Modern Charge Density Studies: the Entanglement of Experiment and Theory Piero Macchi
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Radial Basis Functions
Acta Numerica (2000), pp. 1–38 c Cambridge University Press, 2000 Radial basis functions M. D. Buhmann Mathematical Institute, Justus Liebig University, 35392 Giessen, Germany E-mail: [email protected] Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their usefulness from the theoretical point of view and contributes useful new classes of radial basis function. We consider particularly the new results on convergence rates of interpolation with radial basis functions, as well as some of the various achievements on approximation on spheres, and the efficient numerical computation of interpolants for very large sets of data. Several examples of useful applications are stated at the end of the paper. CONTENTS 1 Introduction 1 2 Convergence rates 5 3 Compact support 11 4 Iterative methods for implementation 16 5 Interpolation on spheres 25 6 Applications 28 References 34 1. Introduction There is a multitude of ways to approximate a function of many variables: multivariate polynomials, splines, tensor product methods, local methods and global methods. All of these approaches have many advantages and some disadvantages, but if the dimensionality of the problem (the number of variables) is large, which is often the case in many applications from stat- istics to neural networks, our choice of methods is greatly reduced, unless 2 M. D. Buhmann we resort solely to tensor product methods. -
On Fourier Transforms of Radial Functions and Distributions
ON FOURIER TRANSFORMS OF RADIAL FUNCTIONS AND DISTRIBUTIONS LOUKAS GRAFAKOS AND GERALD TESCHL Abstract. We find a formula that relates the Fourier transform of a radial function on Rn with the Fourier transform of the same function defined on Rn+2. This formula enables one to explicitly calculate the Fourier transform of any radial function f(r) in any dimension, provided one knows the Fourier transform of the one-dimensional function t 7! f(jtj) and the two-dimensional function (x1; x2) 7! f(j(x1; x2)j). We prove analogous results for radial tempered distributions. 1. Introduction The Fourier transform of a function Φ in L1(Rn) is defined by the con- vergent integral Z −2πix·ξ Fn(Φ)(ξ) = Φ(x)e dx : Rn If the function Φ is radial, i.e., Φ(x) = '(jxj) for some function ' on the line, then its Fourier transform is also radial and we use the notation Fn(Φ)(ξ) = Fn(')(r) ; where r = jξj. In this article, we will show that there is a relationship be- tween Fn(')(r) and Fn+2(')(r) as functions of the positive real variable r. We have the following result. Theorem 1.1. Let n ≥ 1. Suppose that f is a function on the real line such that the functions f(j · j) are in L1(Rn+2) and also in L1(Rn). Then we have 1 1 d (1) F (f)(r) = − F (f)(r) r > 0 : n+2 2π r dr n Moreover, the following formula is valid for all even Schwartz functions ' on the real line: 1 1 d (2) F (')(r) = F s−n+1 ('(s)sn) (r); r > 0 : n+2 2π r2 n ds 1991 Mathematics Subject Classification. -
Radial Functions on Compact Support
Research Collection Journal Article Radial functions on compact support Author(s): Buhmann, M. D. Publication Date: 1998 Permanent Link: https://doi.org/10.3929/ethz-b-000422962 Originally published in: Proceedings of the Edinburgh Mathematical Society 41(1), http://doi.org/10.1017/S0013091500019416 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Proceedings of the Edinburgh Mathematical Society (1998) 41, 33-46 © RADIAL FUNCTIONS ON COMPACT SUPPORT by M. D. BUHMANN (Received 22nd November 1995) In this paper, radial basis functions that are compactly supported and give rise to positive definite interpolation matrices for scattered data are discussed. They are related to the well-known thin plate spline radial functions which are highly useful in applications for gridfree approximation methods. Also, encouraging approximation results for the compactly supported radial functions are shown. 1991 Mathematics subject classification: 41A05, 41A25, 41A63, 42A82, 65D05, 65D07. 1. Introduction The theme of this work is the construction of a new class of radial basis functions. Their special features are compact support and good approximation properties when they are used for interpolation (or other approximations). Radial basis functions and their applications have been comprehensively reviewed in several recent papers ([4,6,13], to name just three). Therefore we will not dwell much on explaining radial basis functions here. Be it sufficient to mention that radial basis function schemes are multivariate approximations from linear spaces generated by translates of the form <t>(- - Xj) = <£(|| • -x;||) : R" -+ R, where 4>: R+ -+ R is the radial basis function, || • || : R" -y R+ is the Euclidean norm (the modulus function in one dimension) and Xj e R" are prescribed points (called centres or sometimes knots) in the underlying space. -
