A second-order sufficient optimality condition for risk-neutral bi-level stochastic linear programs
type: Item
instance of: scholarly article
title: A second-order sufficient optimality condition for risk-neutral bi-level stochastic linear programs
zbMATH Open document ID: 1471.90100
author: Matthias Claus
published in: Journal of Optimization Theory and Applications
publication date: 2021-05-20T00:00:00Z
review text: In this paper the author derives sufficient optimality conditions for Lipschitz continuity of the gradient of the expectation functional arising from a bi-level stochastic linear program with random right-hand side in the lower-level constraint system. His main result is used to formulate a second-order sufficient optimality condition for risk-neutral bi-level stochastic linear programs in terms of the generalized Hessians. The author also studies geometric properties of regions of strong stability and derives representation results, which may facilitate the computation or sample-based estimation of gradients of the expectation functional, which enhances gradient descent-based methods.
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 91A65
zbMATH DE Number: 7350186
zbMATH Keywords: stochastic linear programming
zbMATH Keywords: risk-neutral model
zbMATH Keywords: second-order optimality conditions
zbMATH Keywords: Lipschitz gradients
reviewed by: I. M. Stancu-Minasian
MaRDI profile type: Publication
full work available at URL: s10957-020-01775-x
OpenAlex ID: W3102093686
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DOI: 10.1007/S10957-020-01775-X
description: scientific article; zbMATH DE number 7350186