Measure Theory

Total Page:16

File Type:pdf, Size:1020Kb

Measure Theory NOTES ON MEASURE THEORY M. Papadimitrakis Department of Mathematics University of Crete Autumn of 2004 2 Contents 1 σ-algebras 7 1.1 σ-algebras. 7 1.2 Generated σ-algebras. 8 1.3 Algebras and monotone classes. 9 1.4 Restriction of a σ-algebra. 11 1.5 Borel σ-algebras. 12 1.6 Exercises. 17 2 Measures 21 2.1 General measures. 21 2.2 Point-mass distributions. 23 2.3 Complete measures. 25 2.4 Restriction of a measure. 27 2.5 Uniqueness of measures. 28 2.6 Exercises. 28 3 Outer measures 33 3.1 Outer measures. 33 3.2 Construction of outer measures. 35 3.3 Exercises. 36 4 Lebesgue measure on Rn 39 4.1 Volume of intervals. 39 4.2 Lebesgue measure in Rn. ...................... 41 4.3 Lebesgue measure and simple transformations. 45 4.4 Cantor set. 49 4.5 A non-Lebesgue set in R. ...................... 50 4.6 Exercises. 52 5 Borel measures 57 5.1 Lebesgue-Stieltjes measures in R................... 57 5.2 Borel measures. 62 5.3 Metric outer measures. 67 5.4 Hausdorff measure. 68 3 5.5 Exercises. 70 6 Measurable functions 73 6.1 Measurability. 73 6.2 Restriction and gluing. 73 6.3 Functions with arithmetical values. 74 6.4 Composition. 76 6.5 Sums and products. 76 6.6 Absolute value and signum. 78 6.7 Maximum and minimum. 79 6.8 Truncation. 80 6.9 Limits. 81 6.10 Simple functions. 83 6.11 The role of null sets. 86 6.12 Exercises. 87 7 Integrals 93 7.1 Integrals of non-negative simple functions. 93 7.2 Integrals of non-negative functions. 96 7.3 Integrals of complex valued functions. 99 7.4 Integrals over subsets. 106 7.5 Point-mass distributions. 109 7.6 Lebesgue integral. 112 7.7 Lebesgue-Stieltjes integral. 119 7.8 Reduction to integrals over R. 123 7.9 Exercises. 125 8 Product measures 137 8.1 Product σ-algebra. 137 8.2 Product measure. 139 8.3 Multiple integrals. 146 8.4 Surface measure on Sn−1. 153 8.5 Exercises. 161 9 Convergence of functions 167 9.1 a.e. convergence and uniformly a.e. convergence. 167 9.2 Convergence in the mean. 168 9.3 Convergence in measure. 171 9.4 Almost uniform convergence. 174 9.5 Relations between types of convergence. 176 9.6 Exercises. 180 10 Signed measures and complex measures 183 10.1 Signed measures. 183 10.2 The Hahn and Jordan decompositions, I. 185 10.3 The Hahn and Jordan decompositions, II. 191 4 10.4 Complex measures. 195 10.5 Integration. 197 10.6 Lebesgue decomposition, Radon-Nikodym derivative. 200 10.7 Differentiation of indefinite integrals in Rn. 208 10.8 Differentiation of Borel measures in Rn. 215 10.9 Exercises. 217 11 The classical Banach spaces 221 11.1 Normed spaces. 221 11.2 The spaces Lp(X; Σ; µ). 229 11.3 The dual of Lp(X; Σ; µ). 240 11.4 The space M(X; Σ). 247 11.5 The space C0(X) and its dual. 249 11.6 Exercises. 259 5 6 Chapter 1 σ-algebras 1.1 σ-algebras. Definition 1.1 Let X be a non-empty set and Σ a collection of subsets of X. We call Σ a σ-algebra of subsets of X if it is non-empty, closed under complements and closed under countable unions. This means: (i) there exists at least one A ⊆ X so that A 2 Σ, (ii) if A 2 Σ, then Ac 2 Σ, where Ac = X n A, and +1 (iii) if An 2 Σ for all n 2 N, then [n=1An 2 Σ. The pair (X; Σ) of a non-empty set X and a σ-algebra Σ of subsets of X is called a measurable space. Proposition 1.1 Every σ-algebra of subsets of X contains at least the sets ; and X, it is closed under finite unions, under countable intersections, under finite intersections and under set-theoretic differences. Proof: Let Σ be any σ-algebra of subsets of X. c (a) Take any A 2 Σ and consider the sets A1 = A and An = A for all n ≥ 2. c +1 c Then X = A [ A = [n=1An 2 Σ and also ; = X 2 Σ. (b) Let A1;:::;AN 2 Σ. Consider An = AN for all n > N and get that N +1 [n=1An = [n=1An 2 Σ. +1 +1 c c (c) Let An 2 Σ for all n. Then \n=1An = ([n=1An) 2 Σ. N (d) Let A1;:::;AN 2 Σ. Using the result of (b), we get