Abstract:Abstract. Let λ be a nonderogatory eigenvalue of A ∈ C n×n of algebraic multiplicity m. The sensitivity of λ with respect to matrix perturbations of the form A A + Δ, Δ ∈ Δ, is measured by the structured condition number κ Δ (A, λ). Here Δ denotes the set of admissible perturbations. However, if Δ is not a vector space over C, then κ Δ (A, λ) provides only incomplete information about the mobility of λ under small perturbations from Δ. The full information is then given by the set K Δ (x, y) = {y * Δx; Δ ∈ Δ, … Show more
“…Remark 3.9 Note that Theorem 3.8 (iv) also improves the results in [16,Theorem 4.3] and [32, Theorem 3.2], which only state bounds, but no explicit formula for the structured condition number of a simple nonzero eigenvalue. Recently, Karow [15] described the limit sets of the structured pseudospectra for complex skew-symmetric matrices, from which Theorem 3.8 (iv) could also be derived.…”
Section: Symmetric Skew-symmetric and Hermitian Matricesmentioning
The sensitivity of a multiple eigenvalue of a matrix under perturbations can be measured by its Hölder condition number. Various extensions of this concept are considered. A meaningful notion of structured Hölder condition numbers is introduced and it is shown that many existing results on structured condition numbers for simple eigenvalues carry over to multiple eigenvalues. The structures investigated in more detail include real, Toeplitz, Hankel, symmetric, skew-symmetric, Hamiltonian, and skew-Hamiltonian matrices. Furthermore, unstructured and structured Hölder condition numbers for multiple eigenvalues of matrix pencils are introduced. Particular attention is given to symmetric/skew-symmetric, Hermitian and palindromic pencils. It is also shown how matrix polynomial eigenvalue problems can be covered within this framework.
“…Remark 3.9 Note that Theorem 3.8 (iv) also improves the results in [16,Theorem 4.3] and [32, Theorem 3.2], which only state bounds, but no explicit formula for the structured condition number of a simple nonzero eigenvalue. Recently, Karow [15] described the limit sets of the structured pseudospectra for complex skew-symmetric matrices, from which Theorem 3.8 (iv) could also be derived.…”
Section: Symmetric Skew-symmetric and Hermitian Matricesmentioning
The sensitivity of a multiple eigenvalue of a matrix under perturbations can be measured by its Hölder condition number. Various extensions of this concept are considered. A meaningful notion of structured Hölder condition numbers is introduced and it is shown that many existing results on structured condition numbers for simple eigenvalues carry over to multiple eigenvalues. The structures investigated in more detail include real, Toeplitz, Hankel, symmetric, skew-symmetric, Hamiltonian, and skew-Hamiltonian matrices. Furthermore, unstructured and structured Hölder condition numbers for multiple eigenvalues of matrix pencils are introduced. Particular attention is given to symmetric/skew-symmetric, Hermitian and palindromic pencils. It is also shown how matrix polynomial eigenvalue problems can be covered within this framework.
“…The latter have been defined and investigated in [27]. For structured condition numbers of simple eigenvalues, see, e.g., [5,6,8,9,16,22,23,29,30,37,38]. Finally, we apply our results to the case of real perturbations of real matrices.…”
Section: Introductionmentioning
confidence: 99%
“…The sets (4.14) have been investigated in [23]. It has been shown there that these sets are ellipses in many important cases.…”
Section: δ Is Invariant Under Complex Multiplication) Thenmentioning
Abstract. In this paper we study the shape and growth of structured pseudospectra for small matrix perturbations of the formIt is shown that the properly scaled pseudospectra components converge to nontrivial limit sets as δ tends to 0. We discuss the relationship of these limit sets with μ-values and structured eigenvalue condition numbers for multiple eigenvalues.
“…The pseudospectrum is an important aid for shedding light on the sensitivity. Many properties and applications of the pseudospectrum of a matrix are discussed by Trefethen and Embree [23]; see also [6,7,10,13,20]. However, the computation of pseudospectra is a computationally demanding task except for very small matrices.…”
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