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Proximal Augmented Lagrangian method for Quadratic Programs

QPALM is now maintained at kul-optec/QPALM
<details> <summary>View the original README</summary>

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

minimize        0.5 x' Q x + q' x

subject to      l <= A x <= u
<!-- ## Installation First of all, clone this repo with all the submodules! For this, run ``` git clone https://github.com/Benny44/QPALM_vLADEL.git git submodule update --init --recursive ``` ### **Matlab** * To install the mex interface of QPALM, add QPALM and its subfolders to the matlab path. Then run qpalm_make.m. You can test whether QPALM is working using the examples/qpalm_mex_demo.m and examples/qpalm_mex_nonconvex_demo.m. ### **C** * To install a C-callable library, check [Bintray](https://bintray.com/benny44/generic/QPALM) for the binaries. These were compiled against [lapack](https://anaconda.org/conda-forge/lapack) from conda. So install miniconda and run the following commands ``` conda install -c conda-forge lapack export LD_LIBRARY_PATH=path-to-miniconda/lib/:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=path-to-qpalm-binaries/lib/:$LD_LIBRARY_PATH ``` ### **Python** The python interface has been compiled for python version 3.7. If you want to use a different version, do your own install with the instructions on custom compilation below. Follow the instructions for installing the C-library above. Then in an open terminal, do ``` export LD_LIBRARY_PATH=path-to-qpalm-binaries/interfaces/python/build/lib/:$LD_LIBRARY_PATH python3 path-to-qpalm-binaries/interfaces/python/qpalm_python_demo.py ``` ### **Julia** See [QPALM.jl](https://github.com/kul-forbes/QPALM.jl/tree/856c70d2be99a24e5d9a6391be45cf40c48947d4) for the instructions on installing the Julia interface. ## Custom Compilation If you wish to do a custom compilation of the shared libraries, take a look at buildCustom.sh. First install the dependencies ``` conda install -c conda-forge lapack ``` Then change the following lines near the top of the script ``` export MINICONDA_LIB=path-to-miniconda/lib export MINICONDA_INCLUDE=path-to-miniconda/include ``` Furthermore, change the cmake line to have whatever flags you want. To build the release version (with tests), use ``` cmake $curdir -DCMAKE_BUILD_TYPE=release -DCOVERAGE=ON ``` To build the python interface, use instead ``` cmake path-to-QPALM -DCMAKE_BUILD_TYPE=release -DINTERFACES=OFF -DUNITTESTS=OFF -DPYTHON=ON ``` Finally, run the buildCustom.sh script ``` chmod 755 buildCustom.sh ./buildCustom.sh ``` ## Code Examples Basic demos are available for the different ways to call the solver: * For the mex interface of QPALM, check out examples/qpalm_mex_demo.m and examples/qpalm_mex_nonconvex_demo.m. * For the C-version of QPALM, check out examples/qpalm_demo.c. * For the python interface of QPALM, check out interfaces/python/qpalm_python_demo.py. * For the Julia interface of QPALM, check out any of the files in interfaces/QPALM.jl/test/. -->

Documentation

You can now find the the documentation here. This includes all information you need to get started using QPALM.

Benchmarks

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

Citing

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

@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.},
}

License

QPALM is licensed under LGPL v3.0. Some modules are used in this software:

</details>