Preconditioned Krylov subspace method for the solution of least-squares problems

Preconditioned Krylov subspace methods for the solution of least-squares problems

and Kk(BA,Br) = span{Br, (BA)Br, . . . , (BA)k−1Br}, (3) where B ∈ Rn×m is the mapping and preconditioning matrix, and apply Krylov subspace iteration methods on these subspaces. For overdetermined problems, applying the standard CG method to Kk(BA,Br) leads to the preconditioned CGLS [3] or CGNR [9] method while for underdetermined problems it leads to preconditioned CGNE [9] method. The GMRES...

Inner-Iteration Krylov Subspace Methods for Least Squares Problems

Stationary inner iterations in combination with Krylov subspace methods are proposed for least squares problems. The inner iterations are efficient in terms of computational work and memory, and serve as powerful preconditioners also for ill-conditioned and rank-deficient least squares problems. Theoretical justifications for using the inner iterations as preconditioners are presented. Numerica...

Preconditioned Krylov Subspace Methods for Eigenvalue Problems

A Krylov Subspace Method for Quadratic Matrix Polynomials with Application to Constrained Least Squares Problems

We present a Krylov subspace–type projection method for a quadratic matrix polynomial λ2I − λA − B that works directly with A and B without going through any linearization. We discuss a special case when one matrix is a low rank perturbation of the other matrix. We also apply the method to solve quadratically constrained linear least squares problem through a reformulation of Gander, Golub, and...

Multisplitting for regularized least squares with Krylov subspace recycling

The method of multisplitting, implemented as a restricted additive Schwarz type algorithm, is extended for the solution of regularized least squares problems. The presented non-stationary version of the algorithm uses dynamic updating of the weights applied to the subdomains in reconstituting the global solution. Standard convergence results follow from extensive prior literature on linear mult...