Harmonic Analysis Meets Geometric Measure Theory
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Geometric Integration Theory Contents
Steven G. Krantz Harold R. Parks Geometric Integration Theory Contents Preface v 1 Basics 1 1.1 Smooth Functions . 1 1.2Measures.............................. 6 1.2.1 Lebesgue Measure . 11 1.3Integration............................. 14 1.3.1 Measurable Functions . 14 1.3.2 The Integral . 17 1.3.3 Lebesgue Spaces . 23 1.3.4 Product Measures and the Fubini–Tonelli Theorem . 25 1.4 The Exterior Algebra . 27 1.5 The Hausdorff Distance and Steiner Symmetrization . 30 1.6 Borel and Suslin Sets . 41 2 Carath´eodory’s Construction and Lower-Dimensional Mea- sures 53 2.1 The Basic Definition . 53 2.1.1 Hausdorff Measure and Spherical Measure . 55 2.1.2 A Measure Based on Parallelepipeds . 57 2.1.3 Projections and Convexity . 57 2.1.4 Other Geometric Measures . 59 2.1.5 Summary . 61 2.2 The Densities of a Measure . 64 2.3 A One-Dimensional Example . 66 2.4 Carath´eodory’s Construction and Mappings . 67 2.5 The Concept of Hausdorff Dimension . 70 2.6 Some Cantor Set Examples . 73 i ii CONTENTS 2.6.1 Basic Examples . 73 2.6.2 Some Generalized Cantor Sets . 76 2.6.3 Cantor Sets in Higher Dimensions . 78 3 Invariant Measures and the Construction of Haar Measure 81 3.1 The Fundamental Theorem . 82 3.2 Haar Measure for the Orthogonal Group and the Grassmanian 90 3.2.1 Remarks on the Manifold Structure of G(N,M).... 94 4 Covering Theorems and the Differentiation of Integrals 97 4.1 Wiener’s Covering Lemma and its Variants . -
On Stochastic Distributions and Currents
NISSUNA UMANA INVESTIGAZIONE SI PUO DIMANDARE VERA SCIENZIA S’ESSA NON PASSA PER LE MATEMATICHE DIMOSTRAZIONI LEONARDO DA VINCI vol. 4 no. 3-4 2016 Mathematics and Mechanics of Complex Systems VINCENZO CAPASSO AND FRANCO FLANDOLI ON STOCHASTIC DISTRIBUTIONS AND CURRENTS msp MATHEMATICS AND MECHANICS OF COMPLEX SYSTEMS Vol. 4, No. 3-4, 2016 dx.doi.org/10.2140/memocs.2016.4.373 ∩ MM ON STOCHASTIC DISTRIBUTIONS AND CURRENTS VINCENZO CAPASSO AND FRANCO FLANDOLI Dedicated to Lucio Russo, on the occasion of his 70th birthday In many applications, it is of great importance to handle random closed sets of different (even though integer) Hausdorff dimensions, including local infor- mation about initial conditions and growth parameters. Following a standard approach in geometric measure theory, such sets may be described in terms of suitable measures. For a random closed set of lower dimension with respect to the environment space, the relevant measures induced by its realizations are sin- gular with respect to the Lebesgue measure, and so their usual Radon–Nikodym derivatives are zero almost everywhere. In this paper, how to cope with these difficulties has been suggested by introducing random generalized densities (dis- tributions) á la Dirac–Schwarz, for both the deterministic case and the stochastic case. For the last one, mean generalized densities are analyzed, and they have been related to densities of the expected values of the relevant measures. Ac- tually, distributions are a subclass of the larger class of currents; in the usual Euclidean space of dimension d, currents of any order k 2 f0; 1;:::; dg or k- currents may be introduced. -
Measure, Integral and Probability
Marek Capinski´ and Ekkehard Kopp Measure, Integral and Probability Springer-Verlag Berlin Heidelberg NewYork London Paris Tokyo Hong Kong Barcelona Budapest To our children; grandchildren: Piotr, Maciej, Jan, Anna; Luk asz Anna, Emily Preface The central concepts in this book are Lebesgue measure and the Lebesgue integral. Their role as standard fare in UK undergraduate mathematics courses is not wholly secure; yet they provide the principal model for the development of the abstract measure spaces which underpin modern probability theory, while the Lebesgue function spaces remain the main source of examples on which to test the methods of functional analysis and its many applications, such as Fourier analysis and the theory of partial differential equations. It follows that not only budding analysts have need of a clear understanding of the construction and properties of measures and integrals, but also that those who wish to contribute seriously to the applications of analytical methods in a wide variety of areas of mathematics, physics, electronics, engineering and, most recently, finance, need to study the underlying theory with some care. We have found remarkably few texts in the current literature which aim explicitly to provide for these needs, at a level accessible to current under- graduates. There are many good books on modern probability theory, and increasingly they recognize the need for a strong grounding in the tools we develop in this book, but all too often the treatment is either too advanced for an undergraduate audience or else somewhat perfunctory. We hope therefore that the current text will not be regarded as one which fills a much-needed gap in the literature! One fundamental decision in developing a treatment of integration is whether to begin with measures or integrals, i.e. -
