### Refine

#### Year of publication

#### Document Type

- Doctoral Thesis (61)
- Habilitation (2)

#### Has Fulltext

- yes (63) (remove)

#### Keywords

- Optimierung (7)
- Approximationstheorie (6)
- Funktionentheorie (6)
- Partielle Differentialgleichung (6)
- Universalität (6)
- Approximation (5)
- Funktionalanalysis (5)
- universal functions (5)
- Numerische Strömungssimulation (4)
- Optimale Kontrolle (4)

#### Institute

- Mathematik (63) (remove)

The goal of this thesis is to transfer the logarithmic barrier approach, which led to very efficient interior-point methods for convex optimization problems in recent years, to convex semi-infinite programming problems. Based on a reformulation of the constraints into a nondifferentiable form this can be directly done for convex semi- infinite programming problems with nonempty compact sets of optimal solutions. But, by means of an involved max-term this reformulation leads to nondifferentiable barrier problems which can be solved with an extension of a bundle method of Kiwiel. This extension allows to deal with inexact objective values and subgradient information which occur due to the inexact evaluation of the maxima. Nevertheless we are able to prove similar convergence results as for the logarithmic barrier approach in the finite optimization. In the further course of the thesis the logarithmic barrier approach is coupled with the proximal point regularization technique in order to solve ill-posed convex semi-infinite programming problems too. Moreover this coupled algorithm generates sequences converging to an optimal solution of the given semi-infinite problem whereas the pure logarithmic barrier only produces sequences whose accumulation points are such optimal solutions. If there are certain additional conditions fulfilled we are further able to prove convergence rate results up to linear convergence of the iterates. Finally, besides hints for the implementation of the methods we present numerous numerical results for model examples as well as applications in finance and digital filter design.

This work is concerned with the numerical solution of optimization problems that arise in the context of ground water modeling. Both ground water hydraulic and quality management problems are considered. The considered problems are discretized problems of optimal control that are governed by discretized partial differential equations. Aspects of special interest in this work are inaccurate function evaluations and the ensuing numerical treatment within an optimization algorithm. Methods for noisy functions are appropriate for the considered practical application. Also, block preconditioners are constructed and analyzed that exploit the structure of the underlying linear system. Specifically, KKT systems are considered, and the preconditioners are tested for use within Krylov subspace methods. The project was financed by the foundation Stiftung Rheinland-Pfalz für Innovation and carried out in joint work with TGU GmbH, a company of consulting engineers for ground water and water resources.

Diese Arbeit beschäftigt sich mit (frequent) universellen Funktionen bezüglich Differentialoperatoren und gewichteten Shiftoperatoren.rnHierbei wird ein Charakteristikum von Funktionen vom Exponentialtyp untersucht, das bisher im Rahmen der Universalität noch nicht betrachtet wurde: Das konjugierte Indikatordiagramm. Dabei handelt es sich um eine kompakte und konvexe Menge, die einer Funktion vom Exponentialtyp zugeordnet ist und gewisse Rückschlüsse über das Wachstum und die mögliche Nullstellenverteilung zulässt.rnrnMittels einer speziellen Transformation werden (frequent) universelle Funktionen vom Exponentialtyp bezüglich verschiedener Differentialoperatoren ineinander überführt. Hierdurch ist eine genaue Lokalisation der konjugierten Indikatordiagramme möglicher (frequent) universeller Funktionen für diese Operatoren ableitbar.rnDurch Konjugation der Differentiation mit gewichteten Shiftoperatoren über das Hadamardprodukt, wird auch für diese Operatoren eine Lokalisation möglicher konjugierter Indikatordiagramme ihrer (frequent) universellen Funktionen erreicht.rn

