학술논문

An extension of the proximal point algorithm beyond convexity
Document Type
Academic Journal
Source
Journal of Global Optimization. February, 2022, Vol. 82 Issue 2, p313, 17 p.
Subject
Algorithm
Algorithms
Language
English
ISSN
0925-5001
Abstract
We introduce and investigate a new generalized convexity notion for functions called prox-convexity. The proximity operator of such a function is single-valued and firmly nonexpansive. We provide examples of (strongly) quasiconvex, weakly convex, and DC (difference of convex) functions that are prox-convex, however none of these classes fully contains the one of prox-convex functions or is included into it. We show that the classical proximal point algorithm remains convergent when the convexity of the proper lower semicontinuous function to be minimized is relaxed to prox-convexity.
Author(s): Sorin-Mihai Grad [sup.1], Felipe Lara [sup.2] Author Affiliations: (1) grid.10420.37, 0000 0001 2286 1424, Faculty of Mathematics, University of Vienna, , Oskar-Morgenstern-Platz 1, A-1090, Vienna, Austria (2) grid.412182.c, 0000 [...]