Matrix equation techniques for a class of PDE problems with data uncertainty

Linear matrix equations arise in an amazingly growing number of applications. Classically, they have been extensively encountered in Control theory and eigenvalue problems. More recently they have been shown to provide a natural platform in the discretization of certain partial differential equations (PDEs), both in the deterministic setting, and in the presence of uncertainty in the data. We first review some numerical techniques for solving various classes of large scale linear matrix equations commonly occurring in applications. Then we focus on recent developments in the solution of (systems of) linear matrix equations associated with the numerical treatment of various stochastic PDE problems. Contact: paolo.zunino@polimi.it

Valeria Simoncini is Full Professor at Università di Bologna. Her main scientific interests are Matrix Computations, Spectral Perturbation Theory with applications to PDEs, Control and Multivariate Statistics. She is member of the editorial board for several high level international journals