4,432 research outputs found
Sparse implicitization by interpolation: Characterizing non-exactness and an application to computing discriminants
We revisit implicitization by interpolation in order to examine its properties in the context of sparse elimination theory. Based on the computation of a superset of the implicit support, implicitization is reduced to computing the nullspace of a numeric matrix. The approach is applicable to polynomial and rational parameterizations of curves and (hyper)surfaces of any dimension, including the case of parameterizations with base points.
Our support prediction is based on sparse (or toric) resultant theory, in order to exploit the sparsity of the input and the output. Our method may yield a multiple of the implicit equation: we characterize and quantify this situation by relating the nullspace dimension to the predicted support and its geometry. In this case, we obtain more than one multiples of the implicit equation; the latter can be obtained via multivariate polynomial gcd (or factoring).
All of the above techniques extend to the case of approximate computation, thus yielding a method of sparse approximate implicitization, which is important in tackling larger problems. We discuss our publicly available Maple implementation through several examples, including the benchmark of bicubic surface.
For a novel application, we focus on computing the discriminant of a multivariate polynomial, which characterizes the existence of multiple roots and generalizes the resultant of a polynomial system.
This yields an efficient, output-sensitive algorithm for
computing the discriminant polynomial
Implicitization of curves and (hyper)surfaces using predicted support
We reduce implicitization of rational planar parametric curves and (hyper)surfaces to linear algebra, by interpolating the coefficients of the implicit equation.
For predicting the implicit support, we focus on methods that exploit input and output structure in the sense of sparse (or toric) elimination theory, namely by computing the Newton polytope of the implicit polynomial, via sparse resultant theory.
Our algorithm works even in the presence of base points but, in this case, the implicit equation shall be obtained as a factor of the produced polynomial.
We implement our methods on Maple, and some on Matlab as well, and study their numerical stability and efficiency on several classes of curves and surfaces.
We apply our approach to approximate implicitization,
and quantify the accuracy of the approximate output,
which turns out to be satisfactory on all tested examples; we also relate our measures to Hausdorff distance.
In building a square or rectangular matrix, an important issue is (over)sampling the given curve or surface: we conclude that unitary complexes offer the best tradeoff between speed and accuracy when numerical methods are employed, namely SVD, whereas for exact kernel computation random integers is the method of choice.
We compare our prototype to existing software and find that it is rather competitive
Partial Degree Formulae for Plane Offset Curves
In this paper we present several formulae for computing the partial degrees
of the defining polynomial of the offset curve to an irreducible affine plane
curve given implicitly, and we see how these formulae particularize to the case
of rational curves. In addition, we present a formula for computing the degree
w.r.t the distance variable.Comment: 24 pages, no figure
Sparse implicitization by interpolation: Geometric computations using matrix representations
Based on the computation of a superset of the implicit support,
implicitization of a parametrically given hyper-surface is reduced to computing
the nullspace of a numeric matrix. Our approach exploits the sparseness of the
given parametric equations and of the implicit polynomial. In this work, we
study how this interpolation matrix can be used to reduce some key geometric
predicates on the hyper-surface to simple numerical operations on the matrix,
namely membership and sidedness for given query points. We illustrate our
results with examples based on our Maple implementation
Resultant-based Elimination in Ore Algebra
We consider resultant-based methods for elimination of indeterminates of Ore
polynomial systems in Ore algebra. We start with defining the concept of
resultant for bivariate Ore polynomials then compute it by the Dieudonne
determinant of the polynomial coefficients. Additionally, we apply
noncommutative versions of evaluation and interpolation techniques to the
computation process to improve the efficiency of the method. The implementation
of the algorithms will be performed in Maple to evaluate the performance of the
approaches.Comment: An updated (and shorter) version published in the SYNASC '21
proceedings (IEEE CS) with the title "Resultant-based Elimination for Skew
Polynomials
Discovering the roots: Uniform closure results for algebraic classes under factoring
Newton iteration (NI) is an almost 350 years old recursive formula that
approximates a simple root of a polynomial quite rapidly. We generalize it to a
matrix recurrence (allRootsNI) that approximates all the roots simultaneously.
In this form, the process yields a better circuit complexity in the case when
the number of roots is small but the multiplicities are exponentially
large. Our method sets up a linear system in unknowns and iteratively
builds the roots as formal power series. For an algebraic circuit
of size we prove that each factor has size at most a
polynomial in: and the degree of the squarefree part of . Consequently,
if is a -hard polynomial then any nonzero multiple
is equally hard for arbitrary positive 's, assuming
that is at most .
It is an old open question whether the class of poly()-sized formulas
(resp. algebraic branching programs) is closed under factoring. We show that
given a polynomial of degree and formula (resp. ABP) size
we can find a similar size formula (resp. ABP) factor in
randomized poly()-time. Consequently, if determinant requires
size formula, then the same can be said about any of its
nonzero multiples.
As part of our proofs, we identify a new property of multivariate polynomial
factorization. We show that under a random linear transformation ,
completely factors via power series roots. Moreover, the
factorization adapts well to circuit complexity analysis. This with allRootsNI
are the techniques that help us make progress towards the old open problems,
supplementing the large body of classical results and concepts in algebraic
circuit factorization (eg. Zassenhaus, J.NT 1969, Kaltofen, STOC 1985-7 \&
Burgisser, FOCS 2001).Comment: 33 Pages, No figure
Tropical Implicitization Revisited
Tropical implicitization means computing the tropicalization of a unirational
variety from its parametrization. In the case of a hypersurface, this amounts
to finding the Newton polytope of the implicit equation, without computing its
coefficients. We present a new implementation of this procedure in Oscar.jl. It
solves challenging instances, and can be used for classical implicitization as
well. We also develop implicitization in higher codimension via Chow forms, and
we pose several open questions.Comment: 18 pages, 3 figure
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