Download PDF by Unknown Author, G. Meurant: Computer solution of large linear systems

By Unknown Author, G. Meurant

ISBN-10: 044450169X

ISBN-13: 9780444501691

ISBN-10: 1435605225

ISBN-13: 9781435605220

Hardbound. This ebook offers with numerical tools for fixing huge sparse linear structures of equations, quite these coming up from the discretization of partial differential equations. It covers either direct and iterative equipment. Direct tools that are thought of are variations of Gaussian removing and speedy solvers for separable partial differential equations in oblong domain names. The publication stories the classical iterative equipment like Jacobi, Gauss-Seidel and alternating instructions algorithms. a specific emphasis is wear the conjugate gradient in addition to conjugate gradient -like equipment for non symmetric difficulties. most productive preconditioners used to hurry up convergence are studied. A bankruptcy is dedicated to the multigrid procedure and the e-book ends with area decomposition algorithms which are compatible for fixing linear structures on parallel pcs.

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A k, (I- A)Sk = I - A k+l, Sk = ( I - A ) - I ( I Sk - ( I - IISk - ( I - A) -1 = - ( I - A)-III <_ I1(I- Ak+l), A ) - I A k+l, A)-I[I IIAk+ill. But as p(A) < 1, limk-~oo IIAk+lll = 0 and so lim [ISk - (I - A)-~II k---*co = 0. 24. [3 The series I + A + A 2 + . . is said to be the Neumann series for ( I - A ) -1 and Sk (for small k) is frequently used in numerical algorithms to approximate ( I - A) -1 when p(A) < 1. 3. Irreducibility and diagonal dominance In this Section we study the properties of some classes of matrices that will be useful for studying convergence of iterative methods.

We now consider a generalization of M-matrices. Let A be a matrix, define M ( A ) as the matrix having entries mi,j such that = m ,j = -I ,,jl for all i r j. Clearly M(A) is an L-matrix. It is obvious that many different matrices A can lead to the same M(A). So, we define the set 12(A) = {B J[b~,31= ]a~,3J}. ~(A) is called the equimodular set for A. Then, we have the definition of the most general class of matrices we shall consider in this book. 47 A is an H-matrix if and only if M(A) is an M-matrix.

The scalar product of two vectors x and y with complex entries is defined by n y) = i--1 where ~ denotes the conjugate of xi E C. A H denotes the conjugate transpose of A given by (Ag)i,j = aj,i. A complex matrix A which is orthogonal is called unitary. A matrix A is Hermitian if and only if A H -- A. 19 For every matrix A there exists a unitary matrix U such that V HAU = T, where T is an upper triangular matrix. P r o o f . For the proof we follow the lines of Strang [1216]. Every m a t r i x A has at least one eigenvalue, say A1 (which may be of algebraic multiplicity n) and at least one eigenvector x 1 t h a t we can assume h~s norm 1.

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Computer solution of large linear systems by Unknown Author, G. Meurant

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