Notes on Radial and Quasiradial Fourier Multipliers
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Corrective View of Neural Networks: Representation, Memorization and Learning
Proceedings of Machine Learning Research vol 125:1{54, 2020 33rd Annual Conference on Learning Theory A Corrective View of Neural Networks: Representation, Memorization and Learning Guy Bresler [email protected] Department of EECS, MIT Cambridge, MA, USA. Dheeraj Nagaraj [email protected] Department of EECS, MIT Cambridge, MA, USA. Editors: Jacob Abernethy and Shivani Agarwal Abstract We develop a corrective mechanism for neural network approximation: the total avail- able non-linear units are divided into multiple groups and the first group approx- imates the function under consideration, the second approximates the error in ap- proximation produced by the first group and corrects it, the third group approxi- mates the error produced by the first and second groups together and so on. This technique yields several new representation and learning results for neural networks: 1. Two-layer neural networks in the random features regime (RF) can memorize arbi- Rd ~ n trary labels for n arbitrary points in with O( θ4 ) ReLUs, where θ is the minimum distance between two different points. This bound can be shown to be optimal in n up to logarithmic factors. 2. Two-layer neural networks with ReLUs and smoothed ReLUs can represent functions with an error of at most with O(C(a; d)−1=(a+1)) units for a 2 N [ f0g when the function has Θ(ad) bounded derivatives. In certain cases d can be replaced with effective dimension q d. Our results indicate that neural networks with only a single nonlinear layer are surprisingly powerful with regards to representation, and show that in contrast to what is suggested in recent work, depth is not needed in order to represent highly smooth functions. -
Smooth Bumps, a Borel Theorem and Partitions of Smooth Functions on P.C.F. Fractals
SMOOTH BUMPS, A BOREL THEOREM AND PARTITIONS OF SMOOTH FUNCTIONS ON P.C.F. FRACTALS. LUKE G. ROGERS, ROBERT S. STRICHARTZ, AND ALEXANDER TEPLYAEV Abstract. We provide two methods for constructing smooth bump functions and for smoothly cutting off smooth functions on fractals, one using a prob- abilistic approach and sub-Gaussian estimates for the heat operator, and the other using the analytic theory for p.c.f. fractals and a fixed point argument. The heat semigroup (probabilistic) method is applicable to a more general class of metric measure spaces with Laplacian, including certain infinitely ramified fractals, however the cut off technique involves some loss in smoothness. From the analytic approach we establish a Borel theorem for p.c.f. fractals, showing that to any prescribed jet at a junction point there is a smooth function with that jet. As a consequence we prove that on p.c.f. fractals smooth functions may be cut off with no loss of smoothness, and thus can be smoothly decom- posed subordinate to an open cover. The latter result provides a replacement for classical partition of unity arguments in the p.c.f. fractal setting. 1. Introduction Recent years have seen considerable developments in the theory of analysis on certain fractal sets from both probabilistic and analytic viewpoints [1, 14, 26]. In this theory, either a Dirichlet energy form or a diffusion on the fractal is used to construct a weak Laplacian with respect to an appropriate measure, and thereby to define smooth functions. As a result the Laplacian eigenfunctions are well un- derstood, but we have little knowledge of other basic smooth functions except in the case where the fractal is the Sierpinski Gasket [19, 5, 20]. -
Robinson Showed That It Is Possible to Rep- Resent Each Schwartz Distribution T As an Integral T(4) = Sf4, Where F Is Some Nonstandard Smooth Function
