Compare Gram-Schmidt and Householder Orthogonalization Algorithms

Classical Gram-Schmidt and Modified Gram-Schmidt are two algorithms for orthogonalizing a set of vectors. Householder elementary reflectors can be used for the same task. The three algorithms have very different roundoff error properties.


G. W. (Pete) Stewart

My colleague and friend G. W. Stewart is a Distinguished University Professor Emeritus at the Department of Computer Science, University of Maryland. Everybody knows him as “Pete”. He has never been able to satisfactorily explain the origins of “Pete” to me. It somehow goes back through his father to his grandfather and maybe great grandfather, who were also nicknamed “Pete”.

Pete has written several books on numerical linear algebra. For my blog today I am going to rely on the descriptions and pseudocode from his book “Matrix Algorithms, Volume I: Basic Decompositions”. His pseudocode is MATLAB ready.

Classic Gram-Schmidt

The classic Gram-Schmidt algorithm is the first thing you might think of for producing an orthogonal set of vectors. For each vector in your data set, remove its projection onto the data set, normalize what is left, and add it to the orthogonal set. Here is the code. X is the original set of vectors, Q is the resulting set of orthogonal vectors, and R is the set of coefficients, organized into an upper triangular matrix.

   type gs
function [Q,R] = gs(X)
    % Classical Gram-Schmidt.  [Q,R] = gs(X);
    % G. W. Stewart, "Matrix Algorithms, Volume 1", SIAM, 1998.
    [n,p] = size(X);
    Q = zeros(n,p);
    R = zeros(p,p);
    for k = 1:p
        Q(:,k) = X(:,k);
        if k ~= 1
            R(1:k-1,k) = Q(:,k-1)'*Q(:,k);
            Q(:,k) = Q(:,k) - Q(:,1:k-1)*R(1:k-1,k);
        R(k,k) = norm(Q(:,k));
        Q(:,k) = Q(:,k)/R(k,k);

X = Q R

The entire process can be expressed by matrix multiplication, with orthogonal $Q$ and upper triangular $R$.

$$X = QR$$

Modified Gram-Schmidt

This is a rather different algorithm, not just a simple modification of classical Gram-Schmidt. The idea is to orthogonalize against the emerging set of vectors instead of against the original set. There are two variants, a column-oriented one and a row-oriented one. They produce the same results, in different order. Here is the column version,

   type mgs
function [Q,R] =  mgs(X)
    % Modified Gram-Schmidt.  [Q,R] = mgs(X);
    % G. W. Stewart, "Matrix Algorithms, Volume 1", SIAM, 1998.
    [n,p] = size(X);
    Q = zeros(n,p);
    R = zeros(p,p);
    for k = 1:p
        Q(:,k) = X(:,k);
        for i = 1:k-1
            R(i,k) = Q(:,i)'*Q(:,k);
            Q(:,k) = Q(:,k) - R(i,k)*Q(:,i);
        R(k,k) = norm(Q(:,k))';
        Q(:,k) = Q(:,k)/R(k,k);

Householder Reflections

A Householder reflection $H$ transforms a given vector $x$ into a multiple of a unit vector $e_k$ while preserving length, so $Hx = \pm \sigma e_k$ where $\sigma = ||x||$ .

The matrix $H$ is never formed. The reflection is expressed as a rank-one modification of the identity

$$H = I – uu^T \ \mbox{where} \ ||u|| = \sqrt{2}$$

(The use of $\sqrt{2}$ here is Pete’s signature normalization.)

   type housegen
function [u,nu] = housegen(x)
    % [u,nu] = housegen(x)
    % Generate Householder reflection.
    % G. W. Stewart, "Matrix Algorithms, Volume 1", SIAM, 1998.
    % [u,nu] = housegen(x).
    % H = I - uu' with Hx = -+ nu e_1
    %    returns nu = norm(x).
    u = x;
    nu = norm(x);
    if nu == 0
        u(1) = sqrt(2);
    u = x/nu;
    if u(1) >= 0
        u(1) = u(1) + 1;
        nu = -nu;
        u(1) = u(1) - 1;
    u = u/sqrt(abs(u(1)));

