235 research outputs found
A survey on signature-based Gr\"obner basis computations
This paper is a survey on the area of signature-based Gr\"obner basis
algorithms that was initiated by Faug\`ere's F5 algorithm in 2002. We explain
the general ideas behind the usage of signatures. We show how to classify the
various known variants by 3 different orderings. For this we give translations
between different notations and show that besides notations many approaches are
just the same. Moreover, we give a general description of how the idea of
signatures is quite natural when performing the reduction process using linear
algebra. This survey shall help to outline this field of active research.Comment: 53 pages, 8 figures, 11 table
On formulas for decoding binary cyclic codes
We adress the problem of the algebraic decoding of any cyclic code up to the
true minimum distance. For this, we use the classical formulation of the
problem, which is to find the error locator polynomial in terms of the syndroms
of the received word. This is usually done with the Berlekamp-Massey algorithm
in the case of BCH codes and related codes, but for the general case, there is
no generic algorithm to decode cyclic codes. Even in the case of the quadratic
residue codes, which are good codes with a very strong algebraic structure,
there is no available general decoding algorithm. For this particular case of
quadratic residue codes, several authors have worked out, by hand, formulas for
the coefficients of the locator polynomial in terms of the syndroms, using the
Newton identities. This work has to be done for each particular quadratic
residue code, and is more and more difficult as the length is growing.
Furthermore, it is error-prone. We propose to automate these computations,
using elimination theory and Grbner bases. We prove that, by computing
appropriate Grbner bases, one automatically recovers formulas for the
coefficients of the locator polynomial, in terms of the syndroms
On the Complexity of the F5 Gr\"obner basis Algorithm
We study the complexity of Gr\"obner bases computation, in particular in the
generic situation where the variables are in simultaneous Noether position with
respect to the system.
We give a bound on the number of polynomials of degree in a Gr\"obner
basis computed by Faug\`ere's algorithm~(Fau02) in this generic case for
the grevlex ordering (which is also a bound on the number of polynomials for a
reduced Gr\"obner basis, independently of the algorithm used). Next, we analyse
more precisely the structure of the polynomials in the Gr\"obner bases with
signatures that computes and use it to bound the complexity of the
algorithm.
Our estimates show that the version of~ we analyse, which uses only
standard Gaussian elimination techniques, outperforms row reduction of the
Macaulay matrix with the best known algorithms for moderate degrees, and even
for degrees up to the thousands if Strassen's multiplication is used. The
degree being fixed, the factor of improvement grows exponentially with the
number of variables.Comment: 24 page
Moment Varieties of Gaussian Mixtures
The points of a moment variety are the vectors of all moments up to some
order of a family of probability distributions. We study this variety for
mixtures of Gaussians. Following up on Pearson's classical work from 1894, we
apply current tools from computational algebra to recover the parameters from
the moments. Our moment varieties extend objects familiar to algebraic
geometers. For instance, the secant varieties of Veronese varieties are the
loci obtained by setting all covariance matrices to zero. We compute the ideals
of the 5-dimensional moment varieties representing mixtures of two univariate
Gaussians, and we offer a comparison to the maximum likelihood approach.Comment: 17 pages, 2 figure
Polynomial-Time Algorithms for Quadratic Isomorphism of Polynomials: The Regular Case
Let and be
two sets of nonlinear polynomials over
( being a field). We consider the computational problem of finding
-- if any -- an invertible transformation on the variables mapping
to . The corresponding equivalence problem is known as {\tt
Isomorphism of Polynomials with one Secret} ({\tt IP1S}) and is a fundamental
problem in multivariate cryptography. The main result is a randomized
polynomial-time algorithm for solving {\tt IP1S} for quadratic instances, a
particular case of importance in cryptography and somewhat justifying {\it a
posteriori} the fact that {\it Graph Isomorphism} reduces to only cubic
instances of {\tt IP1S} (Agrawal and Saxena). To this end, we show that {\tt
IP1S} for quadratic polynomials can be reduced to a variant of the classical
module isomorphism problem in representation theory, which involves to test the
orthogonal simultaneous conjugacy of symmetric matrices. We show that we can
essentially {\it linearize} the problem by reducing quadratic-{\tt IP1S} to
test the orthogonal simultaneous similarity of symmetric matrices; this latter
problem was shown by Chistov, Ivanyos and Karpinski to be equivalent to finding
an invertible matrix in the linear space of matrices over and to compute the square root in a matrix
algebra. While computing square roots of matrices can be done efficiently using
numerical methods, it seems difficult to control the bit complexity of such
methods. However, we present exact and polynomial-time algorithms for computing
the square root in for various fields (including
finite fields). We then consider \\#{\tt IP1S}, the counting version of {\tt
IP1S} for quadratic instances. In particular, we provide a (complete)
characterization of the automorphism group of homogeneous quadratic
polynomials. Finally, we also consider the more general {\it Isomorphism of
Polynomials} ({\tt IP}) problem where we allow an invertible linear
transformation on the variables \emph{and} on the set of polynomials. A
randomized polynomial-time algorithm for solving {\tt IP} when
is presented. From an algorithmic point
of view, the problem boils down to factoring the determinant of a linear matrix
(\emph{i.e.}\ a matrix whose components are linear polynomials). This extends
to {\tt IP} a result of Kayal obtained for {\tt PolyProj}.Comment: Published in Journal of Complexity, Elsevier, 2015, pp.3
Gr\"obner Bases of Bihomogeneous Ideals generated by Polynomials of Bidegree (1,1): Algorithms and Complexity
Solving multihomogeneous systems, as a wide range of structured algebraic
systems occurring frequently in practical problems, is of first importance.
