Sparse FGLM algorithms

TL;DR

This paper introduces efficient sparse FGLM algorithms for changing monomial orderings in zero-dimensional ideals, significantly improving performance and handling large degrees with a combination of probabilistic, deterministic, and coding theory methods.

Contribution

It develops new sparse algorithms for FGLM order change, combining multiple techniques and providing a top-level adaptive algorithm with superior practical performance.

Findings

Outperforms Magma and Singular implementations on large ideals

Provides complexity analysis for all proposed methods

Estimates sparsity of multiplication matrices for generic systems

Abstract

Given a zero-dimensional ideal I in K[x1,...,xn] of degree D, the transformation of the ordering of its Groebner basis from DRL to LEX is a key step in polynomial system solving and turns out to be the bottleneck of the whole solving process. Thus it is of crucial importance to design efficient algorithms to perform the change of ordering. The main contributions of this paper are several efficient methods for the change of ordering which take advantage of the sparsity of multiplication matrices in the classical FGLM algorithm. Combing all these methods, we propose a deterministic top-level algorithm that automatically detects which method to use depending on the input. As a by-product, we have a fast implementation that is able to handle ideals of degree over 40000. Such an implementation outperforms the Magma and Singular ones, as shown by our experiments. First for the shape position case, two methods are designed based on the Wiedemann algorithm: the first is probabilistic and its complexity to complete the change of ordering is O(D(N1+nlog(D))), where N1 is the number of nonzero entries of a multiplication matrix; the other is deterministic and computes the LEX Groebner basis of the radical of I via Chinese Remainder Theorem. Then for the general case, the designed method is characterized by the Berlekamp-Massey-Sakata algorithm from Coding Theory to handle the multi-dimensional linearly recurring relations. Complexity analyses of all proposed methods are also provided. Furthermore, for generic polynomial systems, we present an explicit formula for the estimation of the sparsity of one main multiplication matrix, and prove its construction is free. With the asymptotic analysis of such sparsity, we are able to show for generic systems the complexity above becomes $O(\sqrt{6/n \pi} D^{2+(n-1)/n}})$.