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.

Prochain evénement

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

Evénements passés

  • 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
  • Groupe de travail approximation et analyse matricielle du 12 mai 2017

    Hassane Sadok (ULCO)

    Convergence properties and implementations of Block Krylov subspaces methods

    Krylov subspace methods are widely used for the iterative solution of
    a large variety of linear systems of equations with one or several
    right hand sides or for solving nonsymmetric eigenvalue problems. The
    solution of linear systems of equations with several right-hand sides
    is considered. Approximate solutions are conveniently computed by
    block GMRES methods. We describe and study three variants of block
    GMRES. These methods are based on three implementations of the block
    Arnoldi method, which differ in their choice of inner product.. The
    Block GMRES is classically implemented by first applying the Arnoldi
    iteration as a block orthogonalization process to create a basis of
    the block Krylov space generated by the matrix of the system from the
    initial residual. Next, the method is solving a block least-squares
    problem, which is equivalent to solving several least squares problems
    implying the same Hessenberg matrix. These laters are usually solved
    by using a block updating procedure for the QR decomposition of the
    Hessenberg matrix based on Givens rotations. A more effective
    alternative was given by M. H. Gutknecht and T. Schmelzer which uses
    the Householder reflectors. We propose a new and simple implementation
    of the block GMRES algorithm, based on a generalization of Ayachour’s
    method given for the GMRES with a single right-hand side. Several
    numerical experiments are provided to illustrate the performance of
    the new implementation.

    Informations : 13:30 - 14:30 B014

Agenda