Distributed Basis Pursuit

TL;DR

This paper introduces D-ADMM, a distributed algorithm for solving Basis Pursuit that minimizes communication in sensor networks, enabling efficient sparse signal recovery without centralized control.

Contribution

The paper presents a novel decentralized algorithm for Basis Pursuit that reduces communication overhead and operates without a central node, suitable for distributed systems.

Findings

Requires less communication than existing algorithms

Operates without a central processing node

Applicable to distributed sensor networks

Abstract

We propose a distributed algorithm for solving the optimization problem Basis Pursuit (BP). BP finds the least L1-norm solution of the underdetermined linear system Ax = b and is used, for example, in compressed sensing for reconstruction. Our algorithm solves BP on a distributed platform such as a sensor network, and is designed to minimize the communication between nodes. The algorithm only requires the network to be connected, has no notion of a central processing node, and no node has access to the entire matrix A at any time. We consider two scenarios in which either the columns or the rows of A are distributed among the compute nodes. Our algorithm, named D-ADMM, is a decentralized implementation of the alternating direction method of multipliers. We show through numerical simulation that our algorithm requires considerably less communications between the nodes than the state-of-the-art algorithms.