THEOREMS, ETC., FOR MATH 516

Size: px
Start display at page:

Download "THEOREMS, ETC., FOR MATH 516"

Transcription

1 THEOREMS, ETC., FOR MATH 516 Results labeled Theorem Ea.b.c (or Proposition Ea.b.c, etc.) refer to Theorem c from section a.b of Evans book (Partial Differential Equations). Proposition 1 (=Proposition 10.2). (a) A bounded linear operator is uniformly continuous. (b) A linear operator which is continuous at one point is bounded. Proposition 2 (=Proposition 10.3). The set of all bounded linear operators from a normed linear space to a Banach space is also a Banach space. Theorem 3 (=Theorem 10.4, Hahn-Banach). Let X be a vector space, let p be a real- valued function on X such that (x + y) p(x) + p(y) and p(αx) = αp(x) for all x and y in X and all α R. Suppose f is a linear functional on a subspace S of X such that f(s) p(s) for all s S. Then there is a linear functional F on X which agrees with f on S such that F (x) p(x) for all x X. Proposition 4 (=Proposition 10.5). Let X be a vector space, let p be a real-valued function on X such that (x + y) p(x) + p(y) and p(αx) = αp(x) for all x and y in X and all α R. Suppose f is a linear functional on a subspace S of X such that f(s) p(s) for all s S. Let G be an Abelian semigroup of linear operators on X and suppose that p(ax) p(x) for all x X and f(as) = f(s) for all s S. Then there is a linear functional F on X which agrees with f on S such that F (x) p(x) and F (Ax) = F (x) for all x X. Proposition 5 (=Proposition 10.6). Let X be a normed linear space and let x be a nonzero element of X. Then there is a bounded linear functional f on X such that f(x) = f x. Proposition 6 (=Proposition 10.7). Let X be a normed linear space, let T be a subspace of X and let y be an element of X such that d(y, T ) δ for some positive δ. Then there is a bounded linear functional f on X such that f(y) = δ, f 1, and f = 0 on T. Lemma 7 (=Lemma 10.9). Let X and Y be Banach spaces and let A be a bounded linear transformation of X onto Y. Then the image of the unit ball of X contains an open ball of Y with center 0. Theorem 8 (=Proposition 10.10, Open Mapping Theorem). Let A be a bounded linear transformation from a Banach space X onto a Banach space Y. Then A is an open mapping. If A is also one-to-one, then A is an isomorphism. Proposition 9 (=Proposition 10.11). Let X be a vector space, let 1 and 2 be two norms on X and suppose that (X, 1 ) and (X, 2 ) are both Banach spaces. If there is a constant C such that x 1 C x 2 for all x X, then there is a constant K such that x 2 K x 1 1

2 2 THEOREMS, ETC., FOR MATH 516 for all x X. Theorem 10 (=Theorem 10.12, Closed Graph Theorem). Let X and Y be Banach spaces and let A: X Y be a linear transformation. If, whenever x n is a convergent sequence in X with limit x such that Ax n is a convergent sequence in Y with limit y, we have y = Ax. Then A is bounded. Proposition 11 (=Proposition 10.13, Uniform Boundedness Principle). Let X be a Banach space, let Y be a normed linear space, and let F be a family of linear operators from X to Y. Suppose that, for every x X, there is a number M x such that T x M x for all T F. Then there is a constant M such that T M for all T F. Proposition 12 (=Proposition 8.5). A collection B of subsets of a set X is a base for a topology on X if and only if (i) For every x X, there is B B such that x B. (ii) For every x X and every B 1 and B 2 in B such that x B 1 B 2, there is B 3 B such that x B 3 B 1 B 2. Proposition 13 (=Proposition 8.6). A topological space satisfies T 1 if and only if points are closed. Lemma 14 (=Lemma 8.7, Urysohn s Lemma). Let A and B be disjoint closed subsets of a normal space X. Then there is a continuous, real-valued function f on X such that 0 f 1 on X, f 0 on A, and f 1 on B. Theorem 15 (=Theorem 8.8, Tietze s Extension Theorem). Let A be a closed subset of a normal space X and let f : A R be continuous. Then there is a continuous function g : X R such that g = f on A. Proposition 16 (=Proposition 10.14). Let X be a vector space. (a) Let T be a topology on X which makes X a topological vector space. Then there is a local base B for T such that (i) If U and V are in B, then there is W B such that W U V. (ii) If U B and x X, then there is V B such that x + V U. (iii) If U B, then there is v B such that V + V U. (iv) If U B and x X, then there is α R such that x αu. (v) If U B and if α [ 1, 1] is nonzero, then αu U and αu B. (b) Conversely, if B is a collection of subsets of X which all contain 0 which satisfies properties (i) (v), then B is a local base for a topology which makes X into a topological vector space. This topology is Hausdorff if and only if (vi) U = {0}. U B Proposition 17 (=Proposition 10.15, Tychonoff). Let X be a finite-dimensional, Hausdorff topological vector space. Then X is isomorphic to R n for some n. Proposition 18 (=Proposition 10.16). A subspace of a topological vector space is closed if and only if it s weakly closed. Theorem 19 (=Theorem 10.17, Alaoglu). S = {f : f 1} is weak compact in X. Lemma 20 (=Lemma 10.18). If K 1 and K 2 are convex, then so are K 1 K 2, λk 1 (for any real λ), and K 1 + K 2.

