A Computational Comparison of Basis Updating Schemes for the Simplex Algorithm on a CPU-GPU System
Department of Applied Informatics, School of Information Sciences, University of Macedonia, Thessaloniki, Greece
American Journal of Operations Research, Vol.3 No.6, 2013
@article{nikolaos2013computational,
title={A Computational Comparison of Basis Updating Schemes for the Simplex Algorithm on a CPU-GPU System},
author={Nikolaos, Ploskas and Nikolaos, Samaras},
journal={American Journal of Operations Research},
volume={3},
pages={497},
year={2013},
publisher={Scientific Research Publishing}
}
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 MATLAB and CUDA environment. Finally, a computational study on randomly generated full dense linear programs is preented to establish the practical value of GPU-based implementation.
November 3, 2013 by hgpu
