A robust FISTA-like algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

Abstract

The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is regarded as the state-of-the-art among a number of proximal gradient-based methods used for addressing large-scale optimization problems with simple but non-differentiable objective functions. However, the efficiency of FISTA in a wide range of applications is hampered by a simple drawback in the line search scheme. The local estimate of the Lipschitz constant, the inverse of which gives the step size, can only increase while the algorithm is running. As a result, FISTA can slow down significantly if the initial estimate of the Lipschitz constant is excessively large or if the local Lipschitz constant decreases in the vicinity of the optimal point. We propose a new FISTA-like method endowed with a robust step size search procedure and demonstrate its effectiveness by means of a rigorous theoretical convergence analysis and simulations.

Details

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publication statusPublished - 16 Jun 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryUnited States
CityNew Orleans
Period05/03/201709/03/2017

    Research areas

  • backtracking, convergence, FISTA, line search

ID: 14451568