Subdifferentials of Convex Operators Valued in the Space of Integrable Functions with Application to Risk-Averse Optimization

Fuente: arXiv
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Main Authors: Dentcheva, Darinka, Ruszczynski, Andrzej
Format: Preprint
Published: 2025
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author Dentcheva, Darinka
Ruszczynski, Andrzej
author_facet Dentcheva, Darinka
Ruszczynski, Andrzej
contents We study differentiability properties of convex operators defined on a Banach space with values in an $\Lc_p$ space and of their compositions with monotonic convex functionals on this space. We develop new tools for operators enjoying an additional feature known as the local property. The new approach and results go beyond the classical theory of normal integrands and lattice-valued operators. We further describe the subdifferentials of compositions of such operators with convex monotonic functionals. The new results are applied to obtain novel optimality conditions in the subdifferential form for a broad class of risk-averse stochastic optimization problems with risk functionals as objectives, with partial information, and with stochastic dominance constraints. While our analysis is motivated by the theory and methods of risk-averse optimization, it addresses problems of a more general structure and has potential for further applications.
format Preprint
id arxiv_https___arxiv_org_abs_2511_05348
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Subdifferentials of Convex Operators Valued in the Space of Integrable Functions with Application to Risk-Averse Optimization
Dentcheva, Darinka
Ruszczynski, Andrzej
Optimization and Control
Functional Analysis
90C15, 90C48, 46B42
We study differentiability properties of convex operators defined on a Banach space with values in an $\Lc_p$ space and of their compositions with monotonic convex functionals on this space. We develop new tools for operators enjoying an additional feature known as the local property. The new approach and results go beyond the classical theory of normal integrands and lattice-valued operators. We further describe the subdifferentials of compositions of such operators with convex monotonic functionals. The new results are applied to obtain novel optimality conditions in the subdifferential form for a broad class of risk-averse stochastic optimization problems with risk functionals as objectives, with partial information, and with stochastic dominance constraints. While our analysis is motivated by the theory and methods of risk-averse optimization, it addresses problems of a more general structure and has potential for further applications.
title Subdifferentials of Convex Operators Valued in the Space of Integrable Functions with Application to Risk-Averse Optimization
topic Optimization and Control
Functional Analysis
90C15, 90C48, 46B42
url https://arxiv.org/abs/2511.05348