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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We consider a standard distributed consensus optimization problem where a set of agents connected over an undirected network minimize the sum of their individual (local) strongly convex costs. Alternating Direction Method of Multipliers (ADMM) and Proximal Method of Multipliers (PMM) have been proved to be effective frameworks for design of exact distributed second order methods (involving calculation of local cost Hessians). However, existing methods involve explicit calculation of local Hessian inverses at each iteration that may be very costly when the dimension of the optimization variable is large. In this article, we develop a novel method, termed Inexact Newton method for Distributed Optimization (INDO), that alleviates the need for Hessian inverse calculation. INDO follows the PMM framework but, unlike existing work, approximates the Newton direction through a generic fixed point method (e.g., Jacobi Overrelaxation) that does not involve Hessian inverses. We prove exact global linear convergence of INDO and provide analytical studies on how the degree of inexactness in the Newton direction calculation affects the overall method&#x0027;s convergence factor. Numerical experiments on several real data sets demonstrate that INDO&#x0027;s speed is on par (or better) as state of the art methods iteration-wise, hence having a comparable communication cost. At the same time, for sufficiently large optimization problem dimensions <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> (even at <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> on the order of couple of hundreds), INDO achieves savings in computational cost by at least an order of magnitude.

A Hessian Inversion-Free Exact Second Order Method for Distributed Consensus Optimization