[2310.05855] Validation of a recently proposed strongly polynomial-time algorithm for the general linear programming problem Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Mathematics > Optimization and Control arXiv:2310.05855 (math) [Submitted on 9 Oct 2023 ( v1 ), last revised 2 Jul 2026 (this version, v5)] Title: Validation of a recently proposed strongly polynomial-time algorithm for the general linear programming problem Authors: Samuel Awoniyi View a PDF of the paper titled Validation of a recently proposed strongly polynomial-time algorithm for the general linear programming problem, by Samuel Awoniyi View PDF Abstract: This article presents a validation of a recently proposed strongly polynomial-time algorithm for the general linear programming problem. The proposed algorithm is an implicit reduction procedure that combines primal and dual linear programming problems into a special system of linear equations constrained by complementarity relations and non-negative variables. Each iteration of the algorithm consists of applying a pair of complementary Gauss-Jordan pivoting operations, guided by a necessary-condition lemma. This validation article demonstrates that the proposed algorithm requires no more than 2(k+n) iterations, where k is the number of constraints and n is the number of variables of given general linear programming problem. Comments: 19 pages. Additional annotations have been provided for the proof of Lemma 6.1. A flow chart has just been added, as suggested by some readers. The results are the same as before. The description have been vastly improved as a result of suggestions by reputable journal reviewers and area editors Subjects: Optimization and Control (math.OC) MSC classes: 90C05 Cite as: arXiv:2310.05855 [math.OC] (or arXiv:2310.05855v5 [math.OC] for this version) https://doi.org/10.48550/arXiv.2310.05855 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Samuel Awoniyi [ view email ] [v1] Mon, 9 Oct 2023 16:50:58 UTC (491 KB) [v2] Wed, 22 Nov 2023 05:25:09 UTC (497 KB) [v3] Sat, 25 Apr 2026 04:08:58 UTC (254 KB) [v4] Wed, 3 Jun 2026 09:25:56 UTC (623 KB) [v5] Thu, 2 Jul 2026 20:41:56 UTC (600 KB) Full-text links: Access Paper: View a PDF of the paper titled Validation of a recently proposed strongly polynomial-time algorithm for the general linear programming problem, by Samuel Awoniyi View PDF view license Current browse context: math.OC < prev | next > new | recent | 2023-10 Change to browse by: math References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from