TY - JOUR A1 - Schindele, Andreas A1 - Borzì, Alfio T1 - Proximal Methods for Elliptic Optimal Control Problems with Sparsity Cost Functional JF - Applied Mathematics N2 - First-order proximal methods that solve linear and bilinear elliptic optimal control problems with a sparsity cost functional are discussed. In particular, fast convergence of these methods is proved. For benchmarking purposes, inexact proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of proximal schemes applied to infinite-dimensional elliptic optimal control problems and to validate the theoretical estimates. KW - semismooth Newton method KW - optimal control KW - elliptic PDE KW - nonsmooth optimization KW - proximal method Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-145850 VL - 7 IS - 9 ER - TY - JOUR A1 - Gaviraghi, Beatrice A1 - Schindele, Andreas A1 - Annunziato, Mario A1 - Borzì, Alfio T1 - On Optimal Sparse-Control Problems Governed by Jump-Diffusion Processes JF - Applied Mathematics N2 - A framework for the optimal sparse-control of the probability density function of a jump-diffusion process is presented. This framework is based on the partial integro-differential Fokker-Planck (FP) equation that governs the time evolution of the probability density function of this process. In the stochastic process and, correspondingly, in the FP model the control function enters as a time-dependent coefficient. The objectives of the control are to minimize a discrete-in-time, resp. continuous-in-time, tracking functionals and its L2- and L1-costs, where the latter is considered to promote control sparsity. An efficient proximal scheme for solving these optimal control problems is considered. Results of numerical experiments are presented to validate the theoretical results and the computational effectiveness of the proposed control framework. KW - jump-diffusion processes KW - partial integro-differential Fokker-Planck Equation KW - optimal control theory KW - nonsmooth optimization KW - proximal methods Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147819 VL - 7 IS - 16 SP - 1978 EP - 2004 ER - TY - THES A1 - Schindele, Andreas T1 - Proximal methods in medical image reconstruction and in nonsmooth optimal control of partial differential equations T1 - Proximale Methoden in der medizinischen Bildrekonstruktion und in der nicht-glatten optimalen Steuerung von partiellen Differenzialgleichungen N2 - Proximal methods are iterative optimization techniques for functionals, J = J1 + J2, consisting of a differentiable part J2 and a possibly nondifferentiable part J1. In this thesis proximal methods for finite- and infinite-dimensional optimization problems are discussed. In finite dimensions, they solve l1- and TV-minimization problems that are effectively applied to image reconstruction in magnetic resonance imaging (MRI). Convergence of these methods in this setting is proved. The proposed proximal scheme is compared to a split proximal scheme and it achieves a better signal-to-noise ratio. In addition, an application that uses parallel imaging is presented. In infinite dimensions, these methods are discussed to solve nonsmooth linear and bilinear elliptic and parabolic optimal control problems. In particular, fast convergence of these methods is proved. Furthermore, for benchmarking purposes, truncated proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of our proximal schemes that need less computation time than the semismooth Newton method in most cases. Results of numerical experiments are presented that successfully validate the theoretical estimates. N2 - Proximale Methoden sind iterative Optimierungsverfahren für Funktionale J = J1 +J2, die aus einem differenzierbaren Teil J2 und einem möglicherweise nichtdifferenzierbaren Teil bestehen. In dieser Arbeit werden proximale Methoden für endlich- und unendlichdimensionale Optimierungsprobleme diskutiert. In endlichen Dimensionen lösen diese `1- und TV-Minimierungsprobleme welche erfolgreich in der Bildrekonstruktion der Magnetresonanztomographie (MRT) angewendet wurden. Die Konvergenz dieser Methoden wurde in diesem Zusammenhang bewiesen. Die vorgestellten proximalen Methoden wurden mit einer geteilten proximalen Methode verglichen und konnten ein besseres Signal-Rausch-Verhältnis erzielen. Zusätzlich wurde eine Anwendung präsentiert, die parallele Bildgebung verwendet. Diese Methoden werden auch für unendlichdimensionale Probleme zur Lösung von nichtglatten linearen und bilinearen elliptischen und parabolischen optimalen Steuerungsproblemen diskutiert. Insbesondere wird die schnelle Konvergenz dieser Methoden bewiesen. Außerdem werden abgeschnittene proximale Methoden mit einem inexakten halbglatten Newtonverfahren verglichen. Die numerischen Ergebnisse demonstrieren die Effektivität der proximalen Methoden, welche im Vergleich zu den halbglatten Newtonverfahren in den meisten Fällen weniger Rechenzeit benötigen. Zusätzlich werden die theoretischen Abschätzungen bestätigt. KW - Optimale Kontrolle KW - Proximal-Punkt-Verfahren KW - Bildrekonstruktion KW - Komprimierte Abtastung KW - Optimal Control KW - Elliptic equations KW - Parabolic equations KW - Proximal Method KW - Semismooth Newton Method KW - Medical image reconstruction KW - Sparsity KW - Total Variation KW - Compressed Sensing KW - Magnetic Resonance Imaging KW - Partielle Differentialgleichung Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-136569 ER -