Improving FISTA's speed of convergence via a novel inertial sequence

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3 Citas (Scopus)


The FISTA (fast iterative shrinkage-thresholding algorithm) is a well-known and fast (theoretical O(k−2) rate of convergence) procedure for solving optimization problems composed by the sum of two convex functions, such that one is smooth (differentiable) and the other is possible nonsmooth. FISTA can be understood as a first order method with one important aspect: it uses a suitable extragradient rule, i.e.: the gradient is evaluated at a linear combination of the past two iterates, whose weights, are usually referred to as the inertial sequence. While problem dependent, it has a direct impact on the FISTA's practical computational performance. In this paper we propose a novel inertial sequence; when compared to well-established alternative choices, in the context of convolutional sparse coding and Wavelet-based inpainting, our proposed inertial sequence can reduce the number of FISTA's global iterations (and thus overall computational time) by 30% ∼ 50% to attain the same level of reduction in the cost functional.

Idioma originalInglés
Título de la publicación alojadaEUSIPCO 2019 - 27th European Signal Processing Conference
EditorialEuropean Signal Processing Conference, EUSIPCO
ISBN (versión digital)9789082797039
EstadoPublicada - set. 2019
Evento27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Espana
Duración: 2 set. 20196 set. 2019

Serie de la publicación

NombreEuropean Signal Processing Conference
ISSN (versión impresa)2219-5491


Conferencia27th European Signal Processing Conference, EUSIPCO 2019
CiudadA Coruna


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