Séminaire et groupe de travail d’Approximation et Analyse matricielle
Le séminaire et groupe de travail d’algèbre réunit les membres de l’équipe Approximation (et toutes personnes intéressées). Responsable : Khalide Jbilou.
Évènements passés
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Séminaire et groupe de travail d’Approximation et Analyse matricielle du 7 décembre 2018
Archraf Badahmane (LMPA-ULCO)
Informations : 13:30 - 14:00 B014 -
Séminaire et groupe de travail d’Approximation et Analyse matricielle du 7 décembre 2018
Yassine Kaouane (LMPA-ULCO)
Informations : 13:00 - 13:30 B014 -
Séminaire et groupe de travail d’Approximation et Analyse matricielle du 7 septembre 2018
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Séminaire et groupe de travail d’Approximation et Analyse matricielle du 15 juin 2018
Marcos Raydan (Universidad Simón Bolívar)
Informations : 11:00 - 12:00 B014The 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. -
Séminaire et groupe de travail d’Approximation et Analyse matricielle du 15 juin 2018
Abderrahim Messaoudi (ENS, Université Mohammed VI, Rabat)
Informations : 10:00 - 11:00 B014Le fichier pdf du résumé est disponible ici.
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Séminaire et groupe de travail d’Approximation et Analyse matricielle du 14 juin 2018
Fadi Dornaika (University of the Basque Country UPV/EHU)
Informations : 13:30 - 14:30 B014The 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.
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Séminaire et groupe de travail d’Approximation et Analyse matricielle du 10 avril 2018
Karim Kreit (Université Caddi Ayad Marrakech)
Informations : 14:30 - 15:00 Salle B014The 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. -
Séminaire et groupe de travail d’Approximation et Analyse matricielle du 16 mars 2018
Achraf Badahmane (ULCO-LMPA)
Informations : 11:00 - 12:00 B014In 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. -
Séminaire et groupe de travail d’Approximation et Analyse matricielle du 9 février 2018
Yassine Kaouane (ULCO)
Informations : 11:00 - 12:00 Salle B014We 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.
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Séminaire et groupe de travail d’Approximation et Analyse matricielle du 30 juin 2017
A. Messaoudi (ENS, Université Mohammed VI, Rabat)
Informations : 11:00 - 12:00 B014Le résumé est diponible ici pdf