The complexity of function approximation on Sobolev spaces with bounded mixed derivative by linear Monte Carlo methods
type: Item
instance of: scholarly article
title: The complexity of function approximation on Sobolev spaces with bounded mixed derivative by linear Monte Carlo methods
zbMATH Open document ID: 1148.41030
author: Liqin Duan
author: Gensun Fang
published in: Journal of Complexity
publication date: 2008-07-21T00:00:00Z
review text: Approximation orders of functions by Monte Carlo methods are considered in this paper. The so-called information based complexity of the problem is of special interest. The approximands are from Sobolev spaces where mixed partial derivatives are assumed to be bounded. The approximation orders are stated explicity. The proof of the main theorem uses Vitali Maiorov's results with respect to discretization.
reviewed by: Martin D. Buhmann
Mathematics Subject Classification ID: 41A63
Mathematics Subject Classification ID: 41A46
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 65D99
zbMATH DE Number: 5303164
zbMATH Keywords: Monte Carlo method
zbMATH Keywords: Sobolev space with bounded mixed derivative
zbMATH Keywords: asymptotic order
MaRDI profile type: Publication
full work available at URL: j.jco.2007.11.001
OpenAlex ID: W2035255232
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DOI: 10.1016/J.JCO.2007.11.001
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description: scientific article; zbMATH DE number 5303164