Mathematical Sciences: Exact and Approximate Parallel Decomposition Algorithms for Stochastic Linear Programs

带补偿的两阶段随机规划问题 在各种各样的领域都有应用。 校长 研究人员和他的合作者开发了一种类似的方法 该方案可以利用新的计算机体系结构， 计算的精确和近似值和次梯度 两阶段随机变量的必要函数 程序. 在此基础上，本项目的重点是 两阶段随机规划的全局算法 追索权 具体来说，他们建议开发分解 适合并行处理的算法，并将 用于并行计算值和次梯度的上述方案 将必要的功能分解到这种分解的实现中 算法 这一提案的进展将导致更好的 算法，将提供更快更准确的结果， 线性规划出现在工业和政府中。 真实的 生活的不确定性将被考虑在内， 方法的随机性。

Two stage stochastic programming problems with recourse have applications in a wide variety of areas. The principal investigator and his collaborators have developed a parallel scheme that can take advantage of novel computer architecture to compute both exact and approximate values and subgradients of the necessary functions associated with two-stage stochastic programs. Based on that work the focus of this project is on overall algorithms for two stage stochastic programs with recourse. Specifically, they propose to develop decomposition algorithms suited for parallel processing and to integrate the above schemes for parallel computation of values and subgradients of necessary functions into implementations of such decomposition algorithms. Progress from this proposal will result in better algorithms which will provide faster more accurate results in linear programming appearing in industry and government. Real life uncertainties will be taken into account by the stochasticity of the method.