Garling's Proof of the Riesz Representation Theorem
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Integral Representation of a Linear Functional on Function Spaces
INTEGRAL REPRESENTATION OF LINEAR FUNCTIONALS ON FUNCTION SPACES MEHDI GHASEMI Abstract. Let A be a vector space of real valued functions on a non-empty set X and L : A −! R a linear functional. Given a σ-algebra A, of subsets of X, we present a necessary condition for L to be representable as an integral with respect to a measure µ on X such that elements of A are µ-measurable. This general result then is applied to the case where X carries a topological structure and A is a family of continuous functions and naturally A is the Borel structure of X. As an application, short solutions for the full and truncated K-moment problem are presented. An analogue of Riesz{Markov{Kakutani representation theorem is given where Cc(X) is replaced with whole C(X). Then we consider the case where A only consists of bounded functions and hence is equipped with sup-norm. 1. Introduction A positive linear functional on a function space A ⊆ RX is a linear map L : A −! R which assigns a non-negative real number to every function f 2 A that is globally non-negative over X. The celebrated Riesz{Markov{Kakutani repre- sentation theorem states that every positive functional on the space of continuous compactly supported functions over a locally compact Hausdorff space X, admits an integral representation with respect to a regular Borel measure on X. In symbols R L(f) = X f dµ, for all f 2 Cc(X). Riesz's original result [12] was proved in 1909, for the unit interval [0; 1]. -
Measure Theory (Graduate Texts in Mathematics)
PaulR. Halmos Measure Theory Springer-VerlagNewYork-Heidelberg-Berlin Managing Editors P. R. Halmos C. C. Moore Indiana University University of California Department of Mathematics at Berkeley Swain Hall East Department of Mathematics Bloomington, Indiana 47401 Berkeley, California 94720 AMS Subject Classifications (1970) Primary: 28 - 02, 28A10, 28A15, 28A20, 28A25, 28A30, 28A35, 28A40, 28A60, 28A65, 28A70 Secondary: 60A05, 60Bxx Library of Congress Cataloging in Publication Data Halmos, Paul Richard, 1914- Measure theory. (Graduate texts in mathematics, 18) Reprint of the ed. published by Van Nostrand, New York, in series: The University series in higher mathematics. Bibliography: p. 1. Measure theory. I. Title. II. Series. [QA312.H26 1974] 515'.42 74-10690 ISBN 0-387-90088-8 All rights reserved. No part of this book may be translated or reproduced in any form without written permission from Springer-Verlag. © 1950 by Litton Educational Publishing, Inc. and 1974 by Springer-Verlag New York Inc. Printed in the United States of America. ISBN 0-387-90088-8 Springer-Verlag New York Heidelberg Berlin ISBN 3-540-90088-8 Springer-Verlag Berlin Heidelberg New York PREFACE My main purpose in this book is to present a unified treatment of that part of measure theory which in recent years has shown itself to be most useful for its applications in modern analysis. If I have accomplished my purpose, then the book should be found usable both as a text for students and as a source of refer ence for the more advanced mathematician. I have tried to keep to a minimum the amount of new and unusual terminology and notation. -
(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. -
Probability Measures on Metric Spaces
Probability measures on metric spaces Onno van Gaans These are some loose notes supporting the first sessions of the seminar Stochastic Evolution Equations organized by Dr. Jan van Neerven at the Delft University of Technology during Winter 2002/2003. They contain less information than the common textbooks on the topic of the title. Their purpose is to present a brief selection of the theory that provides a basis for later study of stochastic evolution equations in Banach spaces. The notes aim at an audience that feels more at ease in analysis than in probability theory. The main focus is on Prokhorov's theorem, which serves both as an important tool for future use and as an illustration of techniques that play a role in the theory. The field of measures on topological spaces has the luxury of several excellent textbooks. The main source that has been used to prepare these notes is the book by Parthasarathy [6]. A clear exposition is also available in one of Bour- baki's volumes [2] and in [9, Section 3.2]. The theory on the Prokhorov metric is taken from Billingsley [1]. The additional references for standard facts on general measure theory and general topology have been Halmos [4] and Kelley [5]. Contents 1 Borel sets 2 2 Borel probability measures 3 3 Weak convergence of measures 6 4 The Prokhorov metric 9 5 Prokhorov's theorem 13 6 Riesz representation theorem 18 7 Riesz representation for non-compact spaces 21 8 Integrable functions on metric spaces 24 9 More properties of the space of probability measures 26 1 The distribution of a random variable in a Banach space X will be a probability measure on X. -