3 THEOREMS, ETC., FOR MATH Lemma 21 (=Lemma 10.19). If 0 is an internal point of a convex set K, then the support function p satisfies the following conditions: (i) p is positively homogeneous. (ii) p is subadditive. (iii) {x : p(x) < 1} K {x : p(x) 1}. Theorem 22 (=Theorem 10.20). If K 1 and K 2 are disjoint convex set in a vector space X and if one of them has an internal point, then there is a nonzero linear functional which separates them. Proposition 23 (=Proposition 10.21). Let X be a vector space. (a) Let N be a family of convex subsets of X containing 0 and suppose that they satisfy the following conditions: (i) if N N, then each point of N is an internal point. (ii) For N 1 and N 2 in N, there is N 3 N such that N 3 N 1 N 2. (iii) If N N and 0 < α 1, then αn N. Then N is a local base for a topology on X which makes X a locally convex topological vector space. (b) Conversely, if X is a locally convex topological vector space, then there is a local base N satisfying (i) (iii). Proposition 24 (=Proposition 10.22). Let F be a closed convex subset of a locally convex topological vector space X and let x X K. Then there is a continuous linear functional f on X such that f(x) < inf{f(y) : y K}. Corollary 25 (=Corollary 10.23). A convex subset of a locally convex topological vector space is strongly closed if and only if it s weakly closed. Corollary 26 (=Corollary 10.24). If x and y are distinct points of a locally convex, Hausdorff topological vector space, then there is a continuous linear functional such that f(x) f(y). Lemma 27 (=Lemma 10.25). Let f be a continuous linear functional defined on a convex subset K of a topological vector space. Then the set of points on which f attains its maximum is a supporting set of K. Theorem 28 (=Theorem 10.26, Krein-Milman). Let K be a compact, convex subset of a locally convex, Hausdorff topological vector space. Then K is the closed convex hull of its extreme points. Proposition 29 (=Proposition 9.2, second part). A compact subset of a Hausdorff space is closed. Proposition 30 (=Proposition 9.5). A one-to-one continuous function from a compact space onto a Hausdorff space is a homeomorphism. Proposition 31 (=Proposition 9.6). The continuous image of a countably compact space is countably compact. Proposition 32 (=Proposition 9.7). A space is countably compact if and only if it has the Bolzano-Weierstrass property.

4 4 THEOREMS, ETC., FOR MATH 516 Proposition 33 (=Proposition 9.8). A sequentially compact space is countably compact. Every first countable, countably compact space is sequentially compact. Proposition 34 (=Proposition 9.10). An upper semicontinuous real-valued function on a countably compact space is bounded above and attains its maximum value. Corollary 35 (=Proposition 9.9). A continuous real-valued function on a countably compact space is bounded above and below, and it attains its maximum and minimum values. Proposition 36 (=Proposition 9.11, Dini). Let f n be a sequence of upper semicontinuous real-valued functions on a countably compact space X such that f n (x) decreases to 0 for each x X. Then f n 0 uniformly on X. Lemma 37 (=Lemma 9.12). Let A be a collection of subsets of a set X with the finite intersection property. Then there is a collection B of subsets of X with the finite intersection property which contains A and which is maximal. Lemma 38 (=Lemma 9.13). Let B be a maximal collection of subsets of a set X with the finite intersection property. (i) Any intersection of finitely many elements of B is an element of B. (ii) If C X and B C for all B B, then C B. Theorem 39 (=Theorem 9.14, Tychonoff). Any product of compact spaces is compact. Proposition 40 (=Proposition 9.15). Let K be a compact subset of a locally compact Hausdorff space X. (i) Then there is an open set U such that K U and U is compact. (ii) For any such U, there is a continuous nonnegative, real-valued function f such that f = 0 on Ũ and f = 1 on K. If K is a G δ set, then this function also has the property that f < 1 on K. Proposition 41 (=Proposition 9.16). Let {O λ } be an open cover of a compact subset K of a locally Hausdorff space X. Then there is a finite collection {ϕ 1,..., ϕ n } of continuous, nonnegative, real-value functions subordinate to {O λ } such that ϕ ϕ n = 1 on K. Proposition 42 (=Proposition 9.29). Let L be a lattice in C(X) for some compact Hausdorff space X. If h, defined by h(x) = inf f L f(x), is continuous, then, for any ε > 0, there is a function g L such that 0 g h ε. Lemma 43 (=Lemma 9.31). Let L be a family of real-valued functions on a set X such that (i) L separates points. (ii) If f L and c R, then f + c and cf are in L. Then for any a and b in R and any x y in X, there is f L such that f(x) = a and f(y) = b. Lemma 44 (=Lemma 9.32). Let L be a lattice in C(X) for some compact space X and suppose L satisfies properties (i) and (ii) from Lemma 43. Let p X and let F be a closed subset of X with p / F. Then for any real numbers a b, there is f L with f a in X, f > b in K and f(p) = a.

5 THEOREMS, ETC., FOR MATH Proposition 45 (=Proposition 9.30). Let L be a lattice in C(X) for some compact space X and suppose L satisfies properties (i) and (ii) from Lemma 43. Then L = C(X). Lemma 46 (=Lemma 9.33). For any ε > 0, there is a polynomial P ε such that t P ε (t) < ε for t [ 1, 1]. Theorem 47 (=Theorem 9.34, Stone-Weierstrass). Let X be a compact space and let A be an algebra in C(X) that contains the constant functions and separates points. Then A = C(X). Theorem 48 (=Corollary 9.35, Weierstrass Theorem). Every continuous function on a closed bounded set of R n can be uniformly approximated by polynomials. Proposition 49 (=Proposition 11.17). Let (X, B) be a measurable space, let µ n be a sequence of measures on (X, B) that converge setwise to a measure µ, and let f n be a sequence of nonnegative measurable functions that converge pointwise to a function f. Then f dµ lim f n dµ n. Proposition 50 (=Proposition 11.18). Let (X, B) be a measurable space, let µ n be a sequence of measures on (X, B) that converge setwise to a measure µ, let g n be a sequence of nonnegative measurable functions that converge pointwise to a integrable function g, and let f n be a sequence of measurable functions that converge pointwise to a function f. Suppose also that f n g and that lim g n dµ n = g dµ. Then lim f n dµ n = f dµ. Lemma 51 (=Lemma 11.19). Every measurable subset of a positive set is positive. The union of countably many positive sets is positive. Lemma 52 (=Lemma 11.20). Let ν be a signed measure. If E is a set such that 0 < νe <, then there is a positive set A with A E and νa > 0. Theorem 53 (= Proposition 11.21, Hahn Decomposition Theorem). Let ν be a signed measure on a measurable space (X, B). Then there is a positive set A such that B = X A is a negative set. Lemma 54 (= Proposition 11.10, approximately). Let D be a countable dense subset of R, let (X, B, µ) be a measure space and suppose that, for every α D, there is B α B such that µ(b α B β ) = 0 for any α < β. Then there is a measurable function f such that f α on B α and f α on B α. If g is any other such function, then g = f a.e. Theorem 55 (=Theorem 11.23, Radon-Nikodym). Let (X, B, µ) be a σ-finite measure space and let ν be a measure on (X, B) which is absolutely continuous with respect to µ. Then there is a nonnegative measurable function f such that νe = f dµ for any E B. If g is another such function, then f = g a.e.[µ]. E

