保凸C^1分段二次多项式插值方法

本导出了二次多项式保凸的充要条件,通过插值部分新节点,得到了一种新的保凸Ｃ＾１分段二镒多项式插值函数。     

C~k连续的保形分段2k次多项式插值

1．引言在每个子区间上，通过插入至多一个内结点，Brodlie和Butt［1］给出了分段三次多项式保形插值算法，Randal［2］等讨论了分段五次多项式插值，作者［31讨论了一般分段奇次多项式的保形插值，并且给1了内结点的位置范围公式．这种插值方法完全解决了一般的分段奇次多项式的保形插值问题．关于分段偶次多项式的保形插值，大多数文献只讨论分段二次保形插值，这里要特别指出的是Shumake［4j导出了二次样条保凸的充要条件，并且给出了一个二次样条保形插值的方法．在每一个子区间上至多插入一个内结点，则一个二次插值样条就可得到．作…     

一类二次保形拟插值函数的研究

通过讨论一种保形拟插值的基函数与二次规范B-样条函数之间的关系,提出了一类二次保形拟插值样条函数,得到了这类保形拟插值函数在具有线性再生性质,并保持原有数据点列的单调性和凸性时分别应满足的条件,并给出几个应用实例.     

分段有理三次保凸插值

给定插值数据{(x_i,y_i)}_(1=0)~n,在许多实际应用中(如VLSI、CAD/CAM等),要求插值函数除满足一定的光滑性条件外,还必须反映插值点集的整体几何性质。例如,通常要求单调(凸)数据产生的插值函数也是单调(凸)的,用标准插值技术象多项式或三次样条,这些要求     

分段低次插值多项式序列的一致收敛性

利用分段线性与三次Hermite插值基函数以及连续模概念,分别推导出分段线性与三次Hermite插值多项式序列一致收敛于被插函数.     

改进Lagrange插值多项式误差上界的系数

当用Lagrange插值多项式逼近函数时,重要的是要了解误差项的性态.本文研究具有等距节点的Lagrange插值多项式,估计了Lagrange插值多项式逼近函数误差项的上界,改进了小于5次Lagrange插值多项式逼近函数误差界的系数.     

C~3连续的三角样条函数与曲线

本文构造了一种三次三角样条函数 ,函数的每一段由三个函数值生成 ,具有C3连续性和较好的逼近性 ,可方便地进行插值 .基于同样的方法得出了一种C3连续的三角样条曲线 ,曲线也有较好的逼近性 ,而且具有局部性、保凸性等特性 .     

有理线性插值曲线和曲面

构造了含参数的分段线性有理插值函数(分子、分母均为一次多项式),通过适当选择形状参数,由此函数产生的曲线一阶连续并且保单调.文中用张量积方法将此结果推广到二元矩形网格上的曲面插值,同时给出了插值函数的误差估计及数值例子.     

一类C~2连续且保凸的插值三次参数样条曲线的构造

本文给出在平面上插值点列为凸的时,构造一类 C~2连续且保凸的插值三次参数样条曲线的方法.这里通过选择插值节点 P_i 处插值曲线 p(t)的切矢方向和长度来代替以往常用的参变量,从而得到一类新的方法.     

保凸分段三次Bézier插值样条曲线

本文讨论分段三次 Bézier曲线的保凸插值 ,对给定的凸数据点列在相邻两型值点之间构造两个三次 Bézier曲线子段 ,两段之间 G2连续的 ,所构造的曲线插值所有型值点且是 G1的和保凸的     

Shape control of curve design by weighted rational spline

Controlling the convexity and the strain energy of the interpolating curve can be carried out by controlling the second-order derivative of the interpolating function. In [1], the rational cubic spline with linear denominator has been used to constrain the convexity and the strain energy of the interpolating curves, but it does not work in some case. This paper deals with the weighted rational cubic spline with linear denominator for this kind of constraint, the sufficient and necessary condition for controlling the convexity and strain energy of the interpolating curves are derived, and a numerical example is given.     

Optimal control computation for integro-differential aerodynamic equations

In order to maintain spectrally accurate solutions, the grids on which a non-linear physical problem is to be solved must also be obtained by spectrally accurate techniques. The purpose of this paper is to describe a pseudospectral computational method of solving integro-differential systems with quadratic performance index. The proposed method is based on the idea of relating grid points to the structure of orthogonal interpolating polynomials. The optimal control and the trajectory are approximated by the m th degree interpolating polynomial. This interpolating polynomial is spectrally constructed using Legendre–Gauss–Lobatto grid points as the collocation points, and Lagrange polynomials as trial functions. The integrals involved in the formulation of the problem are calculated by Gauss–Lobatto integration rule, thereby reducing the problem to a mathematical programming one to which existing well-developed algorithms may be applied. The method is easy to implement and yields very accurate results. An illustrative example is included to confirm the convergence of the pseudospectral Legendre method, and a comparison is made with an existing result in the literature. © 1998 B. G. Teubner Stuttgart–John Wiley & Sons Ltd.     

