In a coursebook I'm reading, some algorithms used to calculate the QR-decomposition of a matrix are introduced, namely Gram-Schmidt orthogonalization and the use of Givens rotations with or without column pivoting.
A question that has been posed on previous exams asks how the QR-decomposition can be used to do low-rank approximation of a matrix. This is unclear to me. I understand how this can be done using singular value decomposition, but cannot seem to grasp how the same can be achieved using the QR-decomposition.
Thanks in advance!