scholarly journals Spectral Limitations of Quadrature Rules and Generalized Spherical Designs

2019 ◽  
Author(s):  
Stefan Steinerberger
Keyword(s):  
Quadrature Rule ◽  
Quadrature Rules ◽  
Dimensional Manifold ◽  
Spherical Designs

Abstract We study manifolds $M$ equipped with a quadrature rule \begin{equation*} \int_{M}{\phi(x)\,\mathrm{d}x} \simeq \sum_{i=1}^{n}{a_i \phi(x_i)}.\end{equation*}We show that $n$-point quadrature rules with nonnegative weights on a compact $d$-dimensional manifold cannot integrate more than at most the 1st $c_{d}n + o(n)$ Laplacian eigenfunctions exactly. The constants $c_d$ are explicitly computed and $c_2 = 4$. The result is new even on $\mathbb{S}^2$ where it generalizes results on spherical designs.

scholarly journals On Appel-Type Quadrature Rules

2002 ◽  
Vol 9 (3) ◽  
pp. 405-412
Author(s):  
C. Belingeri ◽  
B. Germano
Keyword(s):  
Remainder Term ◽  
Quadrature Rule ◽  
Bernoulli Numbers ◽  
Quadrature Rules ◽  
Rule Based ◽  
Numerical Example ◽  
The Given ◽  
Derivatives Of

Abstract The Radon technique is applied in order to recover a quadrature rule based on Appel polynomials and the so called Appel numbers. The relevant formula generalizes both the Euler-MacLaurin quadrature rule and a similar rule using Euler (instead of Bernoulli) numbers and even (instead of odd) derivatives of the given function at the endpoints of the considered interval. In the general case, the remainder term is expressed in terms of Appel numbers, and all derivatives appear. A numerical example is also included.

scholarly journals Variational Integrators in Holonomic Mechanics

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1358
Author(s):  
Shumin Man ◽  
Qiang Gao ◽  
Wanxie Zhong
Keyword(s):  
Variational Principle ◽  
Dynamic Systems ◽  
Time Integration ◽  
Quadrature Rule ◽  
Quadrature Rules ◽  
Variational Integrators ◽  
Lagrange Polynomials ◽  
Holonomic Constraints ◽  
Long Time ◽  
Long Time Integration

Variational integrators for dynamic systems with holonomic constraints are proposed based on Hamilton’s principle. The variational principle is discretized by approximating the generalized coordinates and Lagrange multipliers by Lagrange polynomials, by approximating the integrals by quadrature rules. Meanwhile, constraint points are defined in order to discrete the holonomic constraints. The functional of the variational principle is divided into two parts, i.e., the action of the unconstrained term and the constrained term and the actions of the unconstrained term and the constrained term are integrated separately using different numerical quadrature rules. The influence of interpolation points, quadrature rule and constraint points on the accuracy of the algorithms is analyzed exhaustively. Properties of the proposed algorithms are investigated using examples. Numerical results show that the proposed algorithms have arbitrary high order, satisfy the holonomic constraints with high precision and provide good performance for long-time integration.

scholarly journals A Mixed Quadrature Rule by Blending Clenshaw-Curtis and Gauss-Legendre Quadrature Rules for Approximate Evaluation of Real Definite Integrals

2012 ◽  
Vol 2 ◽  
pp. 10-15
Author(s):  
Anasuya Pati ◽  
Rajani B. Dash ◽  
Pritikanta Patra
Keyword(s):  
Quadrature Rule ◽  
Quadrature Rules ◽  
Approximate Evaluation ◽  
Definite Integrals ◽  
Legendre Quadrature

A mixed quadrature rule blending Clenshaw-Curtis five point rule and Gauss-Legendre three point rule is formed. The mixed rule has been tested and found to be more effective than that of its constituent Clenshaw-Curtis five point rule.

scholarly journals A Note on Finite Quadrature Rules with a Kind of Freud Weight Function

2009 ◽  
Vol 2009 ◽  
pp. 1-8
Author(s):  
Kamal Aghigh ◽  
M. Masjed-Jamei
Keyword(s):  
Weight Function ◽  
Quadrature Rule ◽  
Quadrature Rules ◽  
Zeros Of Polynomials ◽  
Freud Weight ◽  
Weighted Quadrature

We introduce a finite class of weighted quadrature rules with the weight function on as , where are the zeros of polynomials orthogonal with respect to the introduced weight function, are the corresponding coefficients, and is the error value. We show that the above formula is valid only for the finite values of . In other words, the condition must always be satisfied in order that one can apply the above quadrature rule. In this sense, some numerical and analytic examples are also given and compared.

scholarly journals Evaluation of real denite integrals by using of mixed quadrature rules over a Triangular Domain

2016 ◽  
Vol 12 (2) ◽  
pp. 5945-5948
Author(s):  
Rajani Ballav Dash ◽  
Pritikanta Patra ◽  
Dwitikrushna Behera
Keyword(s):  
Quadrature Rule ◽  
Quadrature Rules ◽  
Two Dimensional ◽  
Approximate Evaluation ◽  
Suitable Test ◽  
Triangular Domain ◽  
Definite Integrals

A mixed quadrature rule of higher precision for approximate evaluation of real definite integrals over a triangular domain has been constructed.The relative ecienciesof the proposed mixed quadrature rule has been veried by using suitable test inte-grals.In this paper we present a mixed quadrature i.e. mixed quadrature of anti-Lobatto rule and Fejer's rst rule in one variable.For real denite integral over the triangular surface : f(x; y)j0 x; y 1; x + y 1g in the Cartesian two dimensional (x,y) space.Mathematical transformation from (x,y) space to (; ) space maps the standard triangle in (x,y) space to a standard 2-square in (:) space: f(; )j 1 ; 1g.

