# Apologies to Gram-Schmidt

This is a follow-up to my previous follow-up, posted several days ago. A very careful reader, Bruno Bazzano, contributed a comment pointing out what he called "a small typo" in my code for the classic Gram-Schmidt algorithm. It is more than a small typo, it is a serious blunder. I must correct the code, then do more careful experiments and reword my conclusions.

### Contents

#### Classic Gram-Schmidt

Bruno noticed that in the inner loop of my classic Gram-Schmidt function, the expression `Q(:,k-1)` should be `Q(:,1:k-1)`. I had dropped the two characters, " `1:` ". I need to orthogonalize against all the previous vectors, not just one of them. Here is the corrected code.

```
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(:,1: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

#### Publishing code

By the way, this points out the importance of publishing codes.

#### Comparison

For various matrices `X`, let's again 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`. I have modified the `compare` function in last week's post to return `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 1.52e-16 6.09e-17 5.68e-16 Orthogonality 1.87e-15 1.53e-15 1.96e-15

The `orthogonality` value in the `Classic` column is now at roundoff level. All three algorithms do well with both accuracy and orthogonality on this matrix.

#### Poorly conditioned Hilbert matrices

I want to do two experiments with matrices that are poorly conditioned, but not exactly singular. First, Hilbert matrices of increasing order.

kappa = zeros(8,1); ortherr = zeros(8,3); for n = 1:8 X = hilb(n); kappa(n) = condest(X); ortherr(n,:) = compare(X); end

Plot the results with a logarithmic scale.

semilogy([ortherr eps*kappa eps*kappa.^2],'o-') axis([0 9 10^(-16) 10^5]) legend({'gs','mgs','house','eps*kappa','eps*kappa^2'}, ... 'location','northwest') title('Loss of orthogonality') xlabel('n')

The loss of orthogonality of GS is worse than the other two and roughly varies like the square of the condition number, `kappa`. The loss of orthogonality of MGS varies like `kappa` to the first power. Householder does well with orthogonality.

#### Poorly conditioned stabilized magic square

Another set of nonsingular, but poorly conditioned, matrices, generated by adding a small value to the diagonal of the magic square of order 8

M = magic(8); I = eye(8); for k = 1:8 s = 10^(-k); X = M + s*I; kappa(k) = condest(X); ortherr(k,:) = compare(X); end

Plot the results with a logarithmic scale.

semilogy([ortherr eps*kappa eps*kappa.^2],'o-') axis([0 9 10^(-16) 10^5]) legend({'gs','mgs','house','eps*kappa','eps*kappa^2'}, ... 'location','northwest') title('Loss of orthogonality') xlabel('log10(1/s)')

Again, the orthogonality of GS varies like `kappa^2`, and the orthogonality of MGS varies like `kappa`.

#### Singular

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

n = 8; X = magic(n); compare(X);

Classic Modified Householder QR error 4.78e-17 8.54e-17 4.85e-16 Orthogonality 5.17e+00 2.16e+00 1.30e-15

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. Loss of orthogonality can be much worse than the other two when
`X`is badly conditioned. - Modified Gram-Schmidt.
`Q'*Q`varies like the condition of`X`and fails completely when`X`is singular. - Householder triangularization.
`Q'*Q`is always close to`I`

#### References

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

Ake Bjorck, *Numerical Methods for Least Squares Problems*, SIAM, xvii+407, 1996. <http://epubs.siam.org/doi/book/10.1137/1.9781611971484>

**Category:**- Algorithms,
- Magic Squares,
- Matrices,
- Numerical Analysis

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