论文标题
有效的场景生成用于重尾的机会受到限制优化
Efficient Scenario Generation for Heavy-tailed Chance Constrained Optimization
论文作者
论文摘要
我们考虑使用重型（即幂律类型）风险因素的一类通用的机会约束优化问题。在这种情况下，我们使用场景方法来获得与最佳解决方案的恒定近似值，其计算复杂性在风险公差参数中均匀。我们另外说明在保险网络中偿付能力的背景下，我们的算法效率。
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.
