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

Chance constraints are frequently used to limit the probability of constraintviolations in real-world optimization problems where the constraints involvestochastic components. We study chance-constrained submodular optimizationproblems, which capture a wide range of optimization problems with stochasticconstraints. Previous studies considered submodular problems with stochasticknapsack constraints in the case where uncertainties are the same for each itemthat can be selected. However, uncertainty levels are usually variable withrespect to the different stochastic components in real-world scenarios, andrigorous analysis for this setting is missing in the context of submodularoptimization. This paper provides the first such analysis for this case, wherethe weights of items have the same expectation but different dispersion. Wepresent greedy algorithms that can obtain a high-quality solution, i.e., aconstant approximation ratio to the given optimal solution from thedeterministic setting. In the experiments, we demonstrate that the algorithmsperform effectively on several chance-constrained instances of the maximumcoverage problem and the influence maximization problem.

Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties