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

0098009 Chen这个为期一年的奖项，支持美国-意大利合作研究最佳近似控制理论领域新方法的发展，参与者包括格鲁吉亚理工学院的维多利亚Chen和意大利热那亚大学的Ricardo Zoppoli。 他们研究的目标是扩大统计在运筹学中的作用。S.首席研究员带来了她在统计学习的应用，以随机优化的专业知识，这一合作。 这项工作是补充意大利调查员的经验，在控制理论问题，违反了经典LQG（线性动态系统，二次成本函数，高斯随机变量）。 这项研究的结果，预计将减少所需的计算工作量来解决问题的近似最优控制理论。

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.