2013/01/10 by Chiara Ravazzi, Sophie M. Fosson, Ravazzi, Chiara +3
Computer Science · Engineering · Mathematics · #Distributed #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Optimization and Control (math.OC) #Parallel #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #and Cluster Computing (cs.DC) #cs.DC #cs.IT #math.IT #math.OC
paper · pdf · doi:10.48550/arxiv.1301.2130
Revised version. Main improvements: extension of the convergence theorem to regular graphs; new numerical results and comparisons with other algorithms
openalex publication_date 2013/01/10 · arxiv created 2013/10/14 · arxiv updated 2013/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we introduce a distributed iterative soft thresholding algorithm (DISTA) that consists of three steps: an averaging step, a gradient step, and a soft thresholding operation. We prove the convergence of DISTA in networks represented by regular graphs, and we compare it with existing methods in terms of performance, memory, and complexity.