Block Matrix (block + matrix)

Distribution by Scientific Domains
Engineering75%

Selected Abstracts

Two-grid methods for banded linear systems from DCT III algebra

NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, Issue 2-3 2005
R. H. Chan
Abstract We describe a two-grid and a multigrid method for linear systems whose coefficient matrices are point or block matrices from the cosine algebra generated by a polynomial. We show that the convergence rate of the two-grid method is constant independent of the size of the given matrix. Numerical examples from differential and integral equations are given to illustrate the convergence of both the two-grid and the multigrid method. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Efficient preconditioning techniques for finite-element quadratic discretization arising from linearized incompressible Navier,Stokes equations

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 12 2010
A. El Maliki
Abstract We develop an efficient preconditioning techniques for the solution of large linearized stationary and non-stationary incompressible Navier,Stokes equations. These equations are linearized by the Picard and Newton methods, and linear extrapolation schemes in the non-stationary case. The time discretization procedure uses the Gear scheme and the second-order Taylor,Hood element P2,P1 is used for the approximation of the velocity and the pressure. Our purpose is to develop an efficient preconditioner for saddle point systems. Our tools are the addition of stabilization (penalization) term r,(div(·)), and the use of triangular block matrix as global preconditioner. This preconditioner involves the solution of two subsystems associated, respectively, with the velocity and the pressure and have to be solved efficiently. Furthermore, we use the P1,P2 hierarchical preconditioner recently proposed by the authors, for the block matrix associated with the velocity and an additive approach for the Schur complement approximation. Finally, several numerical examples illustrating the good performance of the preconditioning techniques are presented. Copyright © 2009 John Wiley & Sons, Ltd. [source]

USING LEAST SQUARE SVM FOR NONLINEAR SYSTEMS MODELING AND CONTROL

ASIAN JOURNAL OF CONTROL, Issue 2 2007
Haoran Zhang
ABSTRACT Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. The paper firstly introduces the mathematical model of regression least squares support vector machine (LSSVM), and designs incremental learning algorithms by the calculation formula of block matrix, then uses LSSVM to model nonlinear system, based on which to control nonlinear systems by model predictive method. Simulation experiments indicate that the proposed method provides satisfactory performance, and it achieves superior modeling performance to the conventional method based on neural networks, moreover it achieves well control performance. [source]