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

In this paper we consider distributed convex optimization over time-varyingundirected graphs. We propose a linearized version of primarily averagednetwork dual ascent (PANDA) while requiring less computational costs. Theproposed method, economic primarily averaged network dual ascent (Eco-PANDA),provably converges at R-linear rate to the optimal point given that the agents'objective functions are strongly convex and have Lipschitz continuousgradients. Therefore, the method is competitive, in terms of type of rate, withboth DIGing and PANDA. The proposed method halves the communication costs ofmethods like DIGing while still converging R-linearly and having the same periterate complexity.

Eco-PANDA: A Computationally Economic, Geometrically Converging, Dual Optimization Method on Time-Varying Undirected Graphs