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The information-based complexity of approximation problem by adaptive Monte Carlo methods 被引量:2

The information-based complexity of approximation problem by adaptive Monte Carlo methods
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摘要 In this paper, we study the complexity of information of approximation problem on the multivariate Sobolev space with bounded mixed derivative MW p,α r ( $ \mathbb{T}^d $ ), 1 < p < ∞, in the norm of L q ( $ \mathbb{T}^d $ ), 1 < q < ∞, by adaptive Monte Carlo methods. Applying the discretization technique and some properties of pseudo-s-scale, we determine the exact asymptotic orders of this problem. In this paper, we study the complexity of information of approximation problem on the multivariate Sobolev space with bounded mixed derivative MWpr,α(Td), 1 < p < ∞, in the norm of Lq(Td), 1 < q < ∞, by adaptive Monte Carlo methods. Applying the discretization technique and some properties of pseudo-s-scale, we determine the exact asymptotic orders of this problem.
出处 《Science China Mathematics》 SCIE 2008年第9期1679-1689,共11页 中国科学:数学(英文版)
基金 supported by the National Natural Science Foundation of China (Grant No. 10671019) the Research Fund for the Doctoral Program of Higher Education (Grant No. 20050027007)
关键词 adaptive Monte Carlo method Sobolev space with bounded mixed derivative asymptotic order 41A46 41A63 65C05 65D99 adaptive Monte Carlo method Sobolev space with bounded mixed derivative asymptotic order
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