Distributed Learning for Stochastic Generalized Nash Equilibrium Problems

This paper examines a stochastic formulation of the generalized Nash equilibrium problem where agents are subject to randomness in the environment of unknown statistical distribution. We focus on fully distributed online learning by agents and employ penalized individual cost functions to deal with...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 65; no. 15; pp. 3893 - 3908
Main Authors:	Chung-Kai Yu, van der Schaar, Mihaela, Sayed, Ali H.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01-08-2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive learning Algorithms Competition Cost function Coupling diffusion learning Distance learning Economic analysis Economic models Equilibrium Game theory generalized Nash equilibrium Learning Marketing Nash equilibrium Network topology penalized approximation Probability theory Randomness Resource management Signal processing algorithms Stochastic processes
Online Access:	Get full text
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