Robust univariate spline models for interpolating interval data

We consider spline interpolation problems where information about the approximated function is given by means of interval estimates for the function values over ranges of x-values instead of specific knots. We propose two robust univariate spline models formulated as convex semi-infinite optimization problems. We present simplified equivalent formulations of both models as finite explicit convex optimization problems for splines of degrees up to 3. This makes it possible to use existing convex optimization algorithms and software.     

Remarkable Haar spaces of multivariate piecewise polynomials

Some families of Haar spaces in \(\mathbb {R}^{d},~ d\ge 1,\) whose basis functions are d-variate piecewise polynomials, are highlighted. The starting point is a sequence of univariate piecewise polynomials, called Lobachevsky splines, arised in probability theory and asymptotically related to the normal density function. Then, it is shown that d-variate Lobachevsky splines can be expressed as products of Lobachevsky splines. All these splines have simple analytic expressions and subsets of them are suitable for scattered data interpolation, allowing efficient computation and plain error analysis.     

Free multivariate splines

We introduce a definition of free multivariate splines which generalizes the univariate notion of splines with free knots. We then concentrate on the simplest case, piecewise constant functions and characterize some classes of functions which have a prescribed order of approximation inL _p by these splines. These characterizations involve the classical Besov spaces.     

Smoothing splines using compactly supported, positive definite, radial basis functions

In this paper, we develop a fast algorithm for a smoothing spline estimator in multivariate regression. To accomplish this, we employ general concepts associated with roughness penalty methods in conjunction with the theory of radial basis functions and reproducing kernel Hilbert spaces. It is shown that through the use of compactly supported radial basis functions it becomes possible to recover the band structured matrix feature of univariate spline smoothing and thereby obtain a fast computational algorithm. Given n data points in R ², the new algorithm has complexity O(n ²) compared to O(n ³), the order for the thin plate multivariate smoothing splines.     

Range restricted interpolation using Gregory's rational cubic splines

The construction of range restricted univariate and bivariate interpolants to gridded data is considered. We apply Gregory's rational cubic C¹ splines as well as related rational quintic C² splines. Assume that the lower and upper obstacles are compatible with the data set. Then the tension parameters occurring in the mentioned spline classes can be always determined in such a way that range restricted interpolation is successful.     

The Location of the Control Points in the Case of Box Splines

In this paper a theorem of Greville (1967) for univariate splinesis carried over to multivariate box splines; namely, it is shownhow the vector-valued function s(x)=x can expressed in termsof some translates of a box spline. The result means that thegraph of a spline function s(x), which is given as a linearcombination of box splines, can be viewed as a parametric splinesurface. The control points of the graph are explicitly given.     

Multivariate splines and hyperplane arrangements

In this paper, we use the so-called conformality method of smoothing cofactor (abbr. CSC) and hyperplane arrangements to study truncated powers and box splines in R². By the relation between hyperplane arrangements and truncated powers, we give the expressions of the truncated powers. Moreover, by means of the CSC method, the least smoothness degrees of the truncated powers and the box splines on each direction of partition edges are studied.     

Multivariate F-splines and Fractional Box Splines

We introduce multivariate F-splines, including multivariate F-truncated powers T _f (?|M) and F-box splines B _f (?|M). The classical multivariate polynomial splines and multivariate E-splines can be considered as a special case of multivariate F-splines. We document the main properties of T _f (?|M) and B _f (?|M). Using T _f (?|M), we extend fractional B-splines to fractional box splines and show that these functions satisfy most of the properties of the traditional box splines. Our work unifies and generalizes results due to Dahmen-Micchelli, de Boor-Höllig, Ron and Unser-Blu, and also presents a new tool for computing the integration over polytopes.     

Univariate cubic L 1 interpolating splines based on the first derivative and on 5-point windows: analysis, algorithm and shape-preserving properties

In this paper, univariate cubic L ₁ interpolating splines based on the first derivative and on 5-point windows are introduced. Analytical results for minimizing the local spline functional on 5-point windows are presented and, based on these results, an efficient algorithm for calculating the spline coefficients is set up. It is shown that cubic L ₁ splines based on the first derivative and on 5-point windows preserve linearity of the original data and avoid extraneous oscillation. Computational examples, including comparison with first-derivative-based cubic L ₁ splines calculated by a primal affine algorithm and with second-derivative-based cubic L ₁ splines, show the advantages of the first-derivative-based cubic L ₁ splines calculated by the new algorithm.     

<Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript> spline fits via sliding window process: continuous and discrete cases

The best L ₁ approximation of the Heaviside function and the best ? ₁ approximation of multiscale univariate datasets by a cubic spline have a Gibbs phenomenon near the discontinuity. We show by numerical experiments that the Gibbs phenomenon can be reduced by using L ₁ spline fits which are the best L ₁ approximations in an appropriate spline space obtained by the union of L ₁ interpolation splines. We prove here the existence of L ₁ spline fits for function approximation which has never previously been done to the best of our knowledge. A major disadvantage of this technique is an increased computation time. Thus, we propose a sliding window algorithm on seven nodes which is as efficient as the global method both for functions and datasets with abrupt changes of magnitude, but within a linear complexity on the number of spline nodes.     

