US-Poland Computation Research on Decomposition Methods for Linear Multistage Stocastic Programming

这个美国和波兰研究项目的主要目标 普林斯顿大学的约翰·马尔维博士和 华沙理工大学的Andrzey Ruszczynski是 研究解决大规模问题的计算方法， 具有动态结构的优化问题， 不确定参数 研究人员打算把重点放在 分解型方法，将问题分解为 与时间阶段或场景相对应的较小部分。 他们计划采用以下方法：1）原始和 应用正则化的对偶分解 和增广拉格朗日函数，以及2） 屏障或内点方法。 的结果 项目在线性规划，预计将打开新的 解决大型多阶段随机问题的可能性 编程问题。 这个计算机科学研究项目实现了 通过以下方式提高科学知识的计划目标 让美国和东欧的专家 联合收割机互补人才，集中研究 共同感兴趣的领域的资源， 能力。

The primary objective of this US-Poland research project between Dr. John Mulvey of Princeton University and Dr. Andrzey Ruszczynski of Warsaw University of Technology is to study computational methods for solving large-scale optimization problems that have a dynamic structure and uncertain parameters. The researchers intend to focus on decomposition-type methods which split problems into smaller parts corresponding to time stages or scenarios. They plan to employ the following methods: 1) primal and dual decomposition with the application of regularization and augmented Lagrangians and 2) special versions of barrier or interior point methods. The results of this project in linear programming are expected to open new possibilities for solving large-multistage stochastic programming problems. This project in computer science research fulfills the program objective of advancing scientific knowledge by enabling experts in the United States and Eastern Europe to combine complementary talents and pool research resources in areas of strong mutual interest and competence.