Indag. Mathem., N.S., 11 (1) 129-138 March 30,200O Constructive nonstandard representations of generalized functions by Erik Palmgren* Department of Mathematics,Vppsala University, P. 0. Box 480, S-75106 Uppa& &v&n, e-mail:[email protected] Communicated by Prof. A.S. Troelstra at the meeting of May 31, 1999 ABSTRACT Using techniques of nonstandard analysis Abraham Robinson showed that it is possible to rep- resent each Schwartz distribution T as an integral T(4) = sf4, where f is some nonstandard smooth function. We show that the theory based on this representation can be developed within a constructive setting. I. INTRODUCTION Robinson (1966) demonstrated that Schwartz’ theory of distributions could be given a natural formulation using techniques of nonstandard analysis, so that distributions become certain nonstandard smooth functions. In particular, Dirac’s delta-function may then be taken to be the rational function where E is a positive infinitesimal. As is wellknown, classical nonstandard analysis is based on strongly nonconstructive assumptions. In this paper we present a constructive version of Robinson’s theory using the framework of constructive nonstandard analysis developed in Palmgren (1997, 1998), which was based on Moerdijk’s (1995) sheaf-theoretic nonstandard models. The main *The author gratefully acknowledges support from the Swedish Research Council for Natural Sciences (NFR). 129 points of the present paper are the elimination of the standard part map from Robinson’s theory and a constructive proof of the representation of distribu- tions as nonstandard smooth functions (Theorem 2.6). We note that Schmieden and Laugwitz already in 1958 introduced their ver- sion of nonstandard analysis to give an interpretation of generalized functions which are closer to those of the Mikusinski-Sikorski calculus. -
Delta Functions and Distributions
When functions have no value(s): Delta functions and distributions Steven G. Johnson, MIT course 18.303 notes Created October 2010, updated March 8, 2017. Abstract x = 0. That is, one would like the function δ(x) = 0 for all x 6= 0, but with R δ(x)dx = 1 for any in- These notes give a brief introduction to the mo- tegration region that includes x = 0; this concept tivations, concepts, and properties of distributions, is called a “Dirac delta function” or simply a “delta which generalize the notion of functions f(x) to al- function.” δ(x) is usually the simplest right-hand- low derivatives of discontinuities, “delta” functions, side for which to solve differential equations, yielding and other nice things. This generalization is in- a Green’s function. It is also the simplest way to creasingly important the more you work with linear consider physical effects that are concentrated within PDEs, as we do in 18.303. For example, Green’s func- very small volumes or times, for which you don’t ac- tions are extremely cumbersome if one does not al- tually want to worry about the microscopic details low delta functions. Moreover, solving PDEs with in this volume—for example, think of the concepts of functions that are not classically differentiable is of a “point charge,” a “point mass,” a force plucking a great practical importance (e.g. a plucked string with string at “one point,” a “kick” that “suddenly” imparts a triangle shape is not twice differentiable, making some momentum to an object, and so on. -
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. -
[Math.FA] 1 Apr 1998 E Words Key 1991 Abstract *)Tersac Fscn N H Hr Uhrhsbe Part Been Has Author 93-0452
RENORMINGS OF Lp(Lq) by R. Deville∗, R. Gonzalo∗∗ and J. A. Jaramillo∗∗ (*) Laboratoire de Math´ematiques, Universit´eBordeaux I, 351, cours de la Lib´eration, 33400 Talence, FRANCE. email : [email protected] (**) Departamento de An´alisis Matem´atico, Universidad Complutense de Madrid, 28040 Madrid, SPAIN. email : [email protected] and [email protected] Abstract : We investigate the best order of smoothness of Lp(Lq). We prove in particular ∞ that there exists a C -smooth bump function on Lp(Lq) if and only if p and q are even integers and p is a multiple of q. arXiv:math/9804002v1 [math.FA] 1 Apr 1998 1991 Mathematics Subject Classification : 46B03, 46B20, 46B25, 46B30. Key words : Higher order smoothness, Renormings, Geometry of Banach spaces, Lp spaces. (**) The research of second and the third author has been partially supported by DGICYT grant PB 93-0452. 