The solution of linear least-squares problems is at the heart of many scientific and engineering applications. While any method able to minimize the backward error of such problems is considered numerically stable, the theory states that the forward error depends on the condition number of the matrix in the system of equations. On the one hand, the QR factorization is an efficient method to solve such problems, but the solutions it produces may have large forward errors when the matrix is deficient. On the other hand, QR with column pivoting (QRCP) is able to produce smaller forward errors on deficient matrices, but its cost is prohibitive compared to QR. PAQR is an alternative solution method with the same cost (or smaller) as QR and as accurate as QRCP in practical cases, for the solution of rank-deficient linear least-squares problems. After presenting the algorithm and its implementations on different architectures, we compare its accuracy and performance results on a variety of application problems.
Find out more at https://github.com/PivotingAvoiding