Optimization and pseudospectra, with applications to robust stability

J. V. Burke, A. S. Lewis, M. L. Overton

Research output: Contribution to journalArticlepeer-review


The ε-pseudospectrum of a matrix A is the subset of the complex plane consisting of all eigenvalues of all complex matrices within a distance ε of A, We are interested in two aspects of "optimization and pseudoapectra." The first concerns maximizing the function "real part" over an ε-pseudospectrum of a fixed matrix: this defines a function known as the ε-pseudospectral abscissa of a matrix. We present a bisection algorithm to compute this function. Our second interest is in minimizing the ε-pseudospectral abscissa over a set of feasible matrices. A prerequisite for local optimization of this function is an understanding of its variational properties, the study of which is the main focus of the paper. We show that, in a neighborhood of any nonderogatory matrix, the ε-pseudospectral abscissa is a nonsmooth but locally Lipschitz and subdifferentially regular function for sufficiently small ε; in fact, it can be expressed locally as the maximum of a finite number of smooth functions. Along the way we obtain an eigenvalue perturbation result: near a nonderogatory matrix, the eigenvalues satisfy a Hölder continuity property on matrix space - a property that is well known when only a single perturbation parameter is considered. The pseudospectral abscissa is a powerful modeling tool: not only is it a robust measure of stability, but it also reveals the transient (as opposed to asymptotic) behavior of associated dynamical systems.

Original languageEnglish (US)
Pages (from-to)80-104
Number of pages25
JournalSIAM Journal on Matrix Analysis and Applications
Issue number1
StatePublished - 2004


  • Distance to instability
  • Eigenvalue optimization
  • H norm
  • Nonsmooth analysis
  • Pseudospectrum
  • Robust control
  • Robust optimization
  • Spectral abscissa
  • Stability radius
  • Subdifferential regularity

ASJC Scopus subject areas

  • Analysis


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