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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Often times, in many design problems, there is a need to select a small setof informative or representative elements from a large ground set of entitiesin an optimal fashion. Submodular optimization that provides for a formal wayto solve such problems, has recently received significant attention from thecontrols community where such subset selection problems are abound. However,scaling these approaches to large systems can be challenging because of thehigh computational complexity of the overall flow, in-part due to thehigh-complexity compute-oracles used to determine the objective functionvalues. In this work, we explore a well-known paradigm, namely leader-selectionin a multi-agent networked environment to illustrate strategies for scalablesubmodular optimization. We study the performance of the state-of-the-artstochastic and distributed greedy algorithms as well as explore techniques thataccelerate the computation oracles within the optimization loop. We finallypresent results combining accelerated greedy algorithms with acceleratedcomputation oracles and demonstrate significant speedups with little loss ofoptimality when compared to the baseline ordinary greedy algorithm.

Scaling Submodular Optimization Approaches for Control Applications in Networked Systems