多维变分问题指数惩罚的收敛性

Anurag Jayswal, Ayush Baranwal
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引用次数: 0

摘要

本文描述了一种用指数罚函数处理具有不等式约束的多维变分问题的方法。我们提出了一个无约束的多维变分问题,并研究了所考虑的多维变分问题的最优解与无约束多维变分问题的极小值序列之间的关系。研究了指数惩罚方法的收敛性,表明无约束多维变分问题的最小值序列的收敛子序列趋近于多维变分问题的最优解。此外,还提出了一个说明性应用(最小化生产企业的制造成本函数)来确认所提出结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On convergence of exponential penalty for the multi-dimensional variational problems
In this article, we describe a method to deal with a multi-dimensional variational problem with inequality constraints using an exponential penalty function. We formulate an unconstrained multi-dimensional variational problem and examine the relationships between the optimal solution to the considered multi-dimensional variational problem and the sequence of minimizers of the unconstrained multi-dimensional variational problem. The convergence of the proposed exponential penalty approach is also investigated, which shows that a convergent subsequence of the sequence of minimizers of the unconstrained multi-dimensional variational problem approaches an optimal solution to the multi-dimensional variational problem. Further, an illustrative application (to minimize a manufacturing cost functional of a production firm) is also presented to confirm the effectiveness of the proposed outcomes.
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