Learning how to play Nash, potential games and alternating minimization method for structured nonconvex problems on Riemannian manifolds
J.X. da Cruz Neto(jcruznetouol.com.br)
Abstract: In this paper we consider minimization problems with constraints. We show that if the set of constaints is a Riemannian manifold of non positive curvature and the objective function is lower semicontinuous and satisfies the Kurdyka-Lojasiewicz property, then the alternating proximal algorithm in Euclidean space is naturally extended to solve that class of problems. We prove that the sequence generated by our algorithm is well defined and converges to an inertial Nash equilibrium under mild assumptions about the objective function. As an application, we give a welcome result on the dificult problem of "learning how to play Nash" (convergence, convergence in finite time, speed of convergence, constraints in action spaces in the context of " alternating potential games" with inertia).
Keywords: Nash equilibrium; convergence; finite time; proximal algorithm; alternation; learning in games; inertia; Riemannian manifold; Kurdyka-Lojasiewicz property.
Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )
Category 2: Other Topics (Game Theory )
Category 3: Applications -- Science and Engineering
Citation: Citation: Programa de Engenharia de Sistemas e Computação - UFRJ - Rio de Janeiro, Brazil
Entry Submitted: 03/23/2012
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