Numerical Experiences with a New Generalized Subinterval Selection Criterion for Interval Global Optimization

Numerical Experiences with a New Generalized Subinterval Selection Criterion for Interval Global... The convergence properties are studied for interval global optimization algorithms that select the next subinterval to be subdivided with the largest value of the indicator pf(f k, X) = $$\frac{{f_k - \underline F \left( X \right)}}{{\overline F \left( X \right) - \underline F \left( X \right)}}$$ . In contrast to previous work, here the more general case is investigated, when the global minimum value is unknown, and thus its estimation f k in the iteration k has an important role. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

Numerical Experiences with a New Generalized Subinterval Selection Criterion for Interval Global Optimization

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2003 by Kluwer Academic Publishers
Subject
Mathematics; Numeric Computing; Approximations and Expansions; Computational Mathematics and Numerical Analysis; Mathematical Modeling and Industrial Mathematics
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1023/A:1023086201037
Publisher site
See Article on Publisher Site

Abstract

The convergence properties are studied for interval global optimization algorithms that select the next subinterval to be subdivided with the largest value of the indicator pf(f k, X) = $$\frac{{f_k - \underline F \left( X \right)}}{{\overline F \left( X \right) - \underline F \left( X \right)}}$$ . In contrast to previous work, here the more general case is investigated, when the global minimum value is unknown, and thus its estimation f k in the iteration k has an important role.

Journal

Reliable ComputingSpringer Journals

Published: Oct 17, 2004

References

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