Borel Extensions of Baire Measures
FUNDAMENTA MATHEMATICAE 154 (1997) Borel extensions of Baire measures by J. M. A l d a z (Madrid) Abstract. We show that in a countably metacompact space, if a Baire measure admits a Borel extension, then it admits a regular Borel extension. We also prove that under the special axiom ♣ there is a Dowker space which is quasi-Maˇr´ıkbut not Maˇr´ık,answering a question of H. Ohta and K. Tamano, and under P (c), that there is a Maˇr´ıkDowker space, answering a question of W. Adamski. We answer further questions of H. Ohta and K. Tamano by showing that the union of a Maˇr´ıkspace and a compact space is Maˇr´ık,that under “c is real-valued measurable”, a Baire subset of a Maˇr´ıkspace need not be Maˇr´ık, and finally, that the preimage of a Maˇr´ıkspace under an open perfect map is Maˇr´ık. 1. Introduction. The Borel sets are the σ-algebra generated by the open sets of a topological space, and the Baire sets are the smallest σ- algebra making all real-valued continuous functions measurable. The Borel extension problem asks: Given a Baire measure, when can it be extended to a Borel measure? Whenever one deals with Baire measures on a topological space, it is assumed that the space is completely regular and Hausdorff, so there are enough continuous functions to separate points and closed sets. In 1957 (see [Ma]), J. Maˇr´ıkproved that all normal, countably paracompact spaces have the following property: Every Baire measure extends to a regular Borel measure. -
On Measure-Compactness and Borel Measure-Compactness
CORE Metadata, citation and similar papers at core.ac.uk Provided by Osaka City University Repository Okada, S. and Okazaki, Y. Osaka J. Math. 15 (1978), 183-191 ON MEASURE-COMPACTNESS AND BOREL MEASURE-COMPACTNESS SUSUMU OKADA AND YOSHIAKI OKAZAKI (Received December 6, 1976) 1. Introduction Moran has introduced "measure-compactness' and "strongly measure- compactness' in [7], [8], For measure-compactness, Kirk has independently presented in [4]. For Borel measures, "Borel measure-compactness' and "property B" are introduced by Gardner [1], We study, defining "a weak Radon space", the relation among these proper- ties at the last of §2. Furthermore we investigate the stability of countable unions in §3 and the hereditariness in §4. The authors wish to thank Professor Y. Yamasaki for his suggestions. 2. Preliminaries and fundamental results All spaces considered in this paper are completely regular and Hausdorff. Let Cb(X) be the space of all bounded real-valued continuous functions on a topological space X. By Z(X] we mean the family of zero sets {/" \0) / e Cb(X)} c and U(X) the family of cozero sets {Z \ Z<=Z(X)}. We denote by &a(X) the Baίre field, that is, the smallest σ-algebra containing Z(X) and by 1$(X) the σ-algebra generated by all open subsets of X, which is called the Borel field. A Baire measure [resp. Borel measure} is a totally finite, non-negative coun- tably additive set function defined on Άa(X) [resp. 3)(X)]. For a Baire measure μ, the support of μ is supp μ = {x^Xm, μ(U)>0 for every U^ U(X) containing x} . -
Real Analysis II, Winter 2018
