Scalable Method for Linear Optimization of Industrial Processes
Abstract
In the development of industrial digital twins, the optimization problem of technological and business processes often arises. In many cases, this problem can be reduced to a large-scale linear programming (LP) problem. The article is devoted to the new method for solving large-scale LP problems. This method is called the "apex-method". The apex-method uses the predictor-corrector framework. The predictor step calculates a point belonging to the feasible region of LP problem. The corrector step calculates a sequence of points converging to the exact solution of the LP problem. The article gives a formal description of the apex-method and provides information about its parallel implementation in C++ language by using the MPI library. The results of large-scale computational experiments on a cluster computing system to study the scalability of the apex method are presented.
Cite
@article{arxiv.2006.14921,
title = {Scalable Method for Linear Optimization of Industrial Processes},
author = {Leonid B. Sokolinsky and Irina M. Sokolinskaya},
journal= {arXiv preprint arXiv:2006.14921},
year = {2021}
}