KMS Of Academy of mathematics and systems sciences, CAS
Trace-Penalty Minimization for Large-Scale Eigenspace Computation | |
Wen, Zaiwen1; Yang, Chao2; Liu, Xin3; Zhang, Yin4 | |
2016-03-01 | |
发表期刊 | JOURNAL OF SCIENTIFIC COMPUTING |
ISSN | 0885-7474 |
卷号 | 66期号:3页码:1175-1203 |
摘要 | In a block algorithm for computing relatively high-dimensional eigenspaces of large sparse symmetric matrices, the Rayleigh-Ritz (RR) procedure often constitutes a major bottleneck. Although dense eigenvalue calculations for subproblems in RR steps can be parallelized to a certain level, their parallel scalability, which is limited by some inherent sequential steps, is lower than dense matrix-matrix multiplications. The primary motivation of this paper is to develop a methodology that reduces the use of the RR procedure in exchange for matrix-matrix multiplications. We propose an unconstrained trace-penalty minimization model and establish its equivalence to the eigenvalue problem. With a suitably chosen penalty parameter, this model possesses far fewer undesirable full-rank stationary points than the classic trace minimization model. More importantly, it enables us to deploy algorithms that makes heavy use of dense matrix-matrix multiplications. Although the proposed algorithm does not necessarily reduce the total number of arithmetic operations, it leverages highly optimized operations on modern high performance computers to achieve parallel scalability. Numerical results based on a preliminary implementation, parallelized using OpenMP, show that our approach is promising. |
关键词 | Eigenvalue computation Exact quadratic penalty approach Gradient methods |
DOI | 10.1007/s10915-015-0061-0 |
语种 | 英语 |
资助项目 | Office of Advanced Scientific Computing Research of the U.S. Department of Energy[DE-AC02-05CH11232] |
WOS研究方向 | Mathematics |
WOS类目 | Mathematics, Applied |
WOS记录号 | WOS:000369911500013 |
出版者 | SPRINGER/PLENUM PUBLISHERS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.amss.ac.cn/handle/2S8OKBNM/22005 |
专题 | 计算数学与科学工程计算研究所 |
通讯作者 | Wen, Zaiwen |
作者单位 | 1.Peking Univ, Beijing Int Ctr Math Res, Beijing, Peoples R China 2.Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Computat Res Div, Berkeley, CA 94720 USA 3.Chinese Acad Sci, Acad Math & Syst Sci, State Key Lab Sci & Engn Comp, Beijing, Peoples R China 4.Rice Univ, Dept Computat & Appl Math, Houston, TX USA |
推荐引用方式 GB/T 7714 | Wen, Zaiwen,Yang, Chao,Liu, Xin,et al. Trace-Penalty Minimization for Large-Scale Eigenspace Computation[J]. JOURNAL OF SCIENTIFIC COMPUTING,2016,66(3):1175-1203. |
APA | Wen, Zaiwen,Yang, Chao,Liu, Xin,&Zhang, Yin.(2016).Trace-Penalty Minimization for Large-Scale Eigenspace Computation.JOURNAL OF SCIENTIFIC COMPUTING,66(3),1175-1203. |
MLA | Wen, Zaiwen,et al."Trace-Penalty Minimization for Large-Scale Eigenspace Computation".JOURNAL OF SCIENTIFIC COMPUTING 66.3(2016):1175-1203. |
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