A weakly stable algorithm for Pade´ approximants and the inversion of Hankel matrices
SIAM Journal on Matrix Analysis and Applications
Fraction-free computation of matrix Padé systems
ISSAC '97 Proceedings of the 1997 international symposium on Symbolic and algebraic computation
When are two numerical polynomials relatively prime?
Journal of Symbolic Computation - Special issue on symbolic numeric algebra for polynomials
Journal of Symbolic Computation - Special issue on symbolic numeric algebra for polynomials
A Stable Numerical Method for Inverting Shape from Moments
SIAM Journal on Scientific Computing
Structured Perturbations Part I: Normwise Distances
SIAM Journal on Matrix Analysis and Applications
Early termination in sparse interpolation algorithms
Journal of Symbolic Computation - Special issue: International symposium on symbolic and algebraic computation (ISSAC 2002)
Symbolic-numeric sparse interpolation of multivariate polynomials
Proceedings of the 2006 international symposium on Symbolic and algebraic computation
Proceedings of the 2006 international symposium on Symbolic and algebraic computation
On the numerical condition of a generalized Hankel eigenvalue problem
Numerische Mathematik
On probabilistic analysis of randomization in hybrid symbolic-numeric algorithms
Proceedings of the 2007 international workshop on Symbolic-numeric computation
Symbolic-numeric sparse interpolation of multivariate polynomials
Journal of Symbolic Computation
Diversification improves interpolation
Proceedings of the 36th international symposium on Symbolic and algebraic computation
Proceedings of the 37th International Symposium on Symbolic and Algebraic Computation
Sparse multivariate function recovery from values with noise and outlier errors
Proceedings of the 38th international symposium on International symposium on symbolic and algebraic computation
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We investigate our early termination criterion for sparse polynomial interpolation when substantial noise is present in the values of the polynomial. Our criterion in the exact case uses Monte Carlo randomization which introduces a second source of error. We harness the Gohberg-Semencul formula for the inverse of a Hankel matrix to compute estimates for the structured condition numbers of all arising Hankel matrices in quadratic arithmetic time overall, and explain how false ill-conditionedness can arise from our randomizations. Finally, we demonstrate by experiments that our condition number estimates lead to a viable termination criterion for polynomials with about 20 non-zero terms and of degree about 100, even in the presence of noise of relative magnitude 10-5.