Boosters: A Derivative-Free Algorithm Based on Radial Basis Functions

Derivative-free optimization (DFO) involves the methods used to minimize an expensive objective function when its derivatives are not available. We present here a trust-region algorithm based on Radial Basis Functions (RBFs). The main originality of our approach is the use of RBFs to build the trust...

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Veröffentlicht in:International journal of modelling & simulation Jg. 29; H. 1; S. 26 - 36
Hauptverfasser: Oeuvray, R., Bierlaire, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Calgary Taylor & Francis 01.01.2009
Taylor & Francis Ltd
Schlagworte:
Algorithms
CUTEr
Derivative-free optimization
radial basis functions
trust-region methods
ISSN:0228-6203, 1925-7082
Online-Zugang:Volltext
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Zusammenfassung:Derivative-free optimization (DFO) involves the methods used to minimize an expensive objective function when its derivatives are not available. We present here a trust-region algorithm based on Radial Basis Functions (RBFs). The main originality of our approach is the use of RBFs to build the trust-region models and our management of the interpolation points based on Newton fundamental polynomials. Moreover the complexity of our method is very attractive. We have tested the algorithm against the best state-of-the-art methods (UOBYQA, NEWUOA, DFO). The tests on the problems from the CUTEr collection show that BOOSTERS is performing very well on medium-size problems. Moreover, it is able to solve problems of dimension 200, which is considered very large in DFO.
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ISSN:0228-6203
1925-7082
DOI:10.1080/02286203.2009.11442507