Glaydston C. Bento, Boris S. Mordukhovich, Tiago S. Mota, Antoine Soubeyran
Résumé
This paper is devoted to developing the alternating minimization algorithm for problems of structured nonconvex optimization proposed by Attouch, Bolt´e, Redont, and Soubeyran in 2010. Our main result provides significant improvements of the convergence rate of the algorithm, especially under the low exponent PolyakLojasiewicz-Kurdyka condition when we establish either finite termination of this algorithm or its superlinear convergence rate instead of the previously known linear convergence. We also investigate the PLK exponent calculus and discuss applications to noncooperative games and behavioralscience.
Mots clés
Polyak- Lojasiewicz-Kurdyka conditions, Convergence rates, Noncooperative games, Alternating minimization algorithm, Nonsmooth optimization