Real Analysis II, Winter 2018 From the Finnish original “Moderni reaalianalyysi”1 by Ilkka Holopainen adapted by Tuomas Hytönen February 22, 2018 1Version dated September 14, 2011 Contents 1 General theory of measure and integration 2 1.1 Measures . 2 1.11 Metric outer measures . 4 1.20 Regularity of measures, Radon measures . 7 1.31 Uniqueness of measures . 9 1.36 Extension of measures . 11 1.45 Product measure . 14 1.52 Fubini’s theorem . 16 2 Hausdorff measures 21 2.1 Basic properties of Hausdorff measures . 21 2.12 Hausdorff dimension . 24 2.17 Hausdorff measures on Rn ...................... 25 3 Compactness and convergence of Radon measures 30 3.1 Riesz representation theorem . 30 3.13 Weak convergence of measures . 35 3.17 Compactness of measures . 36 4 On the Hausdorff dimension of fractals 39 4.1 Mass distribution and Frostman’s lemma . 39 4.16 Self-similar fractals . 43 5 Differentiation of measures 52 5.1 Besicovitch and Vitali covering theorems . 52 1 Chapter 1 General theory of measure and integration 1.1 Measures Let X be a set and P(X) = fA : A ⊂ Xg its power set. Definition 1.2. A collection M ⊂ P (X) is a σ-algebra of X if 1. ? 2 M; 2. A 2 M ) Ac = X n A 2 M; S1 3. Ai 2 M, i 2 N ) i=1 Ai 2 M. Example 1.3. 1. P(X) is the largest σ-algebra of X; 2. f?;Xg is the smallest σ-algebra of X; 3. Leb(Rn) = the Lebesgue measurable subsets of Rn; 4. -
EXISTENCE of HAAR MEASURE Most of the Measure Theory We've Done in This Class Has Been Within Subsets of Rn, Even Though Measu
EXISTENCE OF HAAR MEASURE ARUN DEBRAY NOVEMBER 19, 2015 Abstract. In this presentation, I will prove that every compact topological group has a unique left-invariant measure with total measure 1. This presentation is for UT’s real analysis prelim class. Most of the measure theory we’ve done in this class has been within subsets of Rn, even though measures are very general tools. Today, we will talk about a measure defined on a larger class of spaces, which generalizes the usual Lesbegue measure. I will define and prove its existence; Spencer will prove its uniqueness and provide some interesting examples; and Gill will talk about an application to harmonic analysis. Definition. A topological group is a topological space G with a group structure: an identity, an associative multiplication map, and an inverse map, such that multiplication G G G and inversion G G are both continuous. × ! ! There are many examples, including Rn under addition; all Lie groups; and all finite groups (with the discrete topology). Since a topological group G has a topology, we can talk about the σ-algebra of Borel sets on it, as usual generated by the open sets. But the group structure means we can also talk about left- and right-invariance: for every g G, we have continuous maps ` : G G and r : G G, sending x gx and x xg, respectively. 2 g ! g ! 7! 7! These should be read “left (resp. right) translation by g” (or “multiplication,” or “action”). Often, we want something to be invariant under these maps. Specifically, we say that a measure is left-invariant if µ(S) = µ(g S) for all g G, and define right-invariance similarly. -
5.2 Complex Borel Measures on R
MATH 245A (17F) (L) M. Bonk / (TA) A. Wu Real Analysis Contents 1 Measure Theory 3 1.1 σ-algebras . .3 1.2 Measures . .4 1.3 Construction of non-trivial measures . .5 1.4 Lebesgue measure . 10 1.5 Measurable functions . 14 2 Integration 17 2.1 Integration of simple non-negative functions . 17 2.2 Integration of non-negative functions . 17 2.3 Integration of real and complex valued functions . 19 2.4 Lp-spaces . 20 2.5 Relation to Riemann integration . 22 2.6 Modes of convergence . 23 2.7 Product measures . 25 2.8 Polar coordinates . 28 2.9 The transformation formula . 31 3 Signed and Complex Measures 35 3.1 Signed measures . 35 3.2 The Radon-Nikodym theorem . 37 3.3 Complex measures . 40 4 More on Lp Spaces 43 4.1 Bounded linear maps and dual spaces . 43 4.2 The dual of Lp ....................................... 45 4.3 The Hardy-Littlewood maximal functions . 47 5 Differentiability 51 5.1 Lebesgue points . 51 5.2 Complex Borel measures on R ............................... 54 5.3 The fundamental theorem of calculus . 58 6 Functional Analysis 61 6.1 Locally compact Hausdorff spaces . 61 6.2 Weak topologies . 62 6.3 Some theorems in functional analysis . 65 6.4 Hilbert spaces . 67 1 CONTENTS MATH 245A (17F) 7 Fourier Analysis 73 7.1 Trigonometric series . 73 7.2 Fourier series . 74 7.3 The Dirichlet kernel . 75 7.4 Continuous functions and pointwise convergence properties . 77 7.5 Convolutions . 78 7.6 Convolutions and differentiation . 78 7.7 Translation operators . -
Math 595: Geometric Measure Theory
