A multinomial spline approximation scheme using spline quasi-interpolants

This paper presents a multinomial spline approximation scheme based on spline quasi-interpolants. The scheme can be considered as an extension of the usual Bernstein approximation for complex exponentials. Error estimates and numerical examples are given to show that this new scheme could produce highly accurate results.     

Three-monotone spline approximation

For r≥3, n∈N and each 3-monotone continuous function f on [a,b] (i.e., f is such that its third divided differences [x₀,x₁,x₂,x₃]f are nonnegative for all choices of distinct points x₀,…,x₃ in [a,b]), we construct a spline s of degree r and of minimal defect (i.e., s∈C^r−1[a,b]) with n−1 equidistant knots in (a,b), which is also 3-monotone and satisfies ‖f−s‖_L∞[a,b]≤cω₄(f,n⁻¹,[a,b])_∞, where ω₄(f,t,[a,b])_∞ is the (usual) fourth modulus of smoothness of f in the uniform norm. This answers in the affirmative the question raised in [8, Remark 3], which was the only remaining unproved Jackson-type estimate for uniform 3-monotone approximation by piecewise polynomial functions (ppfs) with uniformly spaced fixed knots.Moreover, we also prove a similar estimate in terms of the Ditzian–Totik fourth modulus of smoothness for splines with Chebyshev knots, and show that these estimates are no longer valid in the case of 3-monotone spline approximation in the L_p norm with p<∞. At the same time, positive results in the L_p case with p<∞ are still valid if one allows the knots of the approximating ppf to depend on f while still being controlled.These results confirm that 3-monotone approximation is the transition case between monotone and convex approximation (where most of the results are “positive”) and k-monotone approximation with k≥4 (where just about everything is “negative”).     

Perfect spline approximation

Our study of perfect spline approximation reveals: (i) it is closely related to ΣΔ modulation used in one-bit quantization of bandlimited signals. In fact, they share the same recursive formulae, although in different contexts; (ii) the best rate of approximation by perfect splines of order r with equidistant knots of mesh size h is h^r−1. This rate is optimal in the sense that a function can be approximated with a better rate if and only if it is a polynomial of degree <r.The uniqueness of best approximation is studied, too. Along the way, we also give a result on an extremal problem, that is, among all perfect splines with integer knots on , (multiples of) Euler splines have the smallest possible norms.     

Tensor spline approximation

The cyclic-shift tensor-factorization interpolation method recently described by de Boor can be used in particular for least-squares fitting of multivariate data on a rectangular grid and for evaluation of the resulting tensor-product splines, taking advantage of existing linear algebra and univariate spline software. We discuss the computational details of this method, pointing out variants and suggesting techniques for dealing with ill-conditioned least-squares problems.     

Mean square spline approximation

The approximation to a specified function on the real line by fitting a cubic in a piecewise fashion is achieved by minimizing the deviations in the mean square sense. The coefficients of the cubic are determined sequentially employing the method of dynamic programming. Employing this method a known function is approximated and the results of the computation are tabulated.     

Limit theorems in spline approximation

In this paper we establish new asymptotic relations of the form

     

Successive polynomial spline function approximation

The use of successive polynomial spline approximation is established as a method of improving the accuracy of estimates of derivatives of periodic functions approximated by interpolating odd order splines defined on a uniformly spaced set of data points. For the various configurations possible with this multiple-approximation method, bounds for the leading error terms are explicitly given. In particular, for the quintic spline, the variety of approximation sequences is described in detail.     

On spherical spline interpolation and approximation

Spherical spline functions are introduced by use of Green's surface functions with respect to the (Laplace-)Beltrami operator of the (unit) sphere. Natural (spherical) spline functions are used to interpolate data discretely given on the sphere. A method is presented that allows the smoothing of irregularities in measured values or experimental data. Extensions of Peano's theorem and Sard's theory of best approximation to the spherical case are given by integral formulas. Schoenberg's theorem is transcribed into spherical nomenclature.     

Free-knot spline approximation of stochastic processes

We study optimal approximation of stochastic processes by polynomial splines with free knots. The number of free knots is either a priori fixed or may depend on the particular trajectory. For the s-fold integrated Wiener process as well as for scalar diffusion processes we determine the asymptotic behavior of the average L_p-distance to the splines spaces, as the (expected) number of free knots tends to infinity.     

Fast one-sided approximation with spline functions

For the approximation of functions, interpolation compromises approximation error for computational convenience. For a bounded interpolation operator the Lebesque inequality bounds the factor by which the interpolation differs from the best approximation available in the range of the operator. A comparable process for one-sided approximation is not readily apparent. Methods are suggested for the computationally economical construction of one-sided spline approximation to large classes of functions, and criteria for comparing such approximation operators are investigated. Since the operators are generally nonlinear the Lebesque inequality is invalidated as an aid for comparing with the best one-sided approximation in the range of the operator, but comparable inequalities are shown to exist in some cases.     

On saturation for a spline approximation

We obtain a saturation theorem for a wavelet operator which is constructed by B-spline function.     

Moment-preserving spline approximation on finite intervals

Summary Continuing previous wotk, we discuss the problem of approximating a functionf on the interval [0, 1] by a spline function of degreem, withn (variable) knots, matching, as many of the initial moments off as possible. Additional constraints on the derivatives of the approximation at one endpoint of [0, 1] may also be imposed. We show that, if the approximations exist, they can be represented in terms of generalized Gauss-Lobatto and Gauss-Radau quadrature rules relative to appropriate moment functionals or measures (depending off). Pointwise convergence asn, for fixedm>0, is shown for functionsf that are completely monotonic on [0, 1], among others. Numerical examples conclude the paper.The work of the first author was supported by theMinistero della Pubblica Istruzione and by theConsiglio Nazionale delle Ricerche. The work of the second author was supported, in part, by the National Science Foundation under grant DCR-8320561     

Nonuniqueness and selections in spline approximation

This paper studies problems of nonuniqueness for the metric projection ofC(T),T a compact Hausdorff space, onto a finite-dimensional subspaceG, and discusses the results for polynomial spline approximation. Among others, we prove that the metric projection ofC[a, b] ontoS _k,n , the space of polynomial splines of degree less than or equal ton withk simple knots in (a, b), is lower semicontinuous on an open, dense subset ofC[a, b] and, consequently, any standard selection of the projection is continuous on this subset. We further show that continuous selections are not so easy to construct.Communicated by Ronald A. DeVore.     

Numerical solution of stiff and convection-diffusion equations using adaptive spline function approximation

The singular perturbation mathematical model plays an important role in modelling fluid processes which arise in applied mechanics. We have either, the stiff system of initial value problems or convection-diffusion problems. When conventional numerical methods are used to obtain the solution, the stepsize must be limited to small values. Any attempt to use a larger step-size results in the production of nonphysical oscillations in the solution.In this paper we have constructed an adaptive spline function to solve initial and boundary value problems of ordinary and partial differential equations. The numerical methods based on the spline relations when applied to the test models produce oscillation free solutions. The numerical results are presented and discussed.     

A note on mean square spline approximation

A method of obtaining the mean-square spline approximation by the use of dynamic programming is indicated.     

Minimal-norm-derivative spline function in interpolation and approximation

The paper is concerned with applications of quadratic splines with minimal derivative to approximation of functions in approximation and interpolation problems. A smooth spline is constructed on a uniform mesh so as the norm of the spline derivative is minimal; the nodes of the spline and the nodes of interpolations coincide. This approach allows construction of a spline from given values of the function on the mesh without additional assignment of the value of the function derivative at the initial point, because the derivative can be determined from the minimality condition for the norm of the spline derivative in L ₂.