Dual support M-method for solving linear programs with non-negative variables

Authors

  • Abdelhak Hebbache LaMOS Research Unit (Modeling and Optimization of Systems), Faculty of Exact Sciences, Bejaia University, 06000 Bejaia, Algeria
  • Mohand Bentobache Laboratory of Pure and Applied Mathematics, University of Laghouat, 03000 Laghouat, Algeria
  • Mohand Ouamer Bibi LaMOS Research Unit (Modeling and Optimization of Systems), Faculty of Exact Sciences, Bejaia University, 06000 Bejaia, Algeria

DOI:

https://doi.org/10.19139/soic-2310-5070-3541

Keywords:

Dual support method, Linear programming, Big M, Updating formulas, Numerical Experiments

Abstract

This paper presents a version of the dual support method specifically designed to handle linear programming (LP) problems with non-negative variables. Since an initial support feasible solution is generally unavailable in advance, we introduce a dual support M-method to solve LP problems without requiring a prior starting point. Additionally, we develop efficient updating formulas for the inverse matrix and the pseudo-feasible solution utilized within the algorithm. To evaluate the performance of the proposed method against the dual simplex and interior-point methods, we implement the proposed algorithm in MATLAB. Computational experiments evaluating CPU time and the number of iterations across both randomly generated test problems and standard NETLIB benchmarks demonstrate the efficiency of the proposed approach, especially on solving dense problems.

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Published

2026-06-22

How to Cite

Hebbache, A., Bentobache, M., & Bibi, M. O. (2026). Dual support M-method for solving linear programs with non-negative variables. Statistics, Optimization & Information Computing, 16(2), 1727–1745. https://doi.org/10.19139/soic-2310-5070-3541

Issue

Vol. 16 No. 2 (2026)

Section

Research Articles

License

Copyright (c) 2026 Abdelhak Hebbache, Mohand Bentobache, Mohand Ouamer Bibi

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