In 2019, Adams, Ehlting, and Edwards developed a four-variable system of ordinary differential equations modelling phenylalanine metabolism in plants according to Michaelis-Menten kinetics. Analysis of the model suggested that when a series of reactions known as the Shikimate Ester Loop (SEL) is included, phenylalanine flux into primary metabolic pathways is prioritized over flux into secondary metabolic pathways when the availability of shikimate, a phenylalanine precursor, is low. Adams et al. called this mechanism of metabolic regulation the Precursor Shutoff Valve (PSV). Here, we attempt to simplify Adams and colleagues’ model by reducing the system to three variables and replacing the Michaelis-Menten terms with piecewise-defined approximations we call ramp functions. We examine equilibria and stability in this simplified model, and show that PSV-type regulation is still present in the version with the SEL. Then, we define a class of systems structurally similar to the simplified Adams model called biochemical ramp systems. We study the properties of the Jacobian matrices of these systems and then explore equilibria and stability in systems of n ≥ 2 variables. Finally, we make several suggestions regarding future work on biochemical ramp systems. / Graduate

Les architectures parallèles sont aujourd'hui présentes dans tous les systèmes informatiques, allant des smartphones aux supercalculateurs en passant par les ordinateurs de bureau. Programmer efficacement ces architectures en fonction des applications requiert un effort pluridisciplinaire portant sur les langages dédiés (Domain Specific Languages - DSL), les techniques de génération de code et d'optimisation, et les algorithmes numériques propres aux applications. Dans cette thèse, nous présentons une méthode de programmation haut niveau prenant en compte les caractéristiques des architectures hétérogènes et les propriétés existantes des matrices pour produire un solveur générique d'algèbre linéaire dense. Notre modèle de programmation supporte les transferts explicites et implicites entre un processeur (CPU) et un processeur graphique qui peut être généraliste (GPU) ou intégré (IGP). Dans la mesure où les GPU sont devenus un outil important pour le calcul haute performance, il est essentiel d'intégrer leur usage dans les plateformes de calcul. Une architecture récente telle que l'IGP requiert des connaissances supplémentaires pour pouvoir être programmée efficacement. Notre méthodologie a pour but de simplifier le développement sur ces architectures parallèles en utilisant des outils de programmation haut niveau. À titre d'exemple, nous avons développé un solveur de moindres carrés en précision mixte basé sur les équations semi-normales qui n'existait pas dans les bibliothèques actuelles. Nous avons par la suite étendu nos travaux à un modèle de programmation multi-étape ("multi-stage") pour résoudre les problèmes d'interopérabilité entre les modèles de programmation CPU et GPU. Nous utilisons cette technique pour générer automatiquement du code pour accélérateur à partir d'un code effectuant des opérations point par point ou utilisant des squelettes algorithmiques. L'approche multi-étape nous assure que le typage du code généré est valide. Nous avons ensuite montré que notre méthode est applicable à d'autres architectures et algorithmes. Les routines développées ont été intégrées dans une bibliothèque de calcul appelée NT2.Enfin, nous montrons comment la programmation haut niveau peut être appliquée à des calculs groupés et des contractions de tenseurs. Tout d'abord, nous expliquons comment concevoir un modèle de container en utilisant des techniques de programmation basées sur le C++ moderne (C++-14). Ensuite, nous avons implémenté un produit de matrices optimisé pour des matrices de petites tailles en utilisant des instructions SIMD. Pour ce faire, nous avons pris en compte les multiples problèmes liés au calcul groupé ainsi que les problèmes de localité mémoire et de vectorisation. En combinant la programmation haut niveau avec des techniques avancées de programmation parallèle, nous montrons qu'il est possible d'obtenir de meilleures performances que celles des bibliothèques numériques actuelles. / Parallelism in today's computer architectures is ubiquitous whether it be in supercomputers, workstations or on portable devices such as smartphones. Exploiting efficiently these systems for a specific application requires a multidisciplinary effort that concerns Domain Specific Languages (DSL), code generation and optimization techniques and application-specific numerical algorithms. In this PhD thesis, we present a method of high level programming that takes into account the features of heterogenous architectures and the properties of matrices to build a generic dense linear algebra solver. Our programming model supports both implicit or explicit data transfers to and from General-Purpose Graphics Processing Units (GPGPU) and Integrated Graphic Processors (IGPs). As GPUs have become an asset in high performance computing, incorporating their use in general solvers is an important issue. Recent architectures such as IGPs also require further knowledge to program them efficiently. Our methodology aims at simplifying the development on parallel architectures through the use of high level programming techniques. As an example, we developed a least-squares solver based on semi-normal equations in mixed precision that cannot be found in current libraries. This solver achieves similar performance as other mixed-precision algorithms. We extend our approach to a new multistage programming model that alleviates the interoperability problems between the CPU and GPU programming models. Our multistage approach is used to automatically generate GPU code for CPU-based element-wise expressions and parallel skeletons while allowing for type-safe program generation. We illustrate that this work can be applied to recent architectures and algorithms. The resulting code has been incorporated into a C++ library called NT2. Finally, we investigate how to apply high level programming techniques to batched computations and tensor contractions. We start by explaining how to design a simple data container using modern C++14 programming techniques. Then, we study the issues around batched computations, memory locality and code vectorization to implement a highly optimized matrix-matrix product for small sizes using SIMD instructions. By combining a high level programming approach and advanced parallel programming techniques, we show that we can outperform state of the art numerical libraries.

