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Könyv Numerical Methods in Approximation Theory, Vol. 9 D. Braess

Numerical Methods in Approximation Theory, Vol. 9

Szerző: D. Braess, L.L. Schumaker
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Springer Basel
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
19 327 Ft
This book is the official proceedings of a conference on Numerical Methods in Approximation Theory w...

Információk a könyvről

Szerző
D. Braess, L.L. Schumaker
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
1992
oldal
359
EAN
9783034897020
ISBN
3034897022
Enbook ID
02011368
Kiadó
Springer Basel
Súly
575
Méretek
155 x 235 x 21

Teljes leírás

This book is the official proceedings of a conference on Numerical Methods in Approximation Theory which was held at the Mathematisches Forschungs institut in Oberwolfach during the week of November 24~30, 1991. It contains refereed and edited papers by 20 of the 49 participants. The book is dedicated to the memory of Prof. Lothar Collatz who main tained a long and active interest in numerical approximation. It is the ninth in a series of volumes published by Birkhiiuser resulting from conferences on the subject held at Oberwolfach, and co-organized by Prof. Collatz. We now briefly describe the contents of the book. The paper of BASZEN SKI, DELVOS and JESTER deals with blending using sine double series expan sions of functions defined on the unit square. In addition to giving explicit error estimates for partial sums and for interpolating sine polynomials, they also show that Boolean sums yield almost the same asymptotic error estimates as the conventional tensor-product approach, but with a reduced number of terms. The paper of BEATSON and LIGHT discusses approximation by quasi interpolants which are sums of scaled translates of a one-parameter family of functions. They do not require reproduction of low degree polynomials, but nevertheless are able to give error bounds and analyze quasi-interpolation based on Gaussians and exponentials. BINEV and JETTER deal with multivariate interpolation using shifts of a single basis function. They treat both gridded data and scattered data. As examples, they consider box splines and certain radial basis functions.

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