  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.
