Alternating direction optimization algorithms for covariance completion problems

Armin Zare, Mihailo Jovanovic, Tryphon T Georgiou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Second-order statistics of nonlinear dynamical systems can be obtained from experiments or numerical simulations. These statistics are relevant in understanding the fundamental physics, e.g., of fluid flows, and are useful for developing low-complexity models. Such models can be used for the purpose of control design and analysis. In many applications, only certain second-order statistics of a limited number of states are available. Thus, it is of interest to complete partially specified covariance matrices in a way that is consistent with the linearized dynamics. The dynamics impose structural constraints on admissible forcing correlations and state statistics. Solutions to such completion problems can be used to obtain stochastically driven linearized models. Herein, we address the covariance completion problem. We introduce an optimization criterion that combines the nuclear norm together with an entropy functional. The two, together, provide a numerically stable and scalable computational approach which is aimed at low complexity structures for stochastic forcing that can account for the observed statistics. We develop customized algorithms based on alternating direction methods that are well-suited for large scale problems.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2015 American Automatic Control Council.


  • Alternating direction method of multipliers
  • alternating minimization algorithm
  • convex optimization
  • low-rank approximation
  • nuclear norm regularization
  • state covariances
  • structured matrix completion problems


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