A pair (V, G) is called geometric structure, where V is a vector space and G is a subgroup GL(V), which is a set of transmission matrices. In this thesis we classify structures, which are based on properties of quaternions. Geometric structures based on quaternions are called triple structures. Triple structures are four structures with similar properties as quaternions. Quaternions are generated from real numbers and three complex units. We write quaternions in this shape a+bi+cj+dk.

Title: Educational environment additive polygons and polyhedrons Summary: The main intention of the work is to introduce a new mathematical educational environment that would be especially attractive for pupils in the grades 6. -9., but also in the secondary schools, universities or primary schools The work consists of six parts. In the introduction are mentioned the reasons that led me to choose this topic. The second chapter describes the theoretical basis of the work. The third section describes in detail the environment of additive polygons, both its aspects - mathematical and educational one. Analogously, as it is in the third chapter, is processed the fourth chapter that is dedicated to the environment of additive polyhedrons. The fifth chapter is devoted to the linking of the environment of additive polygons and polyhedrons into the linear algebra. In conclusion are provided further opportunities of work with this environment.

In mathematics the parameter is used as a special kind of a variable. The classification of the terms \"variable\" and \"parameter\" is often done by intuition and changes due to different situations and needs. The history of mathematics shows that these two terms represent the same abstract object in mathematics. In today´s mathematics, compared to variables, the parameter is declared as an unknown constant measure. This interpretation of parameters can be used in set theory for describing sets with an infinite number of elements. Due to this perspective the structure of vector spaces can be developed as a special structured set theory. Further, the concept of parameters can be seen as a model for developing mathematics education in linear algebra.

