Fast Algorithms for Parallel Architectures

Abstract

Our work on Fast Algorithms for Parallel Architectures led us to investigate methods for computing all eigenvalues and eigen vectors of a summetric tridiagonal matrix on a distributed-memory MIMD multiprocessor. We have studied only those techniques having the potential for both high accuracy and significant large-grained parallelism. These include the QL method or Cuppen's divide and conquer method based on rank-one updating to compute both eigenvalues and eigen vectors, bisection to determine eigenvalues, and inverse iteration to compute eigen vectors. (KR)

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Document Details

Document Type
Technical Report
Publication Date
Mar 12, 1990
Accession Number
ADA223731

Entities

People

  • Martin H. Schultz

Organizations

  • Yale University

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  • Abstracts
  • Accuracy
  • Algorithms
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  • Computer Science
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  • Contracts
  • Eigenvalues
  • Iterations
  • Mathematics
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  • Linear Algebra
  • Parallel and Distributed Computing.