Smoothing Techniques and Difference of Convex Functions Algorithms for Image Reconstructions

Authors

Mau Nam Nguyen, Portland State UniversityFollow
Le Thi Hoai An, University of Lorraine
Daniel Giles, Santa Barbara City CollegeFollow
Thai An Nguyen, University of Electronic Science and Technology of ChinaFollow

Sponsor

Research of Nguyen Mau Nam was partly supported by the National Science Foundation (Division of Mathematical Sciences) under grant DMS-1716057. Research of Nguyen Thai An was supported by the China Postdoctoral Science Foundation under grant No. 2017M622991.

Published In

Optimization

Document Type

Citation

Publication Date

8-2019

Abstract

In this paper, we study characterizations of differentiability for real-valued functions based on generalized differentiation. These characterizations provide the mathematical foundation for Nesterov's smoothing techniques in infinite dimensions. As an application, we provide a simple approach to image reconstructions based on Nesterov's smoothing and algorithms for minimizing differences of convex (DC) functions that involve the ℓ1−ℓ2 regularization.

Description

Copyright © 2019 Informa UK Limited

Locate the Document

http://doi.org/10.1080/02331934.2019.1648467

DOI

10.1080/02331934.2019.1648467

Persistent Identifier

https://archives.pdx.edu/ds/psu/29649

Citation Details

Mau Nam, N., Hoai An, L. T., Giles, D., & Thai An, N. (2019). Smoothing techniques and difference of convex functions algorithms for image reconstructions. Optimization, 1-33.