MIN-MAX SOLUTIONS FOR PARAMETRIC CONTINUOUS STATIC GAME UNDER ROUGHNESS (PARAMETERS IN THE COST FUNCTION AND FEASIBLE REGION IS A ROUGH SET)

Q3 Mathematics Ural Mathematical Journal Pub Date : 2020-12-26 DOI:10.15826/umj.2020.2.001
Y. Aboelnaga, M. Zidan
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引用次数: 1

Abstract

Any simple perturbation in a part of the game whether in the cost function and/or conditions is a big problem because it will require a game re-solution to obtain the perturbed optimal solution. This is a waste of time because there are methods required several steps to obtain the optimal solution, then at the end we may find that there is no solution. Therefore, it was necessary to find a method to ensure that the game optimal solution exists in the case of a change in the game data. This is the aim of this paper. We first provided a continuous static game rough treatment with Min-Max solutions, then a parametric study for the processing game and called a parametric rough continuous static game (PRCSG). In a Parametric study, a solution approach is provided based on the parameter existence in the cost function that reflects the perturbation that may occur to it to determine the parameter range in which the optimal solution point keeps in the surely region that is called the stability set of the \(1^{st}\) kind. Also the sets of possible upper and lower stability to which the optimal solution belongs are characterized. Finally, numerical examples are given to clarify the solution algorithm.
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粗糙条件下参数连续静态对策的最小-最大解(参数在代价函数和可行区域为粗糙集)
在博弈的一部分中的任何简单扰动,无论是在成本函数和/或条件中,都是一个大问题,因为它需要博弈重解来获得扰动的最优解。这是浪费时间,因为有一些方法需要几个步骤才能获得最优解,然后最后我们可能会发现没有解。因此,有必要找到一种方法来确保在游戏数据发生变化的情况下存在游戏最优解。这就是本文的目的。我们首先提供了一个具有Min-Max解的连续静态对策粗处理,然后对处理对策进行了参数研究,称之为参数粗糙连续静态对策(PRCSG)。在参数研究中,基于成本函数中参数的存在性,提供了一种求解方法,该方法反映了它可能发生的扰动,以确定最优解点保持在被称为\(1^{st}\)类稳定集的确定区域中的参数范围。还刻画了最优解所属的可能的上稳定性和下稳定性的集合。最后,通过算例对求解算法进行了说明。
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
Ural Mathematical Journal
Ural Mathematical Journal Mathematics-Mathematics (all)
CiteScore
1.30
自引率
0.00%
发文量
12
审稿时长
16 weeks
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