De nombreux phénomènes physiques peuvent être étudiés au moyen de modélisations et de simulations numériques, courantes dans les applications scientifiques. Pour être calculable sur un ordinateur, des techniques de discrétisation appropriées doivent être considérées, conduisant souvent à un ensemble d’équations linéaires dont les caractéristiques dépendent des techniques de discrétisation. D’un côté, la méthode des éléments finis conduit généralement à des systèmes linéaires creux, tandis que les méthodes des éléments finis de frontière conduisent à des systèmes linéaires denses. La taille des systèmes linéaires en découlant dépend du domaine où le phénomène physique étudié se produit et tend à devenir de plus en plus grand à mesure que les performances des infrastructures informatiques augmentent. Pour des raisons de robustesse numérique, les techniques de solution basées sur la factorisation de la matrice associée au système linéaire sont la méthode de choix utilisée lorsqu’elle est abordable. A cet égard, les méthodes hiérarchiques basées sur de la compression de rang faible ont permis une importante réduction des ressources de calcul nécessaires pour la résolution de systèmes linéaires denses au cours des deux dernières décennies. Pour les systèmes linéaires creux, leur utilisation reste un défi qui a été étudié à la fois par la communauté des matrices hiérarchiques et la communauté des matrices creuses. D’une part, la communauté des matrices hiérarchiques a d’abord exploité la structure creuse du problème via l’utilisation de la dissection emboitée. Bien que cette approche bénéficie de la structure hiérarchique qui en résulte, elle n’est pas aussi efficace que les solveurs creux en ce qui concerne l’exploitation des zéros et la séparation structurelle des zéros et des non-zéros. D’autre part, la factorisation creuse est accomplie de telle sorte qu’elle aboutit à une séquence d’opérations plus petites et denses, ce qui incite les solveurs à utiliser cette propriété et à exploiter les techniques de compression des méthodes hiérarchiques afin de réduire le coût de calcul de ces opérations élémentaires. Néanmoins, la structure hiérarchique globale peut être perdue si la compression des méthodes hiérarchiques n’est utilisée que localement sur des sous-matrices denses. Nous passons en revue ici les principales techniques employées par ces deux communautés, en essayant de mettre en évidence leurs propriétés communes et leurs limites respectives, en mettant l’accent sur les études qui visent à combler l’écart qui les séparent. Partant de ces observations, nous proposons une classe d’algorithmes hiérarchiques basés sur l’analyse symbolique de la structure des facteurs d’une matrice creuse. Ces algorithmes s’appuient sur une information symbolique pour grouper les inconnues entre elles et construire une structure hiérarchique cohérente avec la disposition des non-zéros de la matrice. Nos méthodes s’appuient également sur la compression de rang faible pour réduire la consommation mémoire des sous-matrices les plus grandes ainsi que le temps que met le solveur à trouver une solution. Nous comparons également des techniques de renumérotation se fondant sur des propriétés géométriques ou topologiques. Enfin, nous ouvrons la discussion à un couplage entre la méthode des éléments finis et la méthode des éléments finis de frontière dans un cadre logiciel unique. / Many physical phenomena may be studied through modeling and numerical simulations, commonplace in scientific applications. To be tractable on a computer, appropriated discretization techniques must be considered, which often lead to a set of linear equations whose features depend on the discretization techniques. Among them, the Finite Element Method usually leads to sparse linear systems whereas the Boundary Element Method leads to dense linear systems. The size of the resulting linear systems depends on the domain where the studied physical phenomenon develops and tends to become larger and larger as the performance of the computer facilities increases. For the sake of numerical robustness, the solution techniques based on the factorization of the matrix associated with the linear system are the methods of choice when affordable. In that respect, hierarchical methods based on low-rank compression have allowed a drastic reduction of the computational requirements for the solution of dense linear systems over the last two decades. For sparse linear systems, their application remains a challenge which has been studied by both the community of hierarchical matrices and the community of sparse matrices. On the one hand, the first step taken by the community of hierarchical matrices most often takes advantage of the sparsity of the problem through the use of nested dissection. While this approach benefits from the hierarchical structure, it is not, however, as efficient as sparse solvers regarding the exploitation of zeros and the structural separation of zeros from non-zeros. On the other hand, sparse factorization is organized so as to lead to a sequence of smaller dense operations, enticing sparse solvers to use this property and exploit compression techniques from hierarchical methods in order to reduce the computational cost of these elementary operations. Nonetheless, the globally hierarchical structure may be lost if the compression of hierarchical methods is used only locally on dense submatrices. We here review the main techniques that have been employed by both those communities, trying to highlight their common properties and their respective limits with a special emphasis on studies that have aimed to bridge the gap between them. With these observations in mind, we propose a class of hierarchical algorithms based on the symbolic analysis of the structure of the factors of a sparse matrix. These algorithms rely on a symbolic information to cluster and construct a hierarchical structure coherent with the non-zero pattern of the matrix. Moreover, the resulting hierarchical matrix relies on low-rank compression for the reduction of the memory consumption of large submatrices as well as the time to solution of the solver. We also compare multiple ordering techniques based on geometrical or topological properties. Finally, we open the discussion to a coupling between the Finite Element Method and the Boundary Element Method in a unified computational framework.

