Survey of Metric fixed point theory in random functional analysis

TL;DR

This survey reviews recent advances in metric fixed point theory within random functional analysis, highlighting key theorems and their applications over the past 15 years.

Contribution

It provides a comprehensive overview of fixed point theorems in random metric spaces and modules, emphasizing developments since 2010.

Findings

Progress in fixed point theorems for random metric spaces and modules.

Development of the random Banach contraction principle and Caristi fixed point theorem.

Connections between fixed point theory, random equations, and operators.

Abstract

Based on the idea of randomizing the traditional space theory of functional analysis, random functional analysis has been developed as functional analysis over random metric spaces, random normed modules and random locally convex modules. Since these random frameworks have much more complicated algebraic, topological and geometric structures than their prototypes, the development of fixed point theory in random functional analysis had been almost stagnant before 2010. Unexpectedly, with the deep development of stable set theory fixed point theory in random functional analysis, including both its metric and topological fixed point theory, has made considerable progress in the recent 15 years. The purpose of this paper is to survey the important progress in metric fixed point theory in random functional analysis, including the random Banach contraction mapping principle and Caristi fixed point theorem on complete random metric spaces, and fixed point theorems for random nonexpansive and asymptotically nonexpansive mappings in complete random normed modules. Besides, the connections among the topics surveyed, random equations and random fixed point theorems for random operators are also briefly mentioned.