6 6 THEOREMS, ETC., FOR MATH 516 Proposition 56 (=Proposition 11.24, Lebesgue Decomposition Theorem). Let (X, B, µ) be a σ-finite measure space and let ν be a σ-finite measure on (X, B). Then there are two measures ν 0 and ν 1 such that ν = ν 1 + ν 2, ν 0 and µ are mutually singular, and ν 1 is absolutely continuous with respect to µ. These measures are unique. Lemma 57 (=Lemma 11.27). Let (X, B, µ) be a finite measure space, let g L 1 (µ), let p [1, ), and suppose that there is a constant M such that gϕ dµ M ϕ p for all simple functions ϕ. Then g L q (µ) for q = p/(p 1), and g q M. Theorem 58 (=Theorem 11.29, Riesz Representation). Let (X, B, µ) be a σ-finite measure space, let p [1, ), and let F be a bounded linear functional on L p (µ). Then there is a unique g L q (µ) such that F (f) = fg dµ for all f L p (µ). Theorem 59 (=Theorem 11.30). Let (X, B, µ) be a measure space, let p (1, ), and let F be a bounded linear functional on L p (µ). Then there is a unique g L q (µ) such that F (f) = fg dµ for all f L p (µ). Lemma 60 (=Lemma 12.2). Let µ be a measure on A, an algebra of sets. If A A and A i is a sequence of sets in A such that A i A i, then µa i µa i. Lemma 61 (=Corollary 12.3, Lemma 12.4, Lemma 12.5). Let µ be a measure on A, an algebra of sets. (a) If A A, then µ A = µa. (b) µ is an outer measure. (c) If A A, then A is µ -measurable. Proposition 62 (=Proposition 12.6). Let µ be a measure on A, an algebra of sets, and let E X. (i) For any ε > 0, there is A A σ such that E A and µ A µ E + ε. (ii) There is B A σδ such that E B and µ E = µ B. Proposition 63 (=Proposition 12.7). Let µ be a σ-measure on A, an algebra of sets. (i) A set E X is µ -measurable if and only if there are sets A A σδ and B with µ B = 0 such that E = A B. (ii) If µ B = 0, then there is a set C A σδ such that B C and µ C = 0. Theorem 64 (=Theorem 12.8, Carathéodory). Let µ be a σ-measure on A, an algebra of sets. Then µ, the restriction of µ to the collection of all µ -measurable sets, is a measure (on a σ-algebra of sets) which agrees with µ on A. If µ is finite (or σ-finite), then so is µ. If µ is σ-finite, then µ is the only such extension of µ on the σ-algebra generated by A.

7 THEOREMS, ETC., FOR MATH Proposition 65 (=Proposition 12.9). Let C be a semi-algebra and let µ be a nonnegative set function defined on C with µ = 0 if C. Suppose also that (i) If C C is the union of a finite collection {C 1,..., C n } of disjoints elements of C, then µc = µc i. (ii) If C C is the union of a countable collection {C 1,... } of disjoints elements of C, then µc µc i. Then µ has a unique extension to a measure on A, the algebra generated by C. Lemma 66 (=Lemma 12.14). Let {R i } be a countable collection of disjoint measurable rectangles with R = R i a measurable rectangle. Then λ(r) = λ(r i ). Lemma 67 (=Lemma 12.15). Let E R σδ and x X. Then E x is a measurable function on Y. Lemma 68 (=Lemma 12.16). Let E Rσδ with (µ ν)(e) <. Then g, defined by g(x) = ν(e x ), is a measurable function of x and g dµ = (µ ν)(e). Lemma 69 (=Lemma 12.17). If (µ ν)(e) = 0, then ν(e x ) = 0 for almost all x. Proposition 70 (=Proposition 12.18). Let E be a measurable subset of X Y with (µ ν(e) <. Then, for almost all x, E x is a measurable subset of Y and g, defined by g(x) = ν(e x ), is a measurable function of x. Moreover g dµ = (µ ν)(e). Theorem 71 (=Theorem 12.19, Fubini). Let (X, A, µ) and (Y, B, µ) be σ-finite measure spaces. If f is integrable with respect to µ ν, then (i) For almost all x X, the function f x, defined by f x (y) = f(x, y) is measurable, (i) For almost all y Y, the function f y, defined by f y (x) = f(x, y) is measurable, (ii) The function F 1, defined by F 1 (x) = f(x, y) dν is in L 1 (X), (ii) The function F 2, defined by F 2 (y) = is in L 1 (Y ), (iii) [ ] f dν dµ = [ X Y Y X ] f dµ dν = Y X f(x, y) dµ X Y f d(µ ν). Theorem 72 (=Theorem 12.20, Tonelli). Let (X, A, µ) and (Y, B, µ) be σ-finite measure spaces. If f is nonnegative and measurable with respect to µ ν, then (i) For almost all x X, the function f x, defined by f x (y) = f(x, y) is measurable, (i) For almost all y Y, the function f y, defined by f y (x) = f(x, y) is measurable,

