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

An algorithmic framework to compute sparse or minimal-TV solutions of linearsystems is proposed. The framework includes both the Kaczmarz method and thelinearized Bregman method as special cases and also several new methods such asa sparse Kaczmarz solver. The algorithmic framework has a variety ofapplications and is especially useful for problems in which the linearmeasurements are slow and expensive to obtain. We present examples for onlinecompressed sensing, TV tomographic reconstruction and radio interferometry.

A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing