Variational Analysis of Pseudospectra

The $\epsilon$-pseudospectrum of a square matrix $A$ is the set of eigenvalues attainable when $A$ is perturbed by matrices of spectral norm not greater than $\epsilon$. The pseudospectral abscissa is the largest real part of such an eigenvalue, and the pseudospectral radius is the largest absolute value of such an eigenvalue. We find conditions for the pseudospectrum to be Lipschitz continuous in the set-valued sense and hence find conditions for the pseudospectral abscissa and the pseudospectral radius to be Lipschitz continuous in the single-valued sense. Our approach illustrates diverse techniques of variational analysis. The points at which the pseudospectrum is not Lipschitz (or more properly, does not have the Aubin property) are exactly the critical points of the resolvent norm, which in turn are related to the coalescence points of pseudospectral components.

[1]  B. Mordukhovich Variational analysis and generalized differentiation , 2006 .

[2]  Adrian S. Lewis,et al.  Convexity and Lipschitz Behavior of Small Pseudospectra , 2007, SIAM J. Matrix Anal. Appl..

[3]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[4]  L. Trefethen Spectra and pseudospectra , 2005 .

[5]  F. Clarke Optimization And Nonsmooth Analysis , 1983 .

[6]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[7]  Adrian S. Lewis,et al.  Spectral conditioning and pseudospectral growth , 2007, Numerische Mathematik.

[8]  R. Tyrrell Rockafellar,et al.  Variational Analysis , 1998, Grundlehren der mathematischen Wissenschaften.

[9]  A. Lewis,et al.  Clarke critical values of subanalytic Lipschitz continuous functions , 2005 .

[10]  M. Overton,et al.  Algorithms for the computation of the pseudospectral radius and the numerical radius of a matrix , 2005 .

[11]  Yu. S. Ledyaev,et al.  Nonsmooth analysis and control theory , 1998 .

[12]  G. Watson Characterization of the subdifferential of some matrix norms , 1992 .

[13]  A. Lewis,et al.  Robust stability and a criss‐cross algorithm for pseudospectra , 2003 .

[14]  P. Huard Background to point-to-set maps in mathematical programming , 1979 .

[15]  M. Morari,et al.  Nonlinear parametric optimization using cylindrical algebraic decomposition , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[16]  R. Alam,et al.  On sensitivity of eigenvalues and eigendecompositions of matrices , 2005 .

[17]  M. Coste AN INTRODUCTION TO O-MINIMAL GEOMETRY , 2002 .

[18]  A. Harrabi,et al.  About Hölder condition numbers and the stratification diagram for defective eigenvalues , 2000 .

[19]  J. Demmel A counterexample for two conjectures about stability , 1987 .

[20]  Michael Karow,et al.  EIGENVALUE CONDITION NUMBERS AND A FORMULA OF BURKE, LEWIS AND OVERTON ∗ , 2006 .

[21]  Adrian S. Lewis,et al.  Optimization and Pseudospectra, with Applications to Robust Stability , 2003, SIAM J. Matrix Anal. Appl..

[22]  Diethard Klatte,et al.  On Procedures for Analysing Parametric Optimization Problems , 1982 .