Effective strong convergence of the proximal point algorithm in CAT(0) spaces
Optimization and Control
2018-02-16 v2
Abstract
We apply methods of proof mining to obtain uniform quantitative bounds on the strong convergence of the proximal point algorithm for finding minimizers of convex, lower semicontinuous proper functions in CAT(0) spaces. Thus, for uniformly convex functions we compute rates of convergence, while, for totally bounded CAT(0) spaces we apply methods introduced by Kohlenbach, the first author and Nicolae to compute rates of metastability.
Cite
@article{arxiv.1801.02179,
title = {Effective strong convergence of the proximal point algorithm in CAT(0) spaces},
author = {Laurentiu Leustean and Andrei Sipos},
journal= {arXiv preprint arXiv:1801.02179},
year = {2018}
}