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.



More About This Work

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