In splitting theory of locally convex spaces we investigate evaluable characterizations of the pairs (E, X) of locally convex spaces such that each exact sequence 0 âˆ’> X -> G -> E -> 0 of locally convex spaces splits, i.e. either X -> G has a continuous linear left inverse or G -> E has a continuous linear right inverse. In the thesis at hand we deal with splitting of short exact sequences of so-called PLH spaces, which are defined as projective limits of strongly reduced spectra of strong duals of Fréchet-Hilbert spaces. This class of locally convex spaces contains most of the spaces of interest for application in the theory of partial differential operators as the space of Schwartz distributions , the space of real analytic functions and various spaces of ultradifferentiable functions and ultradistributions. It also contains non-Schwartz spaces as B(2,k,loc)(â„¦) and spaces of smooth and square integrable functions that are not covered by the current theory for PLS spaces. We prove a complete characterizations of the above problem in the case of X being a PLH space and E either being a Fréchet-Hilbert space or a strong dual of one by conditions of type (T ). To this end, we establish the full homological toolbox of Yoneda Ext functors in exact categories for the category of PLH spaces including the long exact sequence, which in particular involves a thorough discussion of the proper concept of exactness. Furthermore, we exhibit the connection to the parameter dependence problem via the Hilbert tensor product for hilbertizable locally convex spaces. We show that the Hilbert tensor product of two PLH spaces is again a PLH space which in particular proves the positive answer to Grothendieck- problème des topologies. In addition to that we give a complete characterization of the vanishing of the first derivative of the functor proj for tensorized PLH spectra if one of the PLH spaces E and X meets some nuclearity assumptions. To apply our results to concrete cases we establish sufficient conditions of (DN)-(â„¦) type and apply them to the parameter dependence problem for partial differential operators with constant coefficients on B(2,k,loc)(â„¦) spaces as well as to the smooth and square integrable parameter dependence problem. Concluding we give a complete solution of all the problems under consideration for PLH spaces of Köthe type.

The main achievement of this thesis is an analysis of the accuracy of computations with Loader's algorithm for the binomial density. This analysis in later progress of work could be used for a theorem about the numerical accuracy of algorithms that compute rectangle probabilities for scan statistics of a multinomially distributed random variable. An example that shall illustrate the practical use of probabilities for scan statistics is the following, which arises in epidemiology: Let n patients arrive at a clinic in d = 365 days, each of the patients with probability 1/d at each of these d days and all patients independently from each other. The knowledge of the probability, that there exist 3 adjacent days, in which together more than k patients arrive, helps deciding, after observing data, if there is a cluster which we would not suspect to have occurred randomly but for which we suspect there must be a reason. Formally, this epidemiological example can be described by a multinomial model. As multinomially distributed random variables are examples of Markov increments, which is a fact already used implicitly by Corrado (2011) to compute the distribution function of the multinomial maximum, we can use a generalized version of Corrado's Algorithm to compute the probability described in our example. To compute its result, the algorithm for rectangle probabilities for Markov increments always uses transition probabilities of the corresponding Markov Chain. In the multinomial case, the transition probabilities of the corresponding Markov Chain are binomial probabilities. Therefore, we start an analysis of accuracy of Loader's algorithm for the binomial density, which for example the statistical software R uses. With the help of accuracy bounds for the binomial density we would be able to derive accuracy bounds for the computation of rectangle probabilities for scan statistics of multinomially distributed random variables. To figure out how sharp derived accuracy bounds are, in examples these can be compared to rigorous upper bounds and rigorous lower bounds which we obtain by interval-arithmetical computations.

In the first part of this work we generalize a method of building optimal confidence bounds provided in Buehler (1957) by specializing an exhaustive class of confidence regions inspired by Sterne (1954). The resulting confidence regions, also called Buehlerizations, are valid in general models and depend on a designated statistic'' that can be chosen according to some desired monotonicity behaviour of the confidence region. For a fixed designated statistic, the thus obtained family of confidence regions indexed by their confidence level is nested. Buehlerizations have furthermore the optimality property of being the smallest (w.r.t. set inclusion) confidence regions that are increasing in their designated statistic. The theory is eventually applied to normal, binomial, and exponential samples. The second part deals with the statistical comparison of pairs of diagnostic tests and establishes relations 1. between the sets of lower confidence bounds, 2. between the sets of pairs of comparable lower confidence bounds, and 3. between the sets of admissible lower confidence bounds in various models for diverse parameters of interest.

Die Probleme bezüglich der Existenz universeller Funktionen und die universelle Approximation von Funktionen sind von klassischer Natur und spielen eine zentrale Rolle. Folgende Untersuchungen sind Gegenstand dieser Arbeit: Universelle Funktionen, die durch Lückenreihen dargestellt werden, sog. eingeschränkte Universalitäten, mehrfache Universalitäten sowie die universelle Approximation messbarer Funktionen. In einem letzten Kapitel werden abschließend ganzzahlige Cesaro-Mittel untersucht. Hier zeigt sich, dass alle bewiesenen Ergebnisse dieser Arbeit über universelle Approximation im Komplement des abgeschlossenen Einheitskreises durch Teilsummen einer Potenzreihe vom Konvergenzradius 1 auch auf die jeweiligen ganzzahligen Cesaro-Transformierten der Teilsummen übertragbar sind.

