Journal of Applied Probability

On ε-optimality of the pursuit learning algorithm

Ryan Martin and Omkar Tilak

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Abstract

Estimator algorithms in learning automata are useful tools for adaptive, real-time optimization in computer science and engineering applications. In this paper we investigate theoretical convergence properties for a special case of estimator algorithms - the pursuit learning algorithm. We identify and fill a gap in existing proofs of probabilistic convergence for pursuit learning. It is tradition to take the pursuit learning tuning parameter to be fixed in practical applications, but our proof sheds light on the importance of a vanishing sequence of tuning parameters in a theoretical convergence analysis.

Article information

Source
J. Appl. Probab., Volume 49, Number 3 (2012), 795-805.

Dates
First available in Project Euclid: 6 September 2012

Permanent link to this document
https://projecteuclid.org/euclid.jap/1346955334

Digital Object Identifier
doi:10.1239/jap/1346955334

Mathematical Reviews number (MathSciNet)
MR3012100

Zentralblatt MATH identifier
1251.68167

Subjects
Primary: 68Q87: Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) [See also 68W20, 68W40]
Secondary: 68W27: Online algorithms 68W40: Analysis of algorithms [See also 68Q25]

Keywords
Convergence indirect estimator algorithm learning automaton

Citation

Martin, Ryan; Tilak, Omkar. On ε-optimality of the pursuit learning algorithm. J. Appl. Probab. 49 (2012), no. 3, 795--805. doi:10.1239/jap/1346955334. https://projecteuclid.org/euclid.jap/1346955334


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