11,982 research outputs found
Parallel sparse interpolation using small primes
To interpolate a supersparse polynomial with integer coefficients, two
alternative approaches are the Prony-based "big prime" technique, which acts
over a single large finite field, or the more recently-proposed "small primes"
technique, which reduces the unknown sparse polynomial to many low-degree dense
polynomials. While the latter technique has not yet reached the same
theoretical efficiency as Prony-based methods, it has an obvious potential for
parallelization. We present a heuristic "small primes" interpolation algorithm
and report on a low-level C implementation using FLINT and MPI.Comment: Accepted to PASCO 201
Reconstructing Rational Functions with
We present the open-source library for the
reconstruction of multivariate rational functions over finite fields. We
discuss the involved algorithms and their implementation. As an application, we
use in the context of integration-by-parts reductions and
compare runtime and memory consumption to a fully algebraic approach with the
program .Comment: 46 pages, 3 figures, 6 tables; v2: matches published versio
Multivariate sparse interpolation using randomized Kronecker substitutions
We present new techniques for reducing a multivariate sparse polynomial to a
univariate polynomial. The reduction works similarly to the classical and
widely-used Kronecker substitution, except that we choose the degrees randomly
based on the number of nonzero terms in the multivariate polynomial, that is,
its sparsity. The resulting univariate polynomial often has a significantly
lower degree than the Kronecker substitution polynomial, at the expense of a
small number of term collisions. As an application, we give a new algorithm for
multivariate interpolation which uses these new techniques along with any
existing univariate interpolation algorithm.Comment: 21 pages, 2 tables, 1 procedure. Accepted to ISSAC 201
Revisit Sparse Polynomial Interpolation based on Randomized Kronecker Substitution
In this paper, a new reduction based interpolation algorithm for black-box
multivariate polynomials over finite fields is given. The method is based on
two main ingredients. A new Monte Carlo method is given to reduce black-box
multivariate polynomial interpolation to black-box univariate polynomial
interpolation over any ring. The reduction algorithm leads to multivariate
interpolation algorithms with better or the same complexities most cases when
combining with various univariate interpolation algorithms. We also propose a
modified univariate Ben-or and Tiwarri algorithm over the finite field, which
has better total complexity than the Lagrange interpolation algorithm.
Combining our reduction method and the modified univariate Ben-or and Tiwarri
algorithm, we give a Monte Carlo multivariate interpolation algorithm, which
has better total complexity in most cases for sparse interpolation of black-box
polynomial over finite fields
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