Learning how to Play Nash, Potential Games and Alternating Minimization Method for Structured Nonconvex Problems on Riemannian Manifolds

Joao Xavier Cruz Neto, Paulo Roberto Oliveira, A. Soares Jr Pedro and Antoine Soubeyran

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Abstract: We consider minimization problems with constraints. We show that if the set of constraints 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 difficult 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: alternation; convergence; finite time; inertia; Kurdyka-Lojasiewicz property; learning in games; Nash equilibrium; proximal algorithm; Riemannian manifold (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

Published in Journal of Convex Analysis, 2013, 20 (2), pp.395-438

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01500875

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