A compact implementation of a recently proposed strongly polynomial-time algorithm for the general LP problem

TL;DR

This paper introduces a space-efficient implementation of a recently developed strongly polynomial-time algorithm for linear programming, utilizing smaller matrices for pivot operations to improve computational efficiency.

Contribution

It presents a novel compact implementation of a strongly polynomial LP algorithm using half-sized matrices for pivoting, enhancing efficiency and practicality.

Findings

Implementation reduces memory usage by half.

Numerical illustration demonstrates effectiveness.

Algorithm maintains polynomial-time complexity.

Abstract

This article presents a compact implementation of a recently proposed strongly polynomial-time algorithm for the general linear programming problem. Each iteration of the algorithm consists of applying a pair of complementary Gauss-Jordan (GJ) pivoting operations. In this compact implementation of the algorithm, the GJ pivoting operations are done inside a matrix that has half the size of the original matrix. A numerical illustration is given.