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arXiv · 1805.03577

Towards Mixed Gr{\"o}bner Basis Algorithms: the Multihomogeneous and Sparse Case

Matías Bender·Jean-Charles Faugère·Elias Tsigaridas
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Abstract

One of the biggest open problems in computational algebra is the design of efficient algorithms for Gr{\"o}bner basis computations that take into account the sparsity of the input polynomials. We can perform such computations in the case of unmixed polynomial systems, that is systems with polynomials having the same support, using the approach of Faug{\`e}re, Spaenlehauer, and Svartz [ISSAC'14]. We present two algorithms for sparse Gr{\"o}bner bases computations for mixed systems. The first one computes with mixed sparse systems and exploits the supports of the polynomials. Under regularity assumptions, it performs no reductions to zero. For mixed, square, and 0-dimensional multihomogeneous polynomial systems, we present a dedicated, and potentially more efficient, algorithm that exploits different algebraic properties that performs no reduction to zero. We give an explicit bound for the maximal degree appearing in the computations.

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Matías Bender, Jean-Charles Faugère, Elias Tsigaridas. 2018-05-09. Towards Mixed Gr{\"o}bner Basis Algorithms: the Multihomogeneous and Sparse Case. https://doi.org/10.1145/3208976.3209018

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