On Spline Approximation for Singular Integral Equations on an Interval

This paper analyses the convergence of spline approximation methods for strongly elliptic singular integral equations on a finite interval. We consider collocation by smooth polynomial splines of odd degree multiplied by a weight function and a Galerkin-Petrov method with spline trial functions of even degree and piecewise constant test functions. We prove the stability of the methods in weighted Sobolev spaces and obtain the optimal orders of convergence in the case of graded meshes.     

Pseudospectral Chebyshev Optimal Control of Constrained Nonlinear Dynamical Systems

A pseudospectral method for generating optimal trajectories of linear and nonlinear constrained dynamic systems is proposed. The method consists of representing the solution of the optimal control problem by an mth degree interpolating polynomial, using Chebyshev nodes, and then discretizing the problem using a cell-averaging technique. The optimal control problem is thereby transformed into an algebraic nonlinear programming problem. Due to its dynamic nature, the proposed method avoids many of the numerical difficulties typically encountered in solving standard optimal control problems. Furthermore, for discontinuous optimal control problems, we develop and implement a Chebyshev smoothing procedure which extracts the piecewise smooth solution from the oscillatory solution near the points of discontinuities. Numerical examples are provided, which confirm the convergence of the proposed method. Moreover, a comparison is made with optimal solutions obtained by closed-form analysis and/or other numerical methods in the literature.     

An algorithm for the interpolation of functions using quintic splines

A method is described for the interpolation of N arbitrarily given data points using fifth degree polynomial spline functions. The interpolating spline is built from a set of basis functions belonging to the fifth degree smooth Hermite space. The resulting algebraic system is symmetric and bloc-tridiagonal. Its solution is calculated using a direct inversion method, namely a block-gaussian elimination without pivoting. Various boundary conditions are provided for independently at each end point. The stability of the algorithm is examined and some examples are given of experimental convergence rates for the interpolation of elementary analytical functions. A listing is given of the two FORTRAN subroutines INSPL5 and SPLIN5 which form the algorithm.     

A note on Halley's method

Summary We introduce the degree of logarithmic convexity which provides a measure of the convexity of a function at each point. Making use of this concept we obtain a new theorem of global convergence for Halley's method.     

Polynomial spline estimation for partial functional linear regression models

Because of its orthogonality, interpretability and best representation, functional principal component analysis approach has been extensively used to estimate the slope function in the functional linear model. However, as a very popular smooth technique in nonparametric/semiparametric regression, polynomial spline method has received little attention in the functional data case. In this paper, we propose the polynomial spline method to estimate a partial functional linear model. Some asymptotic results are established, including asymptotic normality for the parameter vector and the global rate of convergence for the slope function. Finally, we evaluate the performance of our estimation method by some simulation studies.     

Spline collocation for singular integro-differential equations over (0.1)

Summary This paper analyses the convergence of spline collocation methods for singular integro-differential equations over the interval (0.1). As trial functions we utilize smooth polynomial splines the degree of which coincides with the order of the equation. Depending on the choice of collocation points we obtain sufficient and even necessary conditions for the convergence in sobolev norms. We give asymptotic error estimates and some numerical results.     

Efficiently solving total least squares with Tikhonov identical regularization

The Tikhonov identical regularized total least squares (TI) is to deal with the ill-conditioned system of linear equations where the data are contaminated by noise. A standard approach for (TI) is to reformulate it as a problem of finding a zero point of some decreasing concave non-smooth univariate function such that the classical bisection search and Dinkelbach’s method can be applied. In this paper, by exploring the hidden convexity of (TI), we reformulate it as a new problem of finding a zero point of a strictly decreasing, smooth and concave univariate function. This allows us to apply the classical Newton’s method to the reformulated problem, which converges globally to the unique root with an asymptotic quadratic convergence rate. Moreover, in every iteration of Newton’s method, no optimization subproblem such as the extended trust-region subproblem is needed to evaluate the new univariate function value as it has an explicit expression. Promising numerical results based on the new algorithm are reported.     

On the linear convergence of the alternating direction method of multipliers

We analyze the convergence rate of the alternating direction method of multipliers (ADMM) for minimizing the sum of two or more nonsmooth convex separable functions subject to linear constraints. Previous analysis of the ADMM typically assumes that the objective function is the sum of only two convex functions defined on two separable blocks of variables even though the algorithm works well in numerical experiments for three or more blocks. Moreover, there has been no rate of convergence analysis for the ADMM without strong convexity in the objective function. In this paper we establish the global R-linear convergence of the ADMM for minimizing the sum of any number of convex separable functions, assuming that a certain error bound condition holds true and the dual stepsize is sufficiently small. Such an error bound condition is satisfied for example when the feasible set is a compact polyhedron and the objective function consists of a smooth strictly convex function composed with a linear mapping, and a nonsmooth \(\ell _1\) regularizer. This result implies the linear convergence of the ADMM for contemporary applications such as LASSO without assuming strong convexity of the objective function.