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- eigenvalues (3)
- $\mu$-values (2)
- condition numbers (2)
- perturbations (2)
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- Hamiltonian pseudospectra (1)
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In this paper we study the shape and growth of structured pseudospectra
for small matrix perturbations of the form $A \leadsto
A_\Delta=A+B\Delta C$, $\Delta \in \DD$, $\|\Delta\|\leq \delta$.
It is shown that the properly scaled pseudospectra components converge
to non-trivial limit sets as $\delta$ tends to 0.
We discuss the relationship of these limit sets with $\mu$-values and
structured eigenvalue condition numbers for multiple eigenvalues.
Motivated by the analysis of passive control systems, we undertake a detailed perturbation analysis of Hamiltonian matrices that have eigenvalues on the imaginary axis. We construct minimal Hamiltonian perturbations that move and coalesce eigenvalues of opposite sign characteristic to form multiple eigenvalues with mixed sign characteristics, which are then moved from the imaginary axis to specific locations in the complex plane by small Hamiltonian perturbations. We also present a numerical method to compute upper bounds for the minimal perturbations that move all eigenvalues of a given Hamiltonian matrix outside a vertical strip along the imaginary axis.
This paper investigates the effect of structure-preserving perturbations on the eigenvalues
of linearly and nonlinearly structured eigenvalue problems. Particular attention is paid to
structures that form Jordan algebras, Lie algebras, and automorphism groups of a scalar product.
Bounds and computable expressions for structured eigenvalue condition numbers are derived for
these classes of matrices, which include complex symmetric, pseudo symmetric, persymmetric, skewsymmetric,
Hamiltonian, symplectic, and orthogonal matrices. In particular we show that under mild
assumptions on the scalar product, the structured and unstructured eigenvalue condition numbers
are equal for structures in Jordan algebras. For Lie algebras, the effect on the condition number of
incorporating structure varies greatly with the structure. We identify Lie algebras for which structure
does not affect the eigenvalue condition number.
$\mu$-values and spectral value sets for linear perturbation classes defined by a scalar product
(2007)
We study the variation of the spectrum of matrices
under perturbations which are self- or skew-adjoint
with respect to a scalar product.
Computable formulae are given for the associated
$\mu$-values. The results can be used to calculate spectral value
sets for the perturbation classes under consideration.
We discuss the special case of
complex Hamiltonian perturbations of a Hamiltonian matrix in detail.
Let $\lambda$ be a nonderogatory eigenvalue of $A \in \C^{n \times
n}$. The sensitivity of $\lambda$ with respect to matrix
perturbations
$A \leadsto A+\Delta,\Delta \in \DD$, is measured by the structured
condition number $\kappa_\DD(A,\lambda)$. Here $\DD$ denotes the set
of admissible perturbations. However, if $\DD$ is not a vector space
over $\C$ then $\kappa_\DD(A,\lambda)$ provides only incomplete
information about the mobility of $\lambda$ under small
perturbations from $\DD$. The full
information is then given by a certain set $K_\DD(x,y)\subset \C$
which depends on $\DD$ and
a pair of normalized right and left eigenvectors $x,y$. In this paper
we study the sets $K_\DD(x,y)$ and obtain methods for computing
them.
In particular we show that $K_\DD(x,y)$ is an ellipse in some
important cases.
We derive a formula for the backward error of a complex number $\lambda$ when considered as an approximate eigenvalue
of a Hermitian matrix pencil or polynomial with respect to Hermitian perturbations. The same are also obtained for approximate
eigenvalues of matrix pencils and polynomials with related structures like skew-Hermitian, $*$-even and $*$-odd.
Numerical experiments suggest that in many cases there is a significant difference between the backward
errors with respect to perturbations that preserve structure and those with respect to arbitrary perturbations.