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, a class of Decentralized Approximate Newton (DEAN) methods foraddressing convex optimization on a networked system are developed, where nodesin the networked system seek for a consensus that minimizes the sum of theirindividual objective functions through local interactions only. The proposedDEAN algorithms allow each node to repeatedly perform a local approximateNewton update, which leverages tracking the global Newton direction anddissipating the discrepancies among the nodes. Under less restrictive problemassumptions in comparison with most existing second-order methods, the DEANalgorithms enable the nodes to reach a consensus that can be arbitrarily closeto the optimum. Moreover, for a particular DEAN algorithm, the nodes linearlyconverge to a common suboptimal solution with an explicit error bound. Finally,simulations demonstrate the competitive performance of DEAN in convergencespeed, accuracy, and efficiency.

Decentralized Approximate Newton Methods for Convex Optimization on Networked Systems