The Axiom of Choice and Its Implications
THE AXIOM OF CHOICE AND ITS IMPLICATIONS KEVIN BARNUM Abstract. In this paper we will look at the Axiom of Choice and some of the various implications it has. These implications include a number of equivalent statements, and also some less accepted ideas. The proofs discussed will give us an idea of why the Axiom of Choice is so powerful, but also so controversial. Contents 1. Introduction 1 2. The Axiom of Choice and Its Equivalents 1 2.1. The Axiom of Choice and its Well-known Equivalents 1 2.2. Some Other Less Well-known Equivalents of the Axiom of Choice 3 3. Applications of the Axiom of Choice 5 3.1. Equivalence Between The Axiom of Choice and the Claim that Every Vector Space has a Basis 5 3.2. Some More Applications of the Axiom of Choice 6 4. Controversial Results 10 Acknowledgments 11 References 11 1. Introduction The Axiom of Choice states that for any family of nonempty disjoint sets, there exists a set that consists of exactly one element from each element of the family. It seems strange at first that such an innocuous sounding idea can be so powerful and controversial, but it certainly is both. To understand why, we will start by looking at some statements that are equivalent to the axiom of choice. Many of these equivalences are very useful, and we devote much time to one, namely, that every vector space has a basis. We go on from there to see a few more applications of the Axiom of Choice and its equivalents, and finish by looking at some of the reasons why the Axiom of Choice is so controversial. -
(Measure Theory for Dummies) UWEE Technical Report Number UWEETR-2006-0008
A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 UWEE Technical Report Number UWEETR-2006-0008 May 2006 Department of Electrical Engineering University of Washington Box 352500 Seattle, Washington 98195-2500 PHN: (206) 543-2150 FAX: (206) 543-3842 URL: http://www.ee.washington.edu A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 University of Washington, Dept. of EE, UWEETR-2006-0008 May 2006 Abstract This tutorial is an informal introduction to measure theory for people who are interested in reading papers that use measure theory. The tutorial assumes one has had at least a year of college-level calculus, some graduate level exposure to random processes, and familiarity with terms like “closed” and “open.” The focus is on the terms and ideas relevant to applied probability and information theory. There are no proofs and no exercises. Measure theory is a bit like grammar, many people communicate clearly without worrying about all the details, but the details do exist and for good reasons. There are a number of great texts that do measure theory justice. This is not one of them. Rather this is a hack way to get the basic ideas down so you can read through research papers and follow what’s going on. Hopefully, you’ll get curious and excited enough about the details to check out some of the references for a deeper understanding. -
Measure Theory and Probability
Measure theory and probability Alexander Grigoryan University of Bielefeld Lecture Notes, October 2007 - February 2008 Contents 1 Construction of measures 3 1.1Introductionandexamples........................... 3 1.2 σ-additive measures ............................... 5 1.3 An example of using probability theory . .................. 7 1.4Extensionofmeasurefromsemi-ringtoaring................ 8 1.5 Extension of measure to a σ-algebra...................... 11 1.5.1 σ-rings and σ-algebras......................... 11 1.5.2 Outermeasure............................. 13 1.5.3 Symmetric difference.......................... 14 1.5.4 Measurable sets . ............................ 16 1.6 σ-finitemeasures................................ 20 1.7Nullsets..................................... 23 1.8 Lebesgue measure in Rn ............................ 25 1.8.1 Productmeasure............................ 25 1.8.2 Construction of measure in Rn. .................... 26 1.9 Probability spaces ................................ 28 1.10 Independence . ................................. 29 2 Integration 38 2.1 Measurable functions.............................. 38 2.2Sequencesofmeasurablefunctions....................... 42 2.3 The Lebesgue integral for finitemeasures................... 47 2.3.1 Simplefunctions............................ 47 2.3.2 Positivemeasurablefunctions..................... 49 2.3.3 Integrablefunctions........................... 52 2.4Integrationoversubsets............................ 56 2.5 The Lebesgue integral for σ-finitemeasure................. -
