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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 1 to 2 of 2 (0.066 seconds)Spelling suggestions: "subject:"a.unique solvable puzzle"" "subject:"_monique solvable puzzle""
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Algebraic and multilinear-algebraic techniques for fast matrix multiplicationGouaya, Guy Mathias January 2015 (has links)
This dissertation reviews the theory of fast matrix multiplication from a multilinear-algebraic point of view, as
well as recent fast matrix multiplication algorithms based on discrete Fourier transforms over nite groups.
To this end, the algebraic approach is described in terms of group algebras over groups satisfying the triple
product Property, and the construction of such groups via uniquely solvable puzzles.
The higher order singular value decomposition is an important decomposition of tensors that retains some of
the properties of the singular value decomposition of matrices. However, we have proven a novel negative result
which demonstrates that the higher order singular value decomposition yields a matrix multiplication algorithm
that is no better than the standard algorithm. / Mathematical Sciences / M. Sc. (Applied Mathematics)
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| 2 |
Algebraic and multilinear-algebraic techniques for fast matrix multiplicationGouaya, Guy Mathias January 2015 (has links)
This dissertation reviews the theory of fast matrix multiplication from a multilinear-algebraic point of view, as
well as recent fast matrix multiplication algorithms based on discrete Fourier transforms over nite groups.
To this end, the algebraic approach is described in terms of group algebras over groups satisfying the triple
product Property, and the construction of such groups via uniquely solvable puzzles.
The higher order singular value decomposition is an important decomposition of tensors that retains some of
the properties of the singular value decomposition of matrices. However, we have proven a novel negative result
which demonstrates that the higher order singular value decomposition yields a matrix multiplication algorithm
that is no better than the standard algorithm. / Mathematical Sciences / M. Sc. (Applied Mathematics)
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