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 zaia usage as premium feature

ZAIA

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the access of premium content as premium feature

Premium Content

Zendy Tools

Zendy Open

$\Gamma$-maximin, $\Gamma$-maximax and inteval dominance are familiardecision criteria for making decisions under severe uncertainty, whenprobability distributions can only be partially identified. One can apply thesethree criteria by solving sequences of linear programs. In this study, wepresent new algorithms for these criteria and compare their performance toexisting standard algorithms. Specifically, we use efficient ways, based onprevious work, to find common initial feasible points for these algorithms.Exploiting these initial feasible points, we develop early stopping criteria todetermine whether gambles are either $\Gamma$-maximin, $\Gamma$-maximax orinterval dominant. We observe that the primal-dual interior point methodbenefits considerably from these improvements. In our simulation, we find thatour proposed algorithms outperform the standard algorithms when the size of thedomain of lower previsions is less or equal to the sizes of decisions andoutcomes. However, our proposed algorithms do not outperform the standardalgorithms in the case that the size of the domain of lower previsions is muchlarger than the sizes of decisions and outcomes.

Improving and benchmarking of algorithms for $\Gamma$-maximin, $\Gamma$-maximax and interval dominance