Efficient Scenario Generation for Heavy-tailed Chance Constrained Optimization
Optimization and Control
2023-05-09 v3 Probability
Abstract
We consider a generic class of chance-constrained optimization problems with heavy-tailed (i.e., power-law type) risk factors. In this setting, we use the scenario approach to obtain a constant approximation to the optimal solution with a computational complexity that is uniform in the risk tolerance parameter. We additionally illustrate the efficiency of our algorithm in the context of solvency in insurance networks.
Cite
@article{arxiv.2002.02149,
title = {Efficient Scenario Generation for Heavy-tailed Chance Constrained Optimization},
author = {Jose Blanchet and Fan Zhang and Bert Zwart},
journal= {arXiv preprint arXiv:2002.02149},
year = {2023}
}
Comments
31pages, 7 figure