English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper is concerned with the regularization of large-scale discreteinverse problems by means of inexact Krylov methods. Specifically, we derivetwo new inexact Krylov methods that can be efficiently applied to unregularizedor Tikhonov-regularized least squares problems, and we study their theoreticalproperties, including links with their exact counterparts and strategies tomonitor the amount of inexactness. We then apply the new methods to separablenonlinear inverse problems arising in blind deblurring. In this settinginexactness stems from the uncertainty in the parameters defining the blur,which may be recovered using a variable projection method leading to aninner-outer iteration scheme (i.e., one cycle of inner iterations is performedto solve one linear deblurring subproblem for any intermediate values of theblurring parameters computed by a nonlinear least squares solver). The newinexact solvers can naturally handle varying inexact blurring parameters whilesolving the linear deblurring subproblems, allowing for a much reduced numberof total iterations and substantial computational savings with respect to theirexact counterparts.

Regularization by inexact Krylov methods with applications to blind deblurring