DocumentCode
120135
Title
On Convergence Analysis of Iterative Smoothing Methods for a Class of Nonsmooth Convex Minimization Problems
Author
Sanming Liu ; Zhijie Wang
Author_Institution
Dept. of Math. & Phys., Shanghai Dianji Univ. Shanghai, Shanghai, China
fYear
2014
fDate
4-6 July 2014
Firstpage
247
Lastpage
251
Abstract
We consider the problem of minimizing a convex objective which is the sum of a smooth part and a non-smooth part. Inspired by various application, we focus on the case when the non-smooth part is a max function. In this paper, we consider to solve such problems using iterative smoothing-gradient methods. We conduct run-time complexity and convergence analysis of smoothing algorithms.
Keywords
gradient methods; minimisation; convergence analysis; convex objective; iterative smoothing methods; iterative smoothing-gradient methods; max function; nonsmooth convex minimization problems; nonsmooth part; run-time complexity; smooth part; smoothing algorithms; Algorithm design and analysis; Approximation algorithms; Complexity theory; Convergence; Convex functions; Optimization; Smoothing methods; convergence analysis; exponential smoothing technique; non-smooth convex optimization; run-time complexity;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Sciences and Optimization (CSO), 2014 Seventh International Joint Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4799-5371-4
Type
conf
DOI
10.1109/CSO.2014.53
Filename
6923678
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