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

Many practical applications require solving an optimization over large andhigh-dimensional data sets, which makes these problems hard to solve andprohibitively time consuming. In this paper, we propose a parallel distributedalgorithm that uses an adaptive regularizer (PDAR) to solve a jointoptimization problem with separable constraints. The regularizer is adaptiveand depends on the step size between iterations and the iteration number. Weshow theoretical converge of our algorithm to an optimal solution, and use amulti-agent three-bin resource allocation example to illustrate theeffectiveness of the proposed algorithm. Numerical simulations show that ouralgorithm converges to the same optimal solution as other distributed methods,with significantly reduced computational time.

Distributed Optimization via Adaptive Regularization for Large Problems with Separable Constraints