27 research outputs found

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Symbolic Algorithm for Inverting General k-Tridiagonal Interval Matrices

    Get PDF
    The k-tridiagonal matrices have received much attention in recent years. Many different algorithms have been proposed to improve the efficiency of k-tridiagonal matrix estimation. A novel method based on interval analysis has been identified to improve the efficiency of the calculation. This paper presents efficient and reliable computational algorithms for determining the determinant and inverse of general k-tridiagonal interval matrices built on generalized interval arithmetic. This study is based on the Doolittle LU factorization of the interval matrix. Finally, examples are presented to illustrate the algorithms

    New Directions for Contact Integrators

    Get PDF
    Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

    Hybrid direct and interactive solvers for sparse indefinite and overdetermined systems on future exascale architectures

    Get PDF
    In scientific computing, the numerical simulation of systems is crucial to get a deep understanding of the physics underlying real world applications. The models used in simulation are often based on partial differential equations (PDE) which, after fine discretisation, give rise to huge sparse systems of equations to solve. Historically, 2 classes of methods were designed for the solution of such systems: direct methods, robust but expensive in both computations and memory; and iterative methods, cheap but with a very problem-dependent convergence properties. In the context of high performance computing, hybrid direct-iterative methods were then introduced inorder to combine the advantages of both methods, while using efficiently the increasingly largeand fast supercomputing facilities. In this thesis, we focus on the latter type of methods with two complementary research axis.In the first chapter, we detail the mechanisms behind the efficient implementation of multigrid methods. The latter makes use of several levels of increasingly refined grids to solve linear systems with a combination of fine grid smoothing and coarse grid corrections. The efficient parallel implementation of such a scheme is a difficult task. We focus on the solution of the problem on the coarse grid whose scalability is often observed as limiting at very large scales. We propose an agglomeration technique to gather the data of the coarse grid problem on a subset ofthe computing resources in order to minimise the execution time of a direct solver. Combined with a relaxation of the solution accuracy, we demonstrate an increased overall scalability of the multigrid scheme when using our approach compared to classical iterative methods, when the problem is numerically difficult. At extreme scale, this study is carried in the HHG framework(Hierarchical Hybrid Grids) for the solution of a Stokes problem with jumping coefficients, inspired from Earth's mantle convection simulation. The direct solver used on the coarse grid is MUMPS,combined with block low-rank approximation and single precision arithmetic.In the following chapters, we study some hybrid methods derived from the classical row-projection method block Cimmino, and interpreted as domain decomposition methods. These methods are based on the partitioning of the matrix into blocks of rows. Due to its known slow convergence, the original iterative scheme is accelerated with a stabilised block version of the conjugate gradient algorithm. While an optimal choice of block size improves the efficiency of this approach, the convergence stays problem dependent. An alternative solution is then introduced which enforces a convergence in one iteration by embedding the linear system into a carefully augmented space.These two approaches are extended in order to compute the minimum norm solution of in definite systems and the solution of least-squares problems. The latter problems require a partitioning in blocks of columns. We show how to improve the numerical properties of the iterative and pseudo-direct methods with scaling, partitioning and better augmentation methods. Both methods are implemented in the parallel solver ABCD-Solver (Augmented Block Cimmino Distributed solver)whose parallelisation we improve through a combination of load balancing and communication minimising techniques.Finally, for the solution of discretised PDE problems, we propose a new approach which augments the linear system using a coarse representation of the space. The size of the augmentation is controlled by the choice of a more or less refined mesh. We obtain an iterative method with fast linear convergence demonstrated on Helmholtz and Convection-Diffusion problems. The central point of the approach is the iterative construction and solution of a Schur complemen

    Polinomios biortogonales y sus generalizaciones: una perspectiva desde los sistemas integrables

    Get PDF
    La conexión existente entre los polinomios ortogonales y otras ramas de la matemática, la física o la ingeniería es verdaderamente asombrosa. Además, no hay mejor prueba de la utilidad de estos que el propio crecimiento, avance perpetuo y generalización en diversas direcciones de lo que se entendía por polinomio ortogonal en los albores de la teoría. Conforme el concepto se fue generalizando, también fueron evolucionando las técnicas para su estudio, algunas de estas claramente influenciadas por aquellas disciplinas matemáticas con las que iban surgiendo conexiones. La perspectiva que esta tesis adopta frente a los polinomios ortogonales es un ejemplo de este tipo de influencias, compartiendo herramientas y entrelazandose con la teoría de los sistemas integrables. Una posición privilegiada en esta tesis la ocuparían las matrices de Gram semi in nitas; cada cual asociada a una forma sesquilineal adaptada al tipo de biortogonalidad en cuestión. A estas matrices se les impondrán una serie de condiciones cuyo objeto sería el de garantizar la existencia y unicidad de las secuencias biortogonales asociadas a las mismas. El siguiente paso consistiría en buscar simetrías de estas matrices de Gram. Existen dos razones por las que este esfuerzo resulta ventajoso. En primer lugar, cada simetría encontrada podría traducirse en propiedades de las secuencias biortogonales, por ejemplo: una estructura Hankel de la matriz es equivalente a gozar de la recurrencia a tres términos de los polinomios ortogonales; la simetría propia de las matrices asociadas a pesos clásicos (Hermite, Laguerre, Jacobi) implica la existencia del operador diferencial lineal de segundo orden de que los polinomios clásicos son solución; etc..
    corecore