Introduction to Iterative Solvers of Linear Systems

Transcription

1 Introduction to Iterative Solvers of Linear Systems SFB Training Event January 2012 Prof. Dr. Andreas Frommer Typeset by Lukas Krämer, Simon-Wolfgang Mages and Rudolf Rödl

2 1 Classes of Matrices and their Properties A common problem in both mathematics and physics is the solution of linear systems Ax = b, (1) where A C n n and b C n are given and x C n is wanted. The matrix A is supposed to be nonsingular. In these notes the solution to the system will be referred to as x = A 1 b. Special classes of matrices lead to simplified problems. Let us first introduce some special classes of matrices, which have increasingly less nice properties. A matrix A is called hermitian if A = A. If A is hermtian then there exists a unitary matrix U C n n (U U = I) which transforms A to a diagonal matrix Λ R n n, A = UΛU. (2) A closer look on (2) reveals that the diagonal entries of Λ are the real eigenvalues λ i R, i = 1,..., n of A and the columns of U are the corresponding eigenvectors. This can more easily be seen by writing AU = UΛ instead of (2). Note that the λ i are not necessarily different for different i. A matrix A is called normal if A A = AA. Like hermitian matrices, normal matrices are unitary diagonalizable, i.e., A = UΛU, U U = I for some diagonal matrix Λ. Obviously every hermitian matrix is normal, but the eigenvalues of a normal matrix are not necessarily real numbers. A matrix A is called diagonalizable if there exists an invertible matrix V C n n which mediates the transformation of A into diagonal form, A = V ΛV 1, where Λ is a possibly complex valued diagonal matrix. Once more, the columns of V are eigenvectos of the matrix V, but they are not necessarily orthonormal. In the general case A can be brought to the form A = V JV 1 where J C n n is in Jordan canonical form, i.e., it is block diagonal J = diag(j i ), 1

3 with blocks of the form λ i J i = λ i Again, the λ i are not necessarily different for different i. Note that a small perturbation of A would make it diagonalizable, meaning that A would have only Jordan blocks of size 1. In other words, a very tiny perturbation of A can lead to a change in the lower diagonal from 1 to 0. This suggest that we should not seek a numerical algorithm to compute Jordan canonical forms. 2 Grandpa s Methods Some iterative methods are known already for a long time. Examples are the Jacobi method and the Gauß-Seidel method. Both methods rely on splitting the matrix in (1) into the diagonal, lower triangular and upper triangular parts. Equating (1) row by row gives a ii x i + j i a ij x j = b i, i = 1,..., n. This leads to the Jacobi method, developed by Jacobi in 1850, where the (k +1)th iterate is computed as x (k+1) i = 1 b i a ij x (k) j, i = 1,..., n (3) a ii j i for k = 0, 1,.... Computing (3) for each i in the order 1, 2,..., n suggests using the new iterates that are already computed, i.e., x (k+1) j for j < i, which leads directly to the Gauß-Seidel method x (k+1) i = 1 b i a ij x (k+1) j a ij x (k) j. (4) a ii j<i j>i In the following, we show how the iterations (3) and (4) can be written in a more compact form. To this end, let D denote the diagonal part of the matrix A, L the lower triangular part of A, and U the upper triangular part, so that A = D L U. Additionally x (k), k N denotes the approximation to the solution x after k steps of the iteration. 2

4 Jacobi: The defining equation for the Jacobi iteration is in matrix notation Dx (k+1) = b + (L + U)x (k). Gauß-Seidel: The defining equation for the Gauß-Seidel iteration is in matrix notation (D L)x (k+1) = b + Ux (k). General splitting methods. splitting methods, If x solves the problem then Both methods belong to the more general class of iterative A = M N, Mx (k+1) = b + Nx (k). Mx = b + Nx and we find for the error e (k) = x x (k) after the k-th iteration e (k+1) = x x (k+1), Me (k+1) = Ne (k), e (k+1) = M 1 Ne (k). Theorem 1. The iteration Mx k+1 = Nx k + b converges for any x (0) C n if and only if ρ(m 1 N) < 1, where ρ(a) = max { λ : λ is eigenvalue of A} is the spectral radius of A. Proof. 1 We have e (k+1) = ( M 1 N) k+1 e (0), meaning we have to show that ( ) k+1 M 1 k N 0 ρ(m 1 N) < 1. (5) Let us start with. Chose ε > 0 such that ρ(m 1 N) + ε < 1. Then, it can be shown that there exists a norm on C n sucht that the induced operator norm fulfills M 1 N ρ(m 1 N) + ε < 1. This shows that (M 1 N) k+1 (M 1 N) k+1 (ρ(m 1 N) + ε) k+1, where the last expression tends to zero as k. It follows that (M 1 N) k+1 and hence e (k) converge to zero. To show of (2), let λ be an arbitrary eigenvalue of M 1 N with corresponding eigenvector x. Then, λ k is an eigenvalue of (M 1 N) k. It follows λ k x = λ k x = (M 1 N) k x (M 1 N) k x. Since (M 1 N) k 0 we have λ k 0 and hence λ < 1. This is true for every eigenvalue, it follows that the spectral radius is less than 1. 1 Following Numerik linearer Gleichungssysteme by Christian Kanzow, Springer Verlag,

