Bayesian Optimization for Distributionally Robust Chance-constrained Problem
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
复制标题
DOI：
--
复制
发表时间：
2022-01
影响因子：
--
通讯作者：
Yu Inatsu;Shion Takeno;Masayuki Karasuyama;I. Takeuchi
中科院分区：
文献类型：
--
作者：
Yu Inatsu;Shion Takeno;Masayuki Karasuyama;I. Takeuchi
在黑盒函数优化中，不仅需要考虑可控的设计变量，还需要考虑不可控的随机环境变量。在这种情况下，有必要通过考虑环境变量的不确定性来解决优化问题。机会约束问题（Chance-constrained problem，简称CC问题）是在一定的约束满足概率下最大化期望值的问题，是存在环境变量的实际问题之一。在这项研究中，我们考虑分布鲁棒CC（DRCC）问题，并提出了一种新的DRCC贝叶斯优化方法的情况下，环境变量的分布不能精确艾德。我们表明，所提出的方法可以找到一个任意精确的解决方案，在一个有限数量的tri-als高概率，并确认所提出的方法的有效性，通过数值实验。
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 tri-als, and confirm the usefulness of the proposed method through numerical experiments.