Scalable Method for Linear Optimization of Industrial Processes

DOI10.1109/GLOSIC50886.2020.9267854arXiv2006.14921MaRDI QIDQ6343808

Irina M. Sokolinskaya, Leonid B. Sokolinsky

Publication date: 26 June 2020

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.

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