A Computational Comparison of Basis Updating Schemes for the Simplex Algorithm on a CPU-GPU System

摘要

The computation of the basis inverse is the most time-consuming step in simplex type algorithms. This inverse does not have to be computed from scratch at any iteration, but updating schemes can be applied to accelerate this calculation. In this paper, we perform a computational comparison in which the basis inverse is computed with five different updating schemes. Then, we propose a parallel implementation of two updating schemes on a CPU-GPU System using MAT-LAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is presented to establish the practical value of GPU-based implementation.