Continuous State Dynamic Programming Algorithms for Supercomputers

动态规划算法被广泛用于分析 非线性动态系统的控制 这些算法 计算密集型，特别是对于随机问题。 超级计算机和并行处理将促进使用 动态规划算法的现实问题，可以 过去由于计算的原因， 局限性。 该提案集中于以下问题： a）随机动态规划（SDP）的并行处理 和微分动态规划（DDP），B）将DDP耦合到 有限元模型，以及c）SDP的特殊算法， 二进制控制变量 该研究的重点是系统， 连续值的状态变量，因此分析将 检查线性或非线性SDP的计算 状态空间中的插值。 计算对 CPU和使用隐式或显式的并行处理 将结合DPP的有限元模型 研究了 大规模的数值计算结果将在 康奈尔国家超级计算机设施（CNSF） 与CNSF工作人员合作。 调查结果 将适用于几个"真实的"问题， 环境管理以及假设测试 examples.

Dynamic programming algorithms are widely used for analyzing the control of nonlinear dynamic systems. These algorithms are computationally intensive, especially for stochastic problems. Supercomputers and parallel processing will facilitate the use of dynamic programming algorithms for realistic problems that could not be addressed in the past because of computational limitations. The proposal concentrates on the following issues: a) parallel processing of Stochastic Dynamic Programming (SDP) and Differential Dynamic Programming (DDP), b) coupling DDP to finite elements models, and c)special algorithms for SDP with binary control variables. The research focuses on systems with continuously valued state variables, and hence the analysis will examine the computation of SDP with linear or nonlinear interpolation in the state space. The computational effect on CPU and on parallel processing of using implicit or explicit finite element models in conjunction with DPP will be investigated. Large-scale numerical results will be computed at the Cornell National Supercomputer Facility (CNSF) in co-operation with CNSF staff. The results of the investigation will be applied to several "real" problems arising in environmental management as well as to hypothetical test examples.//