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qpdo

The Quadratic Primal-Dual Optimizer

https://github.com/aldma/qpdo

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ○
    Academic publication links
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    Committers with academic emails
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.2%) to scientific vocabulary

Keywords

convex-optimization numerical-optimization optimization quadratic-programming
Last synced: 4 months ago · JSON representation ·

Repository

The Quadratic Primal-Dual Optimizer

Basic Info
  • Host: GitHub
  • Owner: aldma
  • License: gpl-3.0
  • Language: C
  • Default Branch: main
  • Homepage:
  • Size: 1.34 MB
Statistics
  • Stars: 12
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 1
Topics
convex-optimization numerical-optimization optimization quadratic-programming
Created about 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation Roadmap

README.md

QPDO: the Quadratic Primal-Dual Optimizer

QPDO is a numerical solver for optimization problems in the form ``` minimize 0.5 x' Q x + q' x

subject to l <= A x <= u `` wherex in R^nis the decision variable. The symmetric positive semidefinite matrixQ in S+^n, the vectorq in R^n, and the matrixA in R^{m x n}are bounded. The vectorsl in R^m U {-inf}^mandu in R^m U {+inf}^mare extended-real-valued and satisfyli ⩽ u_ifor alli in 1,...,m`.

Method and Citing

QPDO implements a primal-dual Newton proximal method for convex quadratic programming. The proposed method can handle degenerate problems, provides a mechanism for infeasibility detection, and can exploit warm starting, while requiring only convexity. In particular, all linear systems are solvable by construction, independently from the problem data, and an exact linesearch can be performed. Details can be found in the research paper mentioned below, which serves as a user manual for advanced users. If you use QPDO in your work, we kindly ask that you cite the following reference. @article{demarchi2022qpdo, author = {De~Marchi, Alberto}, title = {On a primal-dual {N}ewton proximal method for convex quadratic programs}, journal = {Computational Optimization and Applications}, year = {2022}, volume = {81}, number = {2}, pages = {369--395}, doi = {10.1007/s10589-021-00342-y}, }

Installation

QPDO is implemented in C and provides a MATLAB interface via mex, inspired by OSQP and QPALM.

Clone this repository with the submodule for SuiteSparse, running git clone https://github.com/aldma/qpdo.git cd qpdo git submodule update --init --recursive

Matlab

  • To install the mex interface of QPDO, add QPDO and its subfolders to the MATLAB path. Then go to interfaces/mex/ and run qpdo_make.m. You can test and see how to call QPDO from MATLAB using demo_mex.m in the examples/ folder.

Get in touch

Don't hesitate to share your impression! Would you like to collaborate to build better software? Here we are!

Owner

  • Name: Alberto De Marchi
  • Login: aldma
  • Kind: user
  • Location: Europe

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite the article from the preferred-citation below."
title: "QPDO: the Quadratic Primal-Dual Optimizer"
authors:
  - family-names: "De Marchi"
    given-names: "Alberto"
    orcid: "https://orcid.org/0000-0002-3545-6898"
    affiliation: "Universität der Bundeswehr München"
repository-code: "https://github.com/aldma/qpdo"
type: software
keywords:
  - Convex Optimization
  - Quadratic Programming

preferred-citation:
  type: article
  authors:
  - family-names: "De Marchi"
    given-names: "Alberto"
  title: "On a primal-dual Newton proximal method for convex quadratic programs"
  journal: "Computational Optimization and Applications"
  year: 2022
  doi: 10.1007/s10589-021-00342-y
  url: https://doi.org/10.1007/s10589-021-00342-y

GitHub Events

Total
  • Watch event: 4
Last Year
  • Watch event: 4

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 16
  • Total Committers: 2
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.25
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Alberto De Marchi a****2@l****t 12
Alberto De Marchi a****i@g****m 4
Committer Domains (Top 20 + Academic)
libero.it: 1

Issues and Pull Requests

Last synced: 5 months ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: 8 days
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
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  • feiyuxiaoThu (1)
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