On the least-squares approximation of structured covariances

Fu Lin, Mihailo R. Jovanović

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

Abstract

State covariances of the linear systems satisfy certain constraints imposed by the underlying dynamics. These constraints dictate a particular structure of state covariances. On the other hand, sample covariances (e.g., obtained in experiments) almost always fail to have the required structure. In view of this, it is of interest to approximate sample covariances by positive semi-definite matrices of the required structure. The structured covariance least-squares approximation problem is formulated and the Lyapunov-type matrical linear constraint is converted into an equivalent set of trace constraints. Efficient quasi Newton and generalized Newton methods capable of solving the corresponding unconstrained dual problems with the large number of variables are developed.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 American Control Conference, ACC
Pages2648-2653
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event2007 American Control Conference, ACC - New York, NY, United States
Duration: Jul 9 2007Jul 13 2007

Publication series

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

Other

Other2007 American Control Conference, ACC
CountryUnited States
CityNew York, NY
Period7/9/077/13/07

Keywords

  • Convex optimization
  • Least-squares approximation
  • Sample covariances

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