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Recently, a new Quicksort variant due to Yaroslavskiy was chosen as standard sorting
method for Oracle's Java 7 runtime library. The decision for the change was based on
empirical studies showing that on average, the new algorithm is faster than the formerly
used classic Quicksort. Surprisingly, the improvement was achieved by using a dual pivot
approach — an idea that was considered not promising by several theoretical studies in the
past. In this thesis, I try to find the reason for this unexpected success.
My focus is on the precise and detailed average case analysis, aiming at the flavor of
Knuth's series “The Art of Computer Programming”. In particular, I go beyond abstract
measures like counting key comparisons, and try to understand the efficiency of the
algorithms at different levels of abstraction. Whenever possible, precise expected values are
preferred to asymptotic approximations. This rigor ensures that (a) the sorting methods
discussed here are actually usable in practice and (b) that the analysis results contribute to
a sound comparison of the Quicksort variants.

It is often helpful to compute the intrinsic volumes of a set of which only a pixel image is observed. A computational efficient approach, which is suggested by several authors and used in practice, is to approximate the intrinsic volumes by a linear functional of the pixel configuration histogram. Here we want to examine, whether there is an optimal way of choosing this linear functional, where we will use a quite natural optimality criterion that has already been applied successfully for the estimation of the surface area. We will see that for intrinsic volumes other than volume or surface area this optimality criterion cannot be used, since estimators which ignore the data and return constant values are optimal w.r.t. this criterion. This shows that one has to be very careful, when intrinsic volumes are approximated by a linear functional of the pixel configuration histogram.

We consider a variant of the generalized assignment problem (GAP) where the amount of space used in each bin is restricted to be either zero (if the bin is not opened) or above a given lower bound (a minimum quantity). We provide several complexity results for different versions of the problem and give polynomial time exact algorithms and approximation algorithms for restricted cases.
For the most general version of the problem, we show that it does not admit a polynomial time approximation algorithm (unless P=NP), even for the case of a single bin. This motivates to study dual approximation algorithms that compute solutions violating the bin capacities and minimum quantities by a constant factor. When the number of bins is fixed and the minimum quantity of each bin is at least a factor \(\delta>1\) larger than the largest size of an item in the bin, we show how to obtain a polynomial time dual approximation algorithm that computes a solution violating the minimum quantities and bin capacities by at most a factor \(1-\frac{1}{\delta}\) and \(1+\frac{1}{\delta}\), respectively, and whose profit is at least as large as the profit of the best solution that satisfies the minimum quantities and bin capacities strictly.
In particular, for \(\delta=2\), we obtain a polynomial time (1,2)-approximation algorithm.

Filtering, Approximation and Portfolio Optimization for Shot-Noise Models and the Heston Model
(2012)

We consider a continuous time market model in which stock returns satisfy a stochastic differential equation with stochastic drift, e.g. following an Ornstein-Uhlenbeck process. The driving noise of the stock returns consists not only of Brownian motion but also of a jump part (shot noise or compound Poisson process). The investor's objective is to maximize expected utility of terminal wealth under partial information which means that the investor only observes stock prices but does not observe the drift process. Since the drift of the stock prices is unobservable, it has to be estimated using filtering techniques. E.g., if the drift follows an Ornstein-Uhlenbeck process and without
jump part, Kalman filtering can be applied and optimal strategies can be computed explicitly. Also in other cases, like for an underlying
Markov chain, finite-dimensional filters exist. But for certain jump processes (e.g. shot noise) or certain nonlinear drift dynamics explicit computations, based on discrete observations, are no longer possible or existence of finite dimensional filters is no longer valid. The same
computational difficulties apply to the optimal strategy since it depends on the filter. In this case the model may be approximated by
a model where the filter is known and can be computed. E.g., we use statistical linearization for non-linear drift processes, finite-state-Markov chain approximations for the drift process and/or diffusion approximations for small jumps in the noise term.
In the approximating models, filters and optimal strategies can often be computed explicitly. We analyze and compare different approximation methods, in particular in view of performance of the corresponding utility maximizing strategies.

The direction splitting approach proposed earlier in [6], aiming at the efficient solution of Navier-Stokes equations, is extended and adopted here to solve the Navier-Stokes-Brinkman equations describing incompressible flows in plain and in porous media. The resulting pressure equation is a perturbation of the
incompressibility constrained using a direction-wise factorized operator as proposed in [6]. We prove that this approach is unconditionally stable for the unsteady Navier-Stokes-Brinkman problem. We also provide numerical illustrations of the method's accuracy and efficiency.

