Smoothing Techniques and Difference of Convex Functions Algorithms for Image Reconstructions
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.
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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.
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