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开发新方法的合作研究，获奖者包括佐治亚理工学院的维多利亚·陈和意大利热那亚大学的里卡多·佐波利。他们研究的目的是扩大统计学在运筹学研究中的作用。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了这次合作中。这项工作得到了意大利研究人员在控制理论问题方面的经验的补充，这些问题违反了经典的LQG(线性动态系统、二次成本函数和高斯随机变量)。这一研究结果有望减少近似最优控制理论中求解问题所需的计算量。

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.