Quantum B-splines

Quantum splines are piecewise polynomials whose quantum derivatives (i.e. certain discrete derivatives or equivalently certain divided differences) agree up to some order at the joins. Just like classical splines, quantum splines admit a canonical basis with compact support: the quantum B-splines. These quantum B-splines are the q-analogues of classical B-splines. Here quantum B-spline bases and quantum B-spline curves are investigated, using a new variant of the blossom: the q (quantum)-blossom. The q-blossom of a degree d polynomial is the unique symmetric, multiaffine function in d variables that reduces to the polynomial along the q-diagonal. By applying the q-blossom, algorithms and identities for quantum B-spline bases and quantum B-spline curves are developed, including quantum variants of the de Boor algorithms for recursive evaluation and quantum differentiation, knot insertion procedures for converting from quantum B-spline to piecewise quantum Bézier form, and a quantum variant of Marsden’s identity.     

Polyharmonic cardinal splines: A minimization property

Polyharmonic cardinal splines are distributions which are annihilated by iterates of the Laplacian in the complement of a lattice in Euclidean n-space and satisfy certain continuity conditions. Some of the basic properties were recorded in our earlier paper on the subject. Here we show that such splines solve a variational problem analogous to the univariate case considered by I. J. Schoenberg.     

Stability of spline approximation and the restoration of grid functions

For a functionf(x) ∈ H _ω ^r , defined on a uniform grid approximately, we propose a stable method for approximately restoring the function with the aid of polynomial splines. We derive uniform estimates for the deviations of the spline and its derivatives from the function and its derivatives.     

Quasi-interpolation by thin-plate splines on a square

Quasi-interpolation is one method of generating approximations from a space of translates of dilates of a single function ψ. This method has been applied widely to approximation by radial basis functions. However, such analysis has most often been performed in the setting of an infinite uniform grid of centers. In this paper we develop general error bounds for approximation by quasiinterpolation on ann-cube. The quasi-interpolant analyzed involves a finite number, growing ash ^?n , of translates of dilates of the function ψ, and a bounded number of edge functions. The centers of the translates of dilates of ψ form a uniformly spaced grid within the cube. These error bounds are then applied to approximation by thin-plate splines on a square. The result is an O(ω(f, [-1,1]²,h)) error bound for approximation by thin-plate splines supplemented with eight arctan functions.     

Convergence en moyenne quadratique de l'estimateur de la régression par splines hybrides

In this work, we consider the estimation of a smooth regression function, belonging to C^m [0, 1], by hybrid splines. We give the asymptotic behavior of the integrated mean square error by considering two different assumptions on the noise.     

A Geometric Programming Framework for Univariate Cubic L 1 Smoothing Splines

Univariate cubic L ₁ smoothing splines are capable of providing shape-preserving C ¹-smooth approximation of multi-scale data. The minimization principle for univariate cubic L ₁ smoothing splines results in a nondifferentiable convex optimization problem that, for theoretical treatment and algorithm design, can be formulated as a generalized geometric program. In this framework, a geometric dual with a linear objective function over a convex feasible domain is derived, and a linear system for dual to primal conversion is established. Numerical examples are given to illustrate this approach. Sensitivity analysis for data with uncertainty is presented. This work is supported by research grant #DAAG55-98-D-0003 of the Army Research Office, USA.     

On uniform Lebesgue constants of local exponential splines with equidistant knots

For a linear differential operator L _r of arbitrary order r with constant coefficients and real pairwise different roots of the characteristic polynomial, we study Lebesgue constants (the norms of linear operators from C to C) of local exponential splines corresponding to this operator with a uniform arrangement of knots; such splines were constructed by the authors in earlier papers. In particular, for the third-order operator L ₃ = D(D ² ? β ²) (β > 0), we find the exact values of Lebesgue constants for two types of local splines and compare these values with Lebesgue constants of exponential interpolation splines.     

Banded matrices with banded inverses,II: Locally finite decomposition of spline spaces

Given two function spacesV ₀,V ₁ with compactly supported basis functionsC _i, F_i, i∈Z, respectively, such thatC _i can be written as a finite linear combination of theF _i's, we study the problem of decomposingV ₁ into a direct sum ofV ₀ and some subspaceW ofV ₁ in such a way thatW is spanned by compactly supported functions and that eachF _i can be written as a finite linear combination of the basis functions inV ₀ andW. The problem of finding such locally finite decompositions is shown to be equivalent to solving certain matrix equations involving two-slanted matrices. These relations may be reinterpreted in terms of banded matrices possessing banded inverses. Our approach to solving the matrix equations is based on factorization techniques which work under certain conditions on minors. In particular, we apply these results to univariate splines with arbitrary knot sequences.     

Complex planar splines

In contradistinction to the known theory on complex splines which are defined on the boundary of a region in , we define complex planar splines on a region itself as a complex valued continuous function which is defined piecewise on suitable meshes of that region. The main idea is to use nonholomorphic functions as pieces, since holomorphic pieces would lead to just one holomorphic function on the whole region. Some of the techniques used are available from the theory of finite elements. But we also consider new aspects, namely, mapping properties of a complex planar spline v and the difference f − v, where f is, in general, a holomorphic function. For triangular meshes, rectangular and parallelogrammatic meshes, and meshes on circular sectors, explicit expressions are provided; also properties of the newly introduced complex planar splines are studied.