Jean-Paul Chehab
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jean-Paul Chehab has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hassan Al Moatassime
Université Cadi Ayyad, Faculté des Sciences et Techniques, avenue Abdelkrim Khattabi, B.P. 618 Guéliz Marrakech, Morocco
1 shared publication - Jacques Laminie
Grimaag-Guadeloupe Campus de Fouillole B.P. 592 97157 Pointe à Pitre Cedex and Laboratoire de Mathématique, Université Paris 11, Orsay, France
1 shared publication - Marcos Raydan
Center for Mathematics and Applications (NovaMath) FCT NOVA Portugal
1 shared publication - Mostafa Abounouh1 shared publication
- Olivier Goubet
Laboratoire Amiénois de Mathématique Fondamentale et Appliquée, CNRS UMR 7352, Université de Picardie Jules Verne, 80039 Amiens, France
1 shared publication - Serge Dumont
LAMFA UMR 7352 Université de Picardie Jules Verne -CNRS 33, rue Saint-Leu, 80 000 Amiens France
1 shared publication
Latest Publications
- Open Access
- 1 January 2008
- Published by American Institute of Mathematical Sciences (AIMS) in Communications on Pure and Applied Analysis
- Vol. 7 (2) , 211-227
Discrete Schrödinger equations and dissipative dynamical systems
We introduce a Crank-Nicolson scheme to study numerically the long-time behavior of solutions to a one dimensional damped forced nonlinear Schrödinger equation. We prove the existence of a smooth global attractor for these discretized equations. We also provide some numerical evidences of this asymptotical smoothing effect.
- 1 January 2008
- Published by American Institute of Mathematical Sciences (AIMS) in Communications on Pure and Applied Analysis
- Vol. 7 (2) , 211-227
Open AccessDiscrete Schrödinger equations and dissipative dynamical systems
We introduce a Crank-Nicolson scheme to study numerically the long-time behavior of solutions to a one dimensional damped forced nonlinear Schrödinger equation. We prove the existence of a smooth global attractor for these discretized equations. We also provide some numerical evidences of this asymptotical smoothing effect.
- Open Access
- 1 January 2007
- Published by Brazilian Society for Computational and Applied Mathematics (SBMAC) in Computational and Applied Mathematics
- Vol. 26 (1)
Matrix differential equations and inverse preconditioners
In this article, we propose to model the inverse of a given matrix as the state of a proper first order matrix differential equation. The inverse can correspond to a finite value of the independent variable or can be reached as a steady state. In both cases we derive corresponding dynamical systems and establish stability and convergence results. The application of a numerical time marching scheme is then proposed to compute an approximation of the inverse. The study of the underlying schemes can be done by using tools of numerical analysis instead of linear algebra techniques only. With our approach, we recover some known schemes but also introduce new ones. We derive in addition a masked dynamical system for computing sparse inverse approximations. Finally we give numerical results that illustrate the validity of our approach. Mathematical subject classification: 65F10, 65F35, 65L05, 65L12, 65L20, 65N06.
- 1 January 2007
- Published by Brazilian Society for Computational and Applied Mathematics (SBMAC) in Computational and Applied Mathematics
- Vol. 26 (1)
Open AccessMatrix differential equations and inverse preconditioners
In this article, we propose to model the inverse of a given matrix as the state of a proper first order matrix differential equation. The inverse can correspond to a finite value of the independent variable or can be reached as a steady state. In both cases we derive corresponding dynamical systems and establish stability and convergence results. The application of a numerical time marching scheme is then proposed to compute an approximation of the inverse. The study of the underlying schemes can be done by using tools of numerical analysis instead of linear algebra techniques only. With our approach, we recover some known schemes but also introduce new ones. We derive in addition a masked dynamical system for computing sparse inverse approximations. Finally we give numerical results that illustrate the validity of our approach. Mathematical subject classification: 65F10, 65F35, 65L05, 65L12, 65L20, 65N06.
- 1 October 2005
- Published by Springer Nature in Numerical Algorithms
- Vol. 40 (2) , 103-124
Differential equations and solution of linear systems
- 1 October 2005
- Published by Springer Nature in Numerical Algorithms
- Vol. 40 (2) , 103-124
Differential equations and solution of linear systems
- 26 November 2004
- Published by Elsevier in Applied Numerical Mathematics
- Vol. 55 (1) , 32-47
Implicit and adaptive inverse preconditioned gradient methods for nonlinear problems
- 26 November 2004
- Published by Elsevier in Applied Numerical Mathematics
- Vol. 55 (1) , 32-47
Implicit and adaptive inverse preconditioned gradient methods for nonlinear problems