Disadvantage
- Steps are very expensive to compute. The complexity of one step of the basic QR-method = O(n^3).
- Usually, many steps (much more than n) are required to converge. In fact, the basic QR-method can be arbitrarily slow if the eigenvalues are close to each other.
QR Decomposition in Machine learning
QR decomposition is a way of expressing a matrix as the product of two matrices: Q (an orthogonal matrix) and R (an upper triangular matrix). In this article, I will explain decomposition in Linear Algebra, particularly QR decomposition among many decompositions.