The Relaxation of Complementary Slackness Conditions as a Regularization Method for Optimal Control Problems

Q3 Engineering

Advances in Systems Science and Applications Pub Date : 2019-07-01 DOI:10.25728/ASSA.2019.19.2.708

N. Pilnik, Igor Pospelov, Stanislav Radionov

引用次数: 3

Abstract

A new approach to the transformation of solutions of optimal control problems based on the special form of relaxation of complementary slackness conditions is presented. The proposed approach is tested on the Russian banking system model, which is derived as a solution of a linear nonautonomous optimization problem with mixed constraints. It is shown that the use of this method regularizes the model in a sense it becomes applicable for the forecasting of the main Russian banking indicators.

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互补松弛条件的松弛作为最优控制问题的正则化方法

提出了一种基于互补松弛条件的特殊松弛形式的最优控制问题解变换的新方法。该方法在俄罗斯银行系统模型上进行了验证，该模型是由一个具有混合约束的线性非自治优化问题的解导出的。结果表明，该方法在一定意义上使模型规范化，适用于俄罗斯主要银行指标的预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Systems Science and Applications Engineering-Engineering (all)

CiteScore

1.20

自引率

0.00%

发文量

期刊介绍： Advances in Systems Science and Applications (ASSA) is an international peer-reviewed open-source online academic journal. Its scope covers all major aspects of systems (and processes) analysis, modeling, simulation, and control, ranging from theoretical and methodological developments to a large variety of application areas. Survey articles and innovative results are also welcome. ASSA is aimed at the audience of scientists, engineers and researchers working in the framework of these problems. ASSA should be a platform on which researchers will be able to communicate and discuss both their specialized issues and interdisciplinary problems of systems analysis and its applications in science and industry, including data science, artificial intelligence, material science, manufacturing, transportation, power and energy, ecology, corporate management, public governance, finance, and many others.