Work

Trust-Region Methods for Sparse Relaxation

Year: 2016

Type: preprint

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 apply 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 the computational time to converge.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1602.08813

Authors: Adhikari, Lasith, Erway, Jennifer B., Lockhart, Shelby, Marcia, Roummel F.

Institutions: University of California, Merced, Wake Forest University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, University of California Merced, Division of Civil, Mechanical and Manufacturing Innovation

Awards: G1065499967, G1901991761, G3106196197, Collaborative Research: Trust-Search Methods for Inverse Problems in Imaging, G792402180, Collaborative Research: Trust-Search Methods for Inverse Problems in Imaging

OpenAlex ID: W2290060816

Cites: 17

Cited by:

Related to: 10