Editing QR algorithm (section)

== Interpretation and convergence ==
The QR algorithm can be seen as a more sophisticated variation of the basic [[Power iteration|"power" eigenvalue algorithm]]. Recall that the power algorithm repeatedly multiplies ''A'' times a single vector, normalizing after each iteration. The vector converges to an eigenvector of the largest eigenvalue. Instead, the QR algorithm works with a complete basis of vectors, using QR decomposition to renormalize (and orthogonalize). For a symmetric matrix ''A'', upon convergence, ''AQ'' = ''Q&Lambda;'', where ''&Lambda;'' is the [[diagonal matrix]] of eigenvalues to which ''A'' converged, and where ''Q'' is a composite of all the orthogonal similarity transforms required to get there. Thus the columns of ''Q'' are the eigenvectors.