{ "id": "1603.04637", "version": "v1", "published": "2016-03-15T11:19:52.000Z", "updated": "2016-03-15T11:19:52.000Z", "title": "On the Randomization of Frolov's Algorithm for Multivariate Integration", "authors": [ "David Krieg" ], "comment": "Master Thesis", "categories": [ "math.NA" ], "abstract": "We are concerned with the numerical integration of functions from the Sobolev space $H^{r,\\text{mix}}([0,1]^d)$ of dominating mixed smoothness $r\\in\\mathbb{N}$ over the $d$-dimensional unit cube. K.\\,K.\\,Frolov introduced a deterministic quadrature rule whose worst case error has the order $n^{-r} \\, (\\log n)^{(d-1)/2}$ with respect to the number $n$ of function evaluations. This is known to be optimal. 39 years later, Erich Novak and me introduced a randomized version of this algorithm using $d$ random dilations. We showed that its error is bounded above by a constant multiple of $n^{-r-1/2} \\, (\\log n)^{(d-1)/2}$ in expectation and by $n^{-r} \\, (\\log n)^{(d-1)/2}$ almost surely. The main term $n^{-r-1/2}$ is again optimal and it turns out that the very same algorithm is also optimal for the isotropic Sobolev space $H^s([0,1]^d)$ of smoothness $s>d/2$. We also added a random shift to this algorithm to make it unbiased. Just recently, Mario Ullrich proved in \\cite{ullrichneu} that the expected error of the resulting algorithm on $H^{r,\\text{mix}}([0,1]^d)$ is even bounded above by $n^{-r-1/2}$. This thesis is a review of the mentioned upper bounds and their proofs.", "revisions": [ { "version": "v1", "updated": "2016-03-15T11:19:52.000Z" } ], "analyses": { "subjects": [ "65D30", "65C05", "65Y20", "68Q25" ], "keywords": [ "multivariate integration", "frolovs algorithm", "randomization", "isotropic sobolev space", "deterministic quadrature rule" ], "tags": [ "dissertation" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable" } } }