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The Solution of Singular-Value and Symmetric Eigenvalue Problems on Multiprocessor Arrays

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Parallel Jacobi-like algorithms are presented for computing a singular-value decomposition of an mxn matrix (mn) and an eigenvalue decomposition of an nxn symmetric matrix. A linear array of O(n) processors is proposed for the singular-value problem and the associated algorithm requires time O(mnS), where S is the number of sweeps (typically S≤10). A square array of O(n2) processors with nearest-neighbor communication is proposed for the eigenvalue problem; the associated algorithm requires time O(nS). Key Words And Phrases: Multiprocessor arrays, systolic arrays, singular-value decomposition, eigenvalue decomposition, real symmetric matrices, Hestenes method, Jacobi method, VLSI, real-time computation, parallel algorithms.

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1983-07

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Cornell University

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computer science; technical report

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http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR83-562

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technical report

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