论文标题
可扩展的工业过程线性优化方法
Scalable Method for Linear Optimization of Industrial Processes
论文作者
论文摘要
在工业数字双胞胎的发展中，技术和业务流程的优化问题经常出现。在许多情况下，这个问题可以简化为大规模的线性编程（LP）问题。该文章专门用于解决大规模LP问题的新方法。此方法称为“ Apex-Method”。 Apex-Method使用预测器 - 校正框架。预测变量步骤计算属于LP问题可行区域的点。校正器步骤计算一系列聚合到LP问题的精确解决方案的序列。本文提供了Apex方法的正式描述，并通过使用MPI库提供了有关其并行实现的信息。介绍了群集计算系统上的大规模计算实验的结果，以研究顶点方法的可伸缩性。
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.