In this paper, we propose multi-level Monte Carlo(MLMC) methods that use ensemble level mixed multiscale methods in the simulations of multi-phase flow and transport. The main idea of ensemble level multiscale methods is to construct local multiscale basis functions that can be used for any member of the ensemble. We consider two types of ensemble level mixed multiscale finite element methods, (1) the no-local-solve-online ensemble level method (NLSO) and (2) the local-solve-online ensemble level method (LSO). Both mixed multiscale methods use a number of snapshots of the permeability media to generate a multiscale basis.
As a result, in the offline stage, we construct multiple basis functions for
each coarse region where basis functions correspond to different realizations.
In the no-local-solve-online ensemble level method one uses the whole set of pre-computed basis functions to approximate the solution for an arbitrary realization. In the local-solve-online ensemble level method one uses the pre-computed functions to construct a multiscale basis for a particular realization. With this basis the solution corresponding to this
particular realization is approximated in LSO mixed MsFEM. In both approaches
the accuracy of the method is related to the number of snapshots computed based on different realizations that one uses to pre-compute a
multiscale basis. We note that LSO approaches share similarities with reduced basis methods [11, 21, 22].
In multi-level Monte Carlo methods ([14, 13]), more accurate (and expensive) forward simulations are run with fewer samples while less accurate(and inexpensive) forward simulations are run with a larger number of samples. Selecting the number of expensive and inexpensive simulations carefully, one can show that MLMC methods can provide better accuracy
at the same cost as MC methods. In our simulations, our goal is twofold. First, we would like to compare NLSO and LSO mixed MsFEMs. In particular, we show that NLSO
mixed MsFEM is more accurate compared to LSO mixed MsFEM. Further, we use both approaches in the context of MLMC to speed-up MC
calculations. We present basic aspects of the algorithm and numerical
results for coupled flow and transport in heterogeneous porous media.

By natural or man-made disasters, the evacuation of a whole region or city may become necessary. Apart from private traffic, the evacuation from collection points to secure shelters outside the endangered region will be realized by a bus fleet made available by emergency relief. The arising Bus Evacuation Problem (BEP) is a vehicle scheduling problem, in which a given number of evacuees needs to be transported from a set of collection points to a set of capacitated shelters, minimizing the total evacuation time, i.e., the time needed until the last person is brought to safety.
In this paper we consider an extended version of the BEP, the Robust Bus Evacuation Problem (RBEP), in which the exact numbers of evacuees are not known, but may stem from a set of probable scenarios. However, after a given reckoning time, this uncertainty is eliminated and planners are given exact figures. The problem is to decide for each bus, if it is better to send it right away -- using uncertain numbers of evacuees -- or to wait until the numbers become known.
We present a mixed-integer linear programming formulation for the RBEP and discuss solution approaches; in particular, we present a tabu search framework for finding heuristic solutions of acceptable quality within short computation time. In computational experiments using both randomly generated instances and the real-world scenario of evacuating the city of Kaiserslautern, we compare our solution approaches.

