A Randomized Algorithm for Parsimonious Model Identification

Identifying parsimonious models is generically a "hard" nonconvex problem. Available approaches typically rely on relaxations such as Group Lasso or nuclear norm minimization. Moreover, incorporating stability and model order constraints into the formalism in such methods entails a substan...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 63; no. 2; pp. 532 - 539
Main Authors: Yilmaz, Burak, Bekiroglu, Korkut, Lagoa, Constantino, Sznaier, Mario
Format: Journal Article
Language:English
Published: IEEE 01.02.2018
Subjects:
Atomic norm
Computational modeling
Data models
Frank–Wolfe
Hankel singular values
identification
Linear systems
Noise measurement
Optimization
Stability analysis
Transfer functions
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:Identifying parsimonious models is generically a "hard" nonconvex problem. Available approaches typically rely on relaxations such as Group Lasso or nuclear norm minimization. Moreover, incorporating stability and model order constraints into the formalism in such methods entails a substantial increase in computational complexity. Motivated by these challenges, in this paper we present algorithms for parsimonious linear time invariant system identification aimed at identifying low-complexity models which i) incorporate a priori knowledge on the system (e.g., stability), ii) allow for data with missing/nonuniform measurements, and iii) are able to use data obtained from several runs of the system with different unknown initial conditions. The randomized algorithms proposed are based on the concept of atomic norm and provide a numerically efficient way to identify sparse models from large amounts of noisy data.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2017.2723959