24 August 2017 Limited-memory trust-region methods for sparse relaxation
Author Affiliations +
Proceedings Volume 10394, Wavelets and Sparsity XVII; 103940J (2017); doi: 10.1117/12.2271369
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States
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
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lasith Adhikari, Omar DeGuchy, Jennifer B. Erway, Shelby Lockhart, Roummel F. Marcia, "Limited-memory trust-region methods for sparse relaxation", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940J (24 August 2017); doi: 10.1117/12.2271369; http://dx.doi.org/10.1117/12.2271369

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