The discretization of optimal control problems governed by partial differential equations typically leads to large-scale optimization problems. We consider flow control involving the time-dependent Navier-Stokes equations as state equation which is stamped by exactly this property. In order to avoid the difficulties of dealing with large-scale (discretized) state equations during the optimization process, a reduction of the number of state variables can be achieved by employing a reduced order modelling technique. Using the snapshot proper orthogonal decomposition method, one obtains a low-dimensional model for the computation of an approximate solution to the state equation. In fact, often a small number of POD basis functions suffices to obtain a satisfactory level of accuracy in the reduced order solution. However, the small number of degrees of freedom in a POD based reduced order model also constitutes its main weakness for optimal control purposes. Since a single reduced order model is based on the solution of the Navier-Stokes equations for a specified control, it might be an inadequate model when the control (and consequently also the actual corresponding flow behaviour) is altered, implying that the range of validity of a reduced order model, in general, is limited. Thus, it is likely to meet unreliable reduced order solutions during a control problem solution based on one single reduced order model. In order to get out of this dilemma, we propose to use a trust-region proper orthogonal decomposition (TRPOD) approach. By embedding the POD based reduced order modelling technique into a trust-region framework with general model functions, we obtain a mechanism for updating the reduced order models during the optimization process, enabling the reduced order models to represent the flow dynamics as altered by the control. In fact, a rigorous convergence theory for the TRPOD method is obtained which justifies this procedure also from a theoretical point of view. Benefiting from the trust-region philosophy, the TRPOD method guarantees to save a lot of computational work during the control problem solution, since the original state equation only has to be solved if we intend to update our model function in the trust-region framework. The optimization process itself is completely based on reduced order information only.

Zu den klassischen Verteilungen der mathematischen Statistik zählen die zentralen F- und t-Verteilungen. Die vorliegende Arbeit untersucht Verallgemeinerungen dieser Verteilungen, die sogenannten doppelt nichtzentralen F- und t-Verteilungen, welche in der statistischen Testtheorie von Bedeutung sind. Die Tatsache, dass die zugehörigen Wahrscheinlichkeitsdichten nur in Form von Parameterintegral- bzw. Doppelreihendarstellungen gegeben sind, stellt eine große Herausforderung bei der Untersuchung analytischer Eigenschaften dar. Unter Verwendung von Techniken aus der Theorie der vorzeichenregulären Funktionen gelingt es, die bisher vermutete, jedoch lediglich aus Approximationen abgeleitete, strikt unimodale Gestalt der Dichtefunktion für eine große Klasse doppelt nichtzentraler Verteilungen zu zeigen. Dieses Resultat gestattet die Untersuchung des eindeutig bestimmten Modus als Funktion gewisser Nichtzentralitätsparameter. Hier erweist sich die Theorie der vorzeichenregulären Funktionen als wichtiges Hilfsmittel, um monotone Abhängigkeiten nachzuweisen.