that \n=1An = N c c ([n=1An) 2 Σ. (e) Finally, let A; B 2 Σ. Using the result of (d), we get that AnB = A\Bc 2 Σ. Here are some simple examples. Examples 1. The collection f;;Xg is a σ-algebra of subsets of X. 2. If E ⊆ X is non-empty and different from X, then the collection f;; E; Ec;Xg is a σ-algebra of subsets of X. 7 3. P(X), the collection of all subsets of X, is a σ-algebra of subsets of X. 4. Let X be uncountable. The fA ⊆ X j A is countable or Ac is countableg is a σ-algebra of subsets of X. Firstly, ; is countable and, hence, the collection is non-empty. If A is in the collection, then, considering cases, we see that Ac is also in the collection. Finally, let An be in the collection for all n 2 N. If all +1 c An's are countable, then [n=1An is also countable. If at least one of the An's, say Ac , is countable, then ([+1 A )c ⊆ Ac is also countable. In any case, n0 n=1 n n0 +1 [n=1An belongs to the collection. The following result is useful. Proposition 1.2 Let Σ be a σ-algebra of subsets of X and consider a finite N sequence fAngn=1 or an infinite sequence fAng in Σ. Then there exists a finite N sequence fBngn=1 or, respectively, an infinite sequence fBng in Σ with the properties: (i) Bn ⊆ An for all n = 1;:::;N or, respectively, all n 2 N. N N +1 +1 (ii) [n=1Bn = [n=1An or, respectively, [n=1Bn = [n=1An. (iii) the Bn's are pairwise disjoint. Proof: Trivial, by taking B1 = A1 and Bk = Ak n (A1 [···[ Ak−1) for all k = 2;:::;N or, respectively, all k = 2; 3;::: . 1.2 Generated σ-algebras. Proposition 1.3 The intersection of any σ-algebras of subsets of the same X is a σ-algebra of subsets of X. Proof: Let fΣigi2I be any collection of σ-algebras of subsets of X, indexed by an arbitrary non-empty set I of indices, and consider the intersection Σ = \i2I Σi. Since ; 2 Σi for all i 2 I, we get ; 2 Σ and, hence, Σ is non-empty. Let A 2 Σ. Then A 2 Σi for all i 2 I and, since all Σi's are σ-algebras, c c A 2 Σi for all i 2 I. Therefore A 2 Σ. Let An 2 Σ for all n 2 N. Then An 2 Σi for all i 2 I and all n 2 N +1 and, since all Σi's are σ-algebras, we get [n=1An 2 Σi for all i 2 I. Thus, +1 [n=1An 2 Σ. Definition 1.2 Let X be a non-empty set and E be an arbitrary collection of subsets of X. The intersection of all σ-algebras of subsets of X which include E is called the σ-algebra generated by E and it is denoted by Σ(E). Namely Σ(E) = \fΣ j Σ is a σ-algebra of subsets of X and E ⊆ Σg : Note that there is at least one σ-algebra of subsets of X which includes E and this is P(X). Note also that the term σ-algebra used in the name of Σ(E) is justified by its definition and by Proposition 1.3. Proposition 1.4 Let E be any collection of subsets of the non-empty X. Then Σ(E) is the smallest σ-algebra of subsets of X which includes E. Namely, if Σ is any σ-algebra of subsets of X such that E ⊆ Σ, then Σ(E) ⊆ Σ. 8 Proof: If Σ is any σ-algebra of subsets of X such that E ⊆ Σ, then Σ is one of the σ-algebras whose intersection is denoted Σ(E). Therefore Σ(E) ⊆ Σ. Looking back at two of the examples of σ-algebras, we easily get the following examples. Examples. 1. Let E ⊆ X and E be non-empty and different from X and consider E = fEg. Then Σ(E) = f;; E; Ec;Xg. To see this just observe that f;; E; Ec;Xg is a σ-algebra of subsets of X which contains E and that there can be no smaller σ-algebra of subsets of X containing E, since such a σ-algebra must necessarily contain ;;X and Ec besides E. 2. Let X be an uncountable set and consider E = fA ⊆ X j A is countableg. Then Σ(E) = fA ⊆ XjA is countable or Ac is countableg. The argument is the same as before. fA ⊆ XjA is countable or Ac is countableg is a σ-algebra of subsets of X which contains all countable subsets of X and there is no smaller σ-algebra of subsets of X containing all countable subsets of X, since any such σ-algebra must contain all the complements of countable subsets of X.
