Results 1 
2 of
2
Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications
"... 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 constrain ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
(Show Context)
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 ()Departamento de Matemática, Pontifícia Universidade Católica do Rio de Janeiro, Rio de
J Optim Theory Appl (2009) 142: 399–416 DOI 10.1007/s1095700995236 Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications
, 2009
"... Abstract 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 ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract 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.