Chance-constrained problems and rare events: an importance sampling approach
Barrera
J
author
Homem-De-Mello
T
author
Moreno
E
author
Pagnoncelli
B
K
author
Canessa
G
author
2016
English
We study chance-constrained problems in which the constraints involve the probability of a rare event. We discuss the relevance of such problems and show that the existing sampling-based algorithms cannot be applied directly in this case, since they require an impractical number of samples to yield reasonable solutions. We argue that importance sampling (IS) techniques, combined with a Sample Average Approximation (SAA) approach, can be effectively used in such situations, provided that variance can be reduced uniformly with respect to the decision variables. We give sufficient conditions to obtain such uniform variance reduction, and prove asymptotic convergence of the combined SAA-IS approach. As it often happens with IS techniques, the practical performance of the proposed approach relies on exploiting the structure of the problem under study; in our case, we work with a telecommunications problem with Bernoulli input distributions, and show how variance can be reduced uniformly over a suitable approximation of the feasibility set by choosing proper parameters for the IS distributions. Although some of the results are specific to this problem, we are able to draw general insights that can be useful for other classes of problems. We present numerical results to illustrate our findings.
Chance-constrained programming
Sample average approximation
Importance sampling
Rare-event simulation
WOS:000375568400007
exported from refbase (show.php?record=613), last updated on Thu, 02 Jun 2016 07:39:28 -0400
text
files/502_Barrera_etal2016.pdf
10.1007/s10107-015-0942-x
Barrera_etal2016
Mathematical Programming
Math. Program.
2016
Springer Heidelberg
continuing
periodical
academic journal
157
1
153
189
0025-5610