Jean-Guillaume Dumas: Difference between revisions

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Created page with "Title: Jean-Guillaume Dumas Main Research Question: How can we efficiently compute the dot product of two vectors over word-size finite fields? Methodology: The author compared the practical behaviors of a wide range of implementation techniques using different representations. These techniques include floating point representations, discrete logarithms, tabulations, Montgomery reduction, and delayed modulus. Results: The author found that the Montgomery representa..."
 
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Main Research Question: How can we efficiently compute the dot product of two vectors over word-size finite fields?
Main Research Question: How can we efficiently compute the dot product of two vectors over word-size finite fields?


Methodology: The author compared the practical behaviors of a wide range of implementation techniques using different representations. These techniques include floating point representations, discrete logarithms, tabulations, Montgomery reduction, and delayed modulus.
Methodology: The study compares the practical behaviors of a wide range of implementation techniques using different representations. These techniques include floating point representations, discrete logarithms, tabulations, Montgomery reduction, and delayed modulus.


Results: The author found that the Montgomery representation was the most efficient method for computing the dot product. This method uses a reduction designed by Montgomery, which allows for efficient multiplication and division by using shifts and bit-masks instead of machine remaindering.
Results: The research found that Montgomery representation is a promising approach for achieving efficiency in the computation of the dot product. It allows for significant reduction in the cost of machine remainders, making it a viable alternative to classical representations.


Implications: The results suggest that the Montgomery representation is a practical and efficient method for computing the dot product over word-size finite fields. This could have significant implications for linear algebra routines, matrix multiplication, and other algorithms that rely on the dot product.
Implications: The study's findings have important implications for the field of linear algebra and matrix manipulations. The efficient computation of the dot product over finite fields can lead to faster algorithms for matrix multiplication, triangular system solving, and matrix factorizations. Additionally, the research contributes to the ongoing development of efficient algorithms for iterative methods, further advancing the state of the art in numerical analysis.


In conclusion, the Montgomery representation is a promising technique for computing the dot product over word-size finite fields. It offers efficiency and could have wide-ranging implications for various algorithms and applications.
Link to Article: https://arxiv.org/abs/0404008v2
 
Link to Article: https://arxiv.org/abs/0404008v1
Authors:  
Authors:  
arXiv ID: 0404008v1
arXiv ID: 0404008v2


[[Category:Computer Science]]
[[Category:Computer Science]]
[[Category:Research]]
[[Category:Dot]]
[[Category:Dot]]
[[Category:Product]]
[[Category:Product]]
[[Category:Montgomery]]
[[Category:Representations]]
[[Category:Over]]
[[Category:Matrix]]
[[Category:Word]]

Latest revision as of 15:40, 24 December 2023

Title: Jean-Guillaume Dumas

Main Research Question: How can we efficiently compute the dot product of two vectors over word-size finite fields?

Methodology: The study compares the practical behaviors of a wide range of implementation techniques using different representations. These techniques include floating point representations, discrete logarithms, tabulations, Montgomery reduction, and delayed modulus.

Results: The research found that Montgomery representation is a promising approach for achieving efficiency in the computation of the dot product. It allows for significant reduction in the cost of machine remainders, making it a viable alternative to classical representations.

Implications: The study's findings have important implications for the field of linear algebra and matrix manipulations. The efficient computation of the dot product over finite fields can lead to faster algorithms for matrix multiplication, triangular system solving, and matrix factorizations. Additionally, the research contributes to the ongoing development of efficient algorithms for iterative methods, further advancing the state of the art in numerical analysis.

Link to Article: https://arxiv.org/abs/0404008v2 Authors: arXiv ID: 0404008v2