连接机上奇异值分解的修正

J. Barlow, P. Yoon
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引用次数: 1

摘要

提出了一种m × n(m≥n)矩阵A奇异值分解的全并行更新和降约算法。该算法使用类似的追踪技术来修改[3]中描述的SVD,但需要较少的平面旋转,并且可以几乎相同地实现更新和downdating。在并行实现中,循环存储和连续存储都被考虑。我们证明了后一种方案在分布式存储器MIMD多处理器上优于前者。本文介绍了在32节点连接机(CM-5)上的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modifying the Singular Value Decomposition on the Connection Machine
A fully parallel algorithm for updating and downdating the singular value decompositions (SVD’s) of an m-by-n(m≥n) matrix A is described. The algorithm uses similar chasing techniques for modifying the SVD’s described in [3], but requires fewer plane rotations, and can be implemented almost identically for both updating and downdating. Both cyclic and consecutive storage schemes are considered in parallel implementation. We show that the latter scheme outperforms the former on a distributed memory MIMD multiprocessor. We present the experimental results on the 32-node Connection Machine (CM-5).
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