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 consider the problem of minimizing the sum of two convex functions. One ofthose functions has Lipschitz-continuous gradients, and can be accessed viastochastic oracles, whereas the other is "simple". We provide a Bregman-typealgorithm with accelerated convergence in function values to a ball containingthe minimum. The radius of this ball depends on problem-dependent constants,including the variance of the stochastic oracle. We further show that thisalgorithmic setup naturally leads to a variant of Frank-Wolfe achievingacceleration under parallelization. More precisely, when minimizing a smoothconvex function on a bounded domain, we show that one can achieve an $\epsilon$primal-dual gap (in expectation) in $\tilde{O}(1/ \sqrt{\epsilon})$ iterations,by only accessing gradients of the original function and a linear maximizationoracle with $O(1/\sqrt{\epsilon})$ computing units in parallel. We illustratethis fast convergence on synthetic numerical experiments.

Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization