## QR Decomposition Notes

I find this process easier to reason through by writing out a small 3-column version explicitly:

which gives:

From this and the orthonormal properties of \(Q\), the meaning of the \(R_{ji}\) values are clear, the projection of the \(i\)'th column of \(A\) onto the \(j\)'th column of \(Q\). These allows fixing the values of \(R_{ji}\) and norms of \(Q_{*i}\):

- \(Q_{*i}^T Q_{*j} = 0, \forall i \neq j\): columns of \(Q\) are orthogonal so each \(R_{j*}\) value must account for entire parallel component of \(A_{*j}\) onto each column of \(Q\).
- \(\|Q_{*i}\|_2 = 1, \forall i\): constrains \(\|Q_{*i}\|\) and corresponding \(R_{ji}\) values.

Therefore:

- \(R_{00} = \|A_{*0}\|_2\) and \(Q_{*0} = A_{*0}/R_{00}\)
- \(R_{01} = Q_{*0}^T A_{*1}\), \(s_1 = A_{*1} - R_{01} Q_{*0}\), \(R_{11} = \|s_1\|_2\) \(Q_{*1} = s_1/R_{11}\)
- \(R_{02} = Q_{*0}^T A_{*2},~R_{12} = Q_{*1}^T A_{*2}, s_2 = A_{*2} - R_{02} Q_{*0} - R_{12} Q_{*1}, R_{22} = \| s_2 \|_2, Q_{*2} = s_2/R_{22}\)

or in general:

- \(R_{ji} = Q_{*j}^T A_{*i}, \forall j < i\)
- \(s_{i} = A_{*i} - \sum_j R_{ji} Q_{*j}\)
- \(R_{ii} = \| s_{i} \|_2, Q_{*i} = s_i/R_{ii}\)

and in python:

```
def qr_gs( A, inplace=True ):
'''Decompose A into Q*R with Q orthonormal and R upper triangular using classical Gram-Schmidt (unstable)'''
A = A if inplace else A.copy()
R = np.zeros((A.shape[1],A.shape[1]))
for i in range( A.shape[1] ):
for j in range(i):
R[j,i] = np.sum(A[:,j]*A[:,i])
A[:,i] -= R[j,i]*A[:,j]
R[i,i] = np.linalg.norm(A[:,i])
A[:,i] /= R[i,i]
return A,R
```

The form above generates \(Q\) column by column but has stability issues due to the use of classical Gram-Schmidt. It can be improved by replacing classical Gram-Schmidt with modified Gram-Schmidt:

```
def qr_mgs( A, inplace=True ):
'''Decompose A into Q*R with Q orthonormal and R upper triangular using modified Gram-Schmidt
Assumes columns of A are linearly independent.
'''
A = A if inplace else A.copy()
R = np.zeros((A.shape[1],A.shape[1]))
for i in range( A.shape[1] ):
R[i,i] = np.linalg.norm(A[:,i])
A[:,i] /= R[i,i]
for j in range(i+1,A.shape[1]):
R[i,j] = np.dot(A[:,j],A[:,i])
A[:,j] -= R[i,j]*A[:,i]
return A,R
```

The inner loop of this can be replaced entirely with vectorization in python but the above form gives a reasonable starting point for porting to another language like C or C++. These implementations should also check for \(R_{ii} = 0\) which indicate a rank deficient system.