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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The characteristic feature of inverse problems is their instability withrespect to data perturbations. In order to stabilize the inversion process,regularization methods have to be developed and applied. In this work weintroduce and analyze the concept of filtered diagonal frame decompositionwhich extends the standard filtered singular value decomposition to the framecase. Frames as generalized singular system allows to better adapt to a givenclass of potential solutions. In this paper, we show that filtered diagonalframe decomposition yield a convergent regularization method. Moreover, wederive convergence rates under source type conditions and prove orderoptimality under the assumption that the considered frame is a Riesz-basis.

Regularization of Inverse Problems by Filtered Diagonal Frame Decomposition