Approximate factorization of multivariate polynomials using singular value decomposition

  • Authors:
  • Erich Kaltofen;John P. May;Zhengfeng Yang;Lihong Zhi

  • Affiliations:
  • Department of Mathematics, North Carolina State University, Raleigh, NV, 27695-8205, USA;School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada;Key Lab of Mathematics Mechanization, AMSS, Beijing 100080, China;Key Lab of Mathematics Mechanization, AMSS, Beijing 100080, China

  • Venue:
  • Journal of Symbolic Computation
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficients that contain numerical noise. Our algorithms are based on a generalization of the differential forms introduced by W. Ruppert and S. Gao to many variables, and use singular value decomposition or structured total least squares approximation and Gauss-Newton optimization to numerically compute the approximate multivariate factors. We demonstrate on a large set of benchmark polynomials that our algorithms efficiently yield approximate factorizations within the coefficient noise even when the relative error in the input is substantial (10^-^3).