, B. and Themelis, A. and Patrinos, P.},
    booktitle   = {58th IEEE Conference on Decision and Control},
    title       = {{QPALM}: {A} {N}ewton-type {P}roximal {A}ugmented {L}agrangian {M}ethod for {Q}uadratic {P}rograms},
    year        = {2019},
    volume      = {},
    number      = {},
    pages       = {},
    doi         = {},
    issn        = {},
    month       = {Dec.},
}