Die Dissertation mit dem Thema "Cross-Border-Leasing als Instrument der Kommunalfinanzierung " Eine finanzwirtschaftliche Analyse unter besonderer Berücksichtigung der Risiken " befasst sich am Beispiel des primär steuerinduzierten, grenzüberschreitenden Cross-Border-Leasings (CBL) mit einem innovativen, strukturierten Finanzierungsinstrument, das sich im Spannungsfeld von Rechtsstaatlichkeit und privatwirtschaftlichem Management öffentlicher Akteure befindet. Dazu werden bereits finanzierte und sich im Betrieb befindliche Assets in Variationen von langfristigen Leasingverträge eingebracht. Durch die geschickte Ausnutzung steuerlicher Zurechnungskriterien werden unter Einbindung mehrerer Jurisdiktionen Gewinnverschiebungsmöglichkeiten und Steueroptimierungspotenziale geschaffen, wobei die generierten Zusatzerträge unter den Akteuren aufgeteilt werden. Die Untersuchung orientiert sich an einem umfassenden forschungsleitenden Fragenkatalog, der sehr vielschichtig und zudem interdisziplinär die komplexen Aspekte des CBLs theoretisch sowie praktisch an einem Fallbeispiel untersucht. Zunächst erfolgt die Einbettung des CBLs in den kommunalen Hintergrund. Daran schliesst sich eine Darstellung des Untersuchungsgegenstands im Hinblick auf seine elementare Grundstruktur, Zahlungsströme, Vertragsparteien und deren bilateralen Verpflechtungen an. Daneben erfolgt eine Analyse der öffentlich-rechtlichen Implikationen des CBLs sowie der regulatorischen kommunalaufsichtsrechtlichen Anforderungen. Im zentralen empirischen Teil der Dissertation wird eine idealtypische CBL-Transaktion einer bundesdeutschen Metropole als Fallstudie analysiert: im Rahmen einer erstmaligen wissenschaftlichen Analyse einer Orginaldokumentation werden zunächst die strukturellen Rahmenparameter untersucht, um dann den Finanzierungsvorteil der Transaktion zu ermitteln. Eine Klassifikation erfolgt dabei in diejenigen Risken, die sich unmittelbar im Einflussbereich der Kommune befinden und somit direkt, d.h. durch aktives eigenes Handeln, minimiert oder vermieden werden können und in solche, die aus ihrer Sicht extern sind. Abgerundet wird die Risikoanalyse durch eine Abschätzung der maximalen Risikoposition in Form der Schadensersatzzahlungen, die die Kommune in vertraglich vereinbarten Fällen leisten muss. Dabei ermittelt die Verfasserin den Break-Even der Transaktion und setzt Szenarien sowie mathematische Modelle ein, um die inhärenten Risiken aufgrund ihrer Kostenfolgen sorgfältig gegenüber dem vereinnahmten kurzfristigen Vorteil abzuwägen. Die Untersuchung bedient sich dem anerkannten mathematisch-statistischen Value-at-Risk-Verfahren (VaR), das unter Verwendung von Ansätzen der Wahrscheinlichkeitsverteilung das Marktpreisrisiko zu quantifizieren vermag. Um zu validen Ergebnissen zu gelangen, werden zur Ermittlung des VaRs die beiden bekanntesten (nicht-parametrischen) Tools des VaR-Ansatzes angewendet, um die potenziellen Performanceschwankungen des Depotwertes unter Zugrundelegung bestimmter Wahrscheinlichkeiten abschätzen zu können. Dies ist das Verfahren der Historischen Simulation sowie die als mathematisch sehr anspruchsvoll eingestufte Monte-Carlo-Simulation. Als Weiterentwicklung des VaR-Modells wird zudem der Conditional VaR berechnet, der Aussagen über das Ausmaß der erwarteten Verluste zulässt. Anhand dieser Ergebnisse wird die maximale finanzielle Risikoposition der Kommune, bezogen auf das Kapitaldepot, abgeleitet. Darüber hinaus wird das CBL im Rahmen eines mathematischen Modells insgesamt beurteilt, indem eine Gegenüberstellung von vereinnahmtem Finanzierungsvorteil und den mit Eintrittswahrscheinlichkeiten gewichteten Ausfallrisiken, unter Berücksichtigung des jeweiligen Eintrittszeitpunktes, durchgeführt wird. Diese Vorgehensweise führt zu einer Symbiose aus Finanzierungsvorteil und den Risikomaßzahlen VaR, Expected Shortfall und Expected Loss. Die ermittelten finanzwirtschaftlichen Risikomaßzahlen führen zu überraschenden Ergebnissen, die die propagierte Risikolosigkeit und das vermeintlich attraktive Renditepotenzial derartiger Transaktionen eindeutig verneinen. Aus den gewonnenen Erkenntnissen leitet die Verfasserin praktische Handlungsempfehlungen und Absicherungsmöglichkeiten für kommunale Entscheidungsträger ab. Die sich aufgrund der US-Steuerrechtsänderung vom Februar 2005 ergebenden Auswirkungen auf bestehende Transaktionen wie auch auf Neugeschäfte werden im Ausblick dargelegt.

