Proximal Augmented Lagrangian method for Quadratic Programs
Project description
QPALM is a numerical optimization package that finds stationary points of (possibly nonconvex) quadratic programs, that is
\begin{equation*}
\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}
\end{equation*}
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, you can install it using:
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.
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.
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.},
}
Project details
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qpalm-1.2.3.tar.gz
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BLAKE2b-256 | 9129f3674a58b81c0f8152d7f20ae4bd198707662d51d0bec66a65f76f427f05 |
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Hashes for qpalm-1.2.3-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | 1fa66912aa82b27c6f9be749ed3d057dcadb2aa9c237234c7460df0cfdcd0822 |
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MD5 | cf6bcad0c6b72995866a3564ea9589cc |
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BLAKE2b-256 | 6f0b2e4ef49afbfcb037ff0702e86db17c4be0931b1cb02d69665a41489423db |
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Hashes for qpalm-1.2.3-cp38-cp38-manylinux_2_27_armv7l.whl
Algorithm | Hash digest | |
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SHA256 | de717ba89308f47aff3f3d2eb498c873a5f98beb8d4d037822634b66fe2a2758 |
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MD5 | cf86858112cf977c232a0240969a5538 |
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BLAKE2b-256 | 28f59a56ed20ef2df784cbd933472ec408eb9921b95144c8f8f45b696af9574d |
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Hashes for qpalm-1.2.3-cp38-cp38-manylinux_2_27_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | 534bad4a62259dad5d220fdaecb32abe12822d8c00fb3ea3435a43188e55f6b4 |
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MD5 | 0191e1d57d18ca3e37719d57bd82827b |
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BLAKE2b-256 | 02adda51f20957a2e56268d3fec0a8e4a8f01f790c7f27893fc6daac19e58c11 |
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Hashes for qpalm-1.2.3-cp38-cp38-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | bd455f050b9e86fe82b6a4a2b4913abfe03811426f2a590d3c131b8f8a6d23be |
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MD5 | d7c43da0febe6f30b0d0ea8bd6e4c475 |
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BLAKE2b-256 | 0124a6120785e50c89943e253753b0aeca7635d06c7cfaca97698f4a51fee191 |
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Hashes for qpalm-1.2.3-cp38-cp38-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
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SHA256 | c35c97ff6b847c2d43ebfb3026e9f357bf36fb8e0606204b0baeb404ac75ab86 |
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MD5 | 3debc3e9921f1399b22614d66f839e8b |
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BLAKE2b-256 | 6267e5114e55c1a3abab085cfddf112741c38fb04fe37f2aad5c474c6291063c |
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Hashes for qpalm-1.2.3-cp38-cp38-linux_armv6l.whl
Algorithm | Hash digest | |
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SHA256 | 77ca7e74c7a0eefad485347f20af15c5fae8b8ffee6eb539595f62bf408ba8c8 |
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MD5 | fee4c8bea9fd64c3fb68da840e3fb7ff |
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BLAKE2b-256 | 38767ddce38b8cc387e65f5ef7a83babf931d585a2521d8f3500b0262ae853d1 |
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Hashes for qpalm-1.2.3-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
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SHA256 | e2ff23e596e9722a8abadfe95d3b2207f646f1af27c59f74aa122c54349e5712 |
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MD5 | a04049a3aff3ee842f058c4c7e30ce32 |
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BLAKE2b-256 | cab3ff0d5125bab362d544ac831309f8dce7975833bbea1d4d6669167a00d531 |
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Hashes for qpalm-1.2.3-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
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SHA256 | 2daca5e1939be0de5d8fe8ecb1188f48fee1bc1aa963aeb5d57a6396a282452e |
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MD5 | f922113479d1b37d5b83710ec72cd5f6 |
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BLAKE2b-256 | c02f07943df11da49ca7f2979845e999998ce3cc06fe7dfecdbbdce77423b28c |
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Hashes for qpalm-1.2.3-cp37-cp37m-manylinux_2_27_armv7l.whl
Algorithm | Hash digest | |
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SHA256 | 2eb26066364e6f523239f3f3062d1b657c9cca43a9bd8ad70d2bebb80b1eaadc |
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MD5 | a01aa6e593a8791bcfac8a55d1608741 |
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BLAKE2b-256 | 38645f94a4806f9c9d3949076e905bc67e06f707e94305b7da2156a9794233ba |
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Hashes for qpalm-1.2.3-cp37-cp37m-manylinux_2_27_aarch64.whl
Algorithm | Hash digest | |
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SHA256 | bbbf1be4964ce66d1e2b5b04eea00727e65627c2ceadc1fbe412af41f5630c9d |
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MD5 | 24d5455d5459ed406af08d469d196578 |
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BLAKE2b-256 | abe52c33ff7fb137b0e629ef53402f7e133bb02bbef6ba1598c4301b99128e3d |
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Hashes for qpalm-1.2.3-cp37-cp37m-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | b688fb81cb20fb6a8e3910dfa280f90ba3c6b0c9694679d6223f5dd9a5555141 |
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MD5 | e1c8944f2e6b1e6ee0f424d44c365f22 |
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BLAKE2b-256 | 42ec727172263449e7a23997f9feb847191192215dc61f1e38f1661db6be42d0 |
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Hashes for qpalm-1.2.3-cp37-cp37m-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
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SHA256 | cdf54ac50893b0cb4ef8dfb217d9796c4df0ba5c191e9079c2eeebc4c0dc92d4 |
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MD5 | e5323848d81afdcc6c4309463b542e24 |
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BLAKE2b-256 | 20eecb8fb0938be682e4303b8176f347a44cd5c85ed189f7fdca1d3cd65424bd |
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Hashes for qpalm-1.2.3-cp37-cp37m-linux_armv6l.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a8ec3058390d3d53ed713b71b4f135a88c7847f41b5191b192349ffc6166f2b6 |
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MD5 | 63cd8d25e5be4bcdfd9365f8e19153e4 |
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BLAKE2b-256 | e91e27fa0e77d5deb65c691db5d08f716d7960901074b89944801184fa70627a |