A new class of explicit approximate inverse preconditioning is introduced for solving fourth-order equations, based on the 'coupled equation approach', by the domain decomposition method in conjunction with va...
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. The need for a more accurate modeling of the performance of systems whose functioning mainly dependant on external time parameters such as the number of requests during a particular time phase, led us to a novel app...
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A new class of normalized approximate inverse matrix techniques, based on the concept of sparse normalized approximate factorization procedures are introduced for solving sparse linear systems derived from the finite ...
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A new class of normalized approximate inverse matrix techniques, based on the concept of sparse normalized approximate factorization procedures are introduced for solving sparse linear systems derived from the finite difference discretization of partial differential equations. Normalized explicit preconditioned conjugate gradient type methods in conjunction with normalized approximate inverse matrix techniques are presented for the efficient solution of sparse linear systems. Theoretical results on the rate of convergence of the normalized explicit preconditioned conjugate gradient scheme and estimates of the required computational work are presented. Application of the new proposed methods on two dimensional initial/boundary value problems is discussed and numerical results are given. The parallel and systolic implementation of the dominant computational part is also investigated.
The DOUG (domain decomposition on unstructured grids) package was first developed from 1996-1998 as a parallel solver for scalar elliptic PDEs discretised with finite elements. In this paper we describe its extension ...
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The DOUG (domain decomposition on unstructured grids) package was first developed from 1996-1998 as a parallel solver for scalar elliptic PDEs discretised with finite elements. In this paper we describe its extension to unsymmetric elliptic systems of PDEs, highlighting software design and parallelisation issues. As an application we discuss the performance of the extended package applied to the incompressible Navier-Stokes equations, discretised with mixed finite elements. In particular we focus on discontinuous pressure elements, which are important in many practical applications. We also indicate briefly the application of this solver to Navier-Stokes stability assessment. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.
A new class of approximate inverses for arrowhead and special tridiagonal linear systems, based on the concept of sparse approximate Choleski-type factorization procedures, are introduced for computing fast explicit a...
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A class of finite difference schemes in conjunction with approximate inverse banded matrix techniques based on the concept of LU-type factorization procedures is introduced for computing fast explicit approximate inve...
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A class of finite difference schemes in conjunction with approximate inverse banded matrix techniques based on the concept of LU-type factorization procedures is introduced for computing fast explicit approximate inverses. Explicit preconditioned iterative schemes in conjunction with approximate inverse matrix techniques are presented for the efficient solution of banded linear systems. A theorem on the rate of convergence and estimates of the computational complexity required to reduce the L-infinity-norm of the error is presented. Applications of the method on linear and non-linear systems are discussed and numerical results are given.
In this paper, we present the dependability evaluation of a uniprocessor multitasking system and a multiprocessor system with failures based on the use of performability indicators. The computation of these indicators...
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ISBN:
(纸本)1892512459
In this paper, we present the dependability evaluation of a uniprocessor multitasking system and a multiprocessor system with failures based on the use of performability indicators. The computation of these indicators requiring efficient computational methods is achieved by using explicit approximate inverse preconditioning techniques.
The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing en...
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