Recommended publications
  • Distributions:The Evolutionof a Mathematicaltheory
    Historia Mathematics 10 (1983) 149-183 DISTRIBUTIONS:THE EVOLUTIONOF A MATHEMATICALTHEORY BY JOHN SYNOWIEC INDIANA UNIVERSITY NORTHWEST, GARY, INDIANA 46408 SUMMARIES The theory of distributions, or generalized func- tions, evolved from various concepts of generalized solutions of partial differential equations and gener- alized differentiation. Some of the principal steps in this evolution are described in this paper. La thgorie des distributions, ou des fonctions g&&alis~es, s'est d&eloppeg 2 partir de divers con- cepts de solutions g&&alis6es d'gquations aux d&i- &es partielles et de diffgrentiation g&&alis6e. Quelques-unes des principales &apes de cette &olution sont d&rites dans cet article. Die Theorie der Distributionen oder verallgemein- erten Funktionen entwickelte sich aus verschiedenen Begriffen von verallgemeinerten Lasungen der partiellen Differentialgleichungen und der verallgemeinerten Ableitungen. In diesem Artikel werden einige der wesentlichen Schritte dieser Entwicklung beschrieben. 1. INTRODUCTION The founder of the theory of distributions is rightly con- sidered to be Laurent Schwartz. Not only did he write the first systematic paper on the subject [Schwartz 19451, which, along with another paper [1947-19481, already contained most of the basic ideas, but he also wrote expository papers on distributions for electrical engineers [19481. Furthermore, he lectured effec- tively on his work, for example, at the Canadian Mathematical Congress [1949]. He showed how to apply distributions to partial differential equations [19SObl and wrote the first general trea- tise on the subject [19SOa, 19511. Recognition of his contri- butions came in 1950 when he was awarded the Fields' Medal at the International Congress of Mathematicians, and in his accep- tance address to the congress Schwartz took the opportunity to announce his celebrated "kernel theorem" [195Oc].
    [Show full text]
  • Lectures on Fractal Geometry and Dynamics
    Lectures on fractal geometry and dynamics Michael Hochman∗ March 25, 2012 Contents 1 Introduction 2 2 Preliminaries 3 3 Dimension 3 3.1 A family of examples: Middle-α Cantor sets . 3 3.2 Minkowski dimension . 4 3.3 Hausdorff dimension . 9 4 Using measures to compute dimension 13 4.1 The mass distribution principle . 13 4.2 Billingsley's lemma . 15 4.3 Frostman's lemma . 18 4.4 Product sets . 22 5 Iterated function systems 24 5.1 The Hausdorff metric . 24 5.2 Iterated function systems . 26 5.3 Self-similar sets . 31 5.4 Self-affine sets . 36 6 Geometry of measures 39 6.1 The Besicovitch covering theorem . 39 6.2 Density and differentiation theorems . 45 6.3 Dimension of a measure at a point . 48 6.4 Upper and lower dimension of measures . 50 ∗Send comments to [email protected] 1 6.5 Hausdorff measures and their densities . 52 1 Introduction Fractal geometry and its sibling, geometric measure theory, are branches of analysis which study the structure of \irregular" sets and measures in metric spaces, primarily d R . The distinction between regular and irregular sets is not a precise one but informally, k regular sets might be understood as smooth sub-manifolds of R , or perhaps Lipschitz graphs, or countable unions of the above; whereas irregular sets include just about 1 everything else, from the middle- 3 Cantor set (still highly structured) to arbitrary d Cantor sets (irregular, but topologically the same) to truly arbitrary subsets of R . d For concreteness, let us compare smooth sub-manifolds and Cantor subsets of R .
