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 short note, we provide a simple version of an acceleratedforward-backward method (a.k.a. Nesterov's accelerated proximal gradientmethod) possibly relying on approximate proximal operators and allowing toexploit strong convexity of the objective function. The method supports bothrelative and absolute errors, and its behavior is illustrated on a set ofstandard numerical experiments. Using the same developments, we further providea version of the accelerated proximal hybrid extragradient method of Monteiroand Svaiter (2013) possibly exploiting strong convexity of the objectivefunction.

A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly convex objectives