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/

Zendy Plus

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

Zendy Tools

Zendy Open

Decomposition techniques for linear programming are difficult to extend toconic optimization problems with general non-polyhedral convex cones becausethe conic inequalities introduce an additional nonlinear coupling between thevariables. However in many applications the convex cones have a partiallyseparable structure that allows them to be characterized in terms of simplerlower-dimensional cones. The most important example is sparse semidefiniteprogramming with a chordal sparsity pattern. Here partial separability derivesfrom the clique decomposition theorems that characterize positive semidefiniteand positive-semidefinite-completable matrices with chordal sparsity patterns.The paper describes a decomposition method that exploits partial separabilityin conic linear optimization. The method is based on Spingarn's method forequality constrained convex optimization, combined with a fast interior-pointmethod for evaluating proximal operators.

Decomposition in conic optimization with partially separable structure