Partitioning sparse rectangular matrices for parallel processing
作者： Tamara G. Kolda
DOI: 10.1007/BFB0018528
关键词: Parallel processing (DSP implementation) 、 Adaptive algorithm 、 Matrix (mathematics) 、 Computer science 、 Matrix decomposition 、 Matrix calculus 、 Diagonalizable matrix 、 Algorithm 、 Spectral method 、 Symmetric matrix 、 Graph partition
摘要: The authors are interested in partitioning sparse rectangular matrices for parallel processing. problem has been well-studied the square symmetric case, but received very little attention. They will formalize matrix and discuss several methods solving it. extend spectral method to case compare this three new -- alternating two hybrid methods. be shown best.
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