On orthogonal polynomials for certain non-definite linear functionals

Similar documents
MA2501 Numerical Methods Spring 2015

Scattered Data Interpolation with Polynomial Precision and Conditionally Positive Definite Functions

Gaussian interval quadrature rule for exponential weights

Quadrature Rules With an Even Number of Multiple Nodes and a Maximal Trigonometric Degree of Exactness

Recurrence Relations and Fast Algorithms

Chapter 4: Interpolation and Approximation. October 28, 2005

Optimal global rates of convergence for interpolation problems with random design

CALCULATION OF GAUSS-KRONROD QUADRATURE RULES. 1. Introduction A(2n+ 1)-point Gauss-Kronrod integration rule for the integral.

Cubic Splines. Antony Jameson. Department of Aeronautics and Astronautics, Stanford University, Stanford, California, 94305

ETNA Kent State University

GAUSS-KRONROD QUADRATURE FORMULAE A SURVEY OF FIFTY YEARS OF RESEARCH

University of Houston, Department of Mathematics Numerical Analysis, Fall 2005

Nonnegative linearization for little q-laguerre polynomials and Faber basis

Error Estimates for Trigonometric Interpolation. of Periodic Functions in Lip 1. Knut Petras

Gauss Hermite interval quadrature rule

Wavelets in Scattering Calculations

Numerical Methods I Orthogonal Polynomials

Romberg Integration and Gaussian Quadrature

Fixed point iteration and root finding

Preliminary Examination in Numerical Analysis

GENERALIZED GAUSS RADAU AND GAUSS LOBATTO FORMULAE

12.0 Properties of orthogonal polynomials

Orthogonality of hat functions in Sobolev spaces

On a max norm bound for the least squares spline approximant. Carl de Boor University of Wisconsin-Madison, MRC, Madison, USA. 0.

W. Lenski and B. Szal ON POINTWISE APPROXIMATION OF FUNCTIONS BY SOME MATRIX MEANS OF CONJUGATE FOURIER SERIES

Electronic Transactions on Numerical Analysis Volume 50, 2018

The multiplicity of a spline zero

Interpolation and Cubature at Geronimus Nodes Generated by Different Geronimus Polynomials

Power Series Solutions to the Legendre Equation

Numerical Analysis Preliminary Exam 10 am to 1 pm, August 20, 2018

Biorthogonal Spline Type Wavelets

A trigonometric orthogonality with respect to a nonnegative Borel measure

Legendre s Equation. PHYS Southern Illinois University. October 13, 2016

Two special equations: Bessel s and Legendre s equations. p Fourier-Bessel and Fourier-Legendre series. p

Scientific Computing

Kernel B Splines and Interpolation

Analogues for Bessel Functions of the Christoffel-Darboux Identity

arxiv: v1 [math.co] 3 Nov 2014

The Closed Form Reproducing Polynomial Particle Shape Functions for Meshfree Particle Methods

Fully Symmetric Interpolatory Rules for Multiple Integrals over Infinite Regions with Gaussian Weight

Numerische Mathematik

Deviation Measures and Normals of Convex Bodies

Scientific Computing: An Introductory Survey

THEOREMS, ETC., FOR MATH 515

COURSE Numerical integration of functions (continuation) 3.3. The Romberg s iterative generation method

Absolute value equations

We consider the problem of finding a polynomial that interpolates a given set of values:

Problem 1A. Find the volume of the solid given by x 2 + z 2 1, y 2 + z 2 1. (Hint: 1. Solution: The volume is 1. Problem 2A.

Best approximation in the 2-norm

On an uniqueness theorem for characteristic functions

GENERALIZED STIELTJES POLYNOMIALS AND RATIONAL GAUSS-KRONROD QUADRATURE

Chapter 1. Preliminaries. The purpose of this chapter is to provide some basic background information. Linear Space. Hilbert Space.

