Hamburg-Harburg University of Technology

The famous Eckart-Young Theorem states that the (normwise) condition number of a matrix is equal to the reciprocal of its distance to the nearest singular matrix. In a recent paper we proved an extension of this to a number of structures common in matrix analysis, i.e. the structured condition number is equal to the reciprocal of the structured distance to the nearest singular matrix. In this talk we present a number of related results on structured eigenvalue perturbations and structured pseudospectra, for normwise and for componentwise perturbations.

This talk is about the Induced Dimension Reduction (IDR) methods developed by Peter Sonneveld and, more recently, Martin van Gijzen. We sketch the history, outline the underlying principle, and give a few details about different points of view on this class of Krylov subspace methods. If time permits, we briefly outline some recent developments in this field and the benefits and drawbacks of these and IDR methods in general.