A matrix A is called completely positive if there exists an entrywise nonnegative matrix B such that A = BB^T. These matrices can be used to obtain convex reformulations of for example nonconvex quadratic or combinatorial problems. One of the main problems with completely positive matrices is checking whether a given matrix is completely positive. This is known to be NP-hard in general. rnrnFor a given matrix completely positive matrix A, it is nontrivial to find a cp-factorization A=BB^T with nonnegative B since this factorization would provide a certificate for the matrix to be completely positive. But this factorization is not only important for the membership to the completely positive cone, it can also be used to recover the solution of the underlying quadratic or combinatorial problem.rnrnIn addition, it is not a priori known how many columns are necessary to generate a cp-factorization for the given matrix. The minimal possible number of columns is called the cp-rank of A and so far it is still an open question how to derive the cp-rank for a given matrix. Some facts on completely positive matrices and the cp-rank will be given in Chapter 2.rnrnMoreover, in Chapter 6, we will see a factorization algorithm, which, for a given completely positive matrix A and a suitable starting point, computes the nonnegative factorization A=BB^T. The algorithm therefore returns a certificate for the matrix to be completely positive. As introduced in Chapter 3, the fundamental idea of the factorization algorithm is to start from an initial square factorization which is not necessarily entrywise nonnegative, and extend this factorization to a matrix for which the number of columns is greater than or equal to the cp-rank of A. Then it is the goal to transform this generated factorization into a cp-factorization.rnrnThis problem can be formulated as a nonconvex feasibility problem, as shown in Section 4.1, and solved by a method which is based on alternating projections, as proven in Chapter 6.rnrnOn the topic of alternating projections, a survey will be given in Chapter 5. Here we will see how to apply this technique to several types of sets like subspaces, convex sets, manifolds and semialgebraic sets. Furthermore, we will see some known facts on the convergence rate for alternating projections between these types of sets. Considering more than two sets yields the so called cyclic projections approach. Here some known facts for subspaces and convex sets will be shown. Moreover, we will see a new convergence result on cyclic projections among a sequence of manifolds in Section 5.4.rnrnIn the context of cp-factorizations, a local convergence result for the introduced algorithm will be given. This result is based on the known convergence for alternating projections between semialgebraic sets.rnrnTo obtain cp-facrorizations with this first method, it is necessary to solve a second order cone problem in every projection step, which is very costly. Therefore, in Section 6.2, we will see an additional heuristic extension, which improves the numerical performance of the algorithm. Extensive numerical tests in Chapter 7 will show that the factorization method is very fast in most instances. In addition, we will see how to derive a certificate for the matrix to be an element of the interior of the completely positive cone.rnrnAs a further application, this method can be extended to find a symmetric nonnegative matrix factorization, where we consider an additional low-rank constraint. Here again, the method to derive factorizations for completely positive matrices can be used, albeit with some further adjustments, introduced in Section 8.1. Moreover, we will see that even for the general case of deriving a nonnegative matrix factorization for a given rectangular matrix A, the key aspects of the completely positive factorization approach can be used. To this end, it becomes necessary to extend the idea of finding a completely positive factorization such that it can be used for rectangular matrices. This yields an applicable algorithm for nonnegative matrix factorization in Section 8.2.rnNumerical results for this approach will suggest that the presented algorithms and techniques to obtain completely positive matrix factorizations can be extended to general nonnegative factorization problems.

This thesis is divided into three main parts: The description of the calibration problem, the numerical solution of this problem and the connection to optimal stochastic control problems. Fitting model prices to given market prices leads to an abstract least squares formulation as calibration problem. The corresponding option price can be computed by solving a stochastic differential equation via the Monte-Carlo method which seems to be preferred by most practitioners. Due to the fact that the Monte-Carlo method is expensive in terms of computational effort and requires memory, more sophisticated stochastic predictor-corrector schemes are established in this thesis. The numerical advantage of these predictor-corrector schemes ispresented and discussed. The adjoint method is applied to the calibration. The theoretical advantage of the adjoint method is discussed in detail. It is shown that the computational effort of gradient calculation via the adjoint method is independent of the number of calibration parameters. Numerical results confirm the theoretical results and summarize the computational advantage of the adjoint method. Furthermore, provides the connection to optimal stochastic control problems is proven in this thesis.rn

Extension of inexact Kleinman-Newton methods to a general monotonicity preserving convergence theory
(2011)

The thesis at hand considers inexact Newton methods in combination with algebraic Riccati equation. A monotone convergence behaviour is proven, which enables a non-local convergence. Above relation is transferred to a general convergence theory for inexact Newton methods securing the monotonicity of the iterates for convex or concave mappings. Several application prove the pratical benefits of the new developed theory.

