Skip to Main content Skip to Navigation
Journal articles

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 Antoine Soubeyran 1
1 GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille
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
Document type :
Journal articles
Complete list of metadata
https://hal-amu.archives-ouvertes.fr/hal-01500875
Contributor : Elisabeth Lhuillier Connect in order to contact the contributor
Submitted on : Monday, April 3, 2017 - 5:13:00 PM
Last modification on : Thursday, November 4, 2021 - 11:58:07 AM

Identifiers

  • HAL Id : hal-01500875, version 1

Collections

UNIV-AMU | GREQAM | CNRS | EHESS | EC-MARSEILLE | AMSE

Citation

Joao Xavier Cruz Neto, Paulo Roberto Oliveira, A. Soares Jr Pedro, Antoine Soubeyran. Learning how to Play Nash, Potential Games and Alternating Minimization Method for Structured Nonconvex Problems on Riemannian Manifolds. Journal of Convex Analysis, Heldermann, 2013, 20 (2), pp.395-438. ⟨hal-01500875⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

227