Optimal algorithms for linear problems with gaussian measures

G. W. Wasilkowski

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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 that a translated spline algorithm is optimal. We also derive optimal information under some additional assumptions concerning the error criterion.

Original languageEnglish
Pages (from-to)727-749
Number of pages23
JournalRocky Mountain Journal of Mathematics
Issue number4
StatePublished - 1986

ASJC Scopus subject areas

  • General Mathematics


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