Stochastic Methods in Distributed Optimization and Game-Theoretic Learning

Tatiana Tatarenko
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Tatiana Tatarenko: TU Darmstadt

Chapter Chapter 4 in Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems, 2017, pp 93-155 from Springer

Abstract: Abstract This chapter studies the way to apply stochastic approximation procedure, known as the Robbins–Monro procedure [RM51], to distributed non-convex optimization as well as to communication- and payoff-based learning in potential games.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-65479-9_4

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DOI: 10.1007/978-3-319-65479-9_4

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