Optimal matrix approximants in structural identification

C. A. Beattie, S. W. Smith

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Problems of model correlation and system identification are central in the design, analysis, and control of large space structures. Of the numerous methods that have been proposed, many are based on finding minimal adjustments to a model matrix sufficient to introduce some desirable quality into that matrix. In this work, several of these methods are reviewed, placed in a modern framework, and linked to other previously known ideas in computational linear algebra and optimization. This new framework provides a point of departure for a number of new methods which are introduced here. Significant among these is a method for stiffness matrix adjustment which preserves the sparsity pattern of an original matrix, requires comparatively modest computational resources, and allows robust handling of noisy modal data. Numerical examples are included to illustrate the methods presented herein.

Original languageEnglish
Pages (from-to)23-56
Number of pages34
JournalJournal of Optimization Theory and Applications
Issue number1
StatePublished - Jul 1992


  • least-change secant updates
  • model adjustment
  • optimal matrix approximation
  • Structural identification

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics


Dive into the research topics of 'Optimal matrix approximants in structural identification'. Together they form a unique fingerprint.

Cite this