A Universal Catalyst for First-Order Optimization - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

A Universal Catalyst for First-Order Optimization

Résumé

We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill conditioned problems where we measure significant improvements.
Fichier principal
Vignette du fichier
paper_appendix.pdf (428.77 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01160728 , version 1 (06-06-2015)
hal-01160728 , version 2 (25-10-2015)

Identifiants

  • HAL Id : hal-01160728 , version 2

Citer

Hongzhou Lin, Julien Mairal, Zaid Harchaoui. A Universal Catalyst for First-Order Optimization. NIPS - Advances in Neural Information Processing Systems, Dec 2015, Montreal, Canada. pp. 3384-3392. ⟨hal-01160728v2⟩
1583 Consultations
644 Téléchargements

Partager

Gmail Facebook X LinkedIn More