The diploma theses is focused on matrix-free preconditioning of a linear system. It gives a very brief introduction into the area of iterative methods, preconditioning and matrix-free environment. The emphasis is put on a detailed description of a variant of LU factorization which can be computed in a matrix-free manner and on a new technique connected with this factorization for preconditioning by incomplete LU factors in matrix-free environment. Its main features are storage of only one of the two incomplete factors and low memory costs during the computation of the stored factor. The thesis closes with numerical experiments demonstrating the efficiency of the proposed technique.

The concept of unique factorization was first recognized in the 1840s, but even then, it was still fairly believed to be automatic. The error of this assumption was exposed largely through attempts to prove Pierre de Fermat's, 1601-1665, last theorem. Once mathematicians discovered that this property did not always hold, it was only natural for them to try to search for the strongest available alternative. Thus began the attempt to generalize unique factorization. Using the ascending chain condition on principle ideals, we will show the conditions under which a ring is a unique factorization domain.

Recommendation systems are gaining more popularity because of the complexity of problems that they provide a solution to. There are many applications of recommendation systems everywhere around us. Implementation of these systems differs and there are two approaches that are most distinguished. First approach is a system without Machine Learning, while the other one includes Machine Learning. The second approach, used in this project, is based on Machine Learning collaborative filtering techniques. These techniques include numerous algorithms and data processing methods. This document describes a process that focuses on building a job recommendation system for a recruitment industry, starting from data acquisition to the final result. Data used in the project is collected from the Pitchler AB company, which provides an online recruitment platform. Result of this project is a machine learning based recommendation system used as an engine for the Pitchler AB IT recruitment platform.

Data are valuable assets to any organizations or individuals. Data are sources of useful information which is a big part of decision making. All sectors have potential to benefit from having information. Commerce, health, and research are some of the fields that have benefited from data. On the other hand, the availability of the data makes it easy for anyone to exploit the data, which in many cases are private confidential data. It is necessary to preserve the confidentiality of the data. We study two categories of privacy: Data Value Hiding and Data Pattern Hiding. Privacy is a huge concern but equally important is the concern of data utility. Data should avoid privacy breach yet be usable. Although these two objectives are contradictory and achieving both at the same time is challenging, having knowledge of the purpose and the manner in which it will be utilized helps. In this research, we focus on some particular situations for clustering and classification problems and strive to balance the utility and privacy of the data. In the first part of this dissertation, we propose Nonnegative Matrix Factorization (NMF) based techniques that accommodate constraints defined explicitly into the update rules. These constraints determine how the factorization takes place leading to the favorable results. These methods are designed to make alterations on the matrices such that user-specified cluster properties are introduced. These methods can be used to preserve data value as well as data pattern. As NMF and K-means are proven to be equivalent, NMF is an ideal choice for pattern hiding for clustering problems. In addition to the NMF based methods, we propose methods that take into account the data structures and the attribute properties for the classification problems. We separate the work into two different parts: linear classifiers and nonlinear classifiers. We propose two different solutions based on the classifiers. We study the effect of distortion on the utility of data. We propose three distortion measurement metrics which demonstrate better characteristics than the traditional metrics. The effectiveness of the measures is examined on different benchmark datasets. The result shows that the methods have the desirable properties such as invariance to translation, rotation, and scaling.

Nonnegative matrix factorization (NMF) is an important data-analysis problem which concerns factoring a given d n matrix A with nonnegative entries into matrices B and C where B and C are d k and k n with nonnegative entries. It has numerous applications including Object recognition, Topic Modelling, Hyper-spectral imaging, Music transcription etc. In general, NMF is intractable and several heuristics exists to solve the problem of NMF. Recently there has been interest in investigating conditions under which NMF can be tractably recovered. We note that existing attempts make unrealistic assumptions and often the associated algorithms tend to be not scalable. In this thesis, we make three major contributions: First, we formulate a model of NMF with assumptions which are natural and is a substantial weakening of separability. Unlike requiring a bound on the error in each column of (A BC) as was done in much of previous work, our assumptions are about aggregate errors, namely spectral norm of (A BC) i.e. jjA BCjj2 should be low. This is a much weaker error assumption and the associated B; C would be much more resilient than existing models. Second, we describe a robust polynomial time SVD-based algorithm, UTSVD, with realistic provable error guarantees and can handle higher levels of noise than previous algorithms. Indeed, experimentally we show that existing NMF models, which are based on separability assumptions, degrade much faster than UTSVD, in the presence of noise. Furthermore, when the data has dominant features, UTSVD significantly outperforms existing models. On real life datasets we again see a similar outperformance of UTSVD on clustering tasks. Finally, under a weaker model, we prove a robust version of uniqueness of NMF, where again, the word \robust" refers to realistic error bounds.

