6,352 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
Symmetry Detection of Rational Space Curves from their Curvature and Torsion
We present a novel, deterministic, and efficient method to detect whether a
given rational space curve is symmetric. By using well-known differential
invariants of space curves, namely the curvature and torsion, the method is
significantly faster, simpler, and more general than an earlier method
addressing a similar problem. To support this claim, we present an analysis of
the arithmetic complexity of the algorithm and timings from an implementation
in Sage.Comment: 25 page
Sparse-grid polynomial interpolation approximation and integration for parametric and stochastic elliptic PDEs with lognormal inputs
By combining a certain approximation property in the spatial domain, and
weighted -summability of the Hermite polynomial expansion coefficients
in the parametric domain obtained in [M. Bachmayr, A. Cohen, R. DeVore and G.
Migliorati, ESAIM Math. Model. Numer. Anal. (2017), 341-363] and [M.
Bachmayr, A. Cohen, D. D\~ung and C. Schwab, SIAM J. Numer. Anal. (2017), 2151-2186], we investigate linear non-adaptive methods of fully
discrete polynomial interpolation approximation as well as fully discrete
weighted quadrature methods of integration for parametric and stochastic
elliptic PDEs with lognormal inputs. We explicitly construct such methods and
prove corresponding convergence rates in of the approximations by them,
where is a number characterizing computation complexity. The linear
non-adaptive methods of fully discrete polynomial interpolation approximation
are sparse-grid collocation methods. Moreover, they generate in a natural way
discrete weighted quadrature formulas for integration of the solution to
parametric and stochastic elliptic PDEs and its linear functionals, and the
error of the corresponding integration can be estimated via the error in the
Bochner space norm of the generating methods
where is the Gaussian probability measure on and
is the energy space. We also briefly consider similar problems for
parametric and stochastic elliptic PDEs with affine inputs, and by-product
problems of non-fully discrete polynomial interpolation approximation and
integration. In particular, the convergence rate of non-fully discrete obtained
in this paper improves the known one
Pseudospectral Model Predictive Control under Partially Learned Dynamics
Trajectory optimization of a controlled dynamical system is an essential part
of autonomy, however many trajectory optimization techniques are limited by the
fidelity of the underlying parametric model. In the field of robotics, a lack
of model knowledge can be overcome with machine learning techniques, utilizing
measurements to build a dynamical model from the data. This paper aims to take
the middle ground between these two approaches by introducing a semi-parametric
representation of the underlying system dynamics. Our goal is to leverage the
considerable information contained in a traditional physics based model and
combine it with a data-driven, non-parametric regression technique known as a
Gaussian Process. Integrating this semi-parametric model with model predictive
pseudospectral control, we demonstrate this technique on both a cart pole and
quadrotor simulation with unmodeled damping and parametric error. In order to
manage parametric uncertainty, we introduce an algorithm that utilizes Sparse
Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We
implement this online learning technique on a cart pole and quadrator, then
demonstrate the use of online learning and obstacle avoidance for the dubin
vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201
Parametric Polyhedra with at least Lattice Points: Their Semigroup Structure and the k-Frobenius Problem
Given an integral matrix , the well-studied affine semigroup
\mbox{ Sg} (A)=\{ b : Ax=b, \ x \in {\mathbb Z}^n, x \geq 0\} can be
stratified by the number of lattice points inside the parametric polyhedra
. Such families of parametric polyhedra appear in
many areas of combinatorics, convex geometry, algebra and number theory. The
key themes of this paper are: (1) A structure theory that characterizes
precisely the subset \mbox{ Sg}_{\geq k}(A) of all vectors b \in \mbox{
Sg}(A) such that has at least solutions. We
demonstrate that this set is finitely generated, it is a union of translated
copies of a semigroup which can be computed explicitly via Hilbert bases
computations. Related results can be derived for those right-hand-side vectors
for which has exactly solutions or fewer
than solutions. (2) A computational complexity theory. We show that, when
, are fixed natural numbers, one can compute in polynomial time an
encoding of \mbox{ Sg}_{\geq k}(A) as a multivariate generating function,
using a short sum of rational functions. As a consequence, one can identify all
right-hand-side vectors of bounded norm that have at least solutions. (3)
Applications and computation for the -Frobenius numbers. Using Generating
functions we prove that for fixed the -Frobenius number can be
computed in polynomial time. This generalizes a well-known result for by
R. Kannan. Using some adaptation of dynamic programming we show some practical
computations of -Frobenius numbers and their relatives
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