A real square QR factorization is with and upper triangular. For an invertible matrix it can be made unique by requiring positive diagonal entries of .
If is orthogonal, then is orthogonal as well. An orthogonal upper triangular matrix is diagonal with entries : its first column has just its first entry, which must have modulus one, and orthogonality then zeros the rest of the first row; repeat inductively on the lower block. Thusand under the positive-diagonal convention , .
The first column is five times . Its orthogonal companion in the first-and-third-coordinate plane is , and the second column has positive residual seven times that vector. The remaining coordinate directions can therefore be read off immediately rather than performing a general four-dimensional orthogonalization. One obtainsThe columns of are visibly orthonormal. Taking their inner products with each column of the given matrix gives the displayed , which also verifies and positive diagonal entries.
At step , choose a real shift , factor , and setThe last equality proves orthogonal similarity at every step. Thus all iterates have the same eigenvalues; if is symmetric, the last expression is symmetric, proving symmetry by induction.
For the specified matrix, let . Then and , so and . It is a Householder reflection, hence orthogonal. With the standard positive-diagonal QR convention, part (a) gives , at the first unshifted step, and therefore . The same repeats forever: the unshifted algorithm is stationary.
If arbitrary diagonal signs in QR are allowed, , for a sign matrix , and the next iterate is . Its off-diagonal magnitudes are unchanged, so even then only sign changes occur, not progress toward a diagonal form. This explains the role of the usual convention in the literal stationary-iterate statement.
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