论文标题
NASA Langley不确定性量化挑战的分布强大的优化方法
A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge
论文作者
论文摘要
我们研究了一种基于强大优化的整合，更具体地说，是一种解决NASA Langley不确定性量化挑战问题的方法，更具体地说，是最近的一系列研究线，称为分布强劲的优化，以及在Monte Carlo模拟中的重要性采样。这种集成方法中的主要计算机制归结为解决采样的线性程序。我们将说明通过与非参数假设检验的连接获得的数值性能和理论统计保证。
We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge problem, based on an integration of robust optimization, more specifically a recent line of research known as distributionally robust optimization, and importance sampling in Monte Carlo simulation. The main computation machinery in this integrated methodology boils down to solving sampled linear programs. We will illustrate both our numerical performances and theoretical statistical guarantees obtained via connections to nonparametric hypothesis testing.
