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

We compare two iterative frequency domain sub-space 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.

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