(1-2) fx\-*f(x)dx = ^^ Z f(x[n)) + R (f), a > - 1,

Slow Growth for Gauss Legendre Sparse Grids

CHAPTER 3 Further properties of splines and B-splines

Separation of Variables in Linear PDE: One-Dimensional Problems

Approximation theory

Section 6.6 Gaussian Quadrature

swapneel/207

Orthogonal Polynomials and Gaussian Quadrature

LECTURE 16 GAUSS QUADRATURE In general for Newton-Cotes (equispaced interpolation points/ data points/ integration points/ nodes).

MONOTONICITY OF THE ERROR TERM IN GAUSS TURÁN QUADRATURES FOR ANALYTIC FUNCTIONS

MAT Linear Algebra Collection of sample exams

Examination paper for TMA4215 Numerical Mathematics

COMPUTATION OF BESSEL AND AIRY FUNCTIONS AND OF RELATED GAUSSIAN QUADRATURE FORMULAE

Multivariate Interpolation with Increasingly Flat Radial Basis Functions of Finite Smoothness

Lecture 4.6: Some special orthogonal functions

Outline. 1 Interpolation. 2 Polynomial Interpolation. 3 Piecewise Polynomial Interpolation

NEEDLET APPROXIMATION FOR ISOTROPIC RANDOM FIELDS ON THE SPHERE

Vectors in Function Spaces

LECTURE 15: COMPLETENESS AND CONVEXITY

Notes on Special Functions

15B. Isometries and Functionals 1

ZERO DISTRIBUTION OF POLYNOMIALS ORTHOGONAL ON THE RADIAL RAYS IN THE COMPLEX PLANE* G. V. Milovanović, P. M. Rajković and Z. M.

Lectures notes. Rheology and Fluid Dynamics

On the positivity of linear weights in WENO approximations. Abstract

1. Introduction. A radial basis function (RBF) interpolant of multivariate data (x k, y k ), k = 1, 2,..., n takes the form

MATH 205C: STATIONARY PHASE LEMMA

Power Series Solutions to the Legendre Equation

APPENDIX A. Background Mathematics. A.1 Linear Algebra. Vector algebra. Let x denote the n-dimensional column vector with components x 1 x 2.

LECTURE NOTES ELEMENTARY NUMERICAL METHODS. Eusebius Doedel

Remarks on semi-algebraic functions

The Hardy-Littlewood Function: An Exercise in Slowly Convergent Series

CS 450 Numerical Analysis. Chapter 8: Numerical Integration and Differentiation

LEGENDRE POLYNOMIALS AND APPLICATIONS. We construct Legendre polynomials and apply them to solve Dirichlet problems in spherical coordinates.

A RECONSTRUCTION FORMULA FOR BAND LIMITED FUNCTIONS IN L 2 (R d )

A class of non-convex polytopes that admit no orthonormal basis of exponentials

Asymptotics of Integrals of. Hermite Polynomials

Kernel Method: Data Analysis with Positive Definite Kernels

A finite universal SAGBI basis for the kernel of a derivation. Osaka Journal of Mathematics. 41(4) P.759-P.792

2 Lebesgue integration

ORTHOGONAL POLYNOMIALS OF DISCRETE VARIABLE AND BOUNDEDNESS OF DIRICHLET KERNEL

TP computing lab - Integrating the 1D stationary Schrödinger eq

Measures and Measure Spaces

A CHARACTERIZATION OF STRICT LOCAL MINIMIZERS OF ORDER ONE FOR STATIC MINMAX PROBLEMS IN THE PARAMETRIC CONSTRAINT CASE

Darboux functions. Beniamin Bogoşel

On the simplest expression of the perturbed Moore Penrose metric generalized inverse

(0, 0), (1, ), (2, ), (3, ), (4, ), (5, ), (6, ).