    [Show full text]
  • Complex Measures 1 11
    Tutorial 11: Complex Measures 1 11. Complex Measures In the following, (Ω, F) denotes an arbitrary measurable space. Definition 90 Let (an)n≥1 be a sequence of complex numbers. We a say that ( n)n≥1 has the permutation property if and only if, for ∗ ∗ +∞ 1 all bijections σ : N → N ,theseries k=1 aσ(k) converges in C Exercise 1. Let (an)n≥1 be a sequence of complex numbers. 1. Show that if (an)n≥1 has the permutation property, then the same is true of (Re(an))n≥1 and (Im(an))n≥1. +∞ 2. Suppose an ∈ R for all n ≥ 1. Show that if k=1 ak converges: +∞ +∞ +∞ + − |ak| =+∞⇒ ak = ak =+∞ k=1 k=1 k=1 1which excludes ±∞ as limit. www.probability.net Tutorial 11: Complex Measures 2 Exercise 2. Let (an)n≥1 be a sequence in R, such that the series +∞ +∞ k=1 ak converges, and k=1 |ak| =+∞.LetA>0. We define: + − N = {k ≥ 1:ak ≥ 0} ,N = {k ≥ 1:ak < 0} 1. Show that N + and N − are infinite. 2. Let φ+ : N∗ → N + and φ− : N∗ → N − be two bijections. Show the existence of k1 ≥ 1 such that: k1 aφ+(k) ≥ A k=1 3. Show the existence of an increasing sequence (kp)p≥1 such that: kp aφ+(k) ≥ A k=kp−1+1 www.probability.net Tutorial 11: Complex Measures 3 for all p ≥ 1, where k0 =0. 4. Consider the permutation σ : N∗ → N∗ defined informally by: φ− ,φ+ ,...,φ+ k ,φ− ,φ+ k ,...,φ+ k ,..
    [Show full text]
  • Appendix A. Measure and Integration
    Appendix A. Measure and integration We suppose the reader is familiar with the basic facts concerning set theory and integration as they are presented in the introductory course of analysis. In this appendix, we review them briefly, and add some more which we shall need in the text. Basic references for proofs and a detailed exposition are, e.g., [[ H a l 1 ]] , [[ J a r 1 , 2 ]] , [[ K F 1 , 2 ]] , [[ L i L ]] , [[ R u 1 ]] , or any other textbook on analysis you might prefer. A.1 Sets, mappings, relations A set is a collection of objects called elements. The symbol card X denotes the cardi- nality of the set X. The subset M consisting of the elements of X which satisfy the conditions P1(x),...,Pn(x) is usually written as M = { x ∈ X : P1(x),...,Pn(x) }.A set whose elements are certain sets is called a system or family of these sets; the family of all subsystems of a given X is denoted as 2X . The operations of union, intersection, and set difference are introduced in the standard way; the first two of these are commutative, associative, and mutually distributive. In a { } system Mα of any cardinality, the de Morgan relations , X \ Mα = (X \ Mα)and X \ Mα = (X \ Mα), α α α α are valid. Another elementary property is the following: for any family {Mn} ,whichis { } at most countable, there is a disjoint family Nn of the same cardinality such that ⊂ \ ∪ \ Nn Mn and n Nn = n Mn.Theset(M N) (N M) is called the symmetric difference of the sets M,N and denoted as M #N.
