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A Multiprocessor Algorithm for the Symmetric Tridiagonal Eigenvalue Problem

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Release : 1986
Genre : Eigenvalues
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis A Multiprocessor Algorithm for the Symmetric Tridiagonal Eigenvalue Problem by : Sy-Shin Lo

Download or read book A Multiprocessor Algorithm for the Symmetric Tridiagonal Eigenvalue Problem written by Sy-Shin Lo. This book was released on 1986. Available in PDF, EPUB and Kindle. Book excerpt:

A New O(n2) Algorithm for the Symmetric Tridiagonal Eigenvalue/eigenvector Problem

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Author :
Release : 1997
Genre : Algorithms
Kind : eBook
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Book Synopsis A New O(n2) Algorithm for the Symmetric Tridiagonal Eigenvalue/eigenvector Problem by : Inderjit Singh Dhillon

Download or read book A New O(n2) Algorithm for the Symmetric Tridiagonal Eigenvalue/eigenvector Problem written by Inderjit Singh Dhillon. This book was released on 1997. Available in PDF, EPUB and Kindle. Book excerpt:

Selected Papers from the Second Conference on Parallel Processing for Scientific Computing

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Release : 1987-01-01
Genre : Computers
Kind : eBook
Book Rating : 162/5 ( reviews)

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Book Synopsis Selected Papers from the Second Conference on Parallel Processing for Scientific Computing by : Charles William Gear

Download or read book Selected Papers from the Second Conference on Parallel Processing for Scientific Computing written by Charles William Gear. This book was released on 1987-01-01. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings -- Parallel Computing.

Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing

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Release : 2018-01-03
Genre : Computers
Kind : eBook
Book Rating : 261/5 ( reviews)

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Book Synopsis Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing by : Tetsuya Sakurai

Download or read book Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing written by Tetsuya Sakurai. This book was released on 2018-01-03. Available in PDF, EPUB and Kindle. Book excerpt: This book provides state-of-the-art and interdisciplinary topics on solving matrix eigenvalue problems, particularly by using recent petascale and upcoming post-petascale supercomputers. It gathers selected topics presented at the International Workshops on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2014 and EPASA2015), which brought together leading researchers working on the numerical solution of matrix eigenvalue problems to discuss and exchange ideas – and in so doing helped to create a community for researchers in eigenvalue problems. The topics presented in the book, including novel numerical algorithms, high-performance implementation techniques, software developments and sample applications, will contribute to various fields that involve solving large-scale eigenvalue problems.

High Performance Algorithms for Structured Matrix Problems

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Author :
Release : 1998
Genre : Business & Economics
Kind : eBook
Book Rating : 947/5 ( reviews)

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Book Synopsis High Performance Algorithms for Structured Matrix Problems by : Peter Arbenz

Download or read book High Performance Algorithms for Structured Matrix Problems written by Peter Arbenz. This book was released on 1998. Available in PDF, EPUB and Kindle. Book excerpt: Comprises 10 contributions that summarize the state of the art in the areas of high performance solutions of structured linear systems and structured eigenvalue and singular-value problems. Topics covered range from parallel solvers for sparse or banded linear systems to parallel computation of eigenvalues and singular values of tridiagonal and bidiagonal matrices. Specific paper topics include: the stable parallel solution of general narrow banded linear systems; efficient algorithms for reducing banded matrices to bidiagonal and tridiagonal form; a numerical comparison of look-ahead Levinson and Schur algorithms for non-Hermitian Toeplitz systems; and parallel CG-methods automatically optimized for PC and workstation clusters. Annotation copyrighted by Book News, Inc., Portland, OR

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