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

Since the late 1950's when quasi-Newton methods first appeared, they havebecome one of the most widely used and efficient algorithmic paradigms forunconstrained optimization. Despite their immense practical success, there islittle theory that shows why these methods are so efficient. We provide asemi-local rate of convergence for the randomized BFGS method which can besignificantly better than that of gradient descent, finally giving theoreticalevidence supporting the superior empirical performance of the method.

Fast Linear Convergence of Randomized BFGS