On the convergence of the P-algorithm for one-dimensional global optimization of smooth functions

Document Type

Article

Publication Date

1-1-1999

Abstract

The Wiener process is a widely used statistical model for stochastic global optimization. One of the first optimization algorithms based on a statistical model, the so-called P-algorithm, was based on the Wiener process. Despite many advantages, this process does not give a realistic model for many optimization problems, particularly from the point of view of local behavior. In the present paper, a version of the P-algorithm is constructed based on a stochastic process with smooth sampling functions. It is shown that, in such a case, the algorithm has a better convergence rate than in the case of the Wiener process. A similar convergence rate is proved for a combination of the Wiener model-based P-algorithm with quadratic fit-based local search.

Identifier

0033245903 (Scopus)

Publication Title

Journal of Optimization Theory and Applications

External Full Text Location

https://doi.org/10.1023/A:1022677121193

ISSN

00223239

First Page

479

Last Page

495

Issue

3

Volume

102

Fund Ref

National Research Council

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