Journal Article

A generalized golden-section algorithm for line search


in IMA Journal of Mathematical Control and Information

Volume 15, issue 2, pages 185-214
Published in print June 1998 | ISSN: 0265-0754
Published online June 1998 | e-ISSN: 1471-6887 | DOI:
A generalized golden-section algorithm for line search

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This paper aims at promoting a generalization of the golden-section line-search algorithm, with better performance for functions locally symmetric around their optimum. Any line-search algorithm can be represented as a nonconvergent dynamic system when a suitable renormalization of the uncertainty interval is performed at each iteration. This allows a detailed study of the finite-sample and asymtotic behaviour of the algorithm through a Markov-chain representation. We show that an expansion of the initial uncertainty interval improves this behavior. Some asymptotic characteristics based on the evolution of the length of the uncertainty interval are shown to be related to classical ergodic characteristics of dynamic systems, such as Lyapunov exponent and Kolmogorov entropy. However, other ergodic characteristics, related to the Renyi entropy, are suggested as being more suitable in this context.

Journal Article.  0 words. 

Subjects: Mathematics

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