# Compare Gram-Schmidt and Householder Orthogonalization Algorithms

This is a follow-up to my previous post. 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.

### Contents

#### Pete Stewart

As I did in my previous post, I am using Pete Stewart's 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 include it in 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);
end
R(k,k) = norm(Q(:,k));
Q(:,k) = Q(:,k)/R(k,k);
end
end


#### 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);
end
R(k,k) = norm(Q(:,k))';
Q(:,k) = Q(:,k)/R(k,k);
end
end


#### Householder Reflections

Compute R by applying Householder reflections to X a column at a time. Do not actually compute Q, just save the vectors that generate the reflections. See the description and codes from my previous post.

   type house_qr

function [U,R] = house_qr(A)
% Householder reflections for QR decomposition.
% [U,R] = house_qr(A) returns
% U, the reflector generators for use by house_apply.
% R, the upper triangular factor.
H = @(u,x) x - u*(u'*x);
[m,n] = size(A);
U = zeros(m,n);
R = A;
for j = 1:min(m,n)
u = house_gen(R(j:m,j));
U(j:m,j) = u;
R(j:m,j:n) = H(u,R(j:m,j:n));
R(j+1:m,j) = 0;
end
end


#### Comparison

For various matrices X, let's check how the three algorithms perform on two tasks, accuracy and orthogonality. How close is Q*R to X? And, how close is Q'*Q to I.

   type compare.m

function compare(X);
% compare(X). Compare three QR decompositions.

I = eye(size(X));
qrerr = zeros(1,3);
ortherr = zeros(1,3);

%% Classic Gram Schmidt
[Q,R] = gs(X);
qrerr(1) = norm(Q*R-X,inf)/norm(X,inf);
ortherr(1) = norm(Q'*Q-I,inf);

%% Modified Gram Schmidt
[Q,R] = mgs(X);
qrerr(2) = norm(Q*R-X,inf)/norm(X,inf);
ortherr(2) = norm(Q'*Q-I,inf);

%% Householder QR Decomposition
[U,R] = house_qr(X);
QR = house_apply(U,R);
QQ = house_apply_transpose(U,house_apply(U,I));
qrerr(3) = norm(QR-X,inf)/norm(X,inf);
ortherr(3) = norm(QQ-I,inf);

%% Report results
fprintf('\n                 Classic   Modified  Householder\n')
fprintf('QR error      %10.2e %10.2e %10.2e\n',qrerr)
fprintf('Orthogonality %10.2e %10.2e %10.2e\n',ortherr)


#### Well conditioned.

First, try a well conditioned matrix, a magic square of odd order.

   n = 7;
X = magic(n)

X =
30    39    48     1    10    19    28
38    47     7     9    18    27    29
46     6     8    17    26    35    37
5    14    16    25    34    36    45
13    15    24    33    42    44     4
21    23    32    41    43     3    12
22    31    40    49     2    11    20


Check its condition.

   kappa = condest(X)

kappa =
8.5681


Do the comparison.

   compare(X);

                 Classic   Modified  Householder
QR error        1.73e-16   6.09e-17   5.68e-16
Orthogonality   3.20e+00   1.53e-15   1.96e-15


All three algorithms do well with accuracy, but classic Gram-Schmidt fails with orthogonality.

#### Poorly conditioned.

Next, try a Hilbert matrix that is poorly conditioned, but not exactly singular.

   n = 7;
X = hilb(n)

X =
1.0000    0.5000    0.3333    0.2500    0.2000    0.1667    0.1429
0.5000    0.3333    0.2500    0.2000    0.1667    0.1429    0.1250
0.3333    0.2500    0.2000    0.1667    0.1429    0.1250    0.1111
0.2500    0.2000    0.1667    0.1429    0.1250    0.1111    0.1000
0.2000    0.1667    0.1429    0.1250    0.1111    0.1000    0.0909
0.1667    0.1429    0.1250    0.1111    0.1000    0.0909    0.0833
0.1429    0.1250    0.1111    0.1000    0.0909    0.0833    0.0769


Check its condition.

   kappa = condest(X)

kappa =
9.8519e+08


Do the comparison.

   compare(X)

                 Classic   Modified  Householder
QR error        5.35e-17   5.35e-17   8.03e-16
Orthogonality   5.21e+00   1.22e-08   1.67e-15


All three algorithms do well with accuracy. Classic Gram-Schmidt fails completely with orthogonality. The orthogonality of MGS depends upon kappa. Householder does well with orthogonality.

#### Singular.

Finally, an exactly singular matrix, a magic square of even order.

   n = 8;
X = magic(n)

X =
64     2     3    61    60     6     7    57
9    55    54    12    13    51    50    16
17    47    46    20    21    43    42    24
40    26    27    37    36    30    31    33
32    34    35    29    28    38    39    25
41    23    22    44    45    19    18    48
49    15    14    52    53    11    10    56
8    58    59     5     4    62    63     1


Check its rank.

   rankX = rank(X)

rankX =
3


Do the comparison.

   compare(X)

                 Classic   Modified  Householder
QR error        1.43e-16   8.54e-17   4.85e-16
Orthogonality   5.41e+00   2.16e+00   1.30e-15


Again, all three algorithms do well with accuracy. Both Gram-Schmidts fail completely with orthogonality. Householder still does well with orthogonality.

#### Conclusion

All three of these algorithms provide 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.

• Classic Gram-Schmidt. Q'*Q is almost never close to I.
• Modified Gram-Schmidt. Q'*Q depends upon condition of X and fails completely when X is singular.
• Householder triangularization. Q'*Q is always close to I

#### Reference

G. W. Stewart, Matrix Algorithms: Volume 1: Basic Decompositions, SIAM, xix+458, 1998. <http://epubs.siam.org/doi/book/10.1137/1.9781611971408>

Published with MATLAB® R2016a

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