abstract: Matrix functions are a central topic in scientific computing, with rich connections to several areas of pure and applied mathematics, and applications ranging from differential equations to control theory, Markov models, or theoretical physics.
This course aims to introduce its audience to recent developments in the analysis and computation of matrix functions through their link to quadrature rules and orthogonal polynomials. This elegant approach was mainly developed by Gene Golub and Gerard Meurant, starting in the 1990s, and it has since elicited great interest in the numerical linear algebra community.
From a computational point of view, Golub and Meurant's methods are especially efficient for bounding or estimating bilinear forms involving functions of large, sparse matrices. One interesting application concerns the analysis of complex networks, where certain measures of centrality or communicability (e.g., the popularity of a social networkuser) can be expressed in terms of matrix functions.
The lectures will cover the following topics:
References:
- N. J. Higham, Functions of Matrices: theory and Computation, SIAM 2008.
- G. H. Golub, G. Meurant, Matrices, Moments and Quadrature with Applications, Princeton University Press, 2010.
- E. Estrada, D. J. Higham, Network properties revealed through Matrix functions, SIAM Rev. 52(4), 696-714, 2010.