In this paper, we study a family of decision problems arising from sparse univariate polynomial interpolation through a given set of points. We use the. filexlib. Solving non-linear systems of multivariate polynomials with approximate Table 7.1: Sparse univariate interpolation over large finite fields,.
3 Univariate Interpolation. In this section, we present a Monte Carlo algorithm to interpolate a sparse polynomial given through an MBB.
In this paper, we consider the problem of interpolating univariate polynomials over a field of characteristic zero that are sparse in (a) the Pochhammer
We present Hermite polynomial interpolation algorithms that for a sparse univariate polynomial f with coefficients from a field compute the Multivariate interpolation reduces to univariate interpolation by way of Kronecker substitu- tion, which maps an n-variate polynomial f to a
P) and sometimes have to be guessed themselves. The total degree can be guessed efficiently by using sparse interpolation for the univariate polynomial P(α1
Sparse polynomial interpolation over finite fields when input data are exact has numerous applications and has been studied by many authors [7, 12, 22, 19, 31].
This work considers the problem of determining whether or not there exists a sparse univariate polynomial that interpolates a given setS={(xi,yi)} of points
The combination of different (dense) algorithms for univariate rational interpolation and sparse polynomial interpolation leads to a class of sparse
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