Conditions for exact resultants using the Dixon formulation

  • Authors:
  • Arthur D. Chtcherba;Deepak Kapur

  • Affiliations:
  • Department of Computer Science, University of New Mexico, Albuquerque, NM;Department of Computer Science, University of New Mexico, Albuquerque, NM

  • Venue:
  • ISSAC '00 Proceedings of the 2000 international symposium on Symbolic and algebraic computation
  • Year:
  • 2000

Quantified Score

Hi-index 0.02

Visualization

Abstract

A structural criteria on polynomial systems is developed for which the generalized Dixon formulation of multivariate resultants defined by Kapur, Saxena and Yang (1994) computes the resultant exactly. The concept of a Dixon-exact support (the set of exponent vectors of terms appearing in a polynomial system) is introduced so that the Dixon formulation produces the exact resultant for generic unmixed polynomial systems whose support is Dixon-exact. A geometric operation, called direct-sum, on the supports is defined that preserves the property of supports being Dixon-exact. Generic n-degree systems and multigraded systems are shown to be a special case of generic unmixed polynomial systems whose support is Dixon-exact. Using a scaling techniques discussed by Kapur and Saxena (1997), a wide class of polynomial systems can be identified for which the Dixon formulation produces exact resultants. This analysis can be used to classify terms appearing in the convex hull (also called the Newton polytope) of the support of a polynomial system that can cause extraneous factors in the computation of a projection operation by the generalized Dixon formulation. For the bivariate case, a complete analysis of the terms corresponding to the exponent vectors in the Newton polytope of the support of a polynomial system is given vis a vis their role in producing extraneous factors in a projection operator. A necessary and sufficient condition is developed for a support to be Dixon-exact. Such an analysis is likely to give insights for the general case of elimination of arbitrarily many variables.