{"id":48789194,"url":"https://github.com/petrus1904/superadmm","last_synced_at":"2026-04-13T19:01:05.671Z","repository":{"id":290732791,"uuid":"975392273","full_name":"Petrus1904/superADMM","owner":"Petrus1904","description":"superADMM: Quadratic Program Solver with dynamic weighting ADMM","archived":false,"fork":false,"pushed_at":"2026-03-09T21:09:23.000Z","size":12582,"stargazers_count":7,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-10T01:40:45.739Z","etag":null,"topics":["c","linear-programming","matlab","model-predictive-control","optimization","python","quadratic-programming","solver"],"latest_commit_sha":null,"homepage":"","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-2.1","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Petrus1904.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-04-30T08:37:08.000Z","updated_at":"2026-03-09T21:09:27.000Z","dependencies_parsed_at":null,"dependency_job_id":"98041fce-8070-4d08-9b23-c566ac6c82ba","html_url":"https://github.com/Petrus1904/superADMM","commit_stats":null,"previous_names":["petrus1904/superadmm"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/Petrus1904/superADMM","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Petrus1904%2FsuperADMM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Petrus1904%2FsuperADMM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Petrus1904%2FsuperADMM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Petrus1904%2FsuperADMM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Petrus1904","download_url":"https://codeload.github.com/Petrus1904/superADMM/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Petrus1904%2FsuperADMM/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31766482,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-13T15:25:13.801Z","status":"ssl_error","status_checked_at":"2026-04-13T15:25:09.162Z","response_time":93,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["c","linear-programming","matlab","model-predictive-control","optimization","python","quadratic-programming","solver"],"created_at":"2026-04-13T19:00:38.012Z","updated_at":"2026-04-13T19:01:05.661Z","avatar_url":"https://github.com/Petrus1904.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SuperADMM\nFast Quadratic Program Solver with dynamic weighting ADMM\n\nThis algorithm specifically solves problems of shape\n\n$$\n    \\begin{equation}\n        \\begin{aligned}\n            \u0026 \\underset{x}{\\textbf{minimize}}\n            \u0026 \u0026 \\tfrac12 x^\\top P x + x^\\top q\\\\\n            \u0026 \\textbf{subject to}\n            \u0026 \u0026 l \\le Ax \\le u \\\\\n        \\end{aligned}\n    \\end{equation}\n$$\n\nwhere $x \\in \\mathrm{R}^n$ is the optimization variable, $P \\in \\mathrm{R}^{n \\times n}$ and $q \\in \\mathrm{R}^n$ describe the quadratic cost function ($P$ is positive semi-definite), $A \\in \\mathrm{R}^{m \\times n}$ is the linear constraint mapping and $l \\in\\mathrm{R}^m$, $u\\in\\mathrm{R}^m$ denote the lower and upper bounds of the constraints. Note that by setting $l_i = u_i$ for some index $i$, one can also include equality constraints in the problem. Furthermore, $l_i = -\\infty$ or $u_i = \\infty$ allows users to only consider lower or upper bounds.\n\n# Installation\n\n## Python (Express installation)\n- `pip install superadmm`\n- Enjoy your fast solver in Python with `import superADMM`\n\n## MATLAB (Express installation)\n- Download the latest release (https://github.com/Petrus1904/superADMM/releases)\n- Unzip the contents in some folder\n- Run `superADMM_setup.m`\n- Enjoy your fast solver in MATLAB with `superADMM.m`\n\n## MATLAB (Manual installation)\n- Download or clone this package\n- Ensure that MATLAB `mex` add-on (code-generation) is installed, and `gcc` (MinGW64) is installed as mex compiler\n- Verify with `mex -setup` that `MinGW64` is the default compiler\n- Run `compile.m`\n- Enjoy your fast solver in MATLAB with `superADMM.m`\n\n# How to Cite\n\n**APA:**\n\nVerheijen, P.C.N., Goswami, D., and Lazar, M. (2025). *SuperADMM: Solving Quadratic Programs Faster with Dynamic Weighting ADMM.* arXiv: 2506.11608 [math.OC]. URL: https://arxiv.org/abs/2506.11608\n\n**Bibtex:**\n```\n@misc{superADMM:Verheijen2025,\n      title={{SuperADMM: Solving Quadratic Programs Faster with Dynamic Weighting ADMM}}, \n      author={P. C. N. Verheijen and D. Goswami and M. Lazar},\n      year={2025},\n      eprint={2506.11608},\n      archivePrefix={arXiv},\n      primaryClass={math.OC},\n      url={https://arxiv.org/abs/2506.11608}, \n}\n```\n\n# References\nThe fast execution of various linear algebraic operations is provided by a set of third party libraries, which we list below:\n- [OpenBLAS] Wang Qian, Zhang Xianyi, Zhang Yunquan, Qing Yi, *AUGEM: Automatically Generate High Performance Dense Linear Algebra Kernels on x86 CPUs*, In the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'13), Denver CO, November 2013\n- [LAPACK] E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen, *LAPACK Users' Guide,* 3rd e. Philadelphia, PA: Society for Industrial and Applied Mathematics, 1999.\n- [CSPARSE] T.A. Davis, *Direct Methods for Sparse Linear Systems.* SIAM, 2006\n- [LDL] T.A. Davis, *Algorithm 849: A concise sparse cholesky factorization package,* ACM Trans. Math. Softw. vol. 31, no. 4, p. 587-591, Dec. 2005\n\n# License\nSuperADMM is licensed under LGPL 2.1.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpetrus1904%2Fsuperadmm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpetrus1904%2Fsuperadmm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpetrus1904%2Fsuperadmm/lists"}