1 Introduction The first results on high order differentiability of the norm in a Banach space were given by Kurzweil [10] in 1954, and Bonic and Frampton [2] in 1965. In [2] the best order of differentiability of an equivalent norm on classical Lp-spaces, 1 < p < ∞, is given. The best order of smoothness of equivalent norms and of bump functions in Orlicz spaces has been investigated by Maleev and Troyanski (see [13]). The work of Leonard and Sundaresan [11] contains a systematic investigation of the high order differentiability of the norm function in the Lebesgue-Bochner function spaces Lp(X). Their results relate the continuously n-times differentiability of the norm on X and of the norm on Lp(X). -
Be a Smooth Manifold, and F : M → R a Function
LECTURE 3: SMOOTH FUNCTIONS 1. Smooth Functions Definition 1.1. Let (M; A) be a smooth manifold, and f : M ! R a function. (1) We say f is smooth at p 2 M if there exists a chart f'α;Uα;Vαg 2 A with −1 p 2 Uα, such that the function f ◦ 'α : Vα ! R is smooth at 'α(p). (2) We say f is a smooth function on M if it is smooth at every x 2 M. −1 Remark. Suppose f ◦ 'α is smooth at '(p). Let f'β;Uβ;Vβg be another chart in A with p 2 Uβ. Then by the compatibility of charts, the function −1 −1 −1 f ◦ 'β = (f ◦ 'α ) ◦ ('α ◦ 'β ) must be smooth at 'β(p). So the smoothness of a function is independent of the choice of charts in the given atlas. Remark. According to the chain rule, it is easy to see that if f : M ! R is smooth at p 2 M, and h : R ! R is smooth at f(p), then h ◦ f is smooth at p. We will denote the set of all smooth functions on M by C1(M). Note that this is a (commutative) algebra, i.e. it is a vector space equipped with a (commutative) bilinear \multiplication operation": If f; g are smooth, so are af + bg and fg; moreover, the multiplication is commutative, associative and satisfies the usual distributive laws. 1 n+1 i n Example. Each coordinate function fi(x ; ··· ; x ) = x is a smooth function on S , since ( 2yi −1 1 n 1+jyj2 ; 1 ≤ i ≤ n fi ◦ '± (y ; ··· ; y ) = 1−|yj2 ± 1+jyj2 ; i = n + 1 are smooth functions on Rn. -
Generalized Functions: Homework 1
Generalized Functions: Homework 1 Exercise 1. Prove that there exists a function f Cc∞(R) which isn’t the zero function. ∈ Solution: Consider the function: 1 e x x > 0 g = − 0 x 0 ( ≤ 1 We claim that g C∞(R). Indeed, all of the derivatives of e− x are vanishing when x 0: ∈ n → d 1 1 lim e− x = lim q(x) e− x = 0 x 0 n x 0 → dx → ∙ where q(x) is a rational function of x. The function f = g(x) g(1 x) is smooth, as a multiplication of two smooth functions, has compact support∙ − (the interval 1 1 [0, 1]), and is non-zero (f 2 = e ). Exercise 2. Find a sequence of functions fn n Z (when fn Cc∞ (R) n) that weakly converges to Dirac Delta function.{ } ∈ ∈ ∀ Solution: Consider the sequence of function: ˜ 1 1 fn = g x g + x C∞(R) n − ∙ n ∈ c where g(x) Cc∞(R) is the function defined in the first question. Notice that ∈ 1 1 the support of fn is the interval n , n . Normalizing these functions so that the integral will be equal to 1 we get:− 1 ∞ − f = d(n)f˜ , d(n) = f˜ (x)dx n n n Z −∞ For every test function F (x) C∞(R), we can write F (x) = F (0) + x G(x), ∈ c ∙ where G(x) C∞(R). Therefore we have: ∈ c 1/n ∞ F (x) f (x)dx = F (x) f (x)dx ∙ n ∙ n Z 1Z/n −∞ − 1/n 1/n = F (0) f (x)dx + xG(x) f (x)dx ∙ n ∙ n 1Z/n 1Z/n − − 1/n = F (0) + xG(x) f (x)dx ∙ n 1Z/n − 1 1/n ∞ ∞ So F (x) fn(x)dx F (x) δ(x)dx = 1/n xG(x) fn(x)dx. -
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. -
On the Size of the Sets of Gradients of Bump Functions and Starlike Bodies on the Hilbert Space
