Hermite type moving-least-squares approximations

Z. Komargodski, D. Levin

Research output: Contribution to journalArticlepeer-review

Abstract

The moving-least-squares approach, first presented by McLain [1], is a method for approximating multivariate functions using scattered data information. The method is using local polynomial approximations, incorporating weight functions of different types. Some weights, with certain singularities, induce C interpolation approximation in ℝn. In this work we present a way of generalizing the method to enable Hermite type interpolation, namely, interpolation to derivatives' data as well. The essence of the method is the use of an appropriate metric in the construction of the local polynomial approximations.

Original languageEnglish
Pages (from-to)1223-1232
Number of pages10
JournalComputers and Mathematics with Applications
Volume51
Issue number8 SPEC. ISS.
DOIs
StatePublished - Apr 2006

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