Spherical and Spheroidal Harmonics: Examples and Computations
Spherical and Spheroidal Harmonics: Examples and Computations Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Lin Zhao, B.S., M.S. Graduate Program in Mathematics The Ohio State University 2017 Thesis Committee: Ghaith A. Hiary, Advisor Roman Holowinsky © Copyright by Lin Zhao 2017 Abstract This thesis introduces the theory of spherical and spheroidal harmonics, with em- phasis on algorithms, computations, and explicit examples. In particular, we collect in one place several viewpoints on spherical and spheroidal harmonics, and clarify how the various viewpoints are related and how to transition between them. The ultimate goal is to be able to apply the spheroidal harmonics in calculating the spacing dis- tributions of eigenvalues of the Gaussian unitary ensemble (GUE) random matrices. These eigenvalue statistics are conjectured to model corresponding statistics for zeros of the Riemann zeta function. New results are presented for comparing statistics of zeros and eigenvalues. ii Acknowledgments First and foremost, I am deeply indebted to my advisor, Prof. Ghaith A. Hiary for his guidance over my master’s study in Department of Mathematics. This thesis would not have been possible without his consistent support, great patience, and continual encouragement. I feel very fortunate to be introduced to this interesting topic and it has been a wonderful journey delving into it. I had the opportunity to see broad connections to various aspects of mathematics, as well as interesting applications in many other disciplines. The many discussions with Prof. Hiary have been tremendously helpful. I benefited a lot from his extremely organized and rigorous way of thinking, as well as his meticulousness. -
Radial Functions on Compact Support
Proceedings of the Edinburgh Mathematical Society (1998) 41, 33-46 © RADIAL FUNCTIONS ON COMPACT SUPPORT by M. D. BUHMANN (Received 22nd November 1995) In this paper, radial basis functions that are compactly supported and give rise to positive definite interpolation matrices for scattered data are discussed. They are related to the well-known thin plate spline radial functions which are highly useful in applications for gridfree approximation methods. Also, encouraging approximation results for the compactly supported radial functions are shown. 1991 Mathematics subject classification: 41A05, 41A25, 41A63, 42A82, 65D05, 65D07. 1. Introduction The theme of this work is the construction of a new class of radial basis functions. Their special features are compact support and good approximation properties when they are used for interpolation (or other approximations). Radial basis functions and their applications have been comprehensively reviewed in several recent papers ([4,6,13], to name just three). Therefore we will not dwell much on explaining radial basis functions here. Be it sufficient to mention that radial basis function schemes are multivariate approximations from linear spaces generated by translates of the form <t>(- - Xj) = <£(|| • -x;||) : R" -+ R, where 4>: R+ -+ R is the radial basis function, || • || : R" -y R+ is the Euclidean norm (the modulus function in one dimension) and Xj e R" are prescribed points (called centres or sometimes knots) in the underlying space. The usual way to approximate by these functions is through interpolation at the centres, giving rise to the question of the invertibility of the interpolation matrix {<£(*, — Xj)}tj that we shall address, among other issues, in this article. -
Notes on Radial and Quasiradial Fourier Multipliers
