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 develop a novel randomised block coordinate primal-dual algorithm for aclass of non-smooth ill-posed convex programs. Lying in the midway between thecelebrated Chambolle-Pock primal-dual algorithm and Tseng's acceleratedproximal gradient method, we establish global convergence of the last iterateas well optimal $O(1/k)$ and $O(1/k^{2})$ complexity rates in the convex andstrongly convex case, respectively, $k$ being the iteration count. Motivated bythe increased complexity in the control of distribution level electric powersystems, we test the performance of our method on a second-order conerelaxation of an AC-OPF problem. Distributed control is achieved via thedistributed locational marginal prices (DLMPs), which are obtained \revise{as}dual variables in our optimisation framework.

Random block coordinate methods for inconsistent convex optimisation problems