D. P. Jenkinson, J. C. Mason, A. Crampton
{"title":"Iteratively Weighted Approximation Algorithms For Nonlinear Problems Using Radial Basis Function Examples","authors":"D. P. Jenkinson, J. C. Mason, A. Crampton","doi":"10.1002/anac.200310014","DOIUrl":"10.1002/anac.200310014","url":null,"abstract":"<p>A set of discrete data (<i>x<sub>k</sub>, f</i> (<i>x<sub>k</sub></i>)) (<i>k</i> = 1, 2, …, <i>m</i>) may be fitted in any <i>l<sub>p</sub></i> norm by a nonlinear form derived from a function <i>g</i> (<i>L</i>) of a linear form <i>L</i> = <i>L</i>(<i>x</i>). Such a nonlinear approximation problem may under appropriate conditions be (asymptotically) replaced by the fitting of <i>g</i><sup>–1</sup> (<i>f</i>) by <i>L</i> in any <i>l<sub>p</sub></i> norm with respect to a weight function <i>w</i> = <i>g</i>′ (<i>g</i><sup>–1</sup> (<i>f</i>)). In practice this “direct method” can yield very good results, sometimes coming close to a best approximation. However, to ensure a near-best approximation, by using an iterative procedure based on fitting <i>L</i>, two algorithms are proposed in the <i>l</i><sub>2</sub> norm - one already established by Mason and Upton (1989) and one completely new, based on minimising the two algorithms and multiplicative combinations of errors, respectively. For a general <i>g</i> we prove they converge locally and linearly with small constants. Moreover it is established that they converge to different (nonlinear) “Galerkin type” approximations, the first based on making the explicit error ϵ ≡ <i>f – g</i> (<i>L</i>) orthogonal to a set of functions forming a basis for <i>L</i>, and the second based on making the implicit error ϵ* ≡ <i>w</i>(<i>g</i><sup>–1</sup> (<i>f</i>) – <i>L</i>) orthogonal to such a basis. Finally, and mainly for comparison purposes, the well known Gauss-Newton algorithm is adopted for the determination of a best (nonlinear) approximation. Illustrative problems are tackled and numerical results show how effective all of the algorithms can be. To add a further novel feature, <i>L</i> is here chosen throughout to be a radial basis function (RBF), and, as far as we are aware, this is one of the first successful uses of a (nonlinear) function of an RBF as an approximation form in data fitting. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)</p>","PeriodicalId":100108,"journal":{"name":"Applied Numerical Analysis & Computational Mathematics","volume":"1 1","pages":"165-179"},"PeriodicalIF":0.0,"publicationDate":"2004-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/anac.200310014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87479785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1