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 study stochastic optimization problems with chance and risk constraints,where in the latter, risk is quantified in terms of the conditionalvalue-at-risk (CVaR). We consider the distributionally robust versions of theseproblems, where the constraints are required to hold for a family ofdistributions constructed from the observed realizations of the uncertainty viathe Wasserstein distance. Our main results establish that if the samples aredrawn independently from an underlying distribution and the problems satisfysuitable technical assumptions, then the optimal value and optimizers of thedistributionally robust versions of these problems converge to the respectivequantities of the original problems, as the sample size increases.

Consistency of Distributionally Robust Risk- and Chance-Constrained Optimization under Wasserstein Ambiguity Sets