8 8 THEOREMS, ETC., FOR MATH 516 (ii) The function F 1, defined by F 1 (x) = is in L 1 (X), (ii) The function F 2, defined by F 2 (y) = is in L 1 (Y ), (iii) [ ] f dν dµ = [ X Y Y X ] f dµ dν = Y X f(x, y) dν f(x, y) dµ X Y f d(µ ν). Proposition 73 (=Proposition 12.40). If µ is a Carathéodory outer measure with respect to Γ, then every function in Γ is µ -measurable. Proposition 74 (=Proposition 12.41). Let (X, ρ) be a metric space and let µ be an outer measure on X such that µ A + µ B = µ (A B) whenever ρ(a, B) > 0. Then every Borel set is measurable with respect to µ. Lemma 75 (=Lemma E5.2.1). If u has a weak derivative D α u for some multiindex α, then this weak derivative is unique. Theorem 76 (=Theorem E5.2.1). Suppose u and v are in W k,p (Ω) for some nonnegative integer k and some p [1, ]. (i) For any multi-index α with α k, we have D α u W k α,p (Ω) and D α (D β u) = D β (D α u) = D α+β u if α + β k. (ii) For any real numbers λ and µ, λu + µv W k,p and D α (λu + µv) = λd α u + µd β v. (iii) If V is a connected open subset of Ω, then u W k,p (V ). (iv) If ζ C c (Ω), then ζu W k,p (Ω) and D i (ζu) = D i ζu + ζd i u.

THEOREMS, ETC., FOR MATH 515

THEOREMS, ETC., FOR MATH 515 THEOREMS, ETC., FOR MATH 515 Proposition 1 (=comment on page 17). If A is an algebra, then any finite union or finite intersection of sets in A is also in A. Proposition 2 (=Proposition 1.1). For every

More information

Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm

Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm Chapter 13 Radon Measures Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm (13.1) f = sup x X f(x). We want to identify

More information

MATH MEASURE THEORY AND FOURIER ANALYSIS. Contents

MATH MEASURE THEORY AND FOURIER ANALYSIS. Contents MATH 3969 - MEASURE THEORY AND FOURIER ANALYSIS ANDREW TULLOCH Contents 1. Measure Theory 2 1.1. Properties of Measures 3 1.2. Constructing σ-algebras and measures 3 1.3. Properties of the Lebesgue measure

More information

MATHS 730 FC Lecture Notes March 5, Introduction

MATHS 730 FC Lecture Notes March 5, Introduction 1 INTRODUCTION MATHS 730 FC Lecture Notes March 5, 2014 1 Introduction Definition. If A, B are sets and there exists a bijection A B, they have the same cardinality, which we write as A, #A. If there exists

More information

3 (Due ). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure?

3 (Due ). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure? MA 645-4A (Real Analysis), Dr. Chernov Homework assignment 1 (Due ). Show that the open disk x 2 + y 2 < 1 is a countable union of planar elementary sets. Show that the closed disk x 2 + y 2 1 is a countable

More information

MAT 571 REAL ANALYSIS II LECTURE NOTES. Contents. 2. Product measures Iterated integrals Complete products Differentiation 17

MAT 571 REAL ANALYSIS II LECTURE NOTES. Contents. 2. Product measures Iterated integrals Complete products Differentiation 17 MAT 57 REAL ANALSIS II LECTURE NOTES PROFESSOR: JOHN QUIGG SEMESTER: SPRING 205 Contents. Convergence in measure 2. Product measures 3 3. Iterated integrals 4 4. Complete products 9 5. Signed measures

More information

Aliprantis, Border: Infinite-dimensional Analysis A Hitchhiker s Guide

Aliprantis, Border: Infinite-dimensional Analysis A Hitchhiker s Guide aliprantis.tex May 10, 2011 Aliprantis, Border: Infinite-dimensional Analysis A Hitchhiker s Guide Notes from [AB2]. 1 Odds and Ends 2 Topology 2.1 Topological spaces Example. (2.2) A semimetric = triangle

More information

l(y j ) = 0 for all y j (1)

l(y j ) = 0 for all y j (1) Problem 1. The closed linear span of a subset {y j } of a normed vector space is defined as the intersection of all closed subspaces containing all y j and thus the smallest such subspace. 1 Show that

More information

Bounded and continuous functions on a locally compact Hausdorff space and dual spaces

Bounded and continuous functions on a locally compact Hausdorff space and dual spaces Chapter 6 Bounded and continuous functions on a locally compact Hausdorff space and dual spaces Recall that the dual space of a normed linear space is a Banach space, and the dual space of L p is L q where

More information

3. (a) What is a simple function? What is an integrable function? How is f dµ defined? Define it first

3. (a) What is a simple function? What is an integrable function? How is f dµ defined? Define it first Math 632/6321: Theory of Functions of a Real Variable Sample Preinary Exam Questions 1. Let (, M, µ) be a measure space. (a) Prove that if µ() < and if 1 p < q

More information

The Caratheodory Construction of Measures

The Caratheodory Construction of Measures Chapter 5 The Caratheodory Construction of Measures Recall how our construction of Lebesgue measure in Chapter 2 proceeded from an initial notion of the size of a very restricted class of subsets of R,

More information

1.4 Outer measures 10 CHAPTER 1. MEASURE

1.4 Outer measures 10 CHAPTER 1. MEASURE 10 CHAPTER 1. MEASURE 1.3.6. ( Almost everywhere and null sets If (X, A, µ is a measure space, then a set in A is called a null set (or µ-null if its measure is 0. Clearly a countable union of null sets

More information

Functional Analysis HW #1

Functional Analysis HW #1 Functional Analysis HW #1 Sangchul Lee October 9, 2015 1 Solutions Solution of #1.1. Suppose that X

More information

2 (Bonus). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure?