In this study, two outstanding subgroups of organic-inorganic hybrid materials have been investigated. The first part covers the design, synthesis, characterization and application of seven novel Metal Organic Frameworks (MOFs) containing functionalized biphenyl dicarboxylates as linkers. In the second part, the surface modification of the metal oxides ZrO2, TiO2 and Al2O3 using phosphonate derivates is reported.
Firstly three functionalized MOF structures; ZnBrBPDC, ZnNO2BPDC and ZnNH2BPDC were synthesised using 4,4´-biphenyldicarboxylic acid derivatives with different functional groups (-Br, -NO2, -NH2) Powder X-ray diffraction (PXRD) measurements indicated that the synthesised MOFs posses the interpenetrated IRMOF-9 structure with a cubic topology, which was also confirmed with single crystal X-ray measurements. The chemical structure of the MOF materials was further proved by solid state NMR and IR measurements. N2 adsorption measurements showed Type I isotherms for all three structures with large surface areas. TGA measurements of the evacuated samples were in good agreement with the elemental analysis data. The results proved that their thermal stability is between 325 °C - 450 °C.
Adsorption properties of these MOF structures were tested using light alkanes (CH4, C2H6, C3H8, and n-C4H10) at three different temperatures. For all adsorbents, the maximum uptakes were observed at 273 K. When the temperature was increased, the amount of the adsorbed gas decreased. All three MOFs showed strong affinities for n-butane. The lowest uptakes were observed for CH4.
The effect of functional groups on the IRMOF series was also examined by synthesizing amide functionalized biphenyl linkers. For this purpose, four different linkers containing amides with different alkyl chains (C1-C4) were synthesized and used for the synthesis of four new MOF structures ZnAcBPDC, ZnPrBPDC, ZnBuBPDC and ZnPeBPDC.
PXRD measurements of ZnAcBPDC indicated that the structure contains two different phases. PXRD patterns of ZnPrBPDC, ZnBuBPDC and ZnPeBPDC revealed non-interpenetrated structures which were further proved by single crystal X-ray measurements. The chemical structure of the MOF materials was further confirmed by X-ray spectoscopy, solid state NMR and IR measurements.
N2 adsorption measurements of the MOF structures were carried out using different activation methods. For all four MOFs, Type I isotherms were obtained. ZnAcBPDC showed the highest BET surface area. ZnAcBPDC and ZnBuBPDC were tested for their alkane, alkene and CO2 adsorption capacities.
In the second part of the work, the surface modification of three different metal oxides, ZrO2, TiO2 and Al2O3 was performed. For this purpose firstly three different fluorescent phosphonate derivatives containing thiophene units were synthesized from their halo derivatives in a four step synthesis and then used as coupling molecules for the surface modification. Nine different surfaces were obtained (38@TiO2, 39@TiO2, 40@TiO2, 38@Al2O3, 39@Al2O3, 40@Al2O3, 38@ZrO2, 39@ZrO2, 40@ZrO2).
All three modified metal oxide surfaces were characterized using elemental analysis, solid state NMR and IR spectroscopy. The BET surface areas of the materials were determined by N2 adsorption measurements. TGA was used to determine the stability of the surfaces. Maximum loadings were obtained for ZrO2 surfaces.
Due to the strong luminescence of the coupling molecules, the modified surfaces were checked for their light emission. All ZrO2 and Al2O3 surfaces showed fluorescence with exception of 40@Al2O3. On the other hand, for the modified TiO2 surfaces, no fluorescence could be observed.

Capital budgeting or investment decisions have an essential influence on companies’ performance. Instead of a rational choice, capital budgeting might be regarded as a process of reality construction. Research suggests that decision makers have only limited control over their own cognitive biases in this construction process. It is in this perspective that this paper intends to answer the following research question: What are behavioral determinants for a successful capital-budgeting decision process? The authors identify and discuss three behavioral success factors (reflective prudence, critical communication and outcome independence) for five stages of the capital budgeting process against the backdrop of the findings of the managerial and organizational cognition theory and cognitive psychology.

This papers deals with the minimization of seminorms \(\|L\cdot\|\) on \(\mathbb R^n\) under the constraint of a bounded I-divergence \(D(b,H\cdot)\). The I-divergence is also known as Kullback-Leibler divergence and appears in many models in imaging science, in particular when dealing with Poisson data. Typically, \(H\) represents here, e.g., a linear blur operator and \(L\) is some discrete derivative operator. Our preference for the constrained approach over
the corresponding penalized version is based on the fact that the I-divergence of data
corrupted, e.g., by Poisson noise or multiplicative Gamma noise can be estimated by statistical methods. Our minimization technique rests upon relations between constrained and penalized convex problems and resembles the idea of Morozov's discrepancy principle.
More precisely, we propose first-order primal-dual algorithms which reduce the problem to the solution of certain proximal minimization problems in each iteration step. The most interesting of these proximal minimization problems is an I-divergence constrained least squares problem. We solve this problem by connecting it to the corresponding I-divergence
penalized least squares problem with an appropriately chosen regularization parameter. Therefore, our algorithm produces not only a sequence of vectors which converges to a minimizer of the constrained problem but also a sequence of parameters which convergences to a regularization parameter so that the penalized problem has the same solution as our constrained one. In other words, the solution of this penalized problem fulfills the I-divergence constraint. We provide the proofs which are necessary to understand
our approach and demonstrate the performance of our algorithms for different
image restoration examples.