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Spatial Queues
(2000)
In the present thesis, a theoretical framework for the analysis of spatial queues is developed. Spatial queues are a generalization of the classical concept of queues as they provide the possibility of assigning properties to the users. These properties may influence the queueing process, but may also be of interest for themselves. As a field of application, mobile communication networks are modeled by spatial queues in order to demonstrate the advantage of including user properties into the queueing model. In this application, the property of main interest is the user's position in the network. After a short introduction, the second chapter contains an examination of the class of Markov-additive jump processes, including expressions for the transition probabilities and the expectation as well as laws of large numbers. Chapter 3 contains the definition and analysis of the central concept of spatial Markovian arrival processes (shortly: SMAPs) as a special case of Markov-additive jump processes, but also as a natural generalization from the well-known concept of BMAPs. In chapters 4 and 5, SMAPs serve as arrival streams for the analyzed periodic SMAP/M/c/c and SMAP/G/infinity queues, respectively. These types of queues find application as models or planning tools for mobile communication networks. The analysis of these queues involves new methods such that even for the special cases of BMAP inputs (i.e. non-spatial queues) new results are obtained. In chapter 6, a procedure for statistical parameter estimation is proposed along with its numerical results. The thesis is concluded by an appendix which collects necessary results from the theories of Markov jump processes and stochastic point fields. For special classes of Markov jump processes, new results have been obtained, too.
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.
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.