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

In this paper a new result of recovery of sparse vectors from deterministicand noisy measurements by l1 minimization is given. The sparse vector israndomly chosen and follows a generic p-sparse model introduced by Candes andal. The main theorem ensures consistency of l1 minimization with highprobability. This first result is secondly extended to compressible vectors.

Consistency of l1 recovery from noisy deterministic measurements