    [Show full text]
  • Arxiv:1404.7630V2 [Math.AT] 5 Nov 2014 Asoffsae,Se[S4 P8,Vr6.Isedof Instead Ver66]
    PROPER BASE CHANGE FOR SEPARATED LOCALLY PROPER MAPS OLAF M. SCHNÜRER AND WOLFGANG SOERGEL Abstract. We introduce and study the notion of a locally proper map between topological spaces. We show that fundamental con- structions of sheaf theory, more precisely proper base change, pro- jection formula, and Verdier duality, can be extended from contin- uous maps between locally compact Hausdorff spaces to separated locally proper maps between arbitrary topological spaces. Contents 1. Introduction 1 2. Locally proper maps 3 3. Proper direct image 5 4. Proper base change 7 5. Derived proper direct image 9 6. Projection formula 11 7. Verdier duality 12 8. The case of unbounded derived categories 15 9. Remindersfromtopology 17 10. Reminders from sheaf theory 19 11. Representability and adjoints 21 12. Remarks on derived functors 22 References 23 arXiv:1404.7630v2 [math.AT] 5 Nov 2014 1. Introduction The proper direct image functor f! and its right derived functor Rf! are defined for any continuous map f : Y → X of locally compact Hausdorff spaces, see [KS94, Spa88, Ver66]. Instead of f! and Rf! we will use the notation f(!) and f! for these functors. They are embedded into a whole collection of formulas known as the six-functor-formalism of Grothendieck. Under the assumption that the proper direct image functor f(!) has finite cohomological dimension, Verdier proved that its ! derived functor f! admits a right adjoint f . Olaf Schnürer was supported by the SPP 1388 and the SFB/TR 45 of the DFG. Wolfgang Soergel was supported by the SPP 1388 of the DFG. 1 2 OLAFM.SCHNÜRERANDWOLFGANGSOERGEL In this article we introduce in 2.3 the notion of a locally proper map between topological spaces and show that the above results even hold for arbitrary topological spaces if all maps whose proper direct image functors are involved are locally proper and separated.
    [Show full text]
  • MATH 418 Assignment #7 1. Let R, S, T Be Positive Real Numbers Such That R
    MATH 418 Assignment #7 1. Let r, s, t be positive real numbers such that r + s + t = 1. Suppose that f, g, h are nonnegative measurable functions on a measure space (X, S, µ). Prove that Z r s t r s t f g h dµ ≤ kfk1kgk1khk1. X s t Proof . Let u := g s+t h s+t . Then f rgsht = f rus+t. By H¨older’s inequality we have Z Z rZ s+t r s+t r 1/r s+t 1/(s+t) r s+t f u dµ ≤ (f ) dµ (u ) dµ = kfk1kuk1 . X X X For the same reason, we have Z Z s t s t s+t s+t s+t s+t kuk1 = u dµ = g h dµ ≤ kgk1 khk1 . X X Consequently, Z r s t r s+t r s t f g h dµ ≤ kfk1kuk1 ≤ kfk1kgk1khk1. X 2. Let Cc(IR) be the linear space of all continuous functions on IR with compact support. (a) For 1 ≤ p < ∞, prove that Cc(IR) is dense in Lp(IR). Proof . Let X be the closure of Cc(IR) in Lp(IR). Then X is a linear space. We wish to show X = Lp(IR). First, if I = (a, b) with −∞ < a < b < ∞, then χI ∈ X. Indeed, for n ∈ IN, let gn the function on IR given by gn(x) := 1 for x ∈ [a + 1/n, b − 1/n], gn(x) := 0 for x ∈ IR \ (a, b), gn(x) := n(x − a) for a ≤ x ≤ a + 1/n and gn(x) := n(b − x) for b − 1/n ≤ x ≤ b.
    [Show full text]
  • Version of 21.8.15 Chapter 43 Topologies and Measures II The
    Version of 21.8.15 Chapter 43 Topologies and measures II The first chapter of this volume was ‘general’ theory of topological measure spaces; I attempted to distinguish the most important properties a topological measure can have – inner regularity, τ-additivity – and describe their interactions at an abstract level. I now turn to rather more specialized investigations, looking for features which offer explanations of the behaviour of the most important spaces, radiating outwards from Lebesgue measure. In effect, this chapter consists of three distinguishable parts and two appendices. The first three sections are based on ideas from descriptive set theory, in particular Souslin’s operation (§431); the properties of this operation are the foundation for the theory of two classes of topological space of particular importance in measure theory, the K-analytic spaces (§432) and the analytic spaces (§433). The second part of the chapter, §§434-435, collects miscellaneous results on Borel and Baire measures, looking at the ways in which topological properties of a space determine properties of the measures it carries. In §436 I present the most important theorems on the representation of linear functionals by integrals; if you like, this is the inverse operation to the construction of integrals from measures in §122. The ideas continue into §437, where I discuss spaces of signed measures representing the duals of spaces of continuous functions, and topologies on spaces of measures. The first appendix, §438, looks at a special topic: the way in which the patterns in §§434-435 are affected if we assume that our spaces are not unreasonably complex in a rather special sense defined in terms of measures on discrete spaces.
