New search direction based on a class of parametric kernel functions with a Full Newton step Infeasible O(nL) Interior point Methods for Linear Optimization

Authors

  • Samir BOUALI Research Laboratory of Informatics, Data Sciences and Artificial Intelligence, School of Information Sciences, Rabat, Morocco

DOI:

https://doi.org/10.19139/soic-2310-5070-3076

Keywords:

Linear programming, Infeasible interior-point method, Full-Newton step, Polynomial complexity, Kernel function

Abstract

In this paper which is inspired by the work of Roos [7] (SIAM J. Optim. 16(4):1110-1136, 2006), we analysed a new search direction based on a class of parametric kernel functions for IIPMs algorithms. The main iteration of the algorithm consists of a feasibility step and several centrality steps. The neighborhood of Newton process is more wider using a sharper quadratic convergence results. The complexity is polynomial and coincides with the currently best known iteration bound based on centrality steps.

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Published

2026-02-04

Issue

Vol. 15 No. 5 (2026)

Section

Research Articles

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How to Cite

New search direction based on a class of parametric kernel functions with a Full Newton step Infeasible O(nL) Interior point Methods for Linear Optimization. (2026). Statistics, Optimization & Information Computing, 15(5), 3731-3751. https://doi.org/10.19139/soic-2310-5070-3076