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 considers the problem of distributed model fitting using thealternating directions method of multipliers (ADMM). ADMM splits the learningproblem into several smaller subproblems, usually by partitioning the datasamples. The different subproblems can be solved in parallel by a set of workercomputing nodes coordinated by a master node, and the subproblems arerepeatedly solved until convergence. At each iteration, the worker nodes mustsolve a convex optimization problem whose difficulty increases with the size ofthe problem. In this paper, we propose a sensitivity-assisted ADMM algorithmthat leverages the parametric sensitivities such that the subproblems solutionscan be approximated using a tangential predictor, thus easing the computationalburden to computing one linear solve. We study the convergence properties ofthe proposed sensitivity-assisted ADMM algorithm. The numerical performance ofthe algorithm is illustrated on a nonlinear parameter estimation problem, and amultilayer perceptron learning problem.

Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization and Statistical Learning