    [Show full text]
  • Extremal Dependence Concepts
    Extremal dependence concepts Giovanni Puccetti1 and Ruodu Wang2 1Department of Economics, Management and Quantitative Methods, University of Milano, 20122 Milano, Italy 2Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L3G1, Canada Journal version published in Statistical Science, 2015, Vol. 30, No. 4, 485{517 Minor corrections made in May and June 2020 Abstract The probabilistic characterization of the relationship between two or more random variables calls for a notion of dependence. Dependence modeling leads to mathematical and statistical challenges and recent developments in extremal dependence concepts have drawn a lot of attention to probability and its applications in several disciplines. The aim of this paper is to review various concepts of extremal positive and negative dependence, including several recently established results, reconstruct their history, link them to probabilistic optimization problems, and provide a list of open questions in this area. While the concept of extremal positive dependence is agreed upon for random vectors of arbitrary dimensions, various notions of extremal negative dependence arise when more than two random variables are involved. We review existing popular concepts of extremal negative dependence given in literature and introduce a novel notion, which in a general sense includes the existing ones as particular cases. Even if much of the literature on dependence is focused on positive dependence, we show that negative dependence plays an equally important role in the solution of many optimization problems. While the most popular tool used nowadays to model dependence is that of a copula function, in this paper we use the equivalent concept of a set of rearrangements.
    [Show full text]
  • An Introduction to Measure Theory Terence
    An introduction to measure theory Terence Tao Department of Mathematics, UCLA, Los Angeles, CA 90095 E-mail address: [email protected] To Garth Gaudry, who set me on the road; To my family, for their constant support; And to the readers of my blog, for their feedback and contributions. Contents Preface ix Notation x Acknowledgments xvi Chapter 1. Measure theory 1 x1.1. Prologue: The problem of measure 2 x1.2. Lebesgue measure 17 x1.3. The Lebesgue integral 46 x1.4. Abstract measure spaces 79 x1.5. Modes of convergence 114 x1.6. Differentiation theorems 131 x1.7. Outer measures, pre-measures, and product measures 179 Chapter 2. Related articles 209 x2.1. Problem solving strategies 210 x2.2. The Radamacher differentiation theorem 226 x2.3. Probability spaces 232 x2.4. Infinite product spaces and the Kolmogorov extension theorem 235 Bibliography 243 vii viii Contents Index 245 Preface In the fall of 2010, I taught an introductory one-quarter course on graduate real analysis, focusing in particular on the basics of mea- sure and integration theory, both in Euclidean spaces and in abstract measure spaces. This text is based on my lecture notes of that course, which are also available online on my blog terrytao.wordpress.com, together with some supplementary material, such as a section on prob- lem solving strategies in real analysis (Section 2.1) which evolved from discussions with my students. This text is intended to form a prequel to my graduate text [Ta2010] (henceforth referred to as An epsilon of room, Vol. I ), which is an introduction to the analysis of Hilbert and Banach spaces (such as Lp and Sobolev spaces), point-set topology, and related top- ics such as Fourier analysis and the theory of distributions; together, they serve as a text for a complete first-year graduate course in real analysis.
