Linear matrix equations such as the Lyapunov and Sylvester equations and their generalizations have classically played an important role in the analysis of dynamical systems, in control theory and in eigenvalue computation. More recently, matrix equations have emerged as a natural linear algebra framework for the discretized version of (systems of) partial differential equations (PDEs), possibly evolving in time. In this new framework, new challenges have arisen. In this talk we review some of the key methodologies for solving large scale linear and quadratic matrix equations. We will also discuss recent matrix-based strategies for the numerical solution of time-dependent problems arising in control and in the analysis of spatial pattern formations in certain electrodeposition models.[-]

We present a novel approach to the solution of time-dependent PDEs via the so-called monolithic or all-at-once formulation. This approach will be explained for simple parabolic problems and its utility in the context of PDE constrained optimization problems will be elucidated.
The underlying linear algebra includes circulant matrix approximations of Toeplitz-structured matrices and allows for effective parallel implementation. Simple computational results will be shown for the heat equation and the wave equation which indicate the potential as a parallel-in-time method.
This is joint work with Elle McDonald (CSIRO, Australia), Jennifer Pestana (Strathclyde University, UK) and Anthony Goddard (Durham University, UK)[-]

In this talk, we present a three-level variant of the parareal algorithm that uses three propagators at the fine, intermediate and coarsest levels. The fine and intermediate levels can both be run in parallel, only the coarsest level propagation is completely sequential. We interpret our algorithm as a variant of three-level MGRIT, and we present a convergence analysis that uses parareal-type assumptions, i.e., those that involve Lipschitz constants on the propagators. We present numerical experiments to illustrate how sharp the estimates are for various time dependent problems.[-]