Linear programming with a feasible direction interior point technique for smooth optimization

¹ Systems Engineering and Computer Science, COPPE, Federal University of Rio de Janeiro, P.O. Box 68511, Rio de Janeiro, RJ 21945-970, Brazil
² Mechanical Engineering Program, COPPE, Federal University of Rio de Janeiro, P.O. Box 68503, CEP 21945-970, CT, Cidade Universitéria, Ilha do Fundão, Rio de Janeiro, Brazil

^* Corresponding author: miluzcav@cos.ufrj.br

Received: 1 September 2022
Accepted: 21 September 2022

Abstract

We propose an adaptation of the Feasible Direction Interior Points Algorithm (FDIPA) of J. Herskovits, for solving large-scale linear programs. At each step, the solution of two linear systems with the same coefficient matrix is determined. This step involves a significant computational effort. Reducing the solution time of linear systems is, therefore, a way to improve the performance of the method. The linear systems to be solved are associated with definite positive symmetric matrices. Therefore, we use Split Preconditioned Conjugate Gradient (SPCG) method to solve them, together with an Incomplete Cholesky preconditioner using Matlab’s ICHOL function. We also propose to use the first iteration of the conjugate gradient, and to presolve before applying the algorithm, in order to reduce the computational cost. Following, we then provide mathematica proof that show that the iterations approach Karush–Kuhn–Tucker points of the problem under reasonable assumptions. Finally, numerical evidence show that the method not only works in theory but is also competitive with more advanced methods.