Aux énergies assez hautes, les processus de QCD peuvent être factorisés en une partie dure, qui peut être calculée en utilisant les méthodes perturbatives des diagrammes de Feynman grâce à la petite valeur de la constante de couplage de l'interaction forte, et une partie non-perturbative qui doit être extraite de données expérimentales, modélisées ou calculées avec d'autres méthodes comme par exemple la QCD sur réseau. Cependant la petite valeur de la constante de couplage dans la partie perturbative peut être compensée par des grands logarithmes émergeant de l'annulation de divergences molles ou colinéaires, ou de la présence d'échelles cinématiques multiples. De telles contributions doivent être resommées, ce qui mène à l'équation d'évolution DGLAP aux énergies modérées et aux équations BFKL et B-JIMWLK dans la limite des hautes énergies. Pour les énergies les plus grandes des effets de recombinaison de gluons amènent à la saturation, qui peut être décrite par le formalisme du CGC ou des ondes de choc. Dans cette thèse, nous nous proposons d'étudier certains processus exclusifs en QCD perturbative afin d'obtenir une meilleure description de la factorisation et des effets de resommation et de saturation. Dans un premier temps nous faisons le premier calcul d'une quantité exclusive au premier ordre sous-dominant (NLO) dans le contexte du formalisme des ondes de choc de QCD. Nous calculons l'amplitude NLO pour la production diffractive ouverte d'une paire quark-antiquark, puis nous parvenons à construire une section efficace finie à l'aide de cette amplitude en étudiant la production diffractive exclusive de deux jets vers l'avant. Des analyses précises phénoménologiques et expérimentales de ce processus devraient améliorer notre compréhension de la resommation à haute énergie grâce à la présence d'un Pomeron échangé en diffraction, ce qui est naturellement décrit par la resommation de logarithmes découlant de la divergence molle de la QCD à haute énergie. Notre résultat reste valable quand l'énergie au centre de masse devient proche de l'échelle de saturation ou lorsque la diffraction a lieu sur une cible dense donc il peut être utilisé pour l'étude des effets de saturation. Dans un deuxième temps, nous montrons que l'étude expérimentale de la photoproduction d'un méson léger et d'un photon à énergies modérées devrait constituer un bon moyen d'appréhender les Distributions de Parton Généralisées (GPDs), l'une des généralisations des blocs non perturbatifs en factorisation collinéaire. En principe une telle étude donnerait accès à la fois aux GPDs conservant l'hélicité ou la renversant. Nous donnons des prédictions numériques pour ce processus à JLAB@12GeV. / At high enough energies, QCD processes can be factorized into a hard part, which can be computed by using the smallness of the strong coupling to apply the perturbative Feynman diagram method, and a non-perturbative part which has to be fitted to experimental data, modeled or computed using other tools like for example lattice QCD. However the smallness of the strong coupling in the perturbative part can be compensated by large logarithms which arise from the cancellation of soft or collinear divergences, or by the presence of multiple kinematic scales. Such logarithmically-enhanced contributions must be resummed, leading to the DGLAP evolution at moderate energies and to the BFKL or B-JIMWLK equation in the high energy limit. For the largest energies gluon recombination effects lead to saturation, which can be described in the color glass condensate (CGC) or shockwave formalism. In this thesis, we propose to study several exclusive perturbative QCD processes in order to get a better understanding of factorization, resummation and saturation effects. In the first part we perform the first computation of an exclusive quantity at Next-to-Leading-Order (NLO) accuracy using the QCD shockwave formalism. We calculate the NLO amplitude for the diffractive production of an open quark-antiquark pair, then we manage to construct a finite cross section using this amplitude by studying the exclusive diffractive production of a dijet. Precise phenomenological and experimental analysis of this process should give a great insight on high energy resummation due to the exchange of a Pomeron in diffraction, which is naturally described by the resummation of logarithms emerging from the soft divergences of high energy QCD. Our result holds as the center of mass energy grows towards the saturation scale or for diffraction off a dense target so one could use it to study saturation effects. In the second part we show how the experimental study of the photoproduction of a light meson and a photon at moderate energy should be a good probe for Generalized Parton Distributions (GPDs), one of the generalizations of the non-perturbative building blocks in collinear factorization. In principle such a study would give access to both helicity-conserving and helicity-flip GPDs. We give numerical predictions for this process at JLAB@12GeV.

