2006
DOI: 10.1016/j.jsc.2005.09.007
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Bruno Buchberger’s PhD thesis 1965: An algorithm for finding the basis elements of the residue class ring of a zero dimensional polynomial ideal

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Cited by 572 publications
(527 citation statements)
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“…Then, it S S S S S S S S S S S S S S S S solves the corresponding system which turns out to be overdefined and sparse. The methods already proposed for solving polynomial system of equations are Gröbner basis including Buchberger algorithm [4], F4 [15], F5 [16] and algorithms like ElimLin [9], XL [6] and its family [7], and Raddum-Semaev algorithm [26]. Converting these equations to Boolean expressions in Conjunctive Normal Form (CNF) [9] and deploying various SAT-solver programs is another strategy [14].…”
Section: Algebraic Analysismentioning
confidence: 99%
“…Then, it S S S S S S S S S S S S S S S S solves the corresponding system which turns out to be overdefined and sparse. The methods already proposed for solving polynomial system of equations are Gröbner basis including Buchberger algorithm [4], F4 [15], F5 [16] and algorithms like ElimLin [9], XL [6] and its family [7], and Raddum-Semaev algorithm [26]. Converting these equations to Boolean expressions in Conjunctive Normal Form (CNF) [9] and deploying various SAT-solver programs is another strategy [14].…”
Section: Algebraic Analysismentioning
confidence: 99%
“…The effective notion stems from Shirshov's Composition Lemma and his algorithm [16] for Lie algebras and independently from Buchberger's algorithm [8] of computing Gröbner bases for commutative algebras. In [2], Bokut applied Shirshov's method to associative algebras, and Bergman mentioned the diamond lemma for ring theory [1].…”
Section: Introductionmentioning
confidence: 99%
“…A Gröbner basis is a special kind of generating set for an ideal which provides a computational framework to determine many properties of the ideal. The notion of Gröbner bases was originally introduced in 1965 by Buchberger in his Ph.D. thesis and he also gave the basic algorithm to compute it [2,3]. Later on, he proposed two criteria for detecting superfluous reductions to improve his algorithm [1].…”
Section: Introductionmentioning
confidence: 99%