a QR decomposition (also called a QR factorization) of a matrix is a decomposition of the matrix into an orthogonal and an upper triangular matrix. QR decomposition is often used to solve the linear least squares problem, and is the basis for a particular eigenvalue algorithm, the QR algorithm.