In-depth comparison of the Berlekamp–Massey–Sakata and the Scalar-FGLM algorithms: the adaptive variants - Archive ouverte HAL Access content directly
Journal Articles Journal of Symbolic Computation Year : 2020

In-depth comparison of the Berlekamp–Massey–Sakata and the Scalar-FGLM algorithms: the adaptive variants

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Jérémy Berthomieu
Jean-Charles Faugère

Abstract

The Berlekamp–Massey–Sakata algorithm and the Scalar-FGLM algorithm both compute the ideal of relations of a multidimensional linear recurrent sequence. Whenever quering a single sequence element is prohibitive, the bottleneck of these algorithms becomes the computation of all the needed sequence terms. As such, having adaptive variants of these algorithms, reducing the number of sequence queries, becomes mandatory. A native adaptive variant of the Scalar-FGLM algorithm was presented by its authors, the so-called Adaptive Scalar-FGLM algorithm. In this paper, our first contribution is to make the Berlekamp–Massey–Sakata algorithm more efficient by making it adaptive to avoid some useless relation test-ings. This variant allows us to divide by four in dimension 2 and by seven in dimension 3 the number of basic operations performed on some sequence family. Then, we compare the two adaptive algorithms. We show that their behaviors differ in a way that it is not possible to tweak one of the algorithms in order to mimic exactly the behavior of the other. We detail precisely the differences and the similarities of both algorithms and conclude that in general the Adaptive Scalar-FGLM algorithm needs fewer queries and performs fewer basic operations than the Adaptive Berlekamp–Massey–Sakata algorithm. We also show that these variants are always more efficient than the original algorithms.
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Dates and versions

hal-01805478 , version 1 (01-06-2018)
hal-01805478 , version 2 (26-07-2019)
hal-01805478 , version 3 (28-08-2019)

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Jérémy Berthomieu, Jean-Charles Faugère. In-depth comparison of the Berlekamp–Massey–Sakata and the Scalar-FGLM algorithms: the adaptive variants. Journal of Symbolic Computation, 2020, 101, pp.270-303. ⟨10.1016/j.jsc.2019.09.001⟩. ⟨hal-01805478v3⟩
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