Abstract
We study the ratio of ℓ1 and ℓ2 norms (ℓ1/ℓ2) as a sparsity-promoting objective in compressed sensing. We first propose a novel criterion that guarantees that an s-sparse signal is the local minimizer of the ℓ1/ℓ2 objective; our criterion is interpretable and useful in practice. We also give the first uniform recovery condition using a geometric characterization of the null space of the measurement matrix, and show that this condition is satisfied for a class of random matrices. We also present analysis on the robustness of the procedure when noise pollutes data. Numerical experiments are provided that compare ℓ1/ℓ2 with some other popular non-convex methods in compressed sensing. Finally, we propose a novel initialization approach to accelerate the numerical optimization procedure. We call this initialization approach support selection, and we demonstrate that it empirically improves the performance of existing ℓ1/ℓ2 algorithms.
Original language | English |
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Pages (from-to) | 486-511 |
Number of pages | 26 |
Journal | Applied and Computational Harmonic Analysis |
Volume | 55 |
DOIs | |
State | Published - Nov 2021 |
Bibliographical note
Publisher Copyright:© 2021 Elsevier Inc.
Keywords
- Compressed sensing
- High-dimensional geometry
- Non-convex optimization
- Random matrices
ASJC Scopus subject areas
- Applied Mathematics