IMA Journal of Numerical Analysis (2017) 37, 1831–1863 doi: 10.1093/imanum/drw065 Advance Access publication on January 11, 2017 Computation of pseudospectral abscissa for large-scale nonlinear eigenvalue problems Downloaded from https://academic.oup.com/imajna/article-abstract/37/4/1831/2894467 by Koc University user on 20 August 2019 Karl Meerbergen Department of Computer Science, KU Leuven, University of Leuven, Heverlee 3001, Belgium Emre Mengi∗ Department of Mathematics, Ko¸c University, Rumelifeneri Yolu, Sarıyer-Istanbul˙ 34450, Turkey ∗Corresponding author:
[email protected] and Wim Michiels and Roel Van Beeumen Department of Computer Science, KU Leuven, University of Leuven, Heverlee 3001, Belgium [Received on 26 June 2015; revised on 21 October 2016] We present an algorithm to compute the pseudospectral abscissa for a nonlinear eigenvalue problem. The algorithm relies on global under-estimator and over-estimator functions for the eigenvalue and singular value functions involved. These global models follow from eigenvalue perturbation theory. The algorithm has three particular features. First, it converges to the globally rightmost point of the pseudospectrum, and it is immune to nonsmoothness. The global convergence assertion is under the assumption that a global lower bound is available for the second derivative of a singular value function depending on one parameter. It may not be easy to deduce such a lower bound analytically, but assigning large negative values works robustly in practice. Second, it is applicable to large-scale problems since the dominant cost per iteration stems from computing the smallest singular value and associated singular vectors, for which efficient iterative solvers can be used.