Applications

2004 ◽  
Author(s):  
Walter Gautschi
Keyword(s):  
Orthogonal Polynomials ◽  
Gaussian Quadrature ◽  
Rational Functions ◽  
Quadrature Rule ◽  
Gauss Quadrature ◽  
Quadrature Rules ◽  
Condition Numbers ◽  
Computational Aspects ◽  
Close Relatives ◽  
Gauss Quadrature Rules

The connection between orthogonal polynomials and quadrature rules has already been elucidated in §1.4. The centerpieces were the Gaussian quadrature rule and its close relatives—the Gauss–Radau and Gauss–Lobatto rules (§1.4.2). There are, however, a number of further extensions of Gauss’s approach to numerical quadrature. Principal among them are Kronrod’s idea of extending an n-point Gauss rule to a (2n + 1)-point rule by inserting n + 1 additional nodes and choosing all weights in such a way as to maximize the degree of exactness (cf. Definition 1.44), and Turán’s extension of the Gauss quadrature rule allowing not only function values, but also derivative values, to appear in the quadrature sum. More recent extensions relate to the concept of accuracy, requiring exactness not only for polynomials of a certain degree, but also for rational functions with prescribed poles. Gauss quadrature can also be adapted to deal with Cauchy principal value integrals, and there are other applications of Gauss’s ideas, for example, in combination with Stieltjes’s procedure or the modified Chebyshev algorithm, to generate polynomials orthogonal on several intervals, or, in comnbination with Lanczos’s algorithm, to estimate matrix functionals. The present section is to discuss these questions in turn, with computational aspects foremost in our mind. We have previously seen in Chapter 2 how Gauss quadrature rules can be effectively employed in the context of computational methods; for example, in computing the absolute and relative condition numbers of moment maps (§2.1.5), or as a means of discretizing measures in the multiple-component discretization method for orthogonal polynomials (§2.2.5) and in Stieltjes-type methods for Sobolev orthogonal polynomials (§2.5.2). It is time now to discuss the actual computation of these, and related, quadrature rules.

scholarly journals Complex Jacobi matrices and quadrature rules

Filomat ◽  
2003 ◽  
pp. 117-134 ◽  
Author(s):  
Gradimir Milovanovic ◽  
Aleksandar Cvetkovic
Keyword(s):  
Convex Hull ◽  
Orthogonal Polynomials ◽  
Jacobi Matrix ◽  
Jacobi Operator ◽  
Quadrature Rule ◽  
Quadrature Rules ◽  
Jacobi Matrices ◽  
Infinite Matrix ◽  
Zeros Of Orthogonal Polynomials ◽  
Uniformly Bounded

Given any sequence of orthogonal polynomials, satisfying the three term recurrence relation xpn(x) = ?n+1pn+1(x) + ?npn(x) + ?npn-1(x), p-1(x)=0 po(x)=1 with ?n ? 0, n ? N, ?0 = 1, an infinite Jacobi matrix can be associated in the following way ? ? ??0 ?1 0...? ? ? ??1 ?1 ?2...? ? ? J = ?0 ?2 ?2...? ?.... ? ?.... ? ?....? ? ? In the general case if the sequences {?n} or {?n} are complex the associated Jacobi matrix is complex. Under the condition that both sequences {?n}and {?n} are uniformly bounded, the associated Jacobi matrix can be understood as a linear operator J acting on ?2, the space of all complex square-summable sequences, where the value of the operator J at the vector x is a product of an infinite vector x and an infinite matrix J in the matrix sense. The case when the sequences {?n} and {?n} are not uniformly bounded, an operator acting on ?2 can not be defined that easily. Additional properties of the sequence of orthogonal polynomials are needed in order to be able to define the operator uniquely. The case when the sequences ?n and ?n are real is very well understood. The spectra of the Jacobi matrix J equals the support of the measure of orthogonality for the given sequence of orthogonal polynomials. All zeros of orthogonal polynomials are real, simple and interlace, contained in the convex hull of the spectra of the Jacobi operator associated with the infinite Jacobi matrix J. Every point in ?(J) attracts zeros of orthogonal polynomials. An application of orthogonal polynomials is the construction of quadrature rules for the approximation of integration with respect to the measure of orthogonality. For arbitrary sequences {?n} and {?n} the situation is changed dramatically. Zeros of orthogonal polynomials need not be simple; they are not real and they do not necessarily lie in the convex hull of ?(J). There is also a little known about convergence results of related quadrature rule. Only in recent years a connection between complex Jacobi matrices and related orthogonal polynomials is interesting again (see [2]). Studies of complex Jacobi operators should lead to a better understanding of related orthogonal polynomials, but also the study of orthogonal polynomials with the complex Jacobi matrices should put more light on the non-hermitian banded symmetric matrices. In this lecture some results are given about complex Jacobi matrices and related quadrature rules, and also some interesting examples are presented.

Sign in / Sign up

Export Citation Format

Share Document