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- W2912184787 abstract "Probability functions are a powerful modelling tool when seeking to account for uncertainty in optimization problems. In practice, such uncertainty may result from different sources for which unequal information is available. A convenient combination with ideas from robust optimization then leads to probust functions, i.e. probability functions acting on generalized semi-infinite inequality systems. In this paper we employ the powerful variational tools developed by Boris Mordukhovich to study generalized differentiation of such probust functions. We also provide explicit outer estimates of the generalized subdifferentials in terms of nominal data." @default.
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- W2912184787 date "2019-02-14" @default.
- W2912184787 modified "2023-10-02" @default.
- W2912184787 title "Generalized gradients for probabilistic/robust (probust) constraints" @default.
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- W2912184787 doi "https://doi.org/10.1080/02331934.2019.1576670" @default.
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