Distributed computation of equilibria in monotone nash games via iterative regularization techniques

Aswin Kannan, Uday V. Shanbhag

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

We consider the development of single-timescale schemes for the distributed computation of equilibria associated with Nash games in which each player solves a convex program. Equilibria associated with such games are wholly captured by the solution set of a variational inequality. Our focus is on a class of games, termed monotone Nash games, that lead to monotone variational inequalities. Distributed extensions of standard approaches for solving such variational problems are characterized by two challenges: (1) Unless suitable assumptions (such as strong monotonicity) are imposed on the mapping arising in the specification of the variational inequality, iterative methods often require the solution of a sequence of regularized problems, a naturally two-timescale process that is harder to implement in practice. (2) Additionally, algorithm parameters for all players (such as steplengths and regularization parameters) have to be chosen centrally and communicated to all players; importantly, these parameters cannot be independently chosen by a player. Motivated by these shortcomings, we present two practically implementable distributed regularization schemes that work on a single timescale; specifically, each scheme requires precisely one gradient or projection step at every iteration. Of these, the first is an iterative Tikhonov regularization (ITR) scheme, while the second is an analogously constructed iterative proximal-point (IPP) method. Both schemes are characterized by the property that the regularization/centering parameter are updated after every iteration, rather than when one has approximately solved the regularized problem. To aid in distributed settings requiring limited coordination across players, the schemes allow players to select their parameters independently and do not insist on central prescription of such parameters. We conclude with an application of these schemes on a networked Cournot game with nonlinear prices.

Original languageEnglish (US)
Pages (from-to)1177-1205
Number of pages29
JournalSIAM Journal on Optimization
Volume22
Issue number4
DOIs
StatePublished - Dec 31 2012

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Iterative Regularization
Distributed Computation
Regularization Technique
Monotone
Game
Iterative methods
Time Scales
Specifications
Regularization Parameter
Iteration
Variational Inequalities
Proximal Point Method
Monotone Variational Inequalities
Convex Program
Tikhonov Regularization
Solution Set
Variational Problem
Monotonicity
Regularization
Projection

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science

Cite this

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Distributed computation of equilibria in monotone nash games via iterative regularization techniques. / Kannan, Aswin; Shanbhag, Uday V.

In: SIAM Journal on Optimization, Vol. 22, No. 4, 31.12.2012, p. 1177-1205.

Research output: Contribution to journalArticle

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