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Our subject of study is strong approximation of stochastic differential equations (SDEs) with respect to the supremum and the L_p error criteria, and we seek approximations that are strongly asymptotically optimal in specific classes of approximations. For the supremum error, we prove strong asymptotic optimality for specific tamed Euler schemes relating to certain adaptive and to equidistant time discretizations. For the L_p error, we prove strong asymptotic optimality for specific tamed Milstein schemes relating to certain adaptive and to equidistant time discretizations. To illustrate our findings, we numerically analyze the SDE associated with the Heston–3/2–model originating from mathematical finance.
This thesis is divided into two parts. The first part is devoted to the curvature estimation of piecewise smooth curves using variation diminishing splines. The variation diminishing property combined with the ability to reconstruct linear functions leads to a convexity preserving approximation that is crucial if additional sign changes in the curvature estimation have to be avoided. To this end, we will first establish the foundations of variation diminishing transforms and introduce the Bernstein and the Schoenberg operator on the space of continuous functions and its generalization to the Lp-spaces. In order to be able to detect C2-singularities in piecewise smooth curves, we establish lower estimates for the approximation error in terms of the second order modulus of smoothness for Schoenberg’s variation diminishing operator. Afterwards, we consider smooth curve approximations using only finitely many samples of the curve, where the approximation, its first, and its second derivative converge uniformly to its corresponding part of the curve to be approximated. In this case, we can show that the estimated curvature converges uniformly to the real curvature if the number of samples goes to infinity. Based on the lower estimates that relates the decay rate of the approximation error with smoothness we propose a multi-scale algorithm to estimate the curvature and to detect C2-singularities. We numerically evaluate our algorithm and compare it to others to show that our algorithm achieves competitive accuracy while our curvature estimations are significantly faster to compute.
The second part deals with generalizations of the established lower estimates for the Schoenberg operator. We will show that such estimates can be obtained for linear operators on a general Banach function space with smooth range provided that the iterates of the operator converge uniformly and a semi-norm defined on the range of the operator annihilates the fixed points of the operator. To this end, we will prove by spectral properties that the iterates of every positive finite-rank operator converge uniformly. As highlight of this thesis, we show a constructive way using a Gramian matrix where the dual fixed points operate on the fixed points of an operator to derive the limit of the iterates for an arbitrary quasi-compact operator defined on a general Banach space.
In his famous paper Gersho stressed that the codecells of optimal quantizers asymptotically make an equal contribution to the distortion of the quantizer. Motivated by this fact, we investigate in this paper quantizers in the scalar case, where each codecell contributes with exactly the same portion to the quantization error. We show that such quantizers of Gersho type - or Gersho quantizers for short - exist for non-atomic scalar distributions. As a main result we prove that Gersho quantizers are asymptotically optimal.
Die Anwendungen der vektoriellen Mehrniveaupassung in der Bildsegmentierung stehen in unmittelbarer Nachbarschaft zum verbreiteten Ansatz, Bilder mit Methoden der Variationsrechnung über einen Energieterm zu segmentieren. Beiden Verfahren ist gemeinsam, daß sie versuchen, das gegebene Bild durch stückweise stetige Funktionen zu approximieren. Die maximalen Teilmengen des Definitionsbereichs, auf denen die approximierende Funktion stetig ist, bilden dann die Segmente. Im Gegensatz zu dem in der Literatur häufig unter dem Schlagwort Mumford-Shah-Modell bekannten Energieminimierungsverfahren ist der Raum der Funktionen, mit denen das Bild approximiert wird, bei der vektoriellen Mehrniveaupassung ein endlicher Vektorraum. Ein weiterer Unterschied zu diesem Ansatz ist das System der erlaubten Mengen. Es werden nur Segmentierungen erlaubt, deren Segmente aus diesem Mengensystem sind. Die durch diese Einschränkung schlankere Theorie führt zu einer gesicherten Existenz einer optimalen Lösung des Segmentierungsproblems, die der gängigen Vorstellung einer Segmentierung genügt. Die Berechnung lokaler Optima ist algorithmisch innerhalb der Theorie umsetzbar.