Sparse Interpolation in Terms of Multivariate Chebyshev Polynomials

The computation can be made more efficient by using recent algorithms specific to linear recurrences with constant coefficients [1] (or, equivalently, to rational func- tions)..

Sparse polynomial interpolation consists in recovering of a sparse representation of a polynomial P given by a blackbox program which computes values of P at as many points

Our main problem here is to find an efficient reduction of the general sparse inter- polation problem for rational functions to the special case of polynomials.. This is typically the

Building on [DZ07], Zannier proved in [Zan10] a toric version of Hilbert irreducibil- ity theorem and then deduce from it an analogue of Bertini’s theorem for covers, where the

Polynomial interpolation is a method in numerical analysis, which purpose is to find the polynomial approximated to the original functionA. Additionaly number of equations may

Organization of calculations or processing of data on the observed samples of the process carried out with the help of the representation of algebraic polynomials refers to the

This error analysis can be used directly to derive an algorithm to evaluate a polynomial in the Chebyshev basis on an interval in Algorithm 21.

Concerning the computation of the capacity of a union of intervals, we do not recommend using our semidefinite procedure or a Remez-type procedure to produce Chebyshev