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Gr{?}bner basis. a "pseudo-polynomial" algorithm for computing the Frobenius number  [PDF]
Marcel Morales,Dung Nguyen Thi
Mathematics , 2015,
Abstract: Let consider $n$ natural numbers $a\_1 ,\ldots , a\_{n} $. Let $S$ be the numerical semigroup generated by $a\_1 ,\ldots , a\_{n} $. Set $A=K[t^{a\_1}, \ldots , t^{a\_n}]=K[{x\_1}, \ldots , {x\_n}]/I$. The aim of this paper is: \begin{enumerate}\item Give an effective pseudo-polynomial algorithm on $a\_1$, which computes The Ap{\'e}ry set and the Frobenius number of $S$. As a consequence it also solves in pseudo-polynomial time the integer knapsack problem : given a natural integer b, b belongs to $S$?\item The \gbb of $I$ for the reverse lexicographic order to $x\_n,\ldots ,x\_1$, without using Buchberger's algorithm. \item $\ini{I} $ for the reverse lexicographic order to $x\_n,\ldots ,x\_1$.\item $A$ as a $K[t^{ a\_1 }]$-module. \end{enumerate} We dont know the complexity of our algorithm. We need to solve the "multiplicative" integer knapsack problem: Find all positive integer solutions $({k\_1}, \ldots , {k\_n})$ of the inequality $\prod\_{i=2}^n (k\_i+1)\leq a\_1+1$. This algorithm is easily implemented. The implementation of this algorithm "frobenius-number-mm", for $n=17 $, can be downloaded in \hfill\breakhttps://www-fourier.ujf-grenoble.fr/~morales/frobenius-number-mm
A new conception for computing gr?bner basis and its applications  [PDF]
Lei Huang
Mathematics , 2010,
Abstract: This paper presents a conception for computing gr\"{o}bner basis. We convert some of gr\"{o}bner-computing algorithms, e.g., F5, extended F5 and GWV algorithms into a special type of algorithm. The new algorithm's finite termination problem can be described by equivalent conditions, so all the above algorithms can be determined when they terminate finitely. At last, a new criterion is presented. It is an improvement for the Rewritten and Signature Criterion.
A Variant of the Gr?bner Basis Algorithm for Computing Hilbert Bases  [PDF]
Natalia Dück,Karl-Heinz Zimmermann
Mathematics , 2013,
Abstract: Gr\"obner bases can be used for computing the Hilbert basis of a numerical submonoid. By using these techniques, we provide an algorithm that calculates a basis of a subspace of a finite-dimensional vector space over a finite prime field given as a matrix kernel.
Signature-Based Gr?bner Basis Algorithms --- Extended MMM Algorithm for computing Gr?bner bases  [PDF]
Yao Sun
Computer Science , 2013,
Abstract: Signature-based algorithms is a popular kind of algorithms for computing Gr\"obner bases, and many related papers have been published recently. In this paper, no new signature-based algorithms and no new proofs are presented. Instead, a view of signature-based algorithms is given, that is, signature-based algorithms can be regarded as an extended version of the famous MMM algorithm. By this view, this paper aims to give an easier way to understand signature-based Gr\"obner basis algorithms.
The Termination of Algorithms for Computing Gr?bner Bases  [PDF]
Senshan Pan,Yupu Hu,BaoCang Wang
Mathematics , 2012,
Abstract: The F5 algorithm is generally believed as one of the fastest algorithms for computing Gr\"{o}bner bases. However, its termination problem is still unclear. Recently, an algorithm GVW and its variant GVWHS have been proposed, and their efficiency are comparable to the F5 algorithm. In the paper, we clarify the concept of an admissible module order. For the first time, the connection between the reducible and rewritable check is discussed here. We show that the top-reduced S-Gr\"{o}bner basis must be finite if the admissible monomial order and the admissible module order are compatible. This paper presents a complete proof of the termination and correctness of the GVWHS algorithm. What is more, it can be seen that the GVWHS is in fact an F5-like algorithm. Different from the GVWHS algorithm, the F5B algorithm may generate redundant sig-polynomials. Taking into account this situation, we prove the termination and correctness of the F5B algorithm. And we notice that the original F5 algorithm slightly differs from the F5B algorithm in the insertion strategy on which the F5-rewritten criterion is based. Exploring the potential ordering of sig-polynomials computed by the original F5 algorithm, we propose an F5GEN algorithm with a generalized insertion strategy, and prove the termination and correctness of it. Therefore, we have a positive answer to the long standing problem of proving the termination of the original F5 algorithm.
