Bayes meets Krylov: preconditioning CGLS for underdetermined systems

Could not fetch data.

Publication date: 23 March 2015

Abstract: The solution of linear inverse problems when the unknown parameters outnumber data requires addressing the problem of a nontrivial null space. After restating the problem within the Bayesian framework, a priori information about the unknown can be utilized for determining the null space contribution to the solution. More specifically, if the solution of the associated linear system is computed by the Conjugate Gradient for Least Squares (CGLS) method, the additional information can be encoded in the form of a right preconditioner. In this paper we study how the right preconditioned changes the Krylov subspaces where the CGLS iterates live, and draw a tighter connection between Bayesian inference and Krylov subspace methods. The advantages of a Krylov-meet-Bayes approach to the solution of underdetermined linear inverse problems is illustrated with computed examples.

This page was built for publication: Bayes meets Krylov: preconditioning CGLS for underdetermined systems

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6260226)