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Proceedings Paper

Limited-memory trust-region methods for sparse relaxation
Author(s): Lasith Adhikari; Omar DeGuchy; Jennifer B. Erway; Shelby Lockhart; Roummel F. Marcia
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Paper Abstract

In this paper, we solve the ℓ2-ℓ1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and applying a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving computational time.

Paper Details

Date Published: 24 August 2017
PDF: 8 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940J (24 August 2017); doi: 10.1117/12.2271369
Show Author Affiliations
Lasith Adhikari, Univ. of Florida (United States)
Omar DeGuchy, Univ. of California, Merced (United States)
Jennifer B. Erway, Wake Forest Univ. (United States)
Shelby Lockhart, Wake Forest Univ. (United States)
Roummel F. Marcia, Univ. of California, Merced (United States)

Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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