Computing the Pessimism of Inclusion Functions

Computing the Pessimism of Inclusion Functions “Computing the pessimism” means bounding the overestimation produced by an inclusion function. There are two important distinctions with classical error analysis. First, we do not consider the image by an inclusion function but the distance between this image and the exact image (in the set-theoretical sense). Second, the bound is computed over a infinite set of intervals. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

Computing the Pessimism of Inclusion Functions

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Publisher
Springer Netherlands
Copyright
Copyright © 2008 by Springer Science+Business Media B.V.
Subject
Mathematics; Numeric Computing; Mathematical Modeling and Industrial Mathematics; Approximations and Expansions; Computational Mathematics and Numerical Analysis
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1007/s11155-007-9044-7
Publisher site
See Article on Publisher Site

Abstract

“Computing the pessimism” means bounding the overestimation produced by an inclusion function. There are two important distinctions with classical error analysis. First, we do not consider the image by an inclusion function but the distance between this image and the exact image (in the set-theoretical sense). Second, the bound is computed over a infinite set of intervals.

Journal

Reliable ComputingSpringer Journals

Published: Feb 5, 2008

References

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