Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/313373 
Autor:innen: 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 17 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 471-492
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
We analyze the tail behavior of solutions to sample average approximations (SAAs) of stochastic programs posed in Hilbert spaces. We require that the integrand be strongly convex with the same convexity parameter for each realization. Combined with a standard condition from the literature on stochastic programming, we establish non-asymptotic exponential tail bounds for the distance between the SAA solutions and the stochastic program’s solution, without assuming compactness of the feasible set. Our assumptions are verified on a class of infinite-dimensional optimization problems governed by affine-linear partial differential equations with random inputs. We present numerical results illustrating our theoretical findings.
Schlagwörter: 
Sample average approximation
PDE-constrained optimization under uncertainty
Linear-quadratic optimal control under uncertainty
Exponential tail bounds
Stochastic programming
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Dokumentversion: 
Published Version

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.