多参数邻近点法的亚稳定性
Metastability of the proximal point algorithm with multi-parameters
摘要：
利用证据挖掘技术分析Yonghong Yao和Muhammad Aslam Noor关于多参数广义邻近点法强收敛性的一个结果. Yao和Noor的结果保证了算法强收敛到算子零点集上的最近投影点。我们的定量分析，指导费尔南多·费雷拉和保罗·奥利瓦的有界功能的解释，提供了一个原始的递归界的亚稳定性的算法的收敛性，在特伦斯道的意义。此外，我们得到了定量的信息迭代的渐近正则性。本文的结果是通过对$\limsup$的算术化而得以实现的。
英文摘要：
In this article we use techniques of proof mining to analyse a result, due to Yonghong Yao and Muhammad Aslam Noor, concerning the strong convergence of a generalized proximal point algorithm which involves multiple parameters. Yao and Noor's result ensures the strong convergence of the algorithm to the nearest projection point onto the set of zeros of the operator. Our quantitative analysis, guided by Fernando Ferreira and Paulo Oliva's bounded functional interpretation, provides a primitive recursive bound on the metastability for the convergence of the algorithm, in the sense of Terence Tao. Furthermore, we obtain quantitative information on the asymptotic regularity of the iteration. The results of this paper are made possible by an arithmetization of the $\limsup$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
