TY - JOUR
T1 - Hermite type moving-least-squares approximations
AU - Komargodski, Z.
AU - Levin, D.
PY - 2006/4
Y1 - 2006/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33746118356&partnerID=8YFLogxK
U2 - 10.1016/j.camwa.2006.04.005
DO - 10.1016/j.camwa.2006.04.005
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AN - SCOPUS:33746118356
SN - 0898-1221
VL - 51
SP - 1223
EP - 1232
JO - Computers and Mathematics with Applications
JF - Computers and Mathematics with Applications
IS - 8 SPEC. ISS.
ER -