Cubic Splines MATH 375. J. Robert Buchanan. Fall Department of Mathematics. J. Robert Buchanan Cubic Splines

f(s) e -i n π s/l d s

Transcription:

On orthogonal polynomials for certain non-definite linear functionals Sven Ehrich a a GSF Research Center, Institute of Biomathematics and Biometry, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany Abstract We consider the non-definite linear functionals L n [f] = w(x)f(n) (x)dx and prove the nonexistence of orthogonal polynomials, with all zeros real, in several cases. The proofs are based on the connection with moment preserving spline approximation. Key words: non-definite linear functionals, orthogonal polynomials, nonexistence, moment preserving spline approximation 1 Introduction and statement of the main results For a weight function w on such that w 1 = w > 0 and all moments w(x)xk dx exist, we consider orthogonal polynomials with respect to the non-definite linear functionals L n [f] = w(x)f (n) (x) dx, n 1, (1) i. e. polynomials P m of degree m satisfying w(x) ( P m (x) x k ) (n) dx = 0, k = 0, 1,..., m 1. (2) In particular, we are interested in the problem whether there exist polynomials P m which satisfy (2) and all of whose zeros are real. Thus, our problem is connected with the existence of real Gauss type quadrature formulas for linear functionals of the type (1). As our main results, we prove several instances of nonexistence of orthogonal polynomials P m with all zeros real for general classes of weight functions w. Preprint submitted to Elsevier Preprint 22 April 1998

Suppose w C n (supp(w)), where supp(w) is the support of w, w (n) = v p n (v), (3) where v 0 is a weight function on such that v 0 and all moments v(x)xk dx exist, and p n (v) is the n-th orthonormal polynomial with respect to v. Then, by partial integration, P m satisfies v(x)p n (v, x)p m (x)x k dx = 0, k = 0, 1,..., m 1. For the special case m = n + 1, P n+1 is called a Stieltjes polynomial (cf. [3,7]). The idea of regarding Stieltjes polynomials as special cases of orthogonal polynomials for non-definite inner products goes back to Gautschi [3, p.44]. The problem whether Stieltjes polynomials, depending on v, have complex zeros, has recently attracted a lot of interest (cf. [8,9]). In this paper, we are interested in the more general problem (2), where w is not necessarily of the type (3). We first consider the problem (2) for the Legendre weight w = χ [ 1,1], which serves as a prototype, and then we obtain generalisations. Theorem 1 Let w = χ [ 1,1] be the Legendre weight and n 1. (i) For 0 m < n+1 2, every polynomial Pm IP m satisfies (2). For n+1 2 m n, P m 0 is the unique solution of (2). (ii) For m = n + 1, P n+1 0 is uniquely defined by (2). All zeros are real for n {0, 1, 2} and for no other n IN. (iii) For n + 1 < m < 2n, every P m 0 which satisfies (2) has at least one pair of complex zeros for m > 5. Furthermore, if m and n are odd, then (2) is not uniquely solvable. (iv) For m 2n, every polynomial of the form P m (x) = (1 x 2 ) n Q(x) with Q IP m 2n satisfies (2). Here IP k is the space of algebraic polynomials of degree k. Note that for w = χ [ 1,1], of course w(x)f (n) (x) dx = f (n 1) (1) f (n 1) ( 1), f C n [ 1, 1], and hence for m 2n the existence of real Gauss type formulas (with multiple nodes) is trivial. On the other hand, the case m = n+1 is of special interest for several reasons. First, the unique solvability is guaranteed by Theorem 1, second, because it is the least number of nodes for a nontrivial discretisation of L n, and third, because of its connection with the Stieltjes polynomials. For this case, Theorem 1 can be considerably generalised. 2

