A randomised primal-dual algorithm for distributed radio-interferometric imaging

TL;DR

This paper introduces a randomized primal-dual algorithm for distributed radio-interferometric imaging, enabling efficient large-scale data processing with improved computational performance over existing methods.

Contribution

It develops a flexible, distributed convex optimization algorithm based on primal-dual iterations with probabilistic data block selection for radio astronomy imaging.

Findings

Feasibility of the randomized approach with limited computing resources

Significant computational advantages over state-of-the-art algorithms

Effective handling of large-scale radio interferometric data

Abstract

Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on primal-dual forward-backward iterations, for solving the radio interferometric imaging problem. It can encompass any convex prior of interest. It allows for the distributed processing of the measured data and introduces further flexibility by employing a probabilistic approach for the selection of the data blocks used at a given iteration. We study the reconstruction performance with respect to the data distribution and we propose the use of nonuniform probabilities for the randomised updates. Our simulations show the feasibility of the randomisation given a limited computing infrastructure as well as important computational advantages when compared to state-of-the-art algorithmic structures.