On Convergence of a P-Algorithm Based on a Statistical Model of Continuously Differentiable Functions

Document Type

Article

Publication Date

3-1-2001

Abstract

This paper is a study of the one-dimensional global optimization problem for continuously differentiable functions. We propose a variant of the so-called P-algorithm, originally proposed for a Wiener process model of an unknown objective function. The original algorithm has proven to be quite effective for global search, though it is not efficient for the local component of the optimization search if the objective function is smooth near the global minimizer. In this paper we construct a P-algorithm for a stochastic model of continuously differentiable functions, namely the once-integrated Wiener process. This process is continuously differentiable, but nowhere does it have a second derivative. We prove convergence properties of the algorithm.

Identifier

0035270952 (Scopus)

Publication Title

Journal of Global Optimization

External Full Text Location

https://doi.org/10.1023/A:1011207622164

ISSN

09255001

First Page

229

Last Page

245

Issue

3

Volume

19

Fund Ref

National Research Council

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