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Optimal Algorithm for Linear Problems with Gaussian Measures

Wasilkowski, Grzegorz W.

We study optimal algorithms for linear problems in two settings: the average case and the probabilistic case settings. We assume that the probability measure is Gaussian. This assumption enables us to consider a general class of error criteria. We prove that in both settings adaption does not help and a translated spline algorithm is optimal. We also devise optimal information under some additional assumptions concerning the error criterion.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-176-85
Published Here
November 1, 2011
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