Variational inequality problems constitute a common basis to investigate the theory and algorithms for many problems in mathematical physics, in economy as well as in natural and technical sciences. They appear in a variety of mathematical applications like convex programming, game theory and economic equilibrium problems, but also in fluid mechanics, physics of solid bodies and others. Many variational inequalities arising from applications are ill-posed. This means, for example, that the solution is not unique, or that small deviations in the data can cause large deviations in the solution. In such a situation, standard solution methods converge very slowly or even fail. In this case, so-called regularization methods are the methods of choice. They have the advantage that an ill-posed original problem is replaced by a sequence of well-posed auxiliary problems, which have better properties (like, e.g., a unique solution and a better conditionality). Moreover, a suitable choice of the regularization term can lead to unconstrained auxiliary problems that are even equivalent to optimization problems. The development and improvement of such methods are a focus of current research, in which we take part with this thesis. We suggest and investigate a logarithmic-quadratic proximal auxiliary problem (LQPAP) method that combines the advantages of the well-known proximal-point algorithm and the so-called auxiliary problem principle. Its exploration and convergence analysis is one of the main results in this work. The LQPAP method continues the recent developments of regularization methods. It includes different techniques presented in literature to improve the numerical stability: The logarithmic-quadratic distance function constitutes an interior point effect which allows to treat the auxiliary problems as unconstrained ones. Furthermore, outer operator approximations are considered. This simplifies the numerical solution of variational inequalities with multi-valued operators since, for example, bundle-techniques can be applied. With respect to the numerical practicability, inexact solutions of the auxiliary problems are allowed using a summable-error criterion that is easy to implement. As a further advantage of the logarithmic-quadratic distance we verify that it is self-concordant (in the sense of Nesterov/Nemirovskii). This motivates to apply the Newton method for the solution of the auxiliary problems. In the numerical part of the thesis the LQPAP method is applied to linearly constrained, differentiable and nondifferentiable convex optimization problems, as well as to nonsymmetric variational inequalities with co-coercive operators. It can often be observed that the sequence of iterates reaches the boundary of the feasible set before being close to an optimal solution. Against this background, we present the strategy of under-relaxation, which robustifies the LQPAP method. Furthermore, we compare the results with an appropriate method based on Bregman distances (BrPAP method). For differentiable, convex optimization problems we describe the implementation of the Newton method to solve the auxiliary problems and carry out different numerical experiments. For example, an adaptive choice of the initial regularization parameter and a combination of an Armijo and a self-concordance step size are evaluated. Test examples for nonsymmetric variational inequalities are hardly available in literature. Therefore, we present a geometric and an analytic approach to generate test examples with known solution(s). To solve the auxiliary problems in the case of nondifferentiable, convex optimization problems we apply the well-known bundle technique. The implementation is described in detail and the involved functions and sequences of parameters are discussed. As far as possible, our analysis is substantiated by new theoretical results. Furthermore, it is explained in detail how the bundle auxiliary problems are solved with a primal-dual interior point method. Such investigations have by now only been published for Bregman distances. The LQPAP bundle method is again applied to several test examples from literature. Thus, this thesis builds a bridge between theoretical and numerical investigations of solution methods for variational inequalities.

Given a compact set K in R^d, the theory of extension operators examines the question, under which conditions on K, the linear and continuous restriction operators r_n:E^n(R^d)→E^n(K),f↦(∂^α f|_K)_{|α|≤n}, n in N_0 and r:E(R^d)→E(K),f↦(∂^α f|_K)_{α in N_0^d}, have a linear and continuous right inverse. This inverse is called extension operator and this problem is known as Whitney's extension problem, named after Hassler Whitney. In this context, E^n(K) respectively E(K) denote spaces of Whitney jets of order n respectively of infinite order. With E^n(R^d) and E(R^d), we denote the spaces of n-times respectively infinitely often continuously partially differentiable functions on R^d. Whitney already solved the question for finite order completely. He showed that it is always possible to construct a linear and continuous right inverse E_n for r_n. This work is concerned with the question of how the existence of a linear and continuous right inverse of r, fulfilling certain continuity estimates, can be characterized by properties of K. On E(K), we introduce a full real scale of generalized Whitney seminorms (|·|_{s,K})_{s≥0}, where |·|_{s,K} coincides with the classical Whitney seminorms for s in N_0. We equip also E(R^d) with a family (|·|_{s,L})_{s≥0} of those seminorms, where L shall be a a compact set with K in L-°. This family of seminorms on E(R^d) suffices to characterize the continuity properties of an extension operator E, since we can without loss of generality assume that E(E(K)) in D^s(L).
In Chapter 2, we introduce basic concepts and summarize the classical results of Whitney and Stein.
In Chapter 3, we modify the classical construction of Whitney's operators E_n and show that |E_n(·)|_{s,L}≤C|·|_{s,K} for s in[n,n+1).
In Chapter 4, we generalize a result of Frerick, Jordá and Wengenroth and show that LMI(1) for K implies the existence of an extension operator E without loss of derivatives, i.e. we have it fulfils |E(·)|_{s,L}≤C|·|_{s,K} for all s≥0. We show that a large class of self similar sets, which includes the Cantor set and the Sierpinski triangle, admits an extensions operator without loss of derivatives.
In Chapter 5 we generalize a result of Frerick, Jordá and Wengenroth and show that WLMI(r) for r≥1 implies the existence of a tame linear extension operator E having a homogeneous loss of derivatives, such that |E(·)|_{s,L}≤C|·|_{(r+ε)s,K} for all s≥0 and all ε>0.
In the last chapter we characterize the existence of an extension operator having an arbitrary loss of derivatives by the existence of measures on K.

