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Motivated by applications to stochastic programming, we introduce and study the {\em expected-integral functionals} in the form \begin{align*} \mathbb{R}^n\times \operatorname{L}^1(T,\mathbb{R}^m)\ni(x,y)\to\operatorname{E}_\varphi(x,y):=\int_T\varphi_t(x,y(t))d\mu \end{align*} defined for extended-real-valued normal integrand functions $\varphi:T\times\mathbb{R}^n\times\mathbb{R}^m\to[-\infty,\infty]$ on complete… Click to show full abstract
Motivated by applications to stochastic programming, we introduce and study the {\em expected-integral functionals} in the form \begin{align*} \mathbb{R}^n\times \operatorname{L}^1(T,\mathbb{R}^m)\ni(x,y)\to\operatorname{E}_\varphi(x,y):=\int_T\varphi_t(x,y(t))d\mu \end{align*} defined for extended-real-valued normal integrand functions $\varphi:T\times\mathbb{R}^n\times\mathbb{R}^m\to[-\infty,\infty]$ on complete finite measure spaces $(T,\mathcal{A},\mu)$. The main goal of this paper is to establish sequential versions of Leibniz's rule for regular subgradients by employing and developing appropriate tools of variational analysis.
Journal Title: Set-valued and Variational Analysis
Year Published: 2021
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