Theorem 2 Let w be convex, symmetric and of bounded support in (a, b). Then P n+1 is uniquely defined by (2) and has at least one pair of complex zeros for n > 2. The Legendre weight can be considered as a multiple of the B-spline of order 1. Theorem 1 can also be generalised to w being a B-spline of higher order, and to linear combinations of such. We denote the B-spline with knots x 1,...,x k by B[x 1,...,x k ], where each knot occurs according to its multiplicity, and we use the normalisation B[x 1,...,x k ](x) dx = 1. Theorem 3 Let r 1, w = R µ=1 d µ B[y µ,...,y r+µ ], n r, n < m < (r + R)(n r + 1). Then P m has at least one pair of complex zeros for m > 2r + 2R + 1. Being mainly interested in orthogonal polynomials P m with real zeros, a natural question is to ask for the largest possible K such that w(x) (P m (x)x k ) (n) dx = 0, k = 0, 1, 2,..., K, under the restriction that all zeros of P m are real. The following result gives an upper bound. Theorem 4 Let r 1, w = R µ=1 d µ B[y µ,...,y r+µ ], n r, n < m < (r + R)(n r + 1). Suppose w(x) (P m (x)x k ) (n) dx = 0, k = 0, 1, 2,..., K, and all zeros of P m are real. Then necessarily K 2r + 2R. Corollary 1 Let r 1, w = R µ=1 d µ B[y µ,...,y r+µ ], n r, n < m < (r + R)(n r + 1). Suppose w(x) f (n) (x) dx = m κ ν 1 ρ=0 a νρ f (ρ) (x ν ) =: Q m [f] for f IP m+2r+2r+1, where all x ν are real and m = m κ ν. Then necessarily m = (r + R)(n r + 1) and Q m [f] = r! R µ=1 d µ dvd (y µ,...,y r+µ )[f (n r) ]. The proofs of the results are based on the connection with a certain moment preserving spline approximation problem (Lemma 1). Such problems are of independent interest and have been considered by many authors [1,2,4 6]. An important application are monosplines and in particular Peano kernels of 3

quadrature formulas (cf., e.g., [5,10]). This connection is worked out in 2. The proofs of the results can be found in 3. 2 Connection with moment preserving spline approximation Let the divided difference (with multiple nodes) dvd(x 1,...,x k )[f] = k κ ν 1 ρ=0 a νρ f (ρ) (x ν ), where k = k κ ν, and each x ν appears κ ν times in x 1,...,x k, be defined by dvd(x 1,..., x k )[p] = { 0, p IP k 2, 1, p(x) = x k 1. Lemma 1 Let 0 < w 1 < n 1, n < m. Suppose the polynomial P m in (2) has real zeros x 1,...,x m, and let c 1,...,c m n be such that for all p IP m 1 m n w(x)p (n) (x) dx = n! w 1 c ν dvd (x ν,...,x ν+n )[p]. Then ψ(x)x k dx = 0, k = 0, 1, 2,..., 2m n 1, (4) where ψ = w w 1 c ν B[x ν,...,x ν+n ]. Conversely, if x 1,...,x m and c 1,...,c m n are given real numbers such that ψ(x) x k dx = 0, k = 0, 1,..., 2m n 1, then P m (x) = c m (x x ν ) satisfies (2). The conditions (4) define a moment preserving spline approximation problem. Gautschi, Milovanović et al. considered the following moment preserving spline approximation problems [1,2,4,6]. For given w, find s S m,n such that (1) I w(x)x j dx = I s(x)x j dx, j = 0, 1,..., 2m + n, 4

(2) I = [0, 1], condition (1) holds for j = 0, 1,...,2m 1 and s (j) (1) = f (j) (1), j = 0, 1,..., n. Here I in (1) may be finite, infinite or half infinite, and S m,n is the space of all real spline functions of the form s(x) = n m a j x j + b k (x ξ k ) n +, j=0 k=1 with real arbitrary but fixed knots ξ 1,..., ξ m. In [1], it is proved that if f C n+1 [0, 1] and f (n+1) never vanishes on (0, 1), then problems (1) (with I = [0, 1]) and (2) have unique solutions. In [5] these results were generalised and related to the theory of monosplines. The situation in Lemma 1 is somewhat different since the approximating spline satisfies the boundary conditions s (j) (ξ 1 ) = 0, j = 0, 1,..., m κ 1, and s (j) (ξ m ) = 0, j = 0, 1,..., m κ m, and integration is on (i.e. on supp(w) supp(s)). Finally we note that in Lemma 1 suitable constants c 1,..., c m n always exist uniquely. Lemma 2 Let 0 < w 1 <, n IN, and x 1 x m be given, x 1 x m. There exist unique real numbers c 1,...,c m n such that for every p IP m 1 m n w(x)p (n) (x) dx = n! w 1 c ν dvd (x ν,...,x ν+n )[p]. 3 Proofs Proof of Lemma 1. Suppose P m has real zeros x 1,...,x m and let c 1,..., c m n be as in the assumption. Then for every p IP 2m 1 and suitable q, r IP m 1 w(x)p (n) (x) dx = w(x)[qp m + r] (n) (x) dx = = n! w 1 = n! w 1 c ν dvd (x ν,..., x ν+n )[r] c ν dvd (x ν,..., x ν+n )[p], w(x)r (n) (x) dx 5