The Radon-Nikodym Theorem
The Radon-Nikodym Theorem Yongheng Zhang Theorem 1 (Johann Radon-Otton Nikodym). Let (X; B; µ) be a σ-finite measure space and let ν be a measure defined on B such that ν µ. Then there is a unique nonnegative measurable function f up to sets of µ-measure zero such that Z ν(E) = fdµ, E for every E 2 B. f is called the Radon-Nikodym derivative of ν with respect to µ and it is often dν denoted by [ dµ ]. The assumption that the measure µ is σ-finite is crucial in the theorem (but we don't have this requirement directly for ν). Consider the following two examples in which the µ's are not σ-finite. Example 1. Let (R; M; ν) be the Lebesgue measure space. Let µ be the counting measure on M. So µ is not σ-finite. For any E 2 M, if µ(E) = 0, then E = ; and hence ν(E) = 0. This shows ν µ. Do we have the associated Radon-Nikodym derivative? Never. Suppose we have it and call it R R f, then for each x 2 R, 0 = ν(fxg) = fxg fdµ = fxg fχfxgdµ = f(x)µ(fxg) = f(x). Hence, f = 0. R This means for every E 2 M, ν(E) = E 0dµ = 0, which contradicts that ν is the Lebesgue measure. Example 2. ν is still the same measure above but we let µ to be defined by µ(;) = 0 and µ(A) = 1 if A 6= ;. Clearly, µ is not σ-finite and ν µ. -
A Direct Approach to Plateau's Problem 1
A DIRECT APPROACH TO PLATEAU'S PROBLEM C. DE LELLIS, F. GHIRALDIN, AND F. MAGGI Abstract. We provide a compactness principle which is applicable to different formulations of Plateau's problem in codimension 1 and which is exclusively based on the theory of Radon measures and elementary comparison arguments. Exploiting some additional techniques in geometric measure theory, we can use this principle to give a different proof of a theorem by Harrison and Pugh and to answer a question raised by Guy David. 1. Introduction Since the pioneering work of Reifenberg there has been an ongoing interest into formulations of Plateau's problem involving the minimization of the Hausdorff measure on closed sets coupled with some notion of \spanning a given boundary". More precisely consider any closed set H ⊂ n+1 n+1 R and assume to have a class P(H) of relatively closed subsets K of R nH, which encodes a particular notion of \K bounds H". Correspondingly there is a formulation of Plateau's problem, namely the minimum for such problem is n m0 := inffH (K): K 2 P(H)g ; (1.1) n and a minimizing sequence fKjg ⊂ P(H) is characterized by the property H (Kj) ! m0. Two good motivations for considering this kind of approach rather than the one based on integer rectifiable currents are that, first, not every interesting boundary can be realized as an integer 3 rectifiable cycle and, second, area minimizing 2-d currents in R are always smooth away from their boundaries, in contrast to what one observes with real world soap films. -
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. -
Geometric Measure Theory
Encyclopedia of Mathematical Physics, [version: May 19, 2007] Vol. 2, pp. 520–527 (J.-P. Fran¸coiseet al. eds.). Elsevier, Oxford 2006. Geometric Measure Theory Giovanni Alberti Dipartimento di Matematica, Universit`adi Pisa L.go Pontecorvo 5, 56127 Pisa Italy e-mail: [email protected] 1. Introduction The aim of these pages is to give a brief, self-contained introduction to that part of Geometric Measure Theory which is more directly related to the Calculus of Variations, namely the theory of currents and its applications to the solution of Plateau problem. (The theory of finite perimeter sets, which is closely related to currents and to the Plateau problem, is treated in the article “Free interfaces and free discontinuities: variational problems”). Named after the belgian physicist J.A.F. Plateau (1801-1883), this problem was originally formulated as follows: find the surface of minimal area spanning a given curve in the space. Nowadays, it is mostly intended in the sense of developing a mathematical framework where the existence