Stochastic Approximation of Functions and Applications

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 23)


We survey recent results on the approximation of functions from Sobolev spaces by stochastic linear algorithms based on function values. The error is measured in various Sobolev norms, including positive and negative degree of smoothness. We also prove some new, related results concerning integration over Lipschitz domains, integration with variable weights, and study tractability of generalized versions of indefinite integration and discrepancy.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of KaiserslauternKaiserslauternGermany

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