Optimization algorithms for nuclear norm based subspace identification with uniformly spaced frequency domain data

We compare two iterative frequency domain subspace identification methods using nuclear norm minimization to more commonly used non-iterative methods by means of an artificially created test problem involving very noisy uniformly spaced frequency data. The two corresponding optimization problems are motivated and their first-order algorithmic solutions based on the alternating direction method of multipliers and the dual accelerated gradient-projection method are stated and compared