# Efficient Scenario Generation for Heavy-tailed Chance Constrained Optimization

arXiv: https://arxiv.org/abs/2002.02149 · HTML: https://mlsanity.com/paper/2002.02149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [An Extreme Value Perspective on Learning Stress Laws](https://mlsanity.com/paper/2607.10700/) (2026)

## Abstract (arXiv)

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.