In recent years, the study of dynamical systems has developed into a central research area in mathematics. Actually, in combination with keywords such as "chaos" or "butterfly effect", parts of this theory have been incorporated in other scientific fields, e.g. in physics, biology, meteorology and economics. In general, a discrete dynamical system is given by a set X and a self-map f of X. The set X can be interpreted as the state space of the system and the function f describes the temporal development of the system. If the system is in state x âˆˆ X at time zero, its state at time n âˆˆ N is denoted by f^n(x), where f^n stands for the n-th iterate of the map f. Typically, one is interested in the long-time behaviour of the dynamical system, i.e. in the behaviour of the sequence (f^n(x)) for an arbitrary initial state x âˆˆ X as the time n increases. On the one hand, it is possible that there exist certain states x âˆˆ X such that the system behaves stably, which means that f^n(x) approaches a state of equilibrium for nâ†’âˆž. On the other hand, it might be the case that the system runs unstably for some initial states x âˆˆ X so that the sequence (f^n(x)) somehow shows chaotic behaviour. In case of a non-linear entire function f, the complex plane always decomposes into two disjoint parts, the Fatou set F_f of f and the Julia set J_f of f. These two sets are defined in such a way that the sequence of iterates (f^n) behaves quite "wildly" or "chaotically" on J_f whereas, on the other hand, the behaviour of (f^n) on F_f is rather "nice" and well-understood. However, this nice behaviour of the iterates on the Fatou set can "change dramatically" if we compose the iterates from the left with just one other suitable holomorphic function, i.e. if we consider sequences of the form (gâˆ˜f^n) on D, where D is an open subset of F_f with f(D)âŠ‚ D and g is holomorphic on D. The general aim of this work is to study the long-time behaviour of such modified sequences. In particular, we will prove the existence of holomorphic functions g on D having the property that the behaviour of the sequence of compositions (gâˆ˜f^n) on the set D becomes quite similarly chaotic as the behaviour of the sequence (f^n) on the Julia set of f. With this approach, we immerse ourselves into the theory of universal families and hypercyclic operators, which itself has developed into an own branch of research. In general, for topological spaces X, Y and a family {T_i: i âˆˆ I} of continuous functions T_i:Xâ†’Y, an element x âˆˆ X is called universal for the family {T_i: i âˆˆ I} if the set {T_i(x): i âˆˆ I} is dense in Y. In case that X is a topological vector space and T is a continuous linear operator on X, a vector x âˆˆ X is called hypercyclic for T if it is universal for the family {T^n: n âˆˆ N}. Thus, roughly speaking, universality and hypercyclicity can be described via the following two aspects: There exists a single object which allows us, via simple analytical operations, to approximate every element of a whole class of objects. In the above situation, i.e. for a non-linear entire function f and an open subset D of F_f with f(D)âŠ‚ D, we endow the space H(D) of holomorphic functions on D with the topology of locally uniform convergence and we consider the map C_f:H(D)â†’H(D), C_f(g):=gâˆ˜f|_D, which is called the composition operator with symbol f. The transform C_f is a continuous linear operator on the Fréchet space H(D). In order to show that the above-mentioned "nice" behaviour of the sequence of iterates (f^n) on the set D âŠ‚ F_f can "change dramatically" if we compose the iterates from the left with another suitable holomorphic function, our aim consists in finding functions g âˆˆ H(D) which are hypercyclic for C_f. Indeed, for each hypercyclic function g for C_f, the set of compositions {gâˆ˜f^n|_D: n âˆˆ N} is dense in H(D) so that the sequence of compositions (gâˆ˜f^n|_D) is kind of "maximally divergent" " meaning that each function in H(D) can be approximated locally uniformly on D via subsequences of (gâˆ˜f^n|_D). This kind of behaviour stands in sharp contrast to the fact that the sequence of iterates (f^n) itself converges, behaves like a rotation or shows some "wandering behaviour" on each component of F_f. To put it in a nutshell, this work combines the theory of non-linear complex dynamics in the complex plane with the theory of dynamics of continuous linear operators on spaces of holomorphic functions. As far as the author knows, this approach has not been investigated before.

