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Stochastic Analysis of Convergence via Dynamic Representation for a Class of Line-search Algorithms

Published online by Cambridge University Press:  01 June 1997

L. PRONZATO
Affiliation:
Laboratoire I3S, CNRS-URA 1376, 250 rue A. Einstein, bât. 4, Sophia Antipolis, 06560 Valbonne, France
H. P. WYNN
Affiliation:
Department of Statistics, University of Warwick, Coventry CV4 7AL, UK
A. A. ZHIGLJAVSKY
Affiliation:
Department of Mathematics, St. Petersburg University, Bibliotechnaya sq.2, St. Petersburg, 198904 Russia

Abstract

Certain convergent search algorithms can be turned into chaotic dynamic systems by renormalisation back to a standard region at each iteration. This allows the machinery of ergodic theory to be used for a new probabilistic analysis of their behaviour. Rates of convergence can be redefined in terms of various entropies and ergodic characteristics (Kolmogorov and Rényi entropies and Lyapunov exponent). A special class of line-search algorithms, which contains the Golden-Section algorithm, is studied in detail. Their associated dynamic systems exhibit a Markov partition property, from which invariant measures and ergodic characteristics can be computed. A case is made that the Rényi entropy is the most appropriate convergence criterion in this environment.

Type
Research Article
Copyright
1997 Cambridge University Press

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