5 Example: 1. Let A be hermitian and positive definite (hpd), M = D L (Gauß-Seidel) ρ(m 1 N) < Let A and D + L + U be hpd ρ(d 1 (L + U)) < 1. Remark: We have in every induced operator norm A < 1 ρ(a) < 1 A = max x =1 Ax. For instance x A = max i a ij x 1 A = max j a ij x 2 A 2 = j i ρ(a A) (= ρ(a) if A is hermitian). 3 Krylov Subspaces Recall the Cayley-Hamilton theorem: There is p Π n so that p(a) = 0 = i α ia i = 0, where Π n denotes the space of polynomials of max degree = n. Make p of minimal degree n α 0 0 (if α 0 was zero, one could factor out one A in contradiction to the minimal degree). We then have n 0 = α i A i A 1 = 1 α i A i 1 : q n α 1(A), i=0 0 i=1 where q n 1 Π n 1. We have thus established that the inverse of A is a polynomial in A. { r (0), Ar (0), A 2 r (0),..., A m 1 r (0)} as the m-th Krylov sub- Idea: Define K m (A, r (0) ) = span space, which is equivalent to n K m (A, r (0) ) = {p m 1 (A)r (0) : p m 1 Π n }. Take r (0) = b Ax (0), A(x x (0) ) = r (0) so x = x (0) + A 1 r (0). It follows that x x 0 + K n (A, r 0 ) for n large enough. 4

6 Krylov subspace framework The following is a general framework for constructing an iterative method for the solution of Ax = b using Krylov subspaces. for m = 1, 2,... extend a basis of K m (A, r (0) ) to one of K m+1 (A, r (0) ) extract an appropriate iterate x (m+1) from x (0) + K m+1 (A, r (0) ) end for 4 The Lanczos Process The Lanczos process is an implementation of the Krylov subspace framework for hermitian matrices. Let A be hermitian. Our purpose is to construct a nested orthonormal basis v 1,..., v m for K m (A, r (0) ): K j (A, r (0) ) = span{v 1,..., v j }, j = 1, 2,... K 1 (A, r (0) ) : K 2 (A, r (0) ) : v 1 = 1 r (0) r(0) v 1, ṽ 2 = Av 1 Av 1, v 1 v 1 v 2 = 1 ṽ 2 ṽ2. K m (A, r (0) ) : v 1,..., v m 1, m 1 ṽ m = Av m 1 Av m 1, v j v j (6) v m = 1 ṽ m ṽm j=1 As A is hermitian equation (6) can be simplified using Av m 1, v j = v m 1, Av j and Av j K j+1 (A, r (0) ) v m 1 for all j < m 2 to give ṽ m = Av m 1 m 1 j=m 2 Av m 1, v j v j. Additionaly when computing ṽ m one can save computations by using the values v m 1, Av m 2 from the calculation of v m 1. 5

7 Lanczos Process Pseudocode: Putting everything together we obtain the following pseudocode. given: r (0) compute: β 1 = r (0), v1 = 1 β 1 r (0), v 0 = 0 for m = 2,... ṽ m = Av m 1 α m = ṽ m, v m 1 w m = ṽ m α m v m 1 β m 1 v m 2 β m = w m v m = 1 β m w m end for Summary: We have β m v m = Av m 1 α m v m 1 β m 1 v m 2, m = 2, 3,... Av m = β m+1 v m+1 + α m+1 v m + β m v m 1 = [v m 1 v m v m+1 ] β m α m+1, β m+1 or in full matrix notation: where AV m = V m+1 T m+1,m, V m = [v 1... v m ], [ T m T m+1,m =, β m+1 α 2 β β T m = βm β m α m+1 The cg-method The method of conjugate gradients by Hestenes and Stiefel, published in 1952, uses the ] 6

8 Lanczos process to compute approximations for the solution of Ax = b. It is motivated as follows. Chose x m x 0 + K m (A, r (0) ) such that b Ax m K m (A, r (0) ) ( Galerkin Property ). This Galerkin property is the defining property of the cg iterates. Computationally, we can get x m = V m ξ m, ξ m C n, at least in principle, as follows. b A(x 0 + V m ξ m ) K m (A, r (0) ) V m(x 0 + V m ξ m ) = 0 V m(r (0) AV m ξ m ) = 0 1 r (0) 0. T mξ m = 0, (7) 0 using 1 V mr (0) = r (0) 0. 0 V mav m = V mv m+1 T m+1,m 0 [ ] T = I m m = T m. In each step of the cg method we have to solve the system (7) which is a tridiagonal system of size m n, hence much easier to solve than the original system. Some technical transformations show that it is possible to cheaply update the iterates x m+1 from x m using a search vector p m and that the residual r m+1 = b Ax m+1 is a multiple of the Lanczos vector v m+1. The full method can be written as follows (See Golub and Van Loan, Matrix Computations, 3rd. Ed.): 7

9 k = 0 r 0 = b Ax 0 while r k 0 k = k + 1 if k = 1 p 1 = r 0 else β k = r T k 1 r k 1/r T k 2 r k 2 p k = r k 1 + β k p k 1 end α k = r k 1 r k 1 /p T k Ap k x k = x k 1 + α k p k r k = r k 1 α k Ap k end x = x k Remark: b Ax m K m (A, r (0) ) is equivalent to x x m, A(x x m ) = min{ x x, A(x x) : x x 0 + K m (A, r (0) )}. The number x x m, A(x x m ) is called the A-Norm of Error. In fact, the map x x, Ax is a norm, as one can easily check. In other words, cg is optimal in the sense that it gets as its mth iterate the vector x m x 0 + K m (A, r (0) ) for what the A-norm of the error is minimal among all vectors from x 0 + K m (A, r (0) ). The convergence behaviour of cg depends on the number κ 2 (A) := A 2 A 1 2. Fast convergence can be expected if κ 2 (A) 1. 8