2 (Bonus). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure? MA 645-4A (Real Analysis), Dr. Chernov Homework assignment 1 (Due 9/5). Prove that every countable set A is measurable and µ(a) = 0. 2 (Bonus). Let A consist of points (x, y) such that either x or y is

More information

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define Homework, Real Analysis I, Fall, 2010. (1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define ρ(f, g) = 1 0 f(x) g(x) dx. Show that

More information

+ 2x sin x. f(b i ) f(a i ) < ɛ. i=1. i=1

+ 2x sin x. f(b i ) f(a i ) < ɛ. i=1. i=1 Appendix To understand weak derivatives and distributional derivatives in the simplest context of functions of a single variable, we describe without proof some results from real analysis (see [7] and

More information

E.7 Alaoglu s Theorem

E.7 Alaoglu s Theorem E.7 Alaoglu s Theorem 359 E.7 Alaoglu s Theorem If X is a normed space, then the closed unit ball in X or X is compact if and only if X is finite-dimensional (Problem A.25). Even so, Alaoglu s Theorem

More information

Section Signed Measures: The Hahn and Jordan Decompositions

Section Signed Measures: The Hahn and Jordan Decompositions 17.2. Signed Measures 1 Section 17.2. Signed Measures: The Hahn and Jordan Decompositions Note. If measure space (X, M) admits measures µ 1 and µ 2, then for any α,β R where α 0,β 0, µ 3 = αµ 1 + βµ 2

More information

MTH 404: Measure and Integration

MTH 404: Measure and Integration MTH 404: Measure and Integration Semester 2, 2012-2013 Dr. Prahlad Vaidyanathan Contents I. Introduction....................................... 3 1. Motivation................................... 3 2. The

More information

CHAPTER 6. Differentiation

CHAPTER 6. Differentiation CHPTER 6 Differentiation The generalization from elementary calculus of differentiation in measure theory is less obvious than that of integration, and the methods of treating it are somewhat involved.

More information

MTG 5316/4302 FALL 2018 REVIEW FINAL

MTG 5316/4302 FALL 2018 REVIEW FINAL MTG 5316/4302 FALL 2018 REVIEW FINAL JAMES KEESLING Problem 1. Define open set in a metric space X. Define what it means for a set A X to be connected in a metric space X. Problem 2. Show that if a set

More information

Math 4121 Spring 2012 Weaver. Measure Theory. 1. σ-algebras

Math 4121 Spring 2012 Weaver. Measure Theory. 1. σ-algebras Math 4121 Spring 2012 Weaver Measure Theory 1. σ-algebras A measure is a function which gauges the size of subsets of a given set. In general we do not ask that a measure evaluate the size of every subset,

More information

Locally convex spaces, the hyperplane separation theorem, and the Krein-Milman theorem

Locally convex spaces, the hyperplane separation theorem, and the Krein-Milman theorem 56 Chapter 7 Locally convex spaces, the hyperplane separation theorem, and the Krein-Milman theorem Recall that C(X) is not a normed linear space when X is not compact. On the other hand we could use semi

More information

INTRODUCTION TO MEASURE THEORY AND LEBESGUE INTEGRATION

INTRODUCTION TO MEASURE THEORY AND LEBESGUE INTEGRATION 1 INTRODUCTION TO MEASURE THEORY AND LEBESGUE INTEGRATION Eduard EMELYANOV Ankara TURKEY 2007 2 FOREWORD This book grew out of a one-semester course for graduate students that the author have taught at

More information

Real Analysis. Jesse Peterson

Real Analysis. Jesse Peterson Real Analysis Jesse Peterson February 1, 2017 2 Contents 1 Preliminaries 7 1.1 Sets.................................. 7 1.1.1 Countability......................... 8 1.1.2 Transfinite induction.....................

More information

g 2 (x) (1/3)M 1 = (1/3)(2/3)M.

g 2 (x) (1/3)M 1 = (1/3)(2/3)M. COMPACTNESS If C R n is closed and bounded, then by B-W it is sequentially compact: any sequence of points in C has a subsequence converging to a point in C Conversely, any sequentially compact C R n is

More information

CHAPTER I THE RIESZ REPRESENTATION THEOREM

CHAPTER I THE RIESZ REPRESENTATION THEOREM CHAPTER I THE RIESZ REPRESENTATION THEOREM We begin our study by identifying certain special kinds of linear functionals on certain special vector spaces of functions. We describe these linear functionals

More information

Real Analysis II, Winter 2018

Real Analysis II, Winter 2018 Real Analysis II, Winter 2018 From the Finnish original Moderni reaalianalyysi 1 by Ilkka Holopainen adapted by Tuomas Hytönen January 18, 2018 1 Version dated September 14, 2011 Contents 1 General theory

More information

AN INTRODUCTION TO GEOMETRIC MEASURE THEORY AND AN APPLICATION TO MINIMAL SURFACES ( DRAFT DOCUMENT) Academic Year 2016/17 Francesco Serra Cassano

AN INTRODUCTION TO GEOMETRIC MEASURE THEORY AND AN APPLICATION TO MINIMAL SURFACES ( DRAFT DOCUMENT) Academic Year 2016/17 Francesco Serra Cassano AN INTRODUCTION TO GEOMETRIC MEASURE THEORY AND AN APPLICATION TO MINIMAL SURFACES ( DRAFT DOCUMENT) Academic Year 2016/17 Francesco Serra Cassano Contents I. Recalls and complements of measure theory.

