Random Function Iterations for Consistent Stochastic Feasibility

Autor:	Neal Hermer, Anja Sturm, D. Russell Luke
Rok vydání:	2019
Předmět:	021103 operations research Control and Optimization Markov chain 010102 general mathematics 0211 other engineering and technologies Random function 02 engineering and technology Fixed point 01 natural sciences Computer Science Applications Optimization and Control (math.OC) Iterated function Signal Processing Convergence (routing) FOS: Mathematics 60J05 52A22 49J55 (Primary) 49J53 65K05 (Secondary) Applied mathematics 0101 mathematics Mathematics - Optimization and Control Projection algorithms Analysis Mathematics
Zdroj:	Numerical Functional Analysis and Optimization. 40:386-420
ISSN:	1532-2467 0163-0563
Popis:	We study the convergence of stochastic fixed point iterations in the consistent case (in the sense of Butnariu and Fl{\aa}m (1995)) in several different settings, under decreasingly restrictive regularity assumptions of the fixed point mappings. The iterations are Markov chains and, for the purposes of this study, convergence is understood in very restrictive terms. We show that sufficient conditions for geometric (linear) convergence in expectation of stochastic projection algorithms presented in Nedi\'c (2011), are in fact necessary for geometric (linear) convergence in expectation more generally of iterated random functions. Comment: 29 pages, 4 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fa3f81581ee7256f3b5bbec6b4869ad https://doi.org/10.1080/01630563.2018.1535507 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.