MATH 595: GEOMETRIC MEASURE THEORY FALL 2015 0. Introduction Geometric measure theory considers the structure of Borel sets and Borel measures in metric spaces. It lies at the border between differential geometry and topology, and services a variety of areas: partial differential equations and the calculus of variations, geometric function theory, number theory, etc. The emphasis is on sets with a fine, irregular structure which cannot be well described by the classical tools of geometric analysis. Mandelbrot introduced the term \fractal" to describe sets of this nature. Dynamical systems provide a rich source of examples: Julia sets for rational maps of one complex variable, limit sets of Kleinian groups, attractors of iterated function systems and nonlinear differential systems, and so on. In addition to its intrinsic interest, geometric measure theory has been a valuable tool for problems arising from real and complex analysis, harmonic analysis, PDE, and other fields. For instance, rectifiability criteria and metric curvature conditions played a key role in Tolsa's resolution of the longstanding Painlev´eproblem on removable sets for bounded analytic functions. Major topics within geometric measure theory which we will discuss include Hausdorff measure and dimension, density theorems, energy and capacity methods, almost sure di- mension distortion theorems, Sobolev spaces, tangent measures, and rectifiability. Rectifiable sets and measures provide a rich measure-theoretic generalization of smooth differential submanifolds and their volume measures. The theory of rectifiable sets can be viewed as an extension of differential geometry in which the basic machinery and tools of the subject (tangent spaces, differential operators, vector bundles) are replaced by approxi- mate, measure-theoretic analogs. -
Products of Two Borel Measures by Roy A
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 269, Number 2, February 1982 PRODUCTS OF TWO BOREL MEASURES BY ROY A. JOHNSON Abstract. Let ¡j, and v be finite Borel measures on Hausdorff spaces X and Y, respectively, and suppose product measures ¡i X x v and p. X2 v are defined on the Borel sets of X X Y by integrating vertical and horizontal cross-section measure, respectively. Sufficient conditions are given so that ¡i x¡ v = p x2v and so that the usual product measure u X v can be extended to a Borel measure on X X Y by means of completion. Examples are given to illustrate these ideas. Throughout this paper p and v will be finite Borel measures on Hausdorff spaces A and Y, respectively. That is, p and v will be countably additive, nonnegative real-valued measures on "35(A) and %(Y), where ÇÔ(A) and %(Y) are the Borel sets of A and Y, respectively. Sometimes p will be regular, which is to say that it is inner regular with respect to the compact sets. At times v will be two-valued, which means that its range consists of the two values 0 and 1. If »»can be expressed as the sum of a countable collection of multiples of two-valued Borel measures, then v is purely atomic. A (not necessarily regular) Borel measure p will be called r-additive if p(C\ Fa) = inf p(Fa) whenever {Fa} is a decreasing family of closed sets [4, p. 96]. Equiva- lently, p is r-additive if p( U Va) = sup p( Va) for each increasingly directed family { Va} of open sets. -
Math 541 Lecture #23 II.15: More on Borel Measures, Part II §15.1
Math 541 Lecture #23 II.15: More on Borel Measures, Part II x15.1: Some Extensions to general Borel Measures. The approximation with closed sets contained in a Borel set E continues to hold for Borel measures that are not necessarily finite, provided µ(E) < 1. Proposition 15.3. Let µ be a Borel measure in RN and let E be a Borel set of finite measure. For every > 0 there exists a closed set Ec, such that Ec, ⊂ E and µ(E − Ec,) ≤ . Proof. Let A be the σ-algebra on which µ is defined. For a fixed Borel set E of finite measure, define µE(A) = µ(E \ A);A 2 A: This set function on A is a measure because µE(A) = µ(E \ A) ≥ 0 for all A 2 A, for a countable collection of pairwise disjoint fAng in A there holds [ µE([An) = µ E \ An [ = µ (E \ An) X = µ(E \ An) X = µE(An); and µE(;) = µ(E \;) = 0 is finite. The measure µE is a finite Borel measure because µE(X) = µ(E) < 1 and the domain of µE is A which contains the Borel sets. Thus by Proposition 15.1 part (1) for each > 0 there is a closed set Ec, such that Ec, ⊂ E and µE(E − Ec,) ≤ . Since E \ (E − Ec,) = E − Ec,, we have by the definition of µE that µ(E − Ec,) = µ(E \ (E − Ec,)) = µE(E − Ec,) ≤ , giving the result. Unfortunately, extending the existence of an open set Eo, such that E ⊂ Eo, and µ(Eo, − E) ≤ to Borel sets E of finite measure fails, as the counting measure example illustrates.