81 research outputs found
Lacunaryx: Computing bounded-degree factors of lacunary polynomials
In this paper, we report on an implementation in the free software Mathemagix
of lacunary factorization algorithms, distributed as a library called
Lacunaryx. These algorithms take as input a polynomial in sparse
representation, that is as a list of nonzero monomials, and an integer , and
compute its irreducible degree- factors. The complexity of these
algorithms is polynomial in the sparse size of the input polynomial and .Comment: 6 page
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
Bounded-degree factors of lacunary multivariate polynomials
In this paper, we present a new method for computing bounded-degree factors
of lacunary multivariate polynomials. In particular for polynomials over number
fields, we give a new algorithm that takes as input a multivariate polynomial f
in lacunary representation and a degree bound d and computes the irreducible
factors of degree at most d of f in time polynomial in the lacunary size of f
and in d. Our algorithm, which is valid for any field of zero characteristic,
is based on a new gap theorem that enables reducing the problem to several
instances of (a) the univariate case and (b) low-degree multivariate
factorization.
The reduction algorithms we propose are elementary in that they only
manipulate the exponent vectors of the input polynomial. The proof of
correctness and the complexity bounds rely on the Newton polytope of the
polynomial, where the underlying valued field consists of Puiseux series in a
single variable.Comment: 31 pages; Long version of arXiv:1401.4720 with simplified proof
Difficulté du résultant et des grands déterminants
21 pagesLe résultant est un polynôme permettant de déterminer si plusieurs polynômes donnés ont une racine commune. Canny a pu donner un algorithme PSPACE calculant le résultant à l'aide de calculs de déterminants, mais pose la question de sa complexité exacte. On s'intéresse ici à donner une estimation plus fine de cette complexité. D'une part, on montre que le résultant est dans AM, et qu'il est NP-difficile sous réduction probabiliste. D'autre part, les matrices en jeu étant descriptibles par des circuits de taille raisonnable, on montre que le calcul du déterminant pour de telles matrices est PSPACE-complet
Symmetric Determinantal Representation of Formulas and Weakly Skew Circuits
We deploy algebraic complexity theoretic techniques for constructing
symmetric determinantal representations of for00504925mulas and weakly skew
circuits. Our representations produce matrices of much smaller dimensions than
those given in the convex geometry literature when applied to polynomials
having a concise representation (as a sum of monomials, or more generally as an
arithmetic formula or a weakly skew circuit). These representations are valid
in any field of characteristic different from 2. In characteristic 2 we are led
to an almost complete solution to a question of B\"urgisser on the
VNP-completeness of the partial permanent. In particular, we show that the
partial permanent cannot be VNP-complete in a finite field of characteristic 2
unless the polynomial hierarchy collapses.Comment: To appear in the AMS Contemporary Mathematics volume on
Randomization, Relaxation, and Complexity in Polynomial Equation Solving,
edited by Gurvits, Pebay, Rojas and Thompso
The Limited Power of Powering: Polynomial Identity Testing and a Depth-four Lower Bound for the Permanent
Polynomial identity testing and arithmetic circuit lower bounds are two
central questions in algebraic complexity theory. It is an intriguing fact that
these questions are actually related. One of the authors of the present paper
has recently proposed a "real {\tau}-conjecture" which is inspired by this
connection. The real {\tau}-conjecture states that the number of real roots of
a sum of products of sparse univariate polynomials should be polynomially
bounded. It implies a superpolynomial lower bound on the size of arithmetic
circuits computing the permanent polynomial. In this paper we show that the
real {\tau}-conjecture holds true for a restricted class of sums of products of
sparse polynomials. This result yields lower bounds for a restricted class of
depth-4 circuits: we show that polynomial size circuits from this class cannot
compute the permanent, and we also give a deterministic polynomial identity
testing algorithm for the same class of circuits.Comment: 16 page
Factoring bivariate lacunary polynomials without heights
We present an algorithm which computes the multilinear factors of bivariate
lacunary polynomials. It is based on a new Gap Theorem which allows to test
whether a polynomial of the form P(X,X+1) is identically zero in time
polynomial in the number of terms of P(X,Y). The algorithm we obtain is more
elementary than the one by Kaltofen and Koiran (ISSAC'05) since it relies on
the valuation of polynomials of the previous form instead of the height of the
coefficients. As a result, it can be used to find some linear factors of
bivariate lacunary polynomials over a field of large finite characteristic in
probabilistic polynomial time.Comment: 25 pages, 1 appendi
Fast in-place accumulated bilinear formulae
Bilinear operations are ubiquitous in computer science and in particular in
computer algebra and symbolic computation. One of the most fundamental
arithmetic operation is the multiplication, and when applied to, e.g.,
polynomials or matrices, its result is a bilinear function of its inputs. In
terms of arithmetic operations, many sub-quadratic (resp. sub-cubic) algorithms
were developed for these tasks. But these fast algorithms come at the expense
of (potentially large) extra temporary space to perform the computation. On the
contrary, classical, quadratic (resp. cubic) algorithms, when computed
sequentially, quite often require very few (constant) extra registers. Further
work then proposed simultaneously ``fast'' and ``in-place'' algorithms, for
both matrix and polynomial operations We here propose algorithms to extend the
latter line of work for accumulated algorithms arising from a bilinear formula.
Indeed one of the main ingredient of the latter line of work is to use the
(free) space of the output as intermediate storage. When the result has to be
accumulated, i.e., if the output is also part of the input, this free space
thus does not even exist. To be able to design accumulated in-place algorithm
we thus relax the in-place model to allow algorithms to also modify their
input, therefore to use them as intermediate storage for instance, provided
that they are restored to their initial state after completion of the
procedure. This is in fact a natural possibility in many programming
environments. Furthermore, this restoration allows for recursive combinations
of such procedures, as the (non concurrent) recursive calls will not mess-up
the state of their callers. We propose here a generic technique transforming
any bilinear algorithm into an in-place algorithm under this model. This then
directly applies to polynomial and matrix multiplication algorithms, including
fast ones
The Multivariate Resultant is NP-hard in any Characteristic
The multivariate resultant is a fundamental tool of computational algebraic
geometry. It can in particular be used to decide whether a system of n
homogeneous equations in n variables is satisfiable (the resultant is a
polynomial in the system's coefficients which vanishes if and only if the
system is satisfiable). In this paper we present several NP-hardness results
for testing whether a multivariate resultant vanishes, or equivalently for
deciding whether a square system of homogeneous equations is satisfiable. Our
main result is that testing the resultant for zero is NP-hard under
deterministic reductions in any characteristic, for systems of low-degree
polynomials with coefficients in the ground field (rather than in an
extension). We also observe that in characteristic zero, this problem is in the
Arthur-Merlin class AM if the generalized Riemann hypothesis holds true. In
positive characteristic, the best upper bound remains PSPACE.Comment: 13 page
- …