of k-dimensional surfaces of minimal volume that span a prescribed boundary can be rigorously proved. Indeed, several solutions have been proposed in the last century, none of which is completely satisfactory. One difficulty is that the infimum of the area among all smooth surfaces with a certain boundary may not be attained. More precisely, it may happen that all minimizing sequences (that is, sequences of smooth surfaces whose area approaches the infimum) converge to a singular surface. Therefore one is forced to consider a larger class of admissible surfaces than just smooth ones (in fact, one might want to do this also for modelling reasons—this is indeed the case with soap films, soap bubbles, and other capillarity problems). -
Rapid Course on Geometric Measure Theory and the Theory of Varifolds
Crash course on GMT Sławomir Kolasiński Rapid course on geometric measure theory and the theory of varifolds I will present an excerpt of classical results in geometric measure theory and the theory of vari- folds as developed in [Fed69] and [All72]. Alternate sources for fragments of the theory are [Sim83], [EG92], [KP08], [KP99], [Zie89], [AFP00]. Rectifiable sets and the area and coarea formulas (1) Area and coarea for maps between Euclidean spaces; see [Fed69, 3.2.3, 3.2.10] or [KP08, 5.1, 5.2] (2) Area and coarea formula for maps between rectifiable sets; see [Fed69, 3.2.20, 3.2.22] or [KP08, 5.4.7, 5.4.8] (3) Approximate tangent and normal vectors and approximate differentiation with respect to a measure and dimension; see [Fed69, 3.2.16] (4) Approximate tangents and densities for Hausdorff measure restricted to an (H m; m) recti- fiable set; see [Fed69, 3.2.19] (5) Characterization of countably (H m; m)-rectifiable sets in terms of covering by submanifolds of class C 1; see [Fed69, 3.2.29] or [KP08, 5.4.3] Sets of finite perimeter (1) Gauss-Green theorem for sets of locally finite perimeter; see [Fed69, 4.5.6] or [EG92, 5.7–5.8] (2) Coarea formula for real valued functions of locally bounded variation; see [Fed69, 4.5.9(12)(13)] or [EG92, 5.5 Theorem 1] (3) Differentiability of approximate and integral type for real valued functions of locally bounded variation; see [Fed69, 4.5.9(26)] or [EG92, 6.1 Theorems 1 and 4] (4) Characterization of sets of locally finite perimeter in terms of the Hausdorff measure of the measure theoretic boundary; see [Fed69, 4.5.11] or [EG92, 5.11 Theorem 1] Varifolds (1) Notation and basic facts for submanifolds of Euclidean space, see [All72, 2.5] or [Sim83, §7] (2) Definitions and notation for varifolds, push forward by a smooth map, varifold disintegration; see [All72, 3.1–3.3] or [Sim83, pp. -
Representation of the Dirac Delta Function in C(R)
Representation of the Dirac delta function in C(R∞) in terms of infinite-dimensional Lebesgue measures Gogi Pantsulaia∗ and Givi Giorgadze I.Vekua Institute of Applied Mathematics, Tbilisi - 0143, Georgian Republic e-mail: [email protected] Georgian Technical University, Tbilisi - 0175, Georgian Republic g.giorgadze Abstract: A representation of the Dirac delta function in C(R∞) in terms of infinite-dimensional Lebesgue measures in R∞ is obtained and some it’s properties are studied in this paper. MSC 2010 subject classifications: Primary 28xx; Secondary 28C10. Keywords and phrases: The Dirac delta function, infinite-dimensional Lebesgue measure. 1. Introduction The Dirac delta function(δ-function) was introduced by Paul Dirac at the end of the 1920s in an effort to create the mathematical tools for the development of quantum field theory. He referred to it as an improper functional in Dirac (1930). Later, in 1947, Laurent Schwartz gave it a more rigorous mathematical definition as a spatial linear functional on the space of test functions D (the set of all real-valued infinitely differentiable functions with compact support). Since the delta function is not really a function in the classical sense, one should not consider the value of the delta function at x. Hence, the domain of the delta function is D and its value for f ∈ D is f(0). Khuri (2004) studied some interesting applications of the delta function in statistics. The purpose of the present paper is an introduction of a concept of the Dirac delta function in the class of all continuous functions defined in the infinite- dimensional topological vector space of all real valued sequences R∞ equipped arXiv:1605.02723v2 [math.CA] 14 May 2016 with Tychonoff topology and a representation of this functional in terms of infinite-dimensional Lebesgue measures in R∞.