Limited-memory trust-region methods for sparse relaxation

Lasith Adhikari; Omar DeGuchy; Jennifer B. Erway; Shelby Lockhart; Roummel F. Marcia

doi:10.1117/12.2271369

24 August 2017 Limited-memory trust-region methods for sparse relaxation

Lasith Adhikari, Omar DeGuchy, Jennifer B. Erway, Shelby Lockhart, Roummel F. Marcia

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103940J (2017) https://doi.org/10.1117/12.2271369
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Lasith Adhikari, Omar DeGuchy, Jennifer B. Erway, Shelby Lockhart, and Roummel F. Marcia "Limited-memory trust-region methods for sparse relaxation", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940J (24 August 2017); https://doi.org/10.1117/12.2271369

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