A Randomized Algorithm for Parsimonious Model Identification

Burak Yilmaz, Korkut Bekiroglu, Constantino Lagoa, Mario Sznaier

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish (US)
Article number7970196
Pages (from-to)532-539
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number2
StatePublished - Feb 2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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