Diese Dissertation beschäftigt sich mit der rigorosen Herleitung effektiver Modelle zur Beschreibung von Schädigungsprozessen. Diese effektiven Modelle werden für verschiedene raten-unabhängige Schädigungsmodelle linear elastischer Materialien hergeleitet. Den Ausgangspunkt stellt dabei ein unidirektionales Mikrostrukturevolutionsmodell dar, dessen Fundament eine Familie geordneter zulässiger Mikrostrukturen bildet. Jede Mikrostruktur dieser Familie besitzt die gleiche intrinsische Längenskala. Zur Herleitung eines effektiven Modells wird das asymptotische Verhalten dieser Längenskala mittels Techniken der Zwei-Skalen-Konvergenz untersucht. Um das Grenzmodell zu identifizieren, bedarf es einer Mikrostrukturregularisierung, die als diskreter Gradient für stückweise konstante Funktionen aufgefasst werden kann. Die Mikrostruktur des effektiven Modells ist punktweise durch ein Einheitszellenproblem gegeben, welches die Mikro- von der Makroskala trennt. Ausgehend vom Homogenisierungsresultat für die unidirektionale Mikrostrukturevolution werden effektive Modelle für Zwei-Phasen-Schädungsprozesse hergeleitet. Die aus zwei Phasen bestehende Mikrostruktur der mikroskopischen Modelle ermöglicht z.B. die Modellierung von Schädigung durch das Wachstum von Inklusionen aus geschädigtem Material verschiedener Form und Größe. Außerdem kann Schädigung durch das Wachstum mikroskopischer Hohlräume und Mikrorissen betrachtet werden. Die Größe der Defekte skaliert mit der intrinsischen Längenskala und die unidirektionale Mikrostrukturevolution verhindert, dass bei fixierter Längenskala die Defekte für fortlaufende Zeit schrumpfen. Das Material des Grenzmodells ist dann in jedem Punkt als Mischung von ungeschädigtem und geschädigtem Material durch das Einheitszellenproblem gegeben. Dabei liefert das Einheitszellenproblem nicht nur das Mischungsverhältnis sondern auch die genaue geometrische Mischungsverteilung, die dem effektiven Material des jeweiligen Materialpunktes zugrunde liegt. / This dissertation at hand deals with the rigorous derivation of such effective models used to describe damage processes. For different rate-independent damage processes in linear elastic material these effective models are derived as the asymptotic limit of microscopic models. The starting point is represented by a unidirectional microstructure evolution model which is based on a family of ordered admissible microstructures. Each microstructure of that family possesses the same intrinsic length scale. To derive an effective model, the asymptotic behavior of this intrinsic length scale is investigated with the help of techniques of the two-scale convergence. For this purpose, a microstructure-regularizing term, which can be understood as a discrete gradient for piecewise constant functions, is needed to identify the limit model. The microstructure of the effective model is given pointwisely by a so-called unit cell problem which separates the microscopic scale from the macroscopic scale. Based on these homogenization results for unidirectional microstructure evolution models, effective models for brutal damage processes are provided. There, the microstructure consists of only two phases, namely undamaged material which comprises defects of damaged material with various sizes and shapes. In this way damage progression can be modeled by the growth of inclusions of weak material, the growth of voids, or the growth of microscopic cracks. The size of the defects is scaled by the intrinsic length scale and the unidirectional microstructure evolution prevents that, for a fixed length scale, the defects shrink for progressing time. According to the unit cell problem, the material of the limit model is then given as a mixture of damaged and undamaged material. In a specific material point of the limit model, that unit cell problem does not only define the mixture ratio but also the exact geometrical mixture distribution.

