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Algorithms for matrix multiplication

TLDR
Strassen''s and Winograd''s algorithms for matrix multiplication are investigated and compared with the normal algorithm, and it is shown that scaling is essential for numerical accuracy using Winog rad''s method.
Abstract
Strassen''s and Winograd''s algorithms for matrix multiplication are investigated and compared with the normal algorithm. Floating-point error bounds are obtained, and it is shown that scaling is essential for numerical accuracy using Winograd''s method. In practical cases Winograd''s method appears to be slightly faster than the other two methods, but the gain is, at most, about 20%. Finally, an attempt to generalize Strassen''s method is described.

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Citations
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Journal ArticleDOI

Performance of Processor-Memory Interconnections for Multiprocessors

TL;DR: The analysis shows that delta networks have a far better performance per cost than crossbars in large multiprocessing systems.
Journal ArticleDOI

Fast linear algebra is stable

TL;DR: It is shown that essentially all standard linear algebra operations, including LU decompositions, QR decomposition, linear equation solving, matrix inversion, solving least squares problems, (generalized) eigenvalue problems and the singular value decomposition can also be done stably (in a normwise sense) in O(nω+η) operations.
Journal ArticleDOI

Fast linear algebra is stable

TL;DR: In this article, it was shown that a large class of fast recursive matrix multiplication algorithms are stable in a norm-wise sense, including LU decomposition, QR decomposition and linear equation solving, matrix inversion, solving least squares problems, eigenvalue problems and singular value decomposition.
Proceedings ArticleDOI

Implementation of Strassen's Algorithm for Matrix Multiplication

TL;DR: The implementation is designed to be used in place of DGEMM, the Level 3 BLAS matrix mulitplication routine, and reconfirms that Strassen's algorithm is practical for realistic size matrices.
Journal ArticleDOI

A noncommutative algorithm for multiplying $3 \times 3$ matrices using 23 multiplications

TL;DR: A noncommutative algorithm performs matrix multiplication without requiring the matrix elements to be commutative under the operation of multiplication, most desirable since it can be employed to multiply two matrices whose elements are themselves matrices.
References
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Journal ArticleDOI

Gaussian elimination is not optimal

TL;DR: In this paper, Cook et al. gave an algorithm which computes the coefficients of the product of two square matrices A and B of order n with less than 4. 7 n l°g 7 arithmetical operations (all logarithms in this paper are for base 2).
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A contribution to the development of ALGOL

TL;DR: The main changes were: (1) verbal improvements and clarifications, many of which were kindly suggested by recipients of the original draft; (2) additional or altered language features, in particular the replacement of tree structures by records as proposed by the second author.
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Organizing matrices and matrix operations for paged memory systems

TL;DR: It is shown that carefully designed matrix algorithms can lead to enormous savings in the number of page faults occurring when only a small part of the total matrix can be in main memory at one time.
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A New Algorithm for Inner Product

TL;DR: A new way of computing the inner product of two vectors is described that can be performed using roughly n3/2 multiplications instead of the n3multiplications which the regular method necessitates.
Journal ArticleDOI

Dynamic storage allocation systems

TL;DR: A method of characterizing dynamic storage allocation systems--accordlng to the functional capabilities provided and the underlying techniques used--is presented.