Smoothing-Norm Preconditioning for Regularizing Minimum-Residual Methods

Per Christian Hansen, Toke Koldborg Jensen

    Research output: Contribution to journalJournal articleResearchpeer-review


    When GMRES (or a similar minimum-residual algorithm such as RRGMRES, MINRES, or MR-II) is applied to a discrete ill-posed problem with a square matrix, in some cases the iterates can be considered as regularized solutions. We show how to precondition these methods in such a way that the iterations take into account a smoothing norm for the solution. This technique is well established for CGLS, but it does not immediately carry over to minimum-residual methods when the smoothing norm is a seminorm or a Sobolev norm. We develop a new technique which works for any smoothing norm of the form $\|L\,x\|_2$ and which preserves symmetry if the coefficient matrix is symmetric. We also discuss the efficient implementation of our preconditioning technique, and we demonstrate its performance with numerical examples in one and two dimensions.
    Original languageEnglish
    JournalS I A M Journal on Matrix Analysis and Applications
    Issue number1
    Pages (from-to)1-14
    Publication statusPublished - 2006

    Cite this