More information

CHAPTER II THE HAHN-BANACH EXTENSION THEOREMS AND EXISTENCE OF LINEAR FUNCTIONALS

CHAPTER II THE HAHN-BANACH EXTENSION THEOREMS AND EXISTENCE OF LINEAR FUNCTIONALS CHAPTER II THE HAHN-BANACH EXTENSION THEOREMS AND EXISTENCE OF LINEAR FUNCTIONALS In this chapter we deal with the problem of extending a linear functional on a subspace Y to a linear functional on the

More information

CHAPTER V DUAL SPACES

CHAPTER V DUAL SPACES CHAPTER V DUAL SPACES DEFINITION Let (X, T ) be a (real) locally convex topological vector space. By the dual space X, or (X, T ), of X we mean the set of all continuous linear functionals on X. By the

More information

Carathéodory s extension of a measure on a semi-ring

Carathéodory s extension of a measure on a semi-ring Carathéodory s extension of a measure on a semi-ring Reinhardt Messerschmidt www.rmesserschmidt.me.uk 7 October 2018 1 Introduction This article presents Carathéodory s extension of a measure on a semi-ring,

More information

PROBLEMS. (b) (Polarization Identity) Show that in any inner product space

PROBLEMS. (b) (Polarization Identity) Show that in any inner product space 1 Professor Carl Cowen Math 54600 Fall 09 PROBLEMS 1. (Geometry in Inner Product Spaces) (a) (Parallelogram Law) Show that in any inner product space x + y 2 + x y 2 = 2( x 2 + y 2 ). (b) (Polarization

More information

Math 209B Homework 2

Math 209B Homework 2 Math 29B Homework 2 Edward Burkard Note: All vector spaces are over the field F = R or C 4.6. Two Compactness Theorems. 4. Point Set Topology Exercise 6 The product of countably many sequentally compact

More information

Notation. General. Notation Description See. Sets, Functions, and Spaces. a b & a b The minimum and the maximum of a and b

Notation. General. Notation Description See. Sets, Functions, and Spaces. a b & a b The minimum and the maximum of a and b Notation General Notation Description See a b & a b The minimum and the maximum of a and b a + & a f S u The non-negative part, a 0, and non-positive part, (a 0) of a R The restriction of the function

More information

Part III Functional Analysis

Part III Functional Analysis Part III Functional Analysis András Zsák Michaelmas 2017 Contents 1 The Hahn Banach extension theorems 1 2 The dual space of L p (µ) and of C(K) 7 3 Weak topologies 12 4 Convexity and the Krein Milman

More information

Stone-Čech compactification of Tychonoff spaces

Stone-Čech compactification of Tychonoff spaces The Stone-Čech compactification of Tychonoff spaces Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto June 27, 2014 1 Completely regular spaces and Tychonoff spaces A topological

More information

Annalee Gomm Math 714: Assignment #2

Annalee Gomm Math 714: Assignment #2 Annalee Gomm Math 714: Assignment #2 3.32. Verify that if A M, λ(a = 0, and B A, then B M and λ(b = 0. Suppose that A M with λ(a = 0, and let B be any subset of A. By the nonnegativity and monotonicity

More information

REAL AND COMPLEX ANALYSIS

REAL AND COMPLEX ANALYSIS REAL AND COMPLE ANALYSIS Third Edition Walter Rudin Professor of Mathematics University of Wisconsin, Madison Version 1.1 No rights reserved. Any part of this work can be reproduced or transmitted in any

More information

Lecture 1 Real and Complex Numbers

Lecture 1 Real and Complex Numbers Lecture 1 Real and Complex Numbers Exercise 1.1. Show that a bounded monotonic increasing sequence of real numbers converges (to its least upper bound). Solution. (This was indicated in class) Let (a n

More information

Measure Theory & Integration

Measure Theory & Integration Measure Theory & Integration Lecture Notes, Math 320/520 Fall, 2004 D.H. Sattinger Department of Mathematics Yale University Contents 1 Preliminaries 1 2 Measures 3 2.1 Area and Measure........................

More information

CHARACTERIZATION OF SOBOLEV AND BV SPACES. by Daniel Spector M.S. Mathematics, Carnegie Mellon, 2009

CHARACTERIZATION OF SOBOLEV AND BV SPACES. by Daniel Spector M.S. Mathematics, Carnegie Mellon, 2009 CHARACTERIZATION OF SOBOLEV AND BV SPACES by Daniel Spector M.S. Mathematics, Carnegie Mellon, 2009 Submitted to the Graduate Faculty of the Department of Mathematics in partial fulfillment of the reuirements

More information

Stanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon Measures

Stanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon Measures 2 1 Borel Regular Measures We now state and prove an important regularity property of Borel regular outer measures: Stanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon

More information

Real Analysis Qualifying Exam May 14th 2016

Real Analysis Qualifying Exam May 14th 2016 Real Analysis Qualifying Exam May 4th 26 Solve 8 out of 2 problems. () Prove the Banach contraction principle: Let T be a mapping from a complete metric space X into itself such that d(tx,ty) apple qd(x,

More information

Continuous Functions on Metric Spaces

Continuous Functions on Metric Spaces Continuous Functions on Metric Spaces Math 201A, Fall 2016 1 Continuous functions Definition 1. Let (X, d X ) and (Y, d Y ) be metric spaces. A function f : X Y is continuous at a X if for every ɛ > 0

More information

TOPOLOGICAL VECTOR SPACES

TOPOLOGICAL VECTOR SPACES TOPOLOGICAL VECTOR SPACES PRADIPTA BANDYOPADHYAY 1. Topological Vector Spaces Let X be a linear space over R or C. We denote the scalar field by K. Definition 1.1. A topological vector space (tvs for short)

More information

02. Measure and integral. 1. Borel-measurable functions and pointwise limits

02. Measure and integral. 1. Borel-measurable functions and pointwise limits (October 3, 2017) 02. Measure and integral Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ [This document is http://www.math.umn.edu/ garrett/m/real/notes 2017-18/02 measure and integral.pdf]

More information

Real Analysis Notes. Thomas Goller

Real Analysis Notes. Thomas Goller Real Analysis Notes Thomas Goller September 4, 2011 Contents 1 Abstract Measure Spaces 2 1.1 Basic Definitions........................... 2 1.2 Measurable Functions........................ 2 1.3 Integration..............................