This thesis is concerned with the development of novel numerical methods for solving nondifferentiable convex optimization problems in real Hilbert spaces and with the investigation of their asymptotic behavior. To this end, we are also making use of monotone operator theory as some of the provided algorithms are originally designed to solve monotone inclusion problems. After introducing basic notations and preliminary results in convex analysis, we derive two numerical methods based on different smoothing strategies for solving nondifferentiable convex optimization problems. The first approach, known as the double smoothing technique, solves the optimization problem with some given a priori accuracy by applying two regularizations to its conjugate dual problem. A special fast gradient method then solves the regularized dual problem such that an approximate primal solution can be reconstructed from it. The second approach affects the primal optimization problem directly by applying a single regularization to it and is capable of using variable smoothing parameters which lead to a more accurate approximation of the original problem as the iteration counter increases. We then derive and investigate different primal-dual methods in real Hilbert spaces. In general, one considerable advantage of primal-dual algorithms is that they are providing a complete splitting philosophy in that the resolvents, which arise in the iterative process, are only taken separately from each maximally monotone operator occurring in the problem description. We firstly analyze the forward-backward-forward algorithm of Combettes and Pesquet in terms of its convergence rate for the objective of a nondifferentiable convex optimization problem. Additionally, we propose accelerations of this method under the additional assumption that certain monotone operators occurring in the problem formulation are strongly monotone. Subsequently, we derive two Douglas–Rachford type primal-dual methods for solving monotone inclusion problems involving finite sums of linearly composed parallel sum type monotone operators. To prove their asymptotic convergence, we use a common product Hilbert space strategy by reformulating the corresponding inclusion problem reasonably such that the Douglas–Rachford algorithm can be applied to it. Finally, we propose two primal-dual algorithms relying on forward-backward and forward-backward-forward approaches for solving monotone inclusion problems involving parallel sums of linearly composed monotone operators. The last part of this thesis deals with different numerical experiments where we intend to compare our methods against algorithms from the literature. The problems which arise in this part are manifold and they reflect the importance of this field of research as convex optimization problems appear in lots of applications of interest.

This thesis is concerned with the development of novel numerical methods for solving nondifferentiable convex optimization problems in real Hilbert spaces and with the investigation of their asymptotic behavior. To this end, we are also making use of monotone operator theory as some of the provided algorithms are originally designed to solve monotone inclusion problems. After introducing basic notations and preliminary results in convex analysis, we derive two numerical methods based on different smoothing strategies for solving nondifferentiable convex optimization problems. The first approach, known as the double smoothing technique, solves the optimization problem with some given a priori accuracy by applying two regularizations to its conjugate dual problem. A special fast gradient method then solves the regularized dual problem such that an approximate primal solution can be reconstructed from it. The second approach affects the primal optimization problem directly by applying a single regularization to it and is capable of using variable smoothing parameters which lead to a more accurate approximation of the original problem as the iteration counter increases. We then derive and investigate different primal-dual methods in real Hilbert spaces. In general, one considerable advantage of primal-dual algorithms is that they are providing a complete splitting philosophy in that the resolvents, which arise in the iterative process, are only taken separately from each maximally monotone operator occurring in the problem description. We firstly analyze the forward-backward-forward algorithm of Combettes and Pesquet in terms of its convergence rate for the objective of a nondifferentiable convex optimization problem. Additionally, we propose accelerations of this method under the additional assumption that certain monotone operators occurring in the problem formulation are strongly monotone. Subsequently, we derive two Douglas–Rachford type primal-dual methods for solving monotone inclusion problems involving finite sums of linearly composed parallel sum type monotone operators. To prove their asymptotic convergence, we use a common product Hilbert space strategy by reformulating the corresponding inclusion problem reasonably such that the Douglas–Rachford algorithm can be applied to it. Finally, we propose two primal-dual algorithms relying on forward-backward and forward-backward-forward approaches for solving monotone inclusion problems involving parallel sums of linearly composed monotone operators. The last part of this thesis deals with different numerical experiments where we intend to compare our methods against algorithms from the literature. The problems which arise in this part are manifold and they reflect the importance of this field of research as convex optimization problems appear in lots of applications of interest.