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On ε-Optimality of the Pursuit Learning Algorithm

Part of: Algorithms - Computer Science Theory of computing

Published online by Cambridge University Press:  04 February 2016

Ryan Martin  and
Omkar Tilak
Ryan Martin*
Affiliation:
University of Illinois at Chicago
Omkar Tilak*
Affiliation:
Indiana University - Purdue University Indianapolis
*
∗ Postal address: Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, 851 S. Morgan St., 322 Science and Engineering Offices, Chicago, Illinois 60607, USA. Email address: [email protected]
∗∗ Postal address: Department of Computer and Information Sciences, Indiana University - Purdue University Indianapolis, 723 W. Michigan St., SL 280, Indianapolis, Indiana 46202, USA. Email address: [email protected]
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Abstract

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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.

Keywords

Convergenceindirect estimator algorithmlearning automaton

MSC classification

Primary: 68Q87: Probability in computer science (algorithm analysis, random structures, phase transitions, etc.)
Secondary: 68W27: Online algorithms 68W40: Analysis of algorithms
Type
Research Article
Information
Journal of Applied Probability , Volume 49 , Issue 3 , September 2012 , pp. 795 - 805
Copyright
© Applied Probability Trust 

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