    [Show full text]
  • Gsm076-Endmatter.Pdf
    http://dx.doi.org/10.1090/gsm/076 Measur e Theor y an d Integratio n This page intentionally left blank Measur e Theor y an d Integratio n Michael E.Taylor Graduate Studies in Mathematics Volume 76 M^^t| American Mathematical Society ^MMOT Providence, Rhode Island Editorial Board David Cox Walter Craig Nikolai Ivanov Steven G. Krantz David Saltman (Chair) 2000 Mathematics Subject Classification. Primary 28-01. For additional information and updates on this book, visit www.ams.org/bookpages/gsm-76 Library of Congress Cataloging-in-Publication Data Taylor, Michael Eugene, 1946- Measure theory and integration / Michael E. Taylor. p. cm. — (Graduate studies in mathematics, ISSN 1065-7339 ; v. 76) Includes bibliographical references. ISBN-13: 978-0-8218-4180-8 1. Measure theory. 2. Riemann integrals. 3. Convergence. 4. Probabilities. I. Title. II. Series. QA312.T387 2006 515/.42—dc22 2006045635 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy a chapter for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. Requests for such permission should be addressed to the Acquisitions Department, American Mathematical Society, 201 Charles Street, Providence, Rhode Island 02904-2294, USA. Requests can also be made by e-mail to [email protected]. © 2006 by the American Mathematical Society.
    [Show full text]
  • Five Lectures on Optimal Transportation: Geometry, Regularity and Applications
    FIVE LECTURES ON OPTIMAL TRANSPORTATION: GEOMETRY, REGULARITY AND APPLICATIONS ROBERT J. MCCANN∗ AND NESTOR GUILLEN Abstract. In this series of lectures we introduce the Monge-Kantorovich problem of optimally transporting one distribution of mass onto another, where optimality is measured against a cost function c(x, y). Connections to geometry, inequalities, and partial differential equations will be discussed, focusing in particular on recent developments in the regularity theory for Monge-Amp`ere type equations. An ap- plication to microeconomics will also be described, which amounts to finding the equilibrium price distribution for a monopolist marketing a multidimensional line of products to a population of anonymous agents whose preferences are known only statistically. c 2010 by Robert J. McCann. All rights reserved. Contents Preamble 2 1. An introduction to optimal transportation 2 1.1. Monge-Kantorovich problem: transporting ore from mines to factories 2 1.2. Wasserstein distance and geometric applications 3 1.3. Brenier’s theorem and convex gradients 4 1.4. Fully-nonlinear degenerate-elliptic Monge-Amp`eretype PDE 4 1.5. Applications 5 1.6. Euclidean isoperimetric inequality 5 1.7. Kantorovich’s reformulation of Monge’s problem 6 2. Existence, uniqueness, and characterization of optimal maps 6 2.1. Linear programming duality 8 2.2. Game theory 8 2.3. Relevance to optimal transport: Kantorovich-Koopmans duality 9 2.4. Characterizing optimality by duality 9 2.5. Existence of optimal maps and uniqueness of optimal measures 10 3. Methods for obtaining regularity of optimal mappings 11 3.1. Rectifiability: differentiability almost everywhere 12 3.2. From regularity a.e.
    [Show full text]
  • The Geometry of Optimal Transportation
    THE GEOMETRY OF OPTIMAL TRANSPORTATION Wilfrid Gangbo∗ School of Mathematics, Georgia Institute of Technology Atlanta, GA 30332, USA [email protected] Robert J. McCann∗ Department of Mathematics, Brown University Providence, RI 02912, USA and Institut des Hautes Etudes Scientifiques 91440 Bures-sur-Yvette, France [email protected] July 28, 1996 Abstract A classical problem of transporting mass due to Monge and Kantorovich is solved. Given measures µ and ν on Rd, we find the measure-preserving map y(x) between them with minimal cost | where cost is measured against h(x − y)withhstrictly convex, or a strictly concave function of |x − y|.This map is unique: it is characterized by the formula y(x)=x−(∇h)−1(∇ (x)) and geometrical restrictions on . Connections with mathematical economics, numerical computations, and the Monge-Amp`ere equation are sketched. ∗Both authors gratefully acknowledge the support provided by postdoctoral fellowships: WG at the Mathematical Sciences Research Institute, Berkeley, CA 94720, and RJM from the Natural Sciences and Engineering Research Council of Canada. c 1995 by the authors. To appear in Acta Mathematica 1997. 1 2 Key words and phrases: Monge-Kantorovich mass transport, optimal coupling, optimal map, volume-preserving maps with convex potentials, correlated random variables, rearrangement of vector fields, multivariate distributions, marginals, Lp Wasserstein, c-convex, semi-convex, infinite dimensional linear program, concave cost, resource allocation, degenerate elliptic Monge-Amp`ere 1991 Mathematics
    [Show full text]