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Fast finite field arithmetic

Abstract : The multiplication of polynomials is a fundamental operation in complexity theory. Indeed, for many arithmetic problems, the complexity of algorithms is expressed in terms of the complexity of polynomial multiplication. For example, the complexity of euclidean division or of multi-point evaluation/interpolation (and others) is often expressed in terms of the complexity of polynomial multiplication. This shows that a better multiplication algorithm allows to perform the corresponding operations faster. A 2016 result gave an improved asymptotic complexity for the multiplication of polynomials over finite fields. This article is the starting point of the thesis; the present work aims to study the implications of the new complexity bound, from a theoretical and practical point of view.The first part focuses on the multiplication of univariate polynomials. This part presents two new algorithms that should make the computation faster in practice (rather than asymptotically speaking). While it is difficult in general to observe the predicted speed-up, some specific cases are particularly favorable. Actually, the second proposed algorithm, which is specific to finite fields, leads to a better implementation for the multiplication in F 2 [X], about twice as fast as state-of-the-art software.The second part deals with the arithmetic of multivariate polynomials modulo an ideal, as considered typically for polynomial system solving. This work assumes a simplified situation, with only two variables and under certain regularity assumptions. In this particular case, there are algorithms whose complexity is asymptotically optimal (up to logarithmic factors), with respect to input/output size. The implementation for this specific case is then significantly faster than general-purpose software, the speed-up becoming larger and larger when the input size increases.
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Submitted on : Tuesday, January 14, 2020 - 4:27:12 PM
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  • HAL Id : tel-02439581, version 1



Robin Larrieu. Fast finite field arithmetic. Symbolic Computation [cs.SC]. Université Paris-Saclay, 2019. English. ⟨NNT : 2019SACLX073⟩. ⟨tel-02439581⟩



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