Dynamic smoothness parameter for fast gradient methods

Authors

Antonio Frangioni, Dipartimento di Informatica, Università di Pisa, Pisa, Italy
Bernard Gendron, Centre Interuniversitaire de Recherche sur les Réseaux d’Entreprise, la Logistique et le Transport (CIRRELT), Montreal, Canada
Enrico Gorgone, Indian Institute of Management BangaloreFollow

Document Type

Article

Publication Title

Optimization Letters

Abstract

We present and computationally evaluate a variant of the fast gradient method by Nesterov that is capable of exploiting information, even if approximate, about the optimal value of the problem. This information is available in some applications, among which the computation of bounds for hard integer programs. We show that dynamically changing the smoothness parameter of the algorithm using this information results in a better convergence profile of the algorithm in practice.

DOI Link

10.1007/S11590-017-1168-Z

Publication Date

20-7-2017

Publisher

Springer

Volume

Vol.12

Recommended Citation

Frangioni, Antonio; Gendron, Bernard; and Gorgone, Enrico, "Dynamic smoothness parameter for fast gradient methods" (2017). Faculty Publications. 551.
https://research.iimb.ac.in/fac_pubs/551