Experimentally, solving these systems with Gr\"obner bases algorithms seems to
be easier than solving homogeneous systems of the same degree. Nevertheless,
the reasons of this behaviour are not clear. In this paper, we focus on
bilinear systems (i.e. bihomogeneous systems where all equations have bidegree
(1,1)). Our goal is to provide a theoretical explanation of the aforementionned
experimental behaviour and to propose new techniques to speed up the Gr\"obner
basis computations by using the multihomogeneous structure of those systems.
The contributions are theoretical and practical. First, we adapt the classical
F5 criterion to avoid reductions to zero which occur when the input is a set of
bilinear polynomials. We also prove an explicit form of the Hilbert series of
bihomogeneous ideals generated by generic bilinear polynomials and give a new
upper bound on the degree of regularity of generic affine bilinear systems.
This leads to new complexity bounds for solving bilinear systems. We propose
also a variant of the F5 Algorithm dedicated to multihomogeneous systems which
exploits a structural property of the Macaulay matrix which occurs on such
inputs. Experimental results show that this variant requires less time and
memory than the classical homogeneous F5 Algorithm.Comment: 31 page
On the Complexity of the Generalized MinRank Problem
We study the complexity of solving the \emph{generalized MinRank problem},
i.e. computing the set of points where the evaluation of a polynomial matrix
has rank at most . A natural algebraic representation of this problem gives
rise to a \emph{determinantal ideal}: the ideal generated by all minors of size
of the matrix. We give new complexity bounds for solving this problem
using Gr\"obner bases algorithms under genericity assumptions on the input
matrix. In particular, these complexity bounds allow us to identify families of
generalized MinRank problems for which the arithmetic complexity of the solving
process is polynomial in the number of solutions. We also provide an algorithm
to compute a rational parametrization of the variety of a 0-dimensional and
radical system of bi-degree . We show that its complexity can be bounded
by using the complexity bounds for the generalized MinRank problem.Comment: 29 page
On the complexity of computing Gr\"obner bases for weighted homogeneous systems
Solving polynomial systems arising from applications is frequently made
easier by the structure of the systems. Weighted homogeneity (or
quasi-homogeneity) is one example of such a structure: given a system of
weights , -homogeneous polynomials are polynomials
which are homogeneous w.r.t the weighted degree
. Gr\"obner bases for weighted homogeneous systems can be
computed by adapting existing algorithms for homogeneous systems to the
weighted homogeneous case. We show that in this case, the complexity estimate
for Algorithm~\F5 \left(\binom{n+\dmax-1}{\dmax}^{\omega}\right) can be
divided by a factor . For zero-dimensional
systems, the complexity of Algorithm~\FGLM (where is the
number of solutions of the system) can be divided by the same factor
. Under genericity assumptions, for
zero-dimensional weighted homogeneous systems of -degree
, these complexity estimates are polynomial in the
weighted B\'ezout bound .
Furthermore, the maximum degree reached in a run of Algorithm \F5 is bounded by
the weighted Macaulay bound , and this bound is
sharp if we can order the weights so that . For overdetermined
semi-regular systems, estimates from the homogeneous case can be adapted to the
weighted case. We provide some experimental results based on systems arising
from a cryptography problem and from polynomial inversion problems. They show
that taking advantage of the weighted homogeneous structure yields substantial
speed-ups, and allows us to solve systems which were otherwise out of reach
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