Abstract: We describe algorithm MINRES-QLP and its FORTRAN 90 implementation for solving symmetric or Hermitian linear systems or least-squares problems. If the system is singular, MINRES-QLP computes the unique minimum-length solution (also known as the pseudoinverse solution), which generally eludes MINRES. In all cases, it overcomes a potential instability in the original MINRES algorithm. A positive-definite preconditioner may be supplied. Our FORTRAN 90 implementation illustrates a design pattern that allows users to make problem data known to the solver but hidden and secure from other program units. In particular, we circumvent the need for reverse communication. Example test programs input and solve real or complex problems specified in Matrix Market format. While we focus here on a FORTRAN 90 implementation, we also provide and maintain MATLAB versions of MINRES and MINRES-QLP.
Authors: Sou-Cheng Terrya Choi and Michael Alan Saunders
Keywords: Algorithms, Krylov subspace method, Lanczos process, conjugate-gradient method, singular least-squares, linear equations, minimum-residual method, pseudoinverse solution, ill-posed problem, regression, sparse matrix, data encapsulation
Publication: ACM Transactions on Mathematical Software (TOMS), 40(2), pp.1-12.
Webpage: https://doi.org/10.1145/2527267