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Scalable Structured Low Rank Matrix Optimization Problems

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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