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

This paper proposes a distributed dual gradient tracking algorithm (DDGT) tosolve resource allocation problems over an unbalanced network, where each nodein the network holds a private cost function and computes the optimal resourceby interacting only with its neighboring nodes. Our key idea is the novel useof the distributed push-pull gradient algorithm (PPG) to solve the dual problemof the resource allocation problem. To study the convergence of the DDGT, wefirst establish the sublinear convergence rate of PPG for non-convex objectivefunctions, which advances the existing results on PPG as they require thestrong-convexity of objective functions. Then we show that the DDGT convergeslinearly for strongly convex and Lipschitz smooth cost functions, andsublinearly without the Lipschitz smoothness. Finally, experimental resultssuggest that DDGT outperforms existing algorithms.

Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks