Alternating Strategies Are Good For Low-Rank Matrix Reconstruction
  This article focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover structured low-rank matrices, such as Hankel structure. We show that merging these two alternating strategies leads to a better performance than the existing alternating least squares (ALS) strategy. The performance is evaluated via numerical simulations. 
Kezhi Li, Martin Sundin, Cristian R. Rojas, Saikat Chatterjee, Magnus   Jansson
math.ST cs.IT math.IT stat.TH