A variable relaxation parameter for the parallel one-sided JRS SVD algorithm
A variable relaxation parameter for the parallel one-sided JRS SVD algorithm
复制标题
并行单边 JRS SVD 算法的可变松弛参数
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Q. Guo
中科院分区：
文献类型：
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作者：
Lei Zhao;Q. Guo
m×n矩阵A的奇异值分解（SVD）的计算在许多领域都有重要意义。许多串行和并行算法，如Jacobi，QR算法已被提出。本文在传统的循环单边Jacobi算法的基础上，利用松弛技术研究了单边JRS算法，并给出了一种新的松弛参数λ可变的方法。实验结果表明，当A的大小和集群中使用的处理器数量不同时，变量λ可以减少扫描次数，加快整个SVD过程。
The computation of the singular value decomposition (SVD) of an m×n matrix A is important in many fields. Many sequential and parallel algorithms such as Jacobi, QR based methods have been proposed. In this paper, we study the one-sided JRS algorithm which is based on traditional cyclic one-sided Jacobi algorithm by using the relaxation technique and give a new method in which the relaxation parameter λ is variable in contrast to the original JRS algorithm. The experiments show that when the size of A and the number of processors used in the cluster are different, the variable λ can decrease the sweeps and accelerate the whole SVD process.