U.S.-Italy Cooperative Research: Statistical Learning for Optimal Approximate Control Theory

这个为期一年的奖项，支持美国和意大利在最优近似控制理论领域发展新方法的合作研究，参与者包括佐治亚理工学院的Victoria Chen和意大利热那亚大学的Ricardo Zoppoli。他们的研究目的是扩大统计学在运筹学中的作用。这位美国首席研究员将她在统计学习应用于随机优化方面的专业知识带到这次合作中。意大利研究者在违反经典LQG（线性动态系统，二次成本函数和高斯随机变量）的控制理论问题方面的经验补充了这项工作。本研究的结果有望减少求解近似最优控制理论问题所需的计算量。

0098009ChenThis one-year award, which supports U.S.-Italy cooperative research on the development of new methodology in the area of optimal approximate control theory, involves Victoria Chen of the Georgia Institute of Technology and Ricardo Zoppoli, University of Genoa, Italy. The objective of their research is to expand the role of statistics in operations research.The U. S. principal investigator brings to this collaboration her expertise in the application of statistical learning to stochastic optimization. This work is complemented by the Italian investigator's experience in control theory problems that violate the classical LQG (linear dynamic systems, quadratic cost functions, and Gaussian random variables). The results of this research are expected to reduce the computational effort required to solve problems in approximate optimal control theory.