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 investigate the convergence rate of the recently proposed subgradient-pushmethod for distributed optimization over time-varying directed graphs. Thesubgradient-push method can be implemented in a distributed way withoutrequiring knowledge of either the number of agents or the graph sequence; eachnode is only required to know its out-degree at each time. Our main result is aconvergence rate of $O \left((\ln t)/t \right)$ for strongly convex functionswith Lipschitz gradients even if only stochastic gradient samples areavailable; this is asymptotically faster than the $O \left((\ln t)/\sqrt{t}\right)$ rate previously known for (general) convex functions.

Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs