14,202 research outputs found
Resultant of an equivariant polynomial system with respect to the symmetric group
Given a system of n homogeneous polynomials in n variables which is
equivariant with respect to the canonical actions of the symmetric group of n
symbols on the variables and on the polynomials, it is proved that its
resultant can be decomposed into a product of several smaller resultants that
are given in terms of some divided differences. As an application, we obtain a
decomposition formula for the discriminant of a multivariate homogeneous
symmetric polynomial
Symmetric tensor decomposition
We present an algorithm for decomposing a symmetric tensor, of dimension n
and order d as a sum of rank-1 symmetric tensors, extending the algorithm of
Sylvester devised in 1886 for binary forms. We recall the correspondence
between the decomposition of a homogeneous polynomial in n variables of total
degree d as a sum of powers of linear forms (Waring's problem), incidence
properties on secant varieties of the Veronese Variety and the representation
of linear forms as a linear combination of evaluations at distinct points. Then
we reformulate Sylvester's approach from the dual point of view. Exploiting
this duality, we propose necessary and sufficient conditions for the existence
of such a decomposition of a given rank, using the properties of Hankel (and
quasi-Hankel) matrices, derived from multivariate polynomials and normal form
computations. This leads to the resolution of polynomial equations of small
degree in non-generic cases. We propose a new algorithm for symmetric tensor
decomposition, based on this characterization and on linear algebra
computations with these Hankel matrices. The impact of this contribution is
two-fold. First it permits an efficient computation of the decomposition of any
tensor of sub-generic rank, as opposed to widely used iterative algorithms with
unproved global convergence (e.g. Alternate Least Squares or gradient
descents). Second, it gives tools for understanding uniqueness conditions, and
for detecting the rank
On Functional Decomposition of Multivariate Polynomials with Differentiation and Homogenization
In this paper, we give a theoretical analysis for the algorithms to compute
functional decomposition for multivariate polynomials based on differentiation
and homogenization which are proposed by Ye, Dai, Lam (1999) and Faugere,
Perret (2006, 2008, 2009). We show that a degree proper functional
decomposition for a set of randomly decomposable quartic homogenous polynomials
can be computed using the algorithm with high probability. This solves a
conjecture proposed by Ye, Dai, and Lam (1999). We also propose a conjecture
such that the decomposition for a set of polynomials can be computed from that
of its homogenization with high probability. Finally, we prove that the right
decomposition factors for a set of polynomials can be computed from its right
decomposition factor space. Combining these results together, we prove that the
algorithm can compute a degree proper decomposition for a set of randomly
decomposable quartic polynomials with probability one when the base field is of
characteristic zero, and with probability close to one when the base field is a
finite field with sufficiently large number under the assumption that the
conjeture is correct
Subresultants and Generic Monomial Bases
Given n polynomials in n variables of respective degrees d_1,...,d_n, and a
set of monomials of cardinality d_1...d_n, we give an explicit
subresultant-based polynomial expression in the coefficients of the input
polynomials whose non-vanishing is a necessary and sufficient condition for
this set of monomials to be a basis of the ring of polynomials in n variables
modulo the ideal generated by the system of polynomials. This approach allows
us to clarify the algorithms for the Bezout construction of the resultant.Comment: 22 pages, uses elsart.cls. Revised version accepted for publication
in the Journal of Symbolic Computatio
Computing low-degree factors of lacunary polynomials: a Newton-Puiseux approach
We present a new algorithm for the computation of the irreducible factors of
degree at most , with multiplicity, of multivariate lacunary polynomials
over fields of characteristic zero. The algorithm reduces this computation to
the computation of irreducible factors of degree at most of univariate
lacunary polynomials and to the factorization of low-degree multivariate
polynomials. The reduction runs in time polynomial in the size of the input
polynomial and in . As a result, we obtain a new polynomial-time algorithm
for the computation of low-degree factors, with multiplicity, of multivariate
lacunary polynomials over number fields, but our method also gives partial
results for other fields, such as the fields of -adic numbers or for
absolute or approximate factorization for instance.
The core of our reduction uses the Newton polygon of the input polynomial,
and its validity is based on the Newton-Puiseux expansion of roots of bivariate
polynomials. In particular, we bound the valuation of where is
a lacunary polynomial and a Puiseux series whose vanishing polynomial
has low degree.Comment: 22 page
Decoupling Multivariate Polynomials Using First-Order Information
We present a method to decompose a set of multivariate real polynomials into
linear combinations of univariate polynomials in linear forms of the input
variables. The method proceeds by collecting the first-order information of the
polynomials in a set of operating points, which is captured by the Jacobian
matrix evaluated at the operating points. The polyadic canonical decomposition
of the three-way tensor of Jacobian matrices directly returns the unknown
linear relations, as well as the necessary information to reconstruct the
univariate polynomials. The conditions under which this decoupling procedure
works are discussed, and the method is illustrated on several numerical
examples
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