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Result: Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications

Title:
Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications
Authors:
PAGNONCELLI, B. K, AHMED, S, SHAPIRO, A
Source:
Journal of optimization theory and applications. 142(2):399-416
Publisher Information:
New York, NY: Springer, 2009.
Publication Year:
2009
Physical Description:
print, 20 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Théorie de la décision. Théorie de l'utilité, Decision theory. Utility theory, Analyse statistique, Statistical analysis, Análisis estadístico, Borne inférieure, Lower bound, Cota inferior, Gestion portefeuille, Portfolio management, Gestión cartera, Loi lognormale, Lognormal distribution, Ley lognormal, Loi n variables, Multivariate distribution, Ley n variables, Méthode approximation, Approximation method, Problème sélection, Selection problem, Problema selección, Sélection portefeuille, Portfolio selection, Selección cartera, Chance constraints, Sample average approximation
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Departamento de Matemática, Pontificia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 22453-900, Brazil
Georgia Institute of Technology, Atlanta, GA 30332, United States
ISSN:
0022-3239
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=21879990
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Operational research. Management
Accession Number:
edscal.21879990
Database:
PASCAL Archive

Further Information

We study sample approximations of chance constrained problems. In particular, we consider the sample average approximation (SAA) approach and discuss the convergence properties of the resulting problem. We discuss how one can use the SAA method to obtain good candidate solutions for chance constrained problems. Numerical experiments are performed to correctly tune the parameters involved in the SAA. In addition, we present a method for constructing statistical lower bounds for the optimal value of the considered problem and discuss how one should tune the underlying parameters. We apply the SAA to two chance constrained problems. The first is a linear portfolio selection problem with returns following a multivariate lognormal distribution. The second is a joint chance constrained version of a simple blending problem.