Programming under Uncertainty and Stochastic Optimal Control
Programming under Uncertainty and Stochastic Optimal Control
复制标题
DOI：
10.1137/0304018
复制
发表时间：
1966-02
期刊：
影响因子：
--
通讯作者：
R. V. Slyke;R. Wets
中科院分区：
文献类型：
--
作者：
R. V. Slyke;R. Wets
翻译后摘要：不确定性下的规划理论扩展到的情况下，决策变量是一个Banach空间的元素。这种方法导致一个非常自然的应用程序的数学规划的计算技术随机最优控制问题。证明了存在一个等价的确定性数学规划，其可行解集是一个凸集，其目标函数可以表示为初始决策变量的凸函数.在第二部分中，我们建立了这类问题的对偶理论，并给出了与随机线性控制问题的极大值原理的一些关系。
Abstract : The theory of programming under uncertainty is extended to the case when the decision variables are elements of a Banach space. This approach leads to a very natural application of the computational techniques of mathematical programming to stochastic optimal control problems. It is shown that there exists an equivalent deterministic mathematical program whose set of feasible solutions is a convex set and whose objective function can be expressed as a convex function of the initial decision variables. In the second part, a duality theory is developed for this class of problems and some of the relations to the maximum principle for stochastic linear control problems are given.