On best proximity point theorems for relatively nonexpansive mappings in locally convex spaces

Abstract

This paper explores the concept of normal structure by introducing a generalized form known as P-proximal normal structure. Focusing on the framework of Hausdorff locally convex spaces, the study establishes optimal proximity outcomes for both cyclic and noncyclic relatively P-nonexpansive mappings. Furthermore, the paper presents in the realm of probabilistic normed spaces, considered as instances of Hausdorff locally convex spaces, some theorems that address the existence of best proximity points within this context.

Keywords

• best proximity point
• hausdorff locally convex space
• cyclic mapping
• noncyclic mapping
• relatively p-nonexpansive mapping
• probabilistic normed space
• p-normal structure
• 46a03
• 47h10
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• 54h25