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 multi-agent, convex optimization programs subject to separableconstraints, where the constraint function of each agent involves only itslocal decision vector, while the decision vectors of all agents are coupled viaa common objective function. We focus on a regularized variant of the so calledJacobi algorithm for decentralized computation in such problems. We firstconsider the case where the objective function is quadratic, and provide afixed-point theoretic analysis showing that the algorithm converges to aminimizer of the centralized problem. Moreover, we quantify the potentialbenefits of such an iterative scheme by comparing it against a scaled projectedgradient algorithm. We then consider the general case and show that all limitpoints of the proposed iteration are optimal solutions of the centralizedproblem. The efficacy of the proposed algorithm is illustrated by applying itto the problem of optimal charging of electric vehicles, where, as opposed toearlier approaches, we show convergence to an optimal charging scheme for afinite, possibly large, number of vehicles.

Regularized Jacobi iteration for decentralized convex optimization with separable constraints