From Event: SPIE Optical Engineering + Applications, 2017
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.
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 (Presented at SPIE Optical Engineering + Applications: August 06, 2017; Published: 24 August 2017); https://doi.org/10.1117/12.2271369.
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