## Low-complexity DCD-based sparse recovery algorithms

Research output: Contribution to journalArticle

## Department/unit(s)

### Publication details

Journal IEEE Access Accepted/In press - 8 Jun 2017 Published (current) - 16 Jun 2017 5 14 12737 - 12750 English

### Abstract

Sparse recovery techniques find applications in many areas. Real-time
implementation of such techniques has been recently an important area for research. In this paper, we propose computationally efficient techniques based
on dichotomous coordinate descent (DCD) iterations for recovery of sparse complex-valued signals. We first consider $\ell_2 \ell_1$ optimization that can incorporate \emph{a priori} information on the solution in the form of a weight vector. We propose a DCD-based algorithm for $\ell_2 \ell_1$ optimization with a fixed $\ell_1$-regularization, and then efficiently incorporate it in reweighting iterations using a \emph{warm start} at each iteration. We then exploit homotopy by sampling the regularization parameter and arrive at an algorithm that, in each homotopy iteration, performs the $\ell_2 \ell_1$ optimization on the current support with a fixed regularization parameter and then updates the support by
adding/removing elements. We propose efficient rules for adding and removing the elements. The performance of the homotopy algorithm is further improved with the reweighting. We then propose an algorithm for $\ell_2 \ell_0$ optimization that exploits homotopy for the $\ell_0$ regularization; it alternates between the least-squares (LS) optimization on the support and the support update, for which we also propose an efficient rule. The algorithm complexity is reduced when DCD iterations with a \emph{warm start} are used for the LS
optimization, and, as most of the DCD operations are additions and bit-shifts, it
is especially suited to real time implementation. The proposed algorithms are investigated in channel estimation scenarios and compared with known sparse recovery techniques such as the matching pursuit (MP) and YALL1 algorithms. The numerical examples show that the proposed techniques achieve a mean-squared error smaller than that of the YALL1 algorithm and complexity comparable to that of the MP algorithm.

### Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

### Research areas

• Coordinate descent, DCD, homotopy, sparse recovery

## Discover related content

Find related publications, people, projects, datasets and more using interactive charts.