Starting with univariate polynomial interpolation we arrive to a natural generalization of fundamental theorem of algebra for certain systems of multivariate algebraic equations.
In this paper, we study how to quickly compute the <-minimal monomial interpolating basis for a multivariate polynomial interpolation problem. We address the notion of "reverse" reduced basis of linearly independent polynomials and design an algorithm for it. Based on the notion, for any monomial ordering we present a new method to read off the <-minimal monomial interpolating basis from monomials appearing in the polynomials representing the interpolation conditions.
We study in this paper the function approximation error of linear interpolation and extrapolation. Several upper bounds are presented along with the conditions under which they are sharp. All results are under the assumptions that the function has Lipschitz continuous gradient and is interpolated on an affinely independent sample set. Errors for quadratic functions and errors of bivariate linear extrapolation are analyzed in depth.
We discuss a pointwise numerical differentiation formula on multivariate scattered data, based on the coefficients of local polynomial interpolation at Discrete Leja Points, written in Taylor's formula monomial basis. Error bounds for the approximation of partial derivatives of any order compatible with the function regularity are provided, as well as sensitivity estimates to functional perturbations, in terms of the inverse Vandermonde coefficients that are active in the differentiation process. Several numerical tests are presented showing the accuracy of the approximation.
The natural kinship between classical theories of interpolation and approximation is well explored. In contrast to this, the interrelation between interpolation and approximation is subtle and this duality is relatively obscure in the context of fractal interpolation. The notion of $α$-fractal function provides a proper foundation for the approximation theoretic facet of univariate fractal interpolation functions (FIFs). However, no comparable approximation theoretic aspects of FIFs has been developed for functions of several variables. The current article intends to open the door for intrigui…
We study approximation properties of general multivariate periodic quasi-interpolation operators, which are generated by distributions/functions $\widetildeφ_j$ and trigonometric polynomials $φ_j$. The class of such operators includes classical interpolation polynomials ($\widetildeφ_j$ is the Dirac delta function), Kantorovich-type operators ($\widetildeφ_j$ is a characteristic function), scaling expansions associated with wavelet constructions, and others. Under different compatibility conditions on $\widetildeφ_j$ and $φ_j$, we obtain upper and lower bound estimates for the $L_p$-error of a…
In this paper, we propose two methods for multivariate Hermite interpolation of manifold-valued functions. On the one hand, we approach the problem via computing suitable weighted Riemannian barycenters. To satisfy the conditions for Hermite interpolation, the sampled derivative information is converted into a condition on the derivatives of the associated weight functions. It turns out that this requires the solution of linear systems of equations, but no vector transport is necessary. This approach treats all given sample data points equally and is intrinsic in the sense that it does not dep…
For $m,n \in \mathbb{N}$, $m\geq 1$ and a given function $f : \mathbb{R}^m\longrightarrow \mathbb{R}$, the polynomial interpolation problem (PIP) is to determine a unisolvent node set $P_{m,n} \subseteq \mathbb{R}^m$ of $N(m,n):=|P_{m,n}|=\binom{m+n}{n}$ points and the uniquely defined polynomial $Q_{m,n,f}\in Π_{m,n}$ in $m$ variables of degree $\mathrm{deg}(Q_{m,n,f})\leq n \in \mathbb{N}$ that fits $f$ on $P_{m,n}$, i.e., $Q_{m,n,f}(p) = f(p)$, $\forall\, p \in P_{m,n}$. For $m=1$ the solution to the PIP is well known. In higher dimensions, however, no closed framework was available. We her…
On exponential convergence of Chebyshev polynomial approximation for multivariate analytic functions
School of Mathematics and Statistics, Huazhong University of Science and Technology(华中科技大学数学与统计学院);Hubei Key Laboratory of Engineering Modeling and Scientific Computing, Huazhong University of Science and Technology(华中科技大学工程建模与科学计算湖北省重点实验室)
Arnoldi-Enhanced Multivariate Hermite Interpolation of Manifold-Valued Data
School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University(西交利物浦大学数理学院);Department of Mathematical Sciences, University of Liverpool(利物浦大学数学科学系)
In this paper we suggest a moment matching method for quadratic-bilinear dynamical systems. Most system-theoretic reduction methods for nonlinear systems rely on multivariate frequency representations. Our approach instead uses univariate frequency representations tailored towards user-pre-defined families of inputs. Then moment matching corresponds to a one-dimensional interpolation problem, not to multi-dimensional interpolation as for the multivariate approaches, i.e., it also involves fewer interpolation frequencies to be chosen. Comparing to former contributions towards nonlinear model re…
Lagrange interpolation at real projections of Leja sequences for the unit disk
Université Paul Sabatier(图卢兹第三大学);Institut de Mathématiques, Université de Toulouse III and CNRS (UMR 5219)(图卢兹第三大学数学研究所及法国国家科学研究中心);Hanoi University of Education(河内师范大学)
A Unified Framework for Efficient Kernel and Polynomial Interpolation
Kahlert School of Computing, University of Utah(犹他大学卡勒特计算学院);Department of Applied Mathematics and Statistics, Colorado School of Mines(科罗拉多矿业大学应用数学与统计系);Department of Mathematics, and Scientific Computing and Imaging (SCI) Institute, University of Utah(犹他大学数学系及科学计算与成像研究所);Stena Center for Financial Technology, University of Utah(犹他大学斯泰纳金融技术研究中心)
In this paper, we propose an interpolation formula for periodic functions. This formula can be regarded as an analog of the Sinc approximation, which is an interpolation formula for functions defined on the entire infinite interval. Theoretical error analysis and numerical examples show that the proposed formula converges exponentially for analytic periodic functions.
$\mathcal{H}_2$-optimal model reduction of linear quadratic-output systems by multivariate rational interpolation
Courant Institute of Mathematical Sciences, New York University(纽约大学库朗数学科学研究所);Max Planck Institute for Dynamics of Complex Technical Systems(马克斯·普朗克复杂技术系统动力学研究所);Virginia Tech(弗吉尼亚理工大学)
Iteration methods based on barycentric rational interpolation are derived that exhibit accelerating orders of convergence. For univariate root search, the derivative-free methods approach quadratic convergence and the first-derivative methods approach cubic convergence. For univariate optimisation, the convergence order of the derivative-free methods approaches 1.62 and the order of the first-derivative methods approaches 2.42. Generally, performance advantages are found with respect to low-memory iteration methods. In optimisation problems where the objective function and gradient is calculat…