Publication
In recent works, sparse models and convex optimization techniques have been applied to radio-interferometric (RI) imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. In this paper, we propose a scalable algorithm for RI imaging that offers a highly parallelizable structure paving the way for next- generation high-dimensional data imaging. The proposed algorithm is based on a proximal linear version of the alternating direction method of multipliers (ADMM).