NOTES ON RADIAL AND QUASIRADIAL FOURIER MULTIPLIERS ANDREAS SEEGER This is an updated version of notes that were originally prepared for lectures at the Summer School on Nonlinear Analysis, Function Spaces and Applications (NAFSA 10), in Tˇreˇst',Czech Republic. They are based on material in various joint papers ([1], [20], [26], [27], [34], [36], [37]). The notes could not have been written without the contribu- tions of my coauthors Jong-Guk Bak, Gustavo Garrig´os,Yaryong Heo, Sanghyuk Lee, Fedya Nazarov, and Keith Rogers. Any deficiencies and errors in these notes are my responsibility. 1. Fourier multipliers Given a translation invariant operator T which is bounded from S(Rd) to some Lq(Rd) there is a tempered distribution K so that d q T f = K ∗ f for all f 2 S(R ) (see [53]). We denote by Convp the space of all K 2 S0(Rd) such that kK ∗ fkq ≤ Ckfkp for all f 2 S(Rd), and set q kKkConvp = supfkK ∗ fkq : f 2 S; kfkp = 1g The convolution operators can also be written as a multiplier transfor- mation K ∗ f = F −1[mfb] where the Fourier transform of a Schwartz function in Rd is given by Z Ff(ξ) = fb(ξ) = f(y)e−ihy,ξidy and F −1[g](x) = (2π)−d R g(ξ)eihx,ξidξ is the inverse Fourier transform q of g. The space of Fourier transforms of distributions in Convp is q denoted by Mp and the Fourier transform is an isometric isomorphism q q q −1 q F : Convp ! Mp , i.e. -
Radial Functions on Compact Support
Proceedings of the Edinburgh Mathematical Society (1998) 41, 33-46 © RADIAL FUNCTIONS ON COMPACT SUPPORT by M. D. BUHMANN (Received 22nd November 1995) In this paper, radial basis functions that are compactly supported and give rise to positive definite interpolation matrices for scattered data are discussed. They are related to the well-known thin plate spline radial functions which are highly useful in applications for gridfree approximation methods. Also, encouraging approximation results for the compactly supported radial functions are shown. 1991 Mathematics subject classification: 41A05, 41A25, 41A63, 42A82, 65D05, 65D07. 1. Introduction The theme of this work is the construction of a new class of radial basis functions. Their special features are compact support and good approximation properties when they are used for interpolation (or other approximations). Radial basis functions and their applications have been comprehensively reviewed in several recent papers ([4,6,13], to name just three). Therefore we will not dwell much on explaining radial basis functions here. Be it sufficient to mention that radial basis function schemes are multivariate approximations from linear spaces generated by translates of the form <t>(- - Xj) = <£(|| • -x;||) : R" -+ R, where 4>: R+ -+ R is the radial basis function, || • || : R" -y R+ is the Euclidean norm (the modulus function in one dimension) and Xj e R" are prescribed points (called centres or sometimes knots) in the underlying space. The usual way to approximate by these functions is through interpolation at the centres, giving rise to the question of the invertibility of the interpolation matrix {<£(*, — Xj)}tj that we shall address, among other issues, in this article. -
Lecture Notes on Harmonic Analysis Updated: April 28, 2020 © Chengchun HAO Institute of Mathematics AMSS CAS - Iii - 4.4
GRADUATE LECTURES IN ANALYSIS LLECTUREECTURE NNOTESOTES ON ON HHARMONICARMONIC AANALYSISNALYSIS CHENGCHUN HAO INSTITUTE OF MATHEMATICS LECTURE NOTES ON HARMONIC ANALYSIS Chengchun Hao Institute of Mathematics, AMSS Chinese Academy of Sciences Email: [email protected] Updated: April 28, 2020 Preface Harmonic analysis, as a subfield of analysis, is particularly interested in the study of quantitative properties on functions, and how these quantitative proper- ties change when apply various operators. In the past two centuries, it has become a vast subject with applications in areas as diverse as signal processing, quantum mechanics, and neuroscience. Most of the material in these notes are excerpted from the book of Stein [Ste70], the book of Stein and Weiss [SW71], the books of Grafakos [Gra14a, Gra14b] and the book of Wang-Huo-Hao-Guo [WHHG11], etc. with some necessary modification. Please email me ([email protected]) with corrections or suggested improvements of any kinds. Chengchun Hao Beijing April 28, 2020 References [Gra14a] Loukas Grafakos. Classical Fourier analysis, volume 249 of Graduate Texts in Mathematics. Springer, New York, third edition, 2014. [Gra14b] Loukas Grafakos. Modern Fourier analysis, volume 250 of Graduate Texts in Mathematics. Springer, New York, third edition, 2014. [Ste70] Elias M. Stein. Singular integrals and differentiability properties of func- tions. Princeton Mathematical Series, No. 30. Princeton University Press, Princeton, N.J., 1970. [SW71] Elias M. Stein and Guido Weiss. Introduction to Fourier analysis on Euclidean spaces. Princeton University Press, Princeton, N.J., 1971. Princeton Mathematical Series, No. 32. [WHHG11] Baoxiang Wang, Zhaohui Huo, Chengchun Hao, and Zihua Guo. Har- monic analysis method for nonlinear evolution equations, volume I.