Optimization Online


Linear System Identification via Atomic Norm Regularization

Parikshit Shah(pshah***at***discovery.wisc.edu)
Badri Bhaskar(bnbhaskar***at***wisc.edu)
Gonnguo Tang(gtang5***at***wisc.edu)
Benjamin Recht(brecht***at***cs.wisc.edu)

Abstract: This paper proposes a new algorithm for linear system identification from noisy measurements. The proposed algorithm balances a data fidelity term with a norm induced by the set of single pole filters. We pose a convex optimization problem that approximately solves the atomic norm minimization problem and identifies the unknown system from noisy linear measurements. This problem can be solved efficiently with standard, freely available software. We provide rigorous statistical guarantees that explicitly bound the estimation error in terms of the stability radius, the Hankel singular values of the true system and the number of measurements. These results in turn yield complexity bounds and asymptotic consistency. We provide numerical experiments demonstrating the efficacy of our method for estimating linear systems from a variety of linear measurements.

Keywords: system identification, atomic norms, Hankel operators, basis pursuit

Category 1: Applications -- Science and Engineering (Control Applications )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )


Download: [PDF]

Entry Submitted: 04/03/2012
Entry Accepted: 04/03/2012
Entry Last Modified: 04/03/2012

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society