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Paul Van Dooren


Paul Van Dooren - Université Catholique de Louvain. Belgium

Paul M. Van Dooren received the engineering degree in computer science and the doctoral degree in applied sciences, both from the Katholieke Universiteit te Leuven, Belgium, in 1974 and 1979, respectively. He held research and teaching positions at the Katholieke Universiteit te Leuven, the University of Southern California, Stanford University, the Australian National University, Philips Research Laboratory Belgium, the University of Illinois at Urbana-Champaign, Florida State University and the Université Catholique de Louvain where he is currently a professor of Mathematical Engineering.

Prof. Van Dooren received the IBM-Belgium Informatics Award in 1974, the Householder Award in Numerical Linear Algebra in 1981, the SIAM Wilkinson Prize of Numerical Analysis and Scientific Computing in 1989 (SIAM is the acronym for the Society of Industrial and Applied Mathematics), the SIAM Outstanding Paper Prize in 2016, and the Hans Schneider Award of the International Linear Algebra Society in 2016. He is a Fellow of IEEE (Institute of Electrical and Electronics Engineers) and of SIAM. He received the Francqui Chair in Antwerp in 2010 and a Cátedra de Excelencia of the Universidad Carlos III de Madrid in 2013 and in 2017. His main interests lie in the areas of numerical linear algebra, systems and control theory, and in numerical methods for large graphs and networks.



Theoretical and Numerical Methods for Matrix Problems arising in Control, Dynamical Systems, and Complex Networks.


This research project presents several open problems that will allow me to collaborate with several investigators of the research groups “Numerical Linear Algebra and Matrix Theory” and "Mathematics Applied to Control, Systems, and Signals" of the Universidad Carlos III de Madrid.

The study of dynamical systems and networks heavily relies on the analysis of polynomial system models, and more specifically on standard and/or generalized state space models. Even though the theory behind these polynomial models is quite well understood, there are still important numerical issues that need to be addressed: algorithms used to compute the various structural elements of these models are still quite sensitive to perturbations and have a computational cost that is often too high. In this project we focus on several such problems and propose new algorithms with improved robustness and complexity properties. These problems include the study of robustness issues of dynamical systems in generalized state space form, and the problem of local linearizations of rational and nonlinear matrix models.