论文标题
通过嵌套随机梯度估计解决贝叶斯风险优化
Solving Bayesian Risk Optimization via Nested Stochastic Gradient Estimation
论文作者
论文摘要
在本文中，我们旨在解决贝叶斯风险优化（BRO），这是一个最近提出的框架，在输入不确定性下制定了模拟优化。为了有效地解决BRO问题，我们得出了嵌套的随机梯度估计器并提出了相应的随机近似算法。我们表明，我们的梯度估计器在渐近公正和一致上是渐进的，并且算法渐近地收敛。我们在双面市场模型上展示了算法的经验性能。我们的估计器在将随机梯度估计的文献扩展到嵌套风险功能的情况下具有独立的兴趣。
In this paper, we aim to solve Bayesian Risk Optimization (BRO), which is a recently proposed framework that formulates simulation optimization under input uncertainty. In order to efficiently solve the BRO problem, we derive nested stochastic gradient estimators and propose corresponding stochastic approximation algorithms. We show that our gradient estimators are asymptotically unbiased and consistent, and that the algorithms converge asymptotically. We demonstrate the empirical performance of the algorithms on a two-sided market model. Our estimators are of independent interest in extending the literature of stochastic gradient estimation to the case of nested risk functions.
