Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43931
Title: A Subgradient method based on gradient sampling for solving convex optimization problems
Authors: Hu, Y
Sim, CK
Yang, X 
Keywords: Convex optimization
Gradient sampling technique
Projection
Subgradient method
Issue Date: 2015
Publisher: Taylor & Francis
Source: Numerical functional analysis and optimization, 2015, v. 36, no. 12, p. 1559-1584 How to cite?
Journal: Numerical functional analysis and optimization 
Abstract: Based on the gradient sampling technique, we present a subgradient algorithm to solve the nondifferentiable convex optimization problem with an extended real-valued objective function. A feature of our algorithm is the approximation of subgradient at a point via random sampling of (relative) gradients at nearby points, and then taking convex combinations of these (relative) gradients. We prove that our algorithm converges to an optimal solution with probability 1. Numerical results demonstrate that our algorithm performs favorably compared with existing subgradient algorithms on applications considered.
URI: http://hdl.handle.net/10397/43931
ISSN: 0163-0563
DOI: 10.1080/01630563.2015.1086788
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Last Week
0
Last month
Citations as of Aug 14, 2017

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
Citations as of Aug 15, 2017

Page view(s)

19
Last Week
1
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.