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Numerical Linear Algebra


1 Arnoldi/Lanczos Iterations


Krylov Subspace Methods
•Given A and b, Krylov subspace

 

  linear systems eigenvalue problems
Hermitian CG Lanczos
Nonhermitian GMRES, BiCG, etc. Arnoldi


Arnoldi Algorithm

•Let be m n matrix with first n columns of Q
and be (n + 1) x n upper-left section of H
•Start by picking a random q1 and then determine q2and
•The nth columns of AQn = Qn+1 can be written as

Aqn = h1nq1 + + hnnqn + hn+1;nqn+1

•Algorithm: Arnoldi Iteration
given random nonzero b, let
for n = 1 to 1, 2, 3,.....


Lanczos Iteration for Symmetric Matrices
•For symmetric A, and Hn are tridiagonal, denoted by and Tn,
respectively. AQn = Qn+1 can be written as three- term recurrence

•where are diagonal entries and are sub-diagonal entries of
•Algorithm: Lanczos Iteration

given random b, let
for n = 1 to 1,2,3,....

Question: What are the meanings of and in ?

Properties of Arnoldi and Lanczos Iterations
•Eigenvalues of Hn (or Tn in Lanczos iterations) are called Ritz values.
•When m = n, Ritz values are eigenvalues .

•Even for n << m, Ritz values are often accurate approximations to
eigenvalues of A!

•For symmetric matrices with evenly spaced eigenvalues, Ritz values
tend to first convert to extreme eigenvalue.

•With rounding errors, Lanczos iteration can suffer from loss of
orthogonality and can in turn lead to spurious “ghost” eigenvalues.

Arnoldi and Polynomial Approximation
•For any

•It can be interpreted as a polynomial in A times b , x = p(A)b, where

•Krylov subspace iterations are often analyzed in terms of matrix
polynomials.
•Let Pn be the set of polynomials of degree n with cn = 1.
•Optimality of Arnoldi/Lanczos approximation: It finds s.t.
•p* is optimal polynomial among that minimizes ,
which is equivalent to minimizing the distance betwen Anb and its
projection in Kn
•p* is the characteristic polynomial of Hn
Ritz values are the roots of an optimal polynomial


2 Conjugate Gradient Method


Krylov Subspace Algorithms
•Create a sequence of Krylov subspaces for Ax = b

and find an approximate (hopefully optimal) solutions x n in Kn
•Only matrix-vector products involved
•For SPD matrices, most famous algorithm is Conjugate Gradient (CG)
method discovered by Hestenes/Stiefel in 1952
•Finds best solution in norm
•Only requires storing 4 vectors (instead of n vectors) due to three-term
recurrence

Motivation of Conjugate Gradients
•If A is m x m SPD, then quadratic function

has unique minimum
Negative gradient of this function is residual vector

so minimum is obtained precisely when Ax = b
•Optimization methods have form

where pn is search direction and is step length chosen to minimize

Line search parameter can be determined analytically as

•In CG, pn is chosen to be A-conjugate (or A-orthogonal) to previous
search directions, i.e., for j < n

Conjugate Gradient Method
 

Algorithm: Conjugate Gradient Method  
 

step length
approximate solution
residual
improvement this step
search direction


•Only one matrix-vector product Ap n-1 per iteration
•Apart from matrix-vector product, operation count per iteration is
O(m)
•If A is sparse with constant number of nonzeros per row, O(m)
operations per iteration.

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