i.e. the linear combination of divided differences is a Gauss type formula. For every f C n () we have n! dvd (x ν,...,x ν+n )[f] = B[x ν,...,x ν+n ] f (n) (x) dx, and hence, with ψ as in the lemma, and for every p IP 2m 1, which is equivalent to the assertion. ψ(x)p (n) (x) dx = 0, Now assume that x 1,..., x m, c 1,...,c m n are given real numbers such that ψ(x) x k dx = 0, k = 0, 1,..., 2m n 1. Let Q m (x) = m (x x ν ), and p IP 2m 1. Then on the one hand, w(x)p (n) (x) dx = with q, r IP m 1, and on the other hand w(x)[q(x)q m (x)] (n) dx + w(x)r (n) (x) dx, w(x)p (n) (x) dx = n! w 1 = n! w 1 c ν dvd (x ν,...,x ν+n )[p] c ν dvd (x ν,...,x ν+n )[r], and hence w(x) (Q m (x)x k ) (n) dx = 0, k = 0, 1, 2,..., m 1. Proof of Theorem 1. For 0 m < n+1 2, the proof is obvious. For n+1 2 m n, suppose P m 0 exists in (2), and let Q(x) = P m (x)x n m. Then n m < m and Q (n) = C 0, which is a contradiction to (2). The uniqueness for m = n + 1 is easy to see since, expanding P n+1 (x) = x n+1 + a n x n + + a 1 x + a 0, the system for the a i is triangular with no zeros on the diagonal. 6

For n < m < 2n, suppose P m has m real zeros x 1 x m, with the possibility of multiplicities. We cannot have x 1 = x 2 = = x m since otherwise, choosing 0 k m 1 such that m n + k is even, we would have 1 1 (x x 1 ) m n+k dx = 0, which is a contradiction. Using Lemma 1, ψ cannot be identical to 0 since m < 2n. Hence ψ must have at least 2m n changes of sign, otherwise the polynomial which changes sign precisely with ψ would yield a contradiction to (2). Using the Budan-Fourier theorem for polynomial splines (cf. [11, Th.4.58]), we obtain that Z (x1,x m) (D + c ν B[x ν,..., x ν+n ] ) m n, (5) where D + f is the right sided derivative and Z (a,b) (s) counts the isolated zeros of the spline s in (a, b) with the following convention (see [11, p.154]): Suppose that s does not vanish identically on any interval containing t, and that s(t ) = D s(t) = = D l 1 s(t) = 0 D s(t), l while s(t+) = D + s(t) = = D+ r 1s(t) = 0 Dr + s(t). Then we say that s has an isolated zero at t of multiplicity α + 1, if α is even and s changes sign at t, z = α + 1, if α is odd and s does not change sign at t, α, otherwise, where α = max{l, r}. (D is the left sided derivative.) Thus (5) implies that there exist at most m n points t 1,...,t m n such that m n c νb[x ν,...,x ν+n ] is monotonic in (x 1, t 1 ), (t m n, x m ) and each of the intervals (t i, t i+1 ), respectively. If (t i, t i+1 ) [ 1, 1] or (t i, t i+1 ) (, 1) (1, ), then ψ has at most one change of sign in (t i, t i+1 ). If 1 [t i, t i+1 ) (1 [t i, t i+1 )) then ψ has at most three changes of sign in [t i, t i+1 ). Hence ψ has at most m n + 5 changes of sign, and 2m n # sign changes m n + 5 m 5. For m = n+1, if 1 (t i, t i+1 ) (1 (t i, t i+1 )) then ψ has at most two changes of sign in (t i, t i+1 ). Hence ψ has at most 4 changes of sign, and n + 2 # sign changes 4 n 2. For n {0, 1, 2} we have respectively P 1 (x) = x, P 2 (x) = x 2 1, P 3 (x) = x 3 2x. 7