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Major works done in Function Field Arithmetic show a strong analogy between the ring of integers Z and the ring of polynomials over a nite eld Fq[T]. While an algorithm has been discovered to factor integers using elliptic curves, the discovery of Drinfeld modules, which are analogous to elliptic curves, made it possible to exhibit an algorithm for factorising polynomials in the ring Fq[T]. In this thesis, we introduce the notion of Drinfeld modules, then we demonstrate the analogy between Drinfeld modules and Elliptic curves. Finally, we present an algorithm for factoring polynomials over a nite eld using Drinfeld modules. / AFRIKAANSE OPSOMMING: 'n Groot deel van die werk wat reeds in funksieliggaam rekenkunde voltooi is toon 'n sterk verband tussen die ring van heelgetalle, Z; en die ring van polinome oor 'n eindige liggaam, F[T]: Terwyl daar alreeds 'n algoritme, wat gebruik maak van elliptiese kurwes, ontwerp is om heelgetalle te faktoriseer, het die ontdekking van Drinfeld modules, wat analoog is aan elliptiese kurwes, dit moontlik gemaak om 'n algoritme te konstrueer om polinome in die ring F[T] te faktoriseer. In hierdie tesis maak ons die konsep van Drinfeld modules bekend deur sekere aspekte daarvan te bestudeer. Ons gaan voort deur 'n voorbeeld te voorsien wat die analoog tussen Drinfeld modules en elliptiese kurwes illustreer. Uiteindelik, deur gebruik te maak van Drinfeld modules, bevestig ons hierdie analoog deur die algoritme vir die faktorisering van polinome oor eindige liggame te veskaf.

The reliance of unmanned aerial vehicles (UAVs) on GPS and other external navigation aids has become a limiting factor for many missions. UAVs are now physically able to fly in many enclosed or obstructed environments, due to the shrinking size and weight of electronics and other systems. These environments, such as urban canyons or enclosed areas, often degrade or deny external signals. Furthermore, many of the most valuable potential missions for UAVs are in hostile or disaster areas, where navigation infrastructure could be damaged, denied, or actively used against the vehicle. It is clear that developing alternative, independent, navigation techniques will increase the operating envelope of UAVs and make them more useful. This thesis presents work in the development of reliable monocular vision-aided inertial navigation for UAVs. The work focuses on developing a stable and accurate navigation solution in a variety of realistic conditions. First, a vision-aided inertial navigation algorithm is developed which assumes uncorrelated feature and vehicle states. Flight test results on a 80 kg UAV are presented, which demonstrate that it is possible to bound the horizontal drift with vision aiding. Additionally, a novel implementation method is developed for integration with a variety of navigation systems. Finally, a vision-aided navigation algorithm is derived within a Bierman-Thornton factored extended Kalman Filter (BTEKF) framework, using fully correlated vehicle and feature states. This algorithm shows improved consistency and accuracy by 2 to 3 orders of magnitude over the previous implementation, both in simulation and flight testing. Flight test results of the BTEKF on large (80 kg) and small (600 g) vehicles show accurate navigation over numerous tests.

Thesis (PhD (Mathematical Sciences))--University of Stellenbosch, 2007. / We study refinable functions where the dilation factor is not always assumed to be 2. In our investigation, the role of convolutions and refinable step functions is emphasized as a framework for understanding various previously published results. Of particular importance is a class of polynomial factors, which was first introduced for dilation factor 2 by Berg and Plonka and which we generalise to general integer dilation factors. We obtain results on the existence of refinable functions corresponding to certain reduced masks which generalise similar results for dilation factor 2, where our proofs do not rely on Fourier methods as those in the existing literature do. We also consider subdivision for general integer dilation factors. In this regard, we extend previous results of De Villiers on refinable function existence and subdivision convergence in the case of positive masks from dilation factor 2 to general integer dilation factors. We also obtain results on the preservation of subdivision convergence, as well as on the convergence rate of the subdivision algorithm, when generalised Berg-Plonka polynomial factors are added to the mask symbol. We obtain sufficient conditions for the occurrence of polynomial sections in refinable functions and construct families of related refinable functions. We also obtain results on the regularity of a refinable function in terms of the mask symbol factorisation. In this regard, we obtain much more general sufficient conditions than those previously published, while for dilation factor 2, we obtain a characterisation of refinable functions with a given number of continuous derivatives. We also study the phenomenon of subsequence convergence in subdivision, which explains some of the behaviour that we observed in non-convergent subdivision processes during numerical experimentation. Here we are able to establish different sets of sufficient conditions for this to occur, with some results similar to standard subdivision convergence, e.g. that the limit function is refinable. These results provide generalisations of the corresponding results for subdivision, since subsequence convergence is a generalisation of subdivision convergence. The nature of this phenomenon is such that the standard subdivision algorithm can be extended in a trivial manner to allow it to work in instances where it previously failed. Lastly, we show how, for masks of length 3, explicit formulas for refinable functions can be used to calculate the exact values of the refinable function at rational points. Various examples with accompanying figures are given throughout the text to illustrate our results.

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