L.M.P.A
Laboratoire de Mathématiques Pures et Appliquées
Joseph Liouville

Groupe de travail approximation et analyse matricielle

Le groupe de travail d’algèbre réunit les membres de l’équipe Approximation (et toutes personnes intéressées). Responsable : Khalide Jbilou.

Evénements passés

  • Groupe de travail approximation et analyse matricielle du 15 juin

    Marcos Raydan (Universidad Simón Bolívar)

    On the computation of large-scale self-consistent-field iterations

    The computation of the subspace spanned by the eigenvectors associated to the N lowest eigenvalues of a large symmetric matrix (or, equivalently, the projection matrix onto that subspace) is a difficult numerical linear algebra problem when the dimensions involved are very large.
    These problems appear when one employs the SCF (Self-Consistent-Field) fixed-point algorithm or its variations for electronic structure calculations, which requires repeated solutions of the problem for different data, in an iterative context. The naive use of consolidated packages as Arpack does not
    lead to practical solutions in large-scale cases. In this paper we combine and enhance well-known purification iterative schemes with a specialized use of Arpack (or any other eigen-package) to address these large-scale challenging problems.

    Informations : 11:00 - 12:00 B014
  • Groupe de travail approximation et analyse matricielle du 15 juin

    Abderrahim Messaoudi (ENS, Université Mohammed VI, Rabat)

    GRPIA : a new algorithm for computing interpolation polynomials

    Le fichier pdf du résumé est disponible ici.

    Informations : 10:00 - 11:00 B014
  • Groupe de travail approximation et analyse matricielle du 14 juin

    Fadi Dornaika (University of the Basque Country UPV/EHU)

    Contribution to machine learning tools : Applications to visual data analysis

    The first part of the talk addresses some of my recent contributions to semi-supervised learning. Data driven graphs and semi-supervised learning constitute a cornerstone of many machine learning algorithms. More precisely, we have introduced a Two phases weighted Regularized Least square method which provides adaptive and informative graphs. We have also proposed inductive and flexible schemes for graph-based semi-supervised learning that provide non-linear projections. The performance of the proposed methods is studied on real image datasets including faces and objects.

    The second part of the talk will briefly present some of my recent works that exploit machine learning tools for visual data analysis. These are as follows : object detection in aerial images, vision-based vehicle localization, parking lot occupancy detection, image based age estimation, assessing face attractiveness, Five Psychology Traits from videos, and Driver Drowsiness detection in videos.

    Informations : 13:30 - 14:30 B014
  • Groupe de travail approximation et analyse matricielle du 10 avril

    Karim Kreit (Université Caddi Ayad Marrakech)

    Groupe de travail approximation et analyse matricielle

    The total variation model has been introduced in image processing since $1992$ by Rudin, Osher and Fatemi (ROF). The (ROF) model eliminates noise from images and preserve the edges by solving a minimization problem related to a total variation energy.
    In this talk, we consider the problem of image restoration with total variation regularization. We transform the problem to a nonlinear constrained optimization in the dual form. We apply the conditional gradient (Frank-Wolfe) method to the dual total variation regularization, and we drive a new method for denoising image. The convergence of this method is proved. Finally we illustrate the effectiveness of our proposed method by some numerical examples.

    Informations : 14:30 - 15:00 Salle B014
  • Groupe de travail approximation et analyse matricielle du 16 mars

    Achraf Badahmane (ULCO-LMPA)

    The preconditioned global MINRES

    In this talk, we propose the preconditioned global MINRES as a new strategy to solve problems $AX=B$ with several right-hand sides.
    The preconditioner is obtained by replacing the block (2,2) by another block of the matrix A.
    We apply the global MINRES method for this problem with several right hand sides and we give new convergence results and analyze the eigenvalue-distribution and the eigenvectors of the preconditioner.
    Finally, numerical results show that our preconditioned global MINRES method, is very efficient for solving problem with several right hand sides.

    Informations : 11:00 - 12:00 B014
  • Groupe de travail approximation et analyse matricielle du 9 février

    Yassine Kaouane (ULCO)

    An adaptive block tangential method for multi-input multi-output dynamical systems.

    We present a new approach for model order reduction in large-scale dynamical systems, with multiple inputs and multiple outputs (MIMO). This approach will be named : Adaptive Block Tangential Arnoldi Algorithm (ABTAA) and is based on interpolation via block tangential Krylov subspaces requiring the selection of shifts and tangent directions via an adaptive procedure. We give some algebraic properties and present some numerical examples to show the effectiveness of the proposed method.

    Informations : 11:00 - 12:00 Salle B014
  • Groupe de travail approximation et analyse matricielle du 30 juin 2017

    A. Messaoudi (ENS, Université Mohammed VI, Rabat)

    New algoritm for computing the interpolation polynomials

    Le résumé est diponible ici pdf

    Informations : 11:00 - 12:00 B014
  • Groupe de travail approximation et analyse matricielle du 23 juin 2017

    Marcos Raydan (Universidad Simón Bolívar)

    Constrained optimization schemes for avoiding resonance in large structures

    The Quadratic Finite Element Model Updating Problem (QFEMUP) concerns
    with updating a symmetric second-order finite element model so that it
    remains symmetric and the updated model reproduces a given set of
    desired eigenvalues and eigenvectors by replacing the corresponding
    ones from the original model. Taking advantage of the special
    structure of the constraint set, it is first shown that the QFEMUP can
    be formulated as a suitable constrained nonlinear programming
    problem. Using this formulation, we present and analyze two different
    methods based on successive optimizations. To avoid that spurious
    modes (eigenvectors) appear in the frequency range of interest
    (eigenvalues) after the model has been updated, additional constraints
    based on a quadratic Rayleigh quotient are dynamically included in the
    constraint set. The results of our numerical experiments on
    illustrative problems show that the algorithms work well in practice.

    Informations : 11:00 - 12:00 B014
  • Groupe de travail approximation et analyse matricielle du 16 juin 2017

    Achraf Badahmane (ULCO)

    Saddle point problems

    In some applications, we have to solve large linear saddle
    point problems with multiple right-hand sides. Instead of applying a
    standard iterative process to the solution of each saddle point
    problem indepentely, it’s more efficient to apply a global process. We
    use different techniques of preconditioning ( Diagonal preconditioner,
    Triangular preconditioner, P0 preconditioner ,.. ) to improve spectral
    proprieties of the saddle point matrix and to accelerate the
    convergence

    Informations : 14:30 - 15:30 B014
  • Groupe de travail approximation et analyse matricielle du 2 juin 2017

    Yassine Kaouane (LMPA, ULCO)

    Adaptive tangential Computational Krylov subspaces methods for model reduction in large-scale dynamical systems

    In this talk, we present two new approaches for model order reduction
    problem, with multiple inputs and multiple outputs (MIMO). The
    Adaptive Global Tangentiel Arlondi Algorithms (AGTAA), and the
    Adaptive Global Tangentiel Lanczos Algorithms (AGTLA).These methods
    are based on a generalization of the global Arnoldi and the global
    Laczos algorithms. The selection of the shifts and the tangent
    directions is done with an adaptive procedure. We give some algebraic
    properties for the global case. Finally, some numerical examples are
    presented to show the effectiveness of the proposed algorithms.

    Key words : Global, Arnoldi, Lanczos, Model reduction, Tangential directions.

    Informations : 14:30 - 15:30 B014, Mi-voix
Autres evénements passés : 0 | 10

Agenda