Wednesday, 16:15 - 16:40 h, Room: H 2038


Maryam Fazel
Algorithms for Hankel matrix rank minimization for system identification and realization

Coauthors: Ting Kei Pong, Defeng Sun, Paul Tseng


We introduce a flexible optimization framework for nuclear norm minimization of matrices with linear structure, including Hankel, Toeplitz and Moment structures, and catalog applications from diverse fields under this framework. We discuss first-order methods for solving the resulting optimization problem, including alternating direction methods,
proximal point algorithm and gradient projection methods. We perform computational experiments comparing these methods on system identification and system realization problems. For the system identification problem, the gradient projection method (accelerated by Nesterov’s extrapolation techniques) outperforms other first-order methods in terms of CPU time on both real and simulated data; while for the system realization problem, the alternating direction method, as applied to a certain primal reformulation, outperforms other first-order methods.


Talk 3 of the invited session Wed.3.H 2038
"Conic and convex programming in statistics and signal processing IV" [...]
Cluster 4
"Conic programming" [...]


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