Upper Bound for Lebesgue Constant of Bivariate Lagrange Interpolation Polynomial on the Second Kind Chebyshev Points

In the paper, we study the upper bound estimation of the Lebesgue constant of the bivariate Lagrange interpolation polynomial based on the common zeros of product Chebyshev polynomials of the second kind on the square − 1,1 2 . And, we prove that the growth order of the Lebesgue constant is O n + 2 2 . This result is different from the Lebesgue constant of Lagrange interpolation polynomial on the unit disk, the growth order of which is O n . And, it is different from the Lebesgue constant of the Lagrange interpolation polynomial based on the common zeros of product Chebyshev polynomials of the first kind on the square − 1,1 2 , the growth order of which is O ln n 2 .

Node Generation for RBF-FD Methods by QR Factorization

Polyharmonic spline (PHS) radial basis functions (RBFs) have been used in conjunction with polynomials to create RBF finite-difference (RBF-FD) methods. In 2D, these methods are usually implemented with Cartesian nodes, hexagonal nodes, or most commonly, quasi-uniformly distributed nodes generated through fast algorithms. We explore novel strategies for computing the placement of sampling points for RBF-FD methods in both 1D and 2D while investigating the benefits of using these points. The optimality of sampling points is determined by a novel piecewise-defined Lebesgue constant. Points are then sampled by modifying a simple, robust, column-pivoting QR algorithm previously implemented to find sets of near-optimal sampling points for polynomial approximation. Using the newly computed sampling points for these methods preserves accuracy while reducing computational costs by mitigating stencil size restrictions for RBF-FD methods. The novel algorithm can also be used to select boundary points to be used in conjunction with fast algorithms that provide quasi-uniformly distributed nodes.

Lebesgue Constants and Optimal Node Systems via Symbolic Computations

Polynomial interpolation is a classical method to approximatecontinuous functions by polynomials. To measure the correctness of theapproximation, Lebesgue constants are introduced. For a given node system $X^{(n+1)}=\{x_1<\ldots<x_{n+1}\}\, (x_j\in [a,b])$, the Lebesgue function $\lambda_n(x)$ is the sum of the modulus of the Lagrange basis polynomials built on $X^{(n+1)}$. The Lebesgue constant $\Lambda_n$ assigned to the function $\lambda_n(x)$ is its maximum over $[a,b]$. The Lebesgue constant bounds the interpolation error, i.e., the interpolation polynomial is at most $(1+\Lambda_n)$ times worse then the best approximation.The minimum of the $\Lambda_n$'s for fixed $n$ and interval $[a,b]$ is called the optimal Lebesgue constant $\Lambda_n^*$.For specific interpolation node systems such as the equidistant system, numerical results for the Lebesgue constants $\Lambda_n$ and their asymptoticbehavior are known \cite{3,7}. However, to give explicit symbolic expression for the minimal Lebesgue constant $\Lambda_n^*$ is computationally difficult. In this work, motivated by Rack \cite{5,6}, we are interested for expressing the minimalLebesgue constants symbolically on $[-1,1]$ and we are also looking for thecharacterization of the those node systems which realize theminimal Lebesgue constants. We exploited the equioscillation property of the Lebesgue function \cite{4} andused quantifier elimination and Groebner Basis as tools \cite{1,2}. Most of the computation is done in Mathematica \cite{8}.

Convergence Analysis for the Chebyshev Collocation Methods to Volterra Integral Equations with a Weakly Singular Kernel

In this paper, a Chebyshev-collocation spectral method is developed for Volterra integral equations (VIEs) of second kind with weakly singular kernel. We first change the equation into an equivalent VIE so that the solution of the new equation possesses better regularity. The integral term in the resulting VIE is approximated by Gauss quadrature formulas using the Chebyshev collocation points. The convergence analysis of this method is based on the Lebesgue constant for the Lagrange interpolation polynomials, approximation theory for orthogonal polynomials, and the operator theory. The spectral rate of convergence for the proposed method is established in theL∞-norm and weightedL2-norm. Numerical results are presented to demonstrate the effectiveness of the proposed method.