More information

Folland: Real Analysis, Chapter 7 Sébastien Picard

Folland: Real Analysis, Chapter 7 Sébastien Picard Folland: Real Analysis, Chapter 7 Sébastien Picard Problem 7.2 Let µ be a Radon measure on X. a. Let N be the union of all open U X such that µ(u) =. Then N is open and µ(n) =. The complement of N is called

More information

Introduction to Functional Analysis

Introduction to Functional Analysis Introduction to Functional Analysis Carnegie Mellon University, 21-640, Spring 2014 Acknowledgements These notes are based on the lecture course given by Irene Fonseca but may differ from the exact lecture

More information

A List of Problems in Real Analysis

A List of Problems in Real Analysis A List of Problems in Real Analysis W.Yessen & T.Ma December 3, 218 This document was first created by Will Yessen, who was a graduate student at UCI. Timmy Ma, who was also a graduate student at UCI,

More information

Signed Measures and Complex Measures

Signed Measures and Complex Measures Chapter 8 Signed Measures Complex Measures As opposed to the measures we have considered so far, a signed measure is allowed to take on both positive negative values. To be precise, if M is a σ-algebra

More information

Eberlein-Šmulian theorem and some of its applications

Eberlein-Šmulian theorem and some of its applications Eberlein-Šmulian theorem and some of its applications Kristina Qarri Supervisors Trond Abrahamsen Associate professor, PhD University of Agder Norway Olav Nygaard Professor, PhD University of Agder Norway

More information

Real Analysis, 2nd Edition, G.B.Folland Elements of Functional Analysis

Real Analysis, 2nd Edition, G.B.Folland Elements of Functional Analysis Real Analysis, 2nd Edition, G.B.Folland Chapter 5 Elements of Functional Analysis Yung-Hsiang Huang 5.1 Normed Vector Spaces 1. Note for any x, y X and a, b K, x+y x + y and by ax b y x + b a x. 2. It

More information

Real Analysis Chapter 4 Solutions Jonathan Conder

Real Analysis Chapter 4 Solutions Jonathan Conder 2. Let x, y X and suppose that x y. Then {x} c is open in the cofinite topology and contains y but not x. The cofinite topology on X is therefore T 1. Since X is infinite it contains two distinct points

More information

Analysis Comprehensive Exam Questions Fall 2008

Analysis Comprehensive Exam Questions Fall 2008 Analysis Comprehensive xam Questions Fall 28. (a) Let R be measurable with finite Lebesgue measure. Suppose that {f n } n N is a bounded sequence in L 2 () and there exists a function f such that f n (x)

More information

Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 2017 Nadia S. Larsen. 17 November 2017.

Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 2017 Nadia S. Larsen. 17 November 2017. Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 017 Nadia S. Larsen 17 November 017. 1. Construction of the product measure The purpose of these notes is to prove the main

More information

Five Mini-Courses on Analysis

Five Mini-Courses on Analysis Christopher Heil Five Mini-Courses on Analysis Metrics, Norms, Inner Products, and Topology Lebesgue Measure and Integral Operator Theory and Functional Analysis Borel and Radon Measures Topological Vector

More information

Reminder Notes for the Course on Measures on Topological Spaces

Reminder Notes for the Course on Measures on Topological Spaces Reminder Notes for the Course on Measures on Topological Spaces T. C. Dorlas Dublin Institute for Advanced Studies School of Theoretical Physics 10 Burlington Road, Dublin 4, Ireland. Email: dorlas@stp.dias.ie

More information

Measure Theory. John K. Hunter. Department of Mathematics, University of California at Davis

Measure Theory. John K. Hunter. Department of Mathematics, University of California at Davis Measure Theory John K. Hunter Department of Mathematics, University of California at Davis Abstract. These are some brief notes on measure theory, concentrating on Lebesgue measure on R n. Some missing

More information

Overview of normed linear spaces

Overview of normed linear spaces 20 Chapter 2 Overview of normed linear spaces Starting from this chapter, we begin examining linear spaces with at least one extra structure (topology or geometry). We assume linearity; this is a natural

More information

Hausdorff measure. Jordan Bell Department of Mathematics, University of Toronto. October 29, 2014

Hausdorff measure. Jordan Bell Department of Mathematics, University of Toronto. October 29, 2014 Hausdorff measure Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto October 29, 2014 1 Outer measures and metric outer measures Suppose that X is a set. A function ν :

More information

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define HILBERT SPACES AND THE RADON-NIKODYM THEOREM STEVEN P. LALLEY 1. DEFINITIONS Definition 1. A real inner product space is a real vector space V together with a symmetric, bilinear, positive-definite mapping,

More information

NOTES ON THE REGULARITY OF QUASICONFORMAL HOMEOMORPHISMS

NOTES ON THE REGULARITY OF QUASICONFORMAL HOMEOMORPHISMS NOTES ON THE REGULARITY OF QUASICONFORMAL HOMEOMORPHISMS CLARK BUTLER. Introduction The purpose of these notes is to give a self-contained proof of the following theorem, Theorem.. Let f : S n S n be a

More information

5 Measure theory II. (or. lim. Prove the proposition. 5. For fixed F A and φ M define the restriction of φ on F by writing.