In this thesis, we mainly investigate geometric properties of optimal codebooks for random elements $X$ in a seperable Banach space $E$. Here, for a natural number $ N $ and a random element $X$ , an $N$-optimal codebook is an $ N $-subset in the underlying Banach space $E$ which gives a best approximation to $ X $ in an average sense. We focus on two types of geometric properties: The global growth behaviour (growing in $N$) for a sequence of $N$-optimal codebooks is described by the maximal (quantization) radius and a so-called quantization ball. For many distributions, such as central-symmetric distributions on $R^d$ as well as Gaussian distributions on general Banach spaces, we are able to estimate the asymptotics of the quantization radius as well as the quantization ball. Furthermore, we investigate local properties of optimal codebooks, in particular the local quantization error and the weights of the Voronoi cells induced by an optimal codebook. In the finite-dimensional setting, we are able to proof for many interesting distributions classical conjectures on the asymptotic behaviour of those properties. Finally, we propose a method to construct sequences of asymptotically optimal codebooks for random elements in infinite dimensional Banach spaces and apply this method to construct codebooks for stochastic processes, such as fractional Brownian Motions.

The subject of this thesis is hypercyclic, mixing, and chaotic C0-semigroups on Banach spaces. After introducing the relevant notions and giving some examples the so called hypercyclicity criterion and its relation with weak mixing is treated. Some new equivalent formulations of the criterion are given which are used to derive a very short proof of the well-known fact that a C0-semigroup is weakly mixing if and only if each of its operators is. Moreover, it is proved that under some "regularity conditions" each hypercyclic C0-semigroup is weakly mixing. Furthermore, it is shown that for a hypercyclic C0-semigroup there is always a dense set of hypercyclic vectors having infinitely differentiable trajectories. Chaotic C0-semigroups are also considered. It is proved that they are always weakly mixing and that in certain cases chaoticity is already implied by the existence of a single periodic point. Moreover, it is shown that strongly elliptic differential operators on bounded C^1-domains never generate chaotic C0-semigroups. A thorough investigation of transitivity, weak mixing, and mixing of weighted compositioin operators follows and complete characterisations of these properties are derived. These results are then used to completely characterise hypercyclicity, weak mixing, and mixing of C0-semigroups generated by first order partial differential operators. Moreover, a characterisation of chaos for these C0-semigroups is attained. All these results are achieved on spaces of p-integrable functions as well as on spaces of continuous functions and illustrated by various concrete examples.

The main topic of this treatise is the solution of two problems from the general theory of linear partial differential equations with constant coefficients. While surjectivity criteria for linear partial differential operators in spaces of smooth functions over an open subset of euclidean space and distributions were proved by B. Malgrange and L. Hörmander in 1955, respectively 1962, concrete evaluation of these criteria is still a highly non-trivial task. In particular, it is well-known that surjectivity in the space of smooth functions over an open subset of euclidean space does not automatically imply surjectivity in the space of distributions. Though, examples for this fact all live in three or higher dimensions. In 1966, F. Trèves conjectured that in the two dimensional setting surjectivity of a linear partial differential operator on the smooth functions indeed implies surjectivity on the space of distributions. An affirmative solution to this problem is presented in this treatise. The second main result solves the so-called problem of (distributional) parameter dependence for solutions of linear partial differential equations with constant coefficients posed by J. Bonet and P. Domanski in 2006. It is shown that, in dimensions three or higher, this problem in general has a negative solution even for hypoelliptic operators. Moreover, it is proved that the two dimensional case is again an exception, because in this setting the problem of parameter dependence always has a positive solution.