On the robust hardness of Gr?bner basis computation  [PDF]
Gwen Spencer,David Rolnick
Computer Science , 2015,
Abstract: We introduce a new problem in the approximate computation of Gr\"{o}bner bases that allows the algorithm to ignore a constant fraction of the generators - of the algorithm's choice - then compute a Gr\"{o}bner basis for the remaining polynomial system. The set ignored is subject to one quite-natural structural constraint. For lexicographic orders, when the discarded fraction is less than $(1/4-\epsilon)$, for $\epsilon>0$, we prove that this problem cannot be solved in polynomial time, even when the original polynomial system has maximum degree 3 and each polynomial contains at most 3 variables. Qualitatively, even for sparse systems composed of low-degree polynomials, we show that Gr\"{o}bner basis computation is robustly hard: even producing a Gr\"{o}bner basis for a large subset of the generators is NP-hard.
Gr?bner Basis in Algebras Extended by Loops
Chalom,G.; Marcos,E.; Oliveira,P.;
Revista de la Uni?3n Matem??tica Argentina , 2007,
Abstract: in this work we extend, to the path algebras context, some results obtained in the commutative context, [2]. the main result is that one can extend the gr?bner bases of an ungraded ideal to one possible definition of homogenization for the non commutative case.
Lifting algorithms for Gr bner basis computation of invariant ideals
不变理想的Gr bner基提升算法

WU Jie,CHEN Yu-Fu,

中国科学院研究生院学报 , 2009,
Abstract: A polynomial invariant under the action of a finite group can be rewritten into generators of the invariant ring by Gr bner basis method. The key question is how to find an efficient way to compute the Gr bner basis of the invariant ideal which is positive dimensional. We introduce a lifting algorithm for this computation process. If we use straight line program to analyze the complexity result, this process can be done within polynomial time.
On the Complexity of Computing Critical Points with Gr?bner Bases  [PDF]
Pierre-Jean Spaenlehauer
Computer Science , 2013,
Abstract: Computing the critical points of a polynomial function $q\in\mathbb Q[X_1,\ldots,X_n]$ restricted to the vanishing locus $V\subset\mathbb R^n$ of polynomials $f_1,\ldots, f_p\in\mathbb Q[X_1,\ldots, X_n]$ is of first importance in several applications in optimization and in real algebraic geometry. These points are solutions of a highly structured system of multivariate polynomial equations involving maximal minors of a Jacobian matrix. We investigate the complexity of solving this problem by using Gr\"obner basis algorithms under genericity assumptions on the coefficients of the input polynomials. The main results refine known complexity bounds (which depend on the maximum $D=\max(deg(f_1),\ldots,deg(f_p),deg(q))$) to bounds which depend on the list of degrees $(deg(f_1),\ldots,deg(f_p),deg(q))$: we prove that the Gr\"obner basis computation can be performed in $\delta^{O(\log(A)/\log(G))}$ arithmetic operations in $\mathbb Q$, where $\delta$ is the algebraic degree of the ideal vanishing on the critical points, and $A$ and $G$ are the arithmetic and geometric average of a multiset constructed from the sequence of degrees. As a by-product, we prove that solving such generic optimization problems with Gr\"obner bases requires at most $D^{O(n)}$ arithmetic operations in $\mathbb Q$, which meets the best known complexity bound for this problem. Finally, we illustrate these complexity results with experiments, giving evidence that these bounds are relevant for applications.
Fast Gr?bner Basis Computation for Boolean Polynomials  [PDF]
Franziska Hinkelmann,Elizabeth Arnold
Mathematics , 2010,
Abstract: We introduce the Macaulay2 package BooleanGB, which computes a Gr\"obner basis for Boolean polynomials using a binary representation rather than symbolic. We compare the runtime of several Boolean models from systems in biology and give an application to Sudoku.
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