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arXiv · 2002.02149

Efficient Scenario Generation for Heavy-tailed Chance Constrained Optimization

Jose Blanchet·Fan Zhang·Bert Zwart
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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.

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Jose Blanchet, Fan Zhang, Bert Zwart. 2020-02-06. Efficient Scenario Generation for Heavy-tailed Chance Constrained Optimization. https://arxiv.org/abs/2002.02149

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