European Numerical Mathematics and
 10:30 25 mins PDE Constrained Optimization Problems for the Waste Clearance of the Human Brain Kent-Andre Mardal, Lars Magnus Valnes, Sebastian Mitusch, Geir Ringstad, Per Kristian Eide, Simon Funke Abstract: The brain is our most energy expensive organ, but its metabolic cycle is not understood. In particular, its waste disposal system is a mystery because it lacks the lymphatic system that is present elsewhere in our body. Understand how waste is cleared under healthy and diseased condition is important as accumulation of waste is associated with dementia such as Alzheimer’s and Parkinson’s diseases. Novel imaging protocols are under investigation for assessing brain clearance on the long time scales hours. In this talk we will present the newly proposed protocols and discuss the PDE constrained optimization problems that arise. Furthermore, a crucial component in this type of investigations is that the data exist everywhere in space, albeit at coarse resolution, but at certain points in time. Order optimal algorithms are derived for some simplified problems. 10:55 25 mins Applications of the PRESB preconditioning method for OPT-PDE problems Owe Axelsson Abstract: The abstract is enclosed in the attachment. 11:20 25 mins Fast Interior Point Solvers and Preconditioning for PDE-Constrained Optimization John Pearson Abstract: In this talk we consider the effective numerical solution of PDE-constrained optimization problems with additional box constraints on the state and control variables. Upon discretization, these may give rise to problems of quadratic or nonlinear programming form: a sensible solution strategy is to apply an interior point method, provided one can solve the large and structured matrix systems that arise at each Newton step. We therefore consider fast and robust preconditioned iterative methods for these systems, examining two cases: (i) where L^2 norms measure the misfit between state and desired state, as well as the control; (ii) with an additional L^1 norm term promoting sparsity in the control. Having motivated and derived our recommended preconditioners, and shown some theoretical results on saddle point systems, we present numerical results demonstrating the potency of our solvers. This talk is based on work with Jacek Gondzio, Margherita Porcelli, and Martin Stoll. 11:45 25 mins Spectral Analysis of Saddle-point Matrices from Optimal Control PDE Problems Fabio Durastante, Isabella Furci Abstract: Optimization problems with constraints given in terms of Partial Differential Equations are a ubiquitous problem of the applied mathematics. We consider here the distributed optimal control for the Poisson equation~[3], and focus on the sequences of saddle-point linear systems stemming from its Finite Element approximation $\mathcal{A}}_N$. Our main objective is then devising an efficient solution strategy for them by proving that the matrix sequence $\{\mathcal{A}_N\}_N$ belongs to the class of \emph{Generalized Locally Toeplitz} sequences~[2] in their most general block multilevel setting. This framework permits describing the spectrum of the matrix sequence in a compact form, i.e., it ensures the existence of a measurable matrix--valued function $\kappa$, called symbol, correlated with the sequence. The knowledge of $\kappa$ enables the detailed study of the distribution and the localization of the spectrum of the sequence together with the behavior of its extremal and outlier eigenvalues, and thus of its conditioning. By exploiting these results, we propose a specific solution strategy based on a preconditioned Krylov method. The study of the complexity, and of the convergence speed of the proposed method is then carried out by means of the information acquired from the symbol of the preconditioned sequence. [1] Durastante, F., and I. Furci. Spectral Analysis of Saddle-point Matrices from Optimization problems with Elliptic PDE Constraints. 2019, arXiv e-prints , arXiv:1903.01869. [2] Garoni, C., and S. Serra-Capizzano. Generalized Locally Toeplitz Sequences: Theory and Applications. Vol. 1. Springer, 2017. [3] Tröltzsch, F. Optimal control of partial differential equations: theory, methods, and applications. Vol. 112. American Mathematical Soc., 2010.