Now to apply $H$ to any other $y$, compute the inner product

$$\tau = u^Ty$$

and then simply subtract

$$Hy = x – \tau u$$

Householder QR factorization

This program does not actually compute the QR orthogonalization, but rather computes R and a matrix U containing vectors that generate the Householder reflectors whose product is Q.

   type hqrd
function [U,R] = hqrd(X)  
    % Householder triangularization.  [U,R] = hqrd(X);
    % Generators of Householder reflections stored in U.
    % H_k = I - U(:,k)*U(:,k)'.
    % prod(H_m ... H_1)X = [R; 0]
    % where m = min(size(X))
    % G. W. Stewart, "Matrix Algorithms, Volume 1", SIAM, 1998.
    [n,p] = size(X);
    U = zeros(size(X));
    m = min(n,p);
    R = zeros(m,m);
    for k = 1:min(n,p)
        [U(k:n,k),R(k,k)] = housegen(X(k:n,k));
        v = U(k:n,k)'*X(k:n,k+1:p);
        X(k:n,k+1:p) = X(k:n,k+1:p) - U(k:n,k)*v;
        R(k,k+1:p) = X(k,k+1:p);


All three of these algorithms compute Q and R that do a good job of reproducing the data X, that is

  • Q * R is always close to X for all three algorithms.

On the other hand, their behavior is very different when it comes to producing orthogonality. Is Q'*Q close to the identity?

  • Classic Gram-Schmidt. Usually very poor orthogonality.
  • Modified Gram-Schmidt. Depends upon condition of X. Fails completely when X is singular.
  • Householder triangularization. Always good orthogonality.
   type compare.m
function compare(X);
% compare(X)
% Compare three QR decompositions,
I = eye(size(X));

%% Classic Gram Schmidt
[Q,R] = gs(X);
qrerr_gs = norm(Q*R-X,inf)/norm(X,inf);
ortherr_gs = norm(Q'*Q-I,inf);

%% Modified Gram Schmidt
[Q,R] = mgs(X);
qrerr_mgs = norm(Q*R-X,inf)/norm(X,inf);
ortherr_mgs = norm(Q'*Q-I,inf);

%% Householder QR Decomposition
[U,R] = hqrd(X);
QR = R;
E = I;
for k = size(X,2):-1:1
    uk = U(:,k);
    QR = QR - uk*(uk'*QR);
    E = E - uk*(uk'*E) - (E*uk)*uk' + uk*(uk'*E*uk)*uk';
qrerr_h = norm(QR-X,inf)/norm(X,inf);
ortherr_h = norm(E-I,inf);

%% Report results 
fprintf('QR error\n')
fprintf('Classic:     %10.3e\n',qrerr_gs)
fprintf('Modified:    %10.3e\n',qrerr_mgs)
fprintf('Householder: %10.3e\n',qrerr_h)
fprintf('Orthogonality error\n')
fprintf('Classic:     %10.3e\n',ortherr_gs)
fprintf('Modified:    %10.3e\n',ortherr_mgs)
fprintf('Householder: %10.3e\n',ortherr_h)

Well conditioned.

QR error
Classic:      1.726e-16
Modified:     6.090e-17
Householder:  3.654e-16

Orthogonality error
Classic:      3.201e+00
Modified:     1.534e-15
Householder:  1.069e-15

Poorly conditioned, nonsingular.

QR error
Classic:      5.352e-17
Modified:     5.352e-17
Householder:  7.172e-16

Orthogonality error
Classic:      5.215e+00
Modified:     1.219e-08
Householder:  1.686e-15


QR error
Classic:      1.435e-16
Modified:     8.540e-17
Householder:  2.460e-16

Orthogonality error
Classic:      5.413e+00
Modified:     2.162e+00
Householder:  2.356e-15


G. W. Stewart, “Matrix Algorithms, Volume 1: Basic Decompositions”, SIAM, 1998.

Published with MATLAB® R2016a

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