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

We introduce the continuized Nesterov acceleration, a close variant ofNesterov acceleration whose variables are indexed by a continuous timeparameter. The two variables continuously mix following a linear ordinarydifferential equation and take gradient steps at random times. This continuizedvariant benefits from the best of the continuous and the discrete frameworks:as a continuous process, one can use differential calculus to analyzeconvergence and obtain analytical expressions for the parameters; and adiscretization of the continuized process can be computed exactly withconvergence rates similar to those of Nesterov original acceleration. We showthat the discretization has the same structure as Nesterov acceleration, butwith random parameters. We provide continuized Nesterov acceleration underdeterministic as well as stochastic gradients, with either additive ormultiplicative noise. Finally, using our continuized framework and expressingthe gossip averaging problem as the stochastic minimization of a certain energyfunction, we provide the first rigorous acceleration of asynchronous gossipalgorithms.

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip