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pro vyhledávání: '"Hermer, Neal"'
We provide conditions that guarantee local rates of convergence in distribution of iterated random functions that are not nonexpansive mappings in locally compact Hadamard spaces. Our results are applied to stochastic instances of common algorithms i
Externí odkaz:
http://arxiv.org/abs/2206.05213
Publikováno v:
Journal of Convex Analysis 30 (2023), No. 4, 1073--1114
We study the convergence of random function iterations for finding an invariant measure of the corresponding Markov operator. We call the problem of finding such an invariant measure the stochastic fixed point problem. This generalizes earlier work s
Externí odkaz:
http://arxiv.org/abs/2205.15897
We study the convergence of random function iterations for finding an invariant measure of the corresponding Markov operator. We call the problem of finding such an invariant measure the stochastic fixed point problem. This generalizes earlier work s
Externí odkaz:
http://arxiv.org/abs/2007.06479
Publikováno v:
Numerical Functional Analysis and Optimization 40(4):386--420 (2019)
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
Externí odkaz:
http://arxiv.org/abs/1808.05426
Autor:
Hermer, Neal1 nealhermer@gmx.de, Luke, D Russell1 r.luke@math.uni-goettingen.de, Sturm, Anja2 asturm@math.uni-goettingen.de
Publikováno v:
Transactions of Mathematics & Its Applications. Jan2023, Vol. 7 Issue 1, p1-31. 31p.
Akademický článek
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Publikováno v:
Numerical Functional Analysis & Optimization. 2019, Vol. 40 Issue 4, p386-420. 35p.