Minkowski Decomposition and Geometric Predicates in Sparse Implicitization

Abstract

Based on the computation of a polytope Q, called the predicted polytope, containing the Newton polytope P of the implicit equation, implicitization of a parametric hypersurface is reduced to computing the nullspace of a numeric matrix. Polytope Q may contain P as a Minkowski summand, thus jeopardizing the efficiency of sparse implicitization. Our contribution is twofold. On one hand we tackle the aforementioned issue in the case of 2D curves and 3D surfaces by Minkowski decomposing Q thus detecting the Minkowski summand relevant to implicitization: we design and implement in Sage a new, public domain, practical, potentially generalizable and worst-case optimal algorithm for Minkowski decomposition in 3D based on integer linear programming. On the other hand, we formulate basic geometric predicates, namely membership and sidedness for given query points, as rank computations on the interpolation matrix, thus avoiding to expand the implicit polynomial. This approach is implemented in Maple.