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Optimization Algorithms for Nuclear Norm Based Subspace Identification with Uniformly Spaced Frequency Domain Data

Author(s):

M. Graf Plessen, V. Semeraro, T. A. Wood, R. S. Smith
Conference/Journal:

American Control Conference, Chicago, IL, pp. 1119-1124
Abstract:

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.

Year:

2015
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { PleEtal:2015:IFA_5099,
    author={M. Graf Plessen and V. Semeraro and T. A. Wood and R. S. Smith},
    title={{Optimization Algorithms for Nuclear Norm Based Subspace
	  Identification with Uniformly Spaced Frequency Domain Data}},
    booktitle={American Control Conference},
    pages={1119--1124},
    year={2015},
    address={Chicago, IL},
    month=jul,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=5099}
}
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