23,400 research outputs found
Solving Degenerate Sparse Polynomial Systems Faster
Consider a system F of n polynomial equations in n unknowns, over an
algebraically closed field of arbitrary characteristic. We present a fast
method to find a point in every irreducible component of the zero set Z of F.
Our techniques allow us to sharpen and lower prior complexity bounds for this
problem by fully taking into account the monomial term structure. As a
corollary of our development we also obtain new explicit formulae for the exact
number of isolated roots of F and the intersection multiplicity of the
positive-dimensional part of Z. Finally, we present a combinatorial
construction of non-degenerate polynomial systems, with specified monomial term
structure and maximally many isolated roots, which may be of independent
interest.Comment: This is the final journal version of math.AG/9702222 (``Toric
Generalized Characteristic Polynomials''). This final version is a major
revision with several new theorems, examples, and references. The prior
results are also significantly improve
Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems
Computational algebraic geometry is the study of roots of polynomials and polynomial systems. We are familiar with the notion of degree, but there are other ways to consider a polynomial: How many variables does it have? How many terms does it have? Considering the sparsity of a polynomial means we pay special attention to the number of terms. One can sometimes profit greatly by making use of sparsity when doing computations by utilizing tools from linear programming and integer matrix factorization. This thesis investigates several problems from the point of view of sparsity. Consider a system F of n polynomials over n variables, with a total of n + k distinct exponent vectors over any local field L. We discuss conjecturally tight bounds on the maximal number of non-degenerate roots F can have over L, with all coordinates having fixed phase, as a function of n, k, and L only. In particular, we give new explicit systems with number of roots approaching the best known upper bounds. We also give a complete classification for when an n-variate n + 2-nomial positive polynomial can be written as a sum of squares of polynomials. Finally, we investigate the problem of approximating roots of polynomials from the viewpoint of sparsity by developing a method of approximating roots for binomial systems that runs more efficiently than other current methods. These results serve as building blocks for proving results for less sparse polynomial systems
New data structure for univariate polynomial approximation and applications to root isolation, numerical multipoint evaluation, and other problems
We present a new data structure to approximate accurately and efficiently a
polynomial of degree given as a list of coefficients. Its properties
allow us to improve the state-of-the-art bounds on the bit complexity for the
problems of root isolation and approximate multipoint evaluation. This data
structure also leads to a new geometric criterion to detect ill-conditioned
polynomials, implying notably that the standard condition number of the zeros
of a polynomial is at least exponential in the number of roots of modulus less
than or greater than .Given a polynomial of degree with
for , isolating all its complex roots or
evaluating it at points can be done with a quasi-linear number of
arithmetic operations. However, considering the bit complexity, the
state-of-the-art algorithms require at least bit operations even for
well-conditioned polynomials and when the accuracy required is low. Given a
positive integer , we can compute our new data structure and evaluate at
points in the unit disk with an absolute error less than in
bit operations, where means
that we omit logarithmic factors. We also show that if is the absolute
condition number of the zeros of , then we can isolate all the roots of
in bit operations. Moreover, our
algorithms are simple to implement. For approximating the complex roots of a
polynomial, we implemented a small prototype in \verb|Python/NumPy| that is an
order of magnitude faster than the state-of-the-art solver \verb/MPSolve/ for
high degree polynomials with random coefficients
Generalization and variations of Pellet's theorem for matrix polynomials
We derive a generalized matrix version of Pellet's theorem, itself based on a
generalized Rouch\'{e} theorem for matrix-valued functions, to generate upper,
lower, and internal bounds on the eigenvalues of matrix polynomials. Variations
of the theorem are suggested to try and overcome situations where Pellet's
theorem cannot be applied.Comment: 20 page
Symbolic-Numeric Tools for Analytic Combinatorics in Several Variables
Analytic combinatorics studies the asymptotic behaviour of sequences through
the analytic properties of their generating functions. This article provides
effective algorithms required for the study of analytic combinatorics in
several variables, together with their complexity analyses. Given a
multivariate rational function we show how to compute its smooth isolated
critical points, with respect to a polynomial map encoding asymptotic
behaviour, in complexity singly exponential in the degree of its denominator.
We introduce a numerical Kronecker representation for solutions of polynomial
systems with rational coefficients and show that it can be used to decide
several properties (0 coordinate, equal coordinates, sign conditions for real
solutions, and vanishing of a polynomial) in good bit complexity. Among the
critical points, those that are minimal---a property governed by inequalities
on the moduli of the coordinates---typically determine the dominant asymptotics
of the diagonal coefficient sequence. When the Taylor expansion at the origin
has all non-negative coefficients (known as the `combinatorial case') and under
regularity conditions, we utilize this Kronecker representation to determine
probabilistically the minimal critical points in complexity singly exponential
in the degree of the denominator, with good control over the exponent in the
bit complexity estimate. Generically in the combinatorial case, this allows one
to automatically and rigorously determine asymptotics for the diagonal
coefficient sequence. Examples obtained with a preliminary implementation show
the wide applicability of this approach.Comment: As accepted to proceedings of ISSAC 201
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