ON THE SIZE OF THE SETS OF GRADIENTS OF BUMP FUNCTIONS AND STARLIKE BODIES ON THE HILBERT SPACE DANIEL AZAGRA AND MAR JIMENEZ-SEVILLA´ Abstract. We study the size of the sets of gradients of bump functions on the Hilbert space `2, and the related question as to how small the set of tangent hyperplanes to a smooth bounded starlike body in `2 can be. We find that those sets can be quite small. On the one hand, the usual norm of the Hilbert space 1 `2 can be uniformly approximated by C smooth Lipschitz functions ψ so that 0 the cones generated by the ranges of its derivatives ψ (`2) have empty interior. 1 This implies that there are C smooth Lipschitz bumps in `2 so that the cones generated by their sets of gradients have empty interior. On the other hand, we 1 construct C -smooth bounded starlike bodies A ⊂ `2, which approximate the unit ball, so that the cones generated by the hyperplanes which are tangent to A have empty interior as well. We also explain why this is the best answer to the above questions that one can expect. RESUM´ E.´ On ´etudie la taille de l’ensemble de valeurs du gradient d’une fonction lisse, non identiquement nulle et `asupport born´edans l’espace de Hilbert `2, et aussi celle de l’ensemble des hyperplans tangents `aun corps ´etoil´edans `2. On montre que ces ensembles peuvent ˆetreassez petits. D’un cˆot´e,la norme de l’espace de Hilbert est approxim´eeuniformement par des fonctions de classe C1 et Lipschitziennes ψ telles que les cˆonesengendr´es par les 0 ensembles de valeurs des d´eriv´ees ψ (`2) sont d’int´erieur vide. -
1.6 Smooth Functions and Partitions of Unity
1.6 Smooth functions and partitions of unity The set C1(M; R) of smooth functions on M inherits much of the structure of R by composition. R is a ring, having addition + : R × R −! R and multiplication × : R × R −! R which are both smooth. As a result, C1(M; R) is as well: One way of seeing why is to use the smooth diagonal map ∆ : M −! M × M, i.e. ∆(p) = (p; p). Then, given functions f ; g 2 C1(M; R) we have the sum f + g, defined by the composition ∆ f ×g + M / M × M / R × R / R : We also have the product f g, defined by the composition ∆ f ×g × M / M × M / R × R / R : Given a smooth map ' : M −! N of manifolds, we obtain a natural operation '∗ : C1(N; R) −! C1(M; R), given by f 7! f ◦ '. This is called the pullback of functions, and defines a homomorphism of rings since ∆ ◦ ' = (' × ') ◦ ∆. The association M 7! C1(M; R) and ' 7! '∗ takes objects and arrows of C1-Man to objects and arrows of the category of rings, respectively, in such a way which respects identities and composition of morphisms. Such a map is called a functor. In this case, it has the peculiar property that it switches the source and target of morphisms. It is therefore a contravariant functor from the category of manifolds to the category of rings, and is the basis for algebraic geometry, the algebraic representation of geometrical objects. It is easy to see from this that any diffeomorphism ' : M −! M defines an automorphism '∗ of C1(M; R), but actually all automorphisms are of this form (Why?). -
Examples Discussion 05
(December 18, 2017) Examples discussion 05 Paul Garrett [email protected] http:=/www.math.umn.edu/egarrett/ [This document is http:=/www.math.umn.edu/egarrett/m/real/examples 2017-18/real-disc-05.pdf] [05.1] Give a persuasive proof that the function 0 (for x ≤ 0) f(x) = e−1=x (for x > 0) is infinitely differentiable at 0. Use this kind of construction to make a smooth step function: 0 for x ≤ 0 and 1 for x ≥ 1, and goes monotonically from 0 to 1 in the interval [0; 1]. Use this to construct a family of smooth cut-off functions ffn : n = 1; 2; 3;:::g: for each n, fn(x) = 1 for x 2 [−n; n], fn(x) = 0 for x 62 [−(n + 1); n + 1], and fn goes monotonically from 0 to 1 in [−(n + 1); −n] and monotonically from 1 to 0 in [n; n + 1]. Discussion: In x > 0, by induction, the derivatives are finite linear linear combinations of functions of the −n −1=x −n −1=x n −x form x e . It suffices to show that limx!0+ x e = 0. Equivalently, that limx!+1 x e = 0, which follows from e−x = 1=ex, and xn xn xn x−ne−1=x = = ≤ −! 0 (as x ! +1) ex P xm xn+1 m≥0 m! (n+1)! (This is perhaps a little better than appeals to L'Hospital's Rule.) Thus, f is smooth at 0, with all derivatives 0 there. === Next, we make a smooth bump function by 8 0 (for x ≤ −1) > <> 1 b(x) = e 1−x2 (for −1 < x < 1) > > : 0 (for x ≥ 1) A similar argument to the previous shows that this is smooth.