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Implicit diagonal estimation is a long-standing problem that is concerned with approximating the diagonal of a matrix that can only be accessed through matrix-vector products. It is of interest in various fields of application, such as network science, material science and machine learning. This thesis provides a comprehensive review of randomized algorithms for implicit diagonal estimation and introduces various enhancements as well as extensions to matrix functions. Three novel diagonal estimators are presented. The first method employs low-rank Nyström approximations. The second approach is based on shifts, forming a generalization of current deflation-based techniques. Additionally, we introduce a method for adaptively determining the number of test vectors, thereby removing the need for prior knowledge about the matrix. Moreover, the median of means principle is incorporated into diagonal estimation. Apart from that, we combine diagonal estimation methods with approaches for approximating the action of matrix functions using polynomial approximations and Krylov subspaces. This enables us to present implicit methods for estimating the diagonal of matrix functions. We provide first of their kind theoretical results for the convergence of these estimators. Subsequently, we present a deflation-based diagonal estimator for monotone functions of normal matrices with improved convergence properties. To validate the effectiveness and practical applicability of our methods, we conduct numerical experiments in real-world scenarios. This includes estimating the subgraph centralities in a protein interaction network, approximating uncertainty in ordinary least squares as well as randomized Jacobi preconditioning. / Implicit diagonalskattning är ett långvarigt problem som handlar om approximationen av diagonalerna i en matris som endast kan nås genom matris-vektorprodukter. Problemet är av intresse inom olika tillämpnings-områden, exempelvis nätverksvetenskap, materialvetenskap och maskininlärning. Detta arbete ger en omfattande översikt över algoritmer för randomiserad diagonalskattning och presenterar flera förbättringar samt utvidgningar till matrisfunktioner. Tre nya diagonalskattare presenteras. Den första metoden använder Nyström-approximationer med låg rang. Den andra metoden är baserad på skift och är en generalisering av de nuvarande deflationsbaserade metoderna. Dessutom presenteras en metod för adaptiv bestämning av antalet testvektorer som inte kräver förhandskunskap om matrisen. Median of Means principen ingår också i uppskattningen av diagonalerna. Dessutom kombinerar vi metoder för att uppskatta diagonalerna med algoritmer för att approximera matris-vektorprodukter med matrisfunktioner med hjälp av polynomapproximationer och Krylov-underutrymmen. Detta gör att vi kan presentera implicita metoder för att uppskatta diagonalerna i matrisfunktioner. Vi ger de första teoretiska resultaten för konvergensen av dessa skattare. Sedan presenterar vi en deflationsbaserad diagonal estimator för monotona funktioner av normala matriser med förbättrade konvergensegenskaper. För att validera våra metoders effektivitet och praktiska användbarhet genomför vi numeriska experiment i verkliga scenarier. Detta inkluderar uppskattning av Subgraph Centrality i nätverk, osäkerhetskvantifiering inom ramen för vanliga minsta kvadratmetoden och randomiserad Jacobi-förkonditionering.

Since data sizes of analytical applications are continuously growing, many data scientists are switching from customized micro-solutions to scalable alternatives, such as statistical and scientific databases. However, many algorithms in data mining and science are expressed in terms of linear algebra, which is barely supported by major database vendors and big data solutions. On the other side, conventional linear algebra algorithms and legacy matrix representations are often not suitable for very large matrices. We propose a strategy for large matrix processing on modern multicore systems that is based on a novel, adaptive tile matrix representation (AT MATRIX). Our solution utilizes multiple techniques inspired from database technology, such as multidimensional data partitioning, cardinality estimation, indexing, dynamic rewrites, and many more in order to optimize the execution time. Based thereon we present a matrix multiplication operator ATMULT, which outperforms alternative approaches. The aim of our solution is to overcome the burden for data scientists of selecting appropriate algorithms and matrix storage representations. We evaluated AT MATRIX together with ATMULT on several real-world and synthetic random matrices.

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