If n < m < 2n and m and n are odd, m = 2k + 1, consider P m (x) = x m + a m 1 x m 1 + + a 1 x + a 0. The conditions (2) lead to a linear system for a m 1,...,a 0 which decomposes into k + 1 equations for the k coefficients a 2k,..., a 0 and k equations for the k +1 coefficients a 2k 1,...,a 1. Hence the system cannot be uniquely solvable. Finally, for m 2n the statement is obvious. Proof of Theorem 2. The uniqueness follows as in the proof of Theorem 1. If the zeros of P n+1 are real, by the symmetry of w they lie symmetric with respect to a+b. For m = n + 1 we have 2 ψ = w w 1 B[x 1,..., x n+1 ]. Using the same argument as in the proof of Theorem (1), there exists a point t such that B[x 1,...,x n+1 ] is monotonically increasing in (x 1, t) and decreasing in (t, x n+1 ). By symmetry, t = a+b. Since w is convex, there can be at 2 most one change of sign of ψ in (max{x 1, a}, a+b ) and in (a+b, min{x 2 2 n+1, b}), respectively. There are two possible changes of sign in the points a and b, and B[x 1,...,x n+1 ] is nonnegative outside (a, b). Hence in this case n + 2 # sign changes 4 n 2. Proof of Theorem 3. Using Lemma 1, ψ cannot be identical to 0 since m < (r + R)(n r + 1). Hence ψ must have at least 2m n changes of sign and at least r 1 fold zeros at the boundaries. Hence ψ (r 1) must have at least 2m n + r 1 changes of sign. The function m n c νb (r 1) [x ν,...,x ν+n ] is a spline of degree n r with knots x 1,..., x m, hence by the Budan-Fourier theorem Z (x1,x m) (D + c ν B (r 1) [x ν,...,x ν+n ] ) m n + r 1. Hence there exist at most m n + r 1 points t 1,...,t m n+r 1 such that m n c νb (r 1) [x ν,..., x ν+n ] is monotonic on each of the intervals (x 1, t 1 ), (t 1, t 2 ),...,(t m n+r 1, x m ), respectively. On the other hand, the function w (r 1) is a piecewise constant spline with r + R knots (i.e. possible jumps) at y 1,..., y r+r. For r + R m n + r, we count the possible changes of sign of ψ as in Theorem 1: every y i being in an interval of monotonicity of m n c νb (r 1) [x ν,...,x ν+n ] leads to at most three changes of sign of ψ (r 1). Suppose first that each y 1,...,y r+r lies in a different interval of monotonicity of m n c ν B (r 1) [x ν,...,x ν+n ]. Then in the remaining intervals we have at most one change of sign of ψ (r 1). If there are more than one of the points y i in an interval of monotonicity, the second and any further point can give only two 8

