Hankel Matrix Rank Minimization with Applications to System Identification and Realization

By Maryam Fazel, Ting Kei Pong, Defeng Sun, Paul Tseng. SIAM Journal on Matrix Analysis and Applications, 2013

544 citations recorded by the source corpus.

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Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion (2015, methodology): “In these works, in contrast to Fazel et al. (2013), Hankel/Toeplitz matrices are constrained to be low rank without convex relaxations.”
Non-convex Optimization for Machine Learning (2017, methodology): “However, for the sake of accuracy and ease of analysis [Fazel et al., 2013], it is advisable to fit a low-order model to the data.”
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems (2017, background): “Hankel matrices arise repeatedly from many different contexts in signal processing and control theory, such as system identification [21], harmonic retrieval, array signal processing [33], subspace-based channel identification [64], etc.”
Non-asymptotic Identification of LTI Systems from a Single Trajectory (2018, unknown): “, [9]) works better than Ho-Kalman.”