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Soft Computing

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04-03-2021 | Methodologies and Application

A class of line search-type methods for nonsmooth convex regularized minimization

Author: Weijun Zhou

Published in: Soft Computing | Issue 10/2021

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Abstract

This paper presents a class of line search-type methods for solving the regularized optimization model whose objective function is the sum of a smooth function and a nonsmooth convex regularized term. This problem has many applications such as in compressive sensing and sparse reconstruction. Three special cases of this class of methods are proposed, and their convergence theorems are established. They are generalizations of some existing BB gradient methods and PRP-type nonlinear conjugate gradient methods for smooth unconstrained optimization problems. The proposed methods are applied to sparse reconstruction, and some numerical results are reported to show their efficiency.

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Title: A class of line search-type methods for nonsmooth convex regularized minimization
Author: Weijun Zhou
Publication date: 04-03-2021
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 10/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-05672-x

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