changes of sign and one more remaining interval. In both cases we obtain for r + R m n + r 2m n + r 1 # sign changes 3(r + R) + m n + r (r + R) = m n + 3r + 2R m 2r + 2R + 1. For r + R > m n + r, we count the possible sign changes as follows: if one of the m n + r 1 points t i lies in one of the r + R 1 intervals [y j, y j+1 ), then there can be at most three changes of sign in [y j, y j+1 ). Suppose first that all t 1,...,t m n+r+1 lie in [y 1,...,y r+r ]. In the remaining R m + n intervals [y j, y j+1 ) there can be at most two changes of sign (we count, by convention, changes of sign at the jumps with the interval right to them). Furthermore there can be one change of sign in y r+r, adding up to a total of 3(m n + r 1) + 2(R m + n) + 1 = m n + 3r + 2R 2. Now for every t i [y 1,...,y r+2 ] there can be one additional change of sign outside [y 1,..., y r+2 ], but at the same time there will be one interval [y i, y i+1 ) less where three changes of sign can appear instead of two. Hence 2m n + r 1 # sign changes m n + 3r + 2R 2 m 2r + 2R 1. Proof of Theorem 4. For every p IP m+k, k K, there exist q IP k, r IP m 1 such that w(x)p (n) (x) dx = w(x)r (n) (x) dx = n! w 1 = w 1 c ν dvd (x ν,..., x ν+n )[r] c ν B[x ν,..., x ν+n ](x)p (n) (x) dx. From the proof of Theorem 3 we obtain for the changes of sign of the function in this case m n w (r 1) w 1 c ν B (r 1) [x ν,...,x ν+n ] m+k n+r # sign changes m n+3r+2r k 2r+2R. 9

Proof of Lemma 2. For notational simplicity, suppose that all x i are distinct. Let p ν (x) = x ν. The system m b k x ν k = k=1 w(x)p (n) ν (x) dx has the usual interpolation matrix, which is the Vandermonde matrix in this case. Hence it is sufficient to show that every functional of the form D[f] = mk=0 b k f(x k ) with D[p] = 0 for p IP n 1 can be written in the form D[f] = d ν dvd(x ν,...,x ν+n )[f] with real d 1,...,d m n. In fact, d ν dvd(x ν,...,x ν+n )[f] = m bk f(x k ), k=0 and the conditions b k = b k, k = 1,..., m n give a triangular system. For b l, l = m n + 1,...,m, we choose f l (x) = m κ=m n+1 κ l (x x κ ), hence f l IP n 1 and D[f l ] = k=1 = 0 = bk f l (x k ) + b l f l (x l ) = k=1 b k f l (x k ) + b l f l (x l ), k=1 b k f l (x k ) + b l f l (x l ) hence b l = b l. References [1] M. Frontini, W. Gautschi and G. V. Milovanović, Moment-preserving spline approximation on finite intervals, Numer. Math. 50 (1987) 503 518. [2] W. Gautschi, Discrete approximations to spherically symmetric distributions, Numer. Math. 44 (1984) 53 60. [3] W. Gautschi, Gauss-Kronrod quadrature a survey, in: G. V. Milovanović, ed., Numerical Methods and Approximation Theory III (Nis, 1988) 39 66. 10

[4] W. Gautschi and G. V. Milovanović, Spline approximations to spherically symmetric distributions, Numer. Math. 49 (1986) 111 121. [5] C. A. Micchelli, Monosplines and moment preserving spline approximation, in: H. Braß, G. Hämmerlin, eds., Numerical Integration III (Intern. Series Numer. Math., vol. 85, Birkhäuser, Basel, 1988) 130 139. [6] G. V. Milovanović and M. A. Kovačević, Moment-preserving spline approximation and Turán quadratures, in: R. P. Agarwal, Y. M. Chow, S. J. Wilson, eds., Numerical Mathematics (Proc. Conf. Singapore, Intern. Series Numer. Math., vol. 86, Birkhäuser, Basel, 1988) 357 365. [7] G. Monegato, Stieltjes polynomials and related quadrature rules, SIAM Rev. 24, 2 (1982) 137 158. [8] S. E. Notaris, An overview of results on the existence or nonexistence and the error term of Gauss-Kronrod quadrature formulae, in: R. V. M. Zahar, ed., Approximation and Computation, A Festschrift in Honour of Walter Gautschi (Intern. Series Numer. Math., vol. 119, Birkhäuser, Basel, 1994) 485 496. [9] F. Peherstorfer and K. Petras, Ultraspherical Gauss-Kronrod quadrature is not possible for λ > 3, manuscript. [10] I. J. Schoenberg, Monosplines and quadrature formulas, in: T. N. E. Greville, ed., Theory and Applications of spline functions (Academic Press, New York, 1969) 157 207. [11] L. Schumaker, Spline functions: basic theory (Wiley-Interscience, 1981). 11