Error Estimates and Poisedness in Multivariate Polynomial Interpolation

We show how to derive error estimates between a function and its interpolating polynomial and between their corresponding derivatives. The derivation is based on a new definition of well-poisedness for the interpolation set, directly connecting the accuracy of the error estimates with the geometry of the points in the set. This definition is equivalent to the boundedness of Lagrange polynomials, but it provides new geometric intuition.  Our approach extracts the error bounds for all of the  derivatives using the same analysis; the error bound for the function values is then derived a posteriori.

We develop an algorithm to build a set of well-poised interpolation points or to modify an existing set to ensure its well-poisedness.  We also comment on the optimal geometries corresponding to the best possible well-poised sets in the case of linear interpolation.

By: Andrew R. Conn, Katya Scheinberg, Luís N. Vicente

Published in: RC22990 in 2003

LIMITED DISTRIBUTION NOTICE:

This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.

rc22990.pdf

Questions about this service can be mailed to reports@us.ibm.com .