Scalable Structured Low Rank Matrix Optimization Problems
published: Aug. 26, 2013, recorded: July 2013, views: 3469
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We consider a class of structured low rank matrix optimization problems. We represent the desired structure by a linear map, termed mutation, that can encode matrices having entries partitioned into known disjoined groups. Our interest arises in particular from concatenated block-Hankel matrices that appear in formulations for input-output linear system identification problems with noisy and/or partially unobserved data. We present an algorithm and test it against an existing alternative.
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