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Scalable Structured Low Rank Matrix Optimization Problems
This video was recorded at International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
