Topological insights into weighted statistical convergence for triple sequence

  • Prasenjit Bal ICFAI University Tripura
  • Parthiba Das

Abstract

This research explores the concept of weighted statistical convergence for triple sequences under a topological perspective, an extension of weighted statistical convergence by incorporating three weight functions $g, h$ and $i$ into the framework, refining the notion of convergence to accommodate variations in density and distribution of sequence elements. The study examines the properties and behaviors of these sequences, emphasizing their applications in topology. We provide characterizations, theorems, and examples to illustrate the nuanced convergence criteria under different topological settings, highlighting the impact of weights on convergence rates and patterns. This generalized approach offers deeper insights and more flexible tools for analyzing convergence in complex structures. The work also discusses the product spaces, which play a critical role in understanding the interaction and convergence properties of multiple sequences simultaneously. By leveraging the structure of product spaces, the study provides a comprehensive view of how triple sequences converge under three weight functions and topological features.

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Published
2025-11-01
Section
Advances in Nonlinear Analysis and Applications