Please use this identifier to cite or link to this item:
Title: A novel quasi-Newton method for composite convex minimization
Authors: Chai, Woon Huei
Ho, Shen-Shyang
Quek, Hiok Chai
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Chai, W. H., Ho, S. & Quek, H. C. (2022). A novel quasi-Newton method for composite convex minimization. Pattern Recognition, 122, 108281-.
Journal: Pattern Recognition
Abstract: A fast parallelable Jacobi iteration type optimization method for non-smooth convex composite optimization is presented. Traditional gradient-based techniques cannot solve the problem. Smooth approximate functions are attempted to be used as a replacement of those non-smooth terms without compromising the accuracy. Recently, proximal mapping concept has been introduced into this field. Techniques which utilize proximal average based proximal gradient have been used to solve the problem. The state-of-art methods only utilize first-order information of the smooth approximate function. We integrate both first and second-order techniques to use both first and second-order information to boost the convergence speed. A convergence rate with a lower bound of O([Formula presented]) is achieved by the proposed method and a super-linear convergence is enjoyed when there is proper second-order information. In experiments, the proposed method converges significantly better than the state of art methods which enjoy O([Formula presented]) convergence.
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2021.108281
Schools: School of Computer Science and Engineering 
Interdisciplinary Graduate School (IGS) 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rolls-Royce@NTU Corporate Lab 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles
IGS Journal Articles
SCSE Journal Articles

Citations 50

Updated on Sep 30, 2023

Web of ScienceTM
Citations 50

Updated on Sep 28, 2023

Page view(s)

Updated on Sep 28, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.