Computational Methods in Markov Chains

马尔可夫链中的计算方法

• 批准号: 8906248
• 负责人: Carl Meyer
• 金额: $ 20.72万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-03-01 至 1993-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8906248&HistoricalAwards=false
• 关键词: Computational; Methods; Markov; Chains

The research concerns the development and analysis of algorithms for computing the stationary probabilities of large scale Markov chains on modern multiprocessor computers. The computation of stationary probabilities for large scale problems is a fundamental concern in a wide variety of applications such as computer modeling, computer performance evaluation, queueing networks, and more generally, in applications where discrete mathematical models are used to understand the dynamics of very large systems comprised of a collection of loosely coupled subsystems. Parallel and vector implementations on a variety of multiprocessor computers will be emphasized and specific techniques under investigation include subspace iteration methods; hybrid iterative-direct algorithms; overlapping block iterative schemes; parallel aggregation and iteration methods; and block elimination algorithms. The architectural aspect of the work involves the identification of those features of contemporary vector and parallel computers which are best suited for implementing these techniques, and in this regard, a variety of machines, including the Alliant FX/series, the Sequent Balance, and the CRAY Y-MP will continue to be used.

本研究涉及的发展和分析， 算法计算的平稳概率大 在现代多处理器计算机上扩展马尔可夫链。的 大规模问题的平稳概率计算 在各种各样的应用中是一个基本问题 如计算机建模、计算机性能评估、 网络，更一般地说，在应用程序中，离散 数学模型被用来理解 由松散耦合的 子系统在各种各样的 多处理器计算机将被强调和具体 正在研究的技术包括子空间迭代 方法;混合迭代-直接算法;重叠块 迭代格式;并行聚合迭代法; 和块消除算法。建筑方面的 工作涉及识别这些特征， 当代矢量和并行计算机，最适合 为了实现这些技术，并且在这方面， 包括Alliant FX/系列、Sequent 平衡，CRAY Y-MP将继续使用。