RSCS:一个并行单纯形算法的Nimrod/O优化工具集

骈文研究 Pub Date : 2004-07-05 DOI:10.1109/ISPDC.2004.44
A. Lewis, D. Abramson, T. Peachey
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引用次数: 13

摘要

本文描述了一种流行的Nelder-Mead单纯形优化算法的并行化方法,该算法可以提高并行和分布式计算资源的性能。单纯形顶点的约简集用于推导通常与局部梯度紧密对齐的搜索方向。当在科学和工程的实际应用中对一系列问题进行测试时,这种Nelder-Mead算法的简化集并发单纯形(RSCS)变体与原始算法以及固有并行多向搜索算法(MDS)相比都具有优势。所有算法都在一个通用的、支持网格的优化工具集中实现和测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RSCS: a parallel simplex algorithm for the Nimrod/O optimization toolset
This paper describes a method of parallelisation of the popular Nelder-Mead simplex optimization algorithms that can lead to enhanced performance on parallel and distributed computing resources. A reducing set of simplex vertices are used to derive search directions generally closely aligned with the local gradient. When tested on a range of problems drawn from real-world applications in science and engineering, this reducing set concurrent simplex (RSCS) variant of the Nelder-Mead algorithm compared favourably with the original algorithm, and also with the inherently parallel multidirectional search algorithm (MDS). All algorithms were implemented and tested in a general-purpose, grid-enabled optimization toolset.
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