论文标题
贝叶斯优化用于分配强大的机会约束问题
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
论文作者
论文摘要
在Black-Box功能优化中，我们不仅需要考虑可控的设计变量，还需要考虑不可控制的随机环境变量。在这种情况下，有必要考虑到环境变量的不确定性来解决优化问题。机会约束（CC）问题是在一定程度的约束满意度概率下最大化预期值的问题，是在存在环境变量的情况下实际上重要的问题之一。在这项研究中，我们考虑了分布鲁棒的CC（DRCC）问题，并针对无法精确指定环境变量的分布提出了一种新颖的DRCC贝叶斯优化方法。我们表明，所提出的方法可以在有限数量的试验中找到具有很高概率的任意准确解决方案，并通过数值实验确认所提出的方法的有用性。
In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables. In such cases, it is necessary to solve the optimization problem by taking into account the uncertainty of the environmental variables. Chance-constrained (CC) problem, the problem of maximizing the expected value under a certain level of constraint satisfaction probability, is one of the practically important problems in the presence of environmental variables. In this study, we consider distributionally robust CC (DRCC) problem and propose a novel DRCC Bayesian optimization method for the case where the distribution of the environmental variables cannot be precisely specified. We show that the proposed method can find an arbitrary accurate solution with high probability in a finite number of trials, and confirm the usefulness of the proposed method through numerical experiments.
