AbstractWe derive explicit computable expressions of structured backward errors of approximate eigenelements of structured matrix polynomials including symmetric, skew-symmetric, Hermitian, skew-Hermitian, even and odd polynomials. We determine minimal structured perturbations for which approximate eigenelements are exact eigenelements of the perturbed polynomials. We also analyze structured pseudospectra of a structured matrix polynomial and establish a partial equality between unstructured and structured pseudospectra. Finally, we analyze the effect of structure preserving linearizations of structured matrix polynomials on the structured backward errors of approximate eigenelements and show that structure preserving linearizations which m...
Abstract. Many applications give rise to nonlinear eigenvalue problems with an underlying structured...
We perform a backward error analysis of polynomial eigenvalue problems solved via linearization. Thr...
Many applications give rise to nonlinear eigenvalue problems with an underlying structured matrix po...
AbstractWe derive explicit computable expressions of structured backward errors of approximate eigen...
Minimal structured perturbations are constructed such that an approximate eigenpair of a nonlinear e...
Abstract. We start by introducing a new class of structured matrix polynomials, namely, the class of...
AbstractThis work is concerned with eigenvalue problems for structured matrix polynomials, including...
Most eigenvalue problems arising in practice are known to be structured. Structure is often introduc...
The most widely used approach for solving the polynomial eigenvalue problem $P(\lambda)x = \bigl(\su...
One of the most frequently used techniques to solve polynomial eigenvalue problems is linearization,...
The standard way of solving numerically a polynomial eigenvalue problem (PEP) is to use a linearizat...
In this thesis, we consider polynomial eigenvalue problems. We extend results on eigenvalue and eige...
Eigenvalue and eigenpair backward errors are computed for matrix pencils arising in optimal control....
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
The classical approach to investigating polynomial eigenvalue problems is linearization, where the u...
Abstract. Many applications give rise to nonlinear eigenvalue problems with an underlying structured...
We perform a backward error analysis of polynomial eigenvalue problems solved via linearization. Thr...
Many applications give rise to nonlinear eigenvalue problems with an underlying structured matrix po...
AbstractWe derive explicit computable expressions of structured backward errors of approximate eigen...
Minimal structured perturbations are constructed such that an approximate eigenpair of a nonlinear e...
Abstract. We start by introducing a new class of structured matrix polynomials, namely, the class of...
AbstractThis work is concerned with eigenvalue problems for structured matrix polynomials, including...
Most eigenvalue problems arising in practice are known to be structured. Structure is often introduc...
The most widely used approach for solving the polynomial eigenvalue problem $P(\lambda)x = \bigl(\su...
One of the most frequently used techniques to solve polynomial eigenvalue problems is linearization,...
The standard way of solving numerically a polynomial eigenvalue problem (PEP) is to use a linearizat...
In this thesis, we consider polynomial eigenvalue problems. We extend results on eigenvalue and eige...
Eigenvalue and eigenpair backward errors are computed for matrix pencils arising in optimal control....
It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the...
The classical approach to investigating polynomial eigenvalue problems is linearization, where the u...
Abstract. Many applications give rise to nonlinear eigenvalue problems with an underlying structured...
We perform a backward error analysis of polynomial eigenvalue problems solved via linearization. Thr...
Many applications give rise to nonlinear eigenvalue problems with an underlying structured matrix po...