5 Measure theory II. (or. lim. Prove the proposition. 5. For fixed F A and φ M define the restriction of φ on F by writing. 5 Measure theory II 1. Charges (signed measures). Let (Ω, A) be a σ -algebra. A map φ: A R is called a charge, (or signed measure or σ -additive set function) if φ = φ(a j ) (5.1) A j for any disjoint

More information

Real Analysis Problems

Real Analysis Problems Real Analysis Problems Cristian E. Gutiérrez September 14, 29 1 1 CONTINUITY 1 Continuity Problem 1.1 Let r n be the sequence of rational numbers and Prove that f(x) = 1. f is continuous on the irrationals.

More information

Math 5052 Measure Theory and Functional Analysis II Homework Assignment 7

Math 5052 Measure Theory and Functional Analysis II Homework Assignment 7 Math 5052 Measure Theory and Functional Analysis II Homework Assignment 7 Prof. Wickerhauser Due Friday, February 5th, 2016 Please do Exercises 3, 6, 14, 16*, 17, 18, 21*, 23*, 24, 27*. Exercises marked

More information

II - REAL ANALYSIS. This property gives us a way to extend the notion of content to finite unions of rectangles: we define

II - REAL ANALYSIS. This property gives us a way to extend the notion of content to finite unions of rectangles: we define 1 Measures 1.1 Jordan content in R N II - REAL ANALYSIS Let I be an interval in R. Then its 1-content is defined as c 1 (I) := b a if I is bounded with endpoints a, b. If I is unbounded, we define c 1

More information

Problem Set 6: Solutions Math 201A: Fall a n x n,

Problem Set 6: Solutions Math 201A: Fall a n x n, Problem Set 6: Solutions Math 201A: Fall 2016 Problem 1. Is (x n ) n=0 a Schauder basis of C([0, 1])? No. If f(x) = a n x n, n=0 where the series converges uniformly on [0, 1], then f has a power series

More information

Chapter 8. General Countably Additive Set Functions. 8.1 Hahn Decomposition Theorem

Chapter 8. General Countably Additive Set Functions. 8.1 Hahn Decomposition Theorem Chapter 8 General Countably dditive Set Functions In Theorem 5.2.2 the reader saw that if f : X R is integrable on the measure space (X,, µ) then we can define a countably additive set function ν on by

More information

Differentiation of Measures and Functions

Differentiation of Measures and Functions Chapter 6 Differentiation of Measures and Functions This chapter is concerned with the differentiation theory of Radon measures. In the first two sections we introduce the Radon measures and discuss two

More information

Part II Probability and Measure

Part II Probability and Measure Part II Probability and Measure Theorems Based on lectures by J. Miller Notes taken by Dexter Chua Michaelmas 2016 These notes are not endorsed by the lecturers, and I have modified them (often significantly)

More information

2) Let X be a compact space. Prove that the space C(X) of continuous real-valued functions is a complete metric space.

2) Let X be a compact space. Prove that the space C(X) of continuous real-valued functions is a complete metric space. University of Bergen General Functional Analysis Problems with solutions 6 ) Prove that is unique in any normed space. Solution of ) Let us suppose that there are 2 zeros and 2. Then = + 2 = 2 + = 2. 2)

More information

Summary of Real Analysis by Royden

Summary of Real Analysis by Royden Summary of Real Analysis by Royden Dan Hathaway May 2010 This document is a summary of the theorems and definitions and theorems from Part 1 of the book Real Analysis by Royden. In some areas, such as

More information

MATH 5616H INTRODUCTION TO ANALYSIS II SAMPLE FINAL EXAM: SOLUTIONS

MATH 5616H INTRODUCTION TO ANALYSIS II SAMPLE FINAL EXAM: SOLUTIONS MATH 5616H INTRODUCTION TO ANALYSIS II SAMPLE FINAL EXAM: SOLUTIONS You may not use notes, books, etc. Only the exam paper, a pencil or pen may be kept on your desk during the test. Calculators are not

More information

Functional Analysis I

Functional Analysis I Functional Analysis I Course Notes by Stefan Richter Transcribed and Annotated by Gregory Zitelli Polar Decomposition Definition. An operator W B(H) is called a partial isometry if W x = X for all x (ker

More information

A VERY BRIEF REVIEW OF MEASURE THEORY

A VERY BRIEF REVIEW OF MEASURE THEORY A VERY BRIEF REVIEW OF MEASURE THEORY A brief philosophical discussion. Measure theory, as much as any branch of mathematics, is an area where it is important to be acquainted with the basic notions and

More information

MATH 650. THE RADON-NIKODYM THEOREM

MATH 650. THE RADON-NIKODYM THEOREM MATH 650. THE RADON-NIKODYM THEOREM This note presents two important theorems in Measure Theory, the Lebesgue Decomposition and Radon-Nikodym Theorem. They are not treated in the textbook. 1. Closed subspaces

More information

(2) E M = E C = X\E M

(2) E M = E C = X\E M 10 RICHARD B. MELROSE 2. Measures and σ-algebras An outer measure such as µ is a rather crude object since, even if the A i are disjoint, there is generally strict inequality in (1.14). It turns out to

More information

Probability and Measure

Probability and Measure Part II Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2018 84 Paper 4, Section II 26J Let (X, A) be a measurable space. Let T : X X be a measurable map, and µ a probability

More information

MAT 578 FUNCTIONAL ANALYSIS EXERCISES

MAT 578 FUNCTIONAL ANALYSIS EXERCISES MAT 578 FUNCTIONAL ANALYSIS EXERCISES JOHN QUIGG Exercise 1. Prove that if A is bounded in a topological vector space, then for every neighborhood V of 0 there exists c > 0 such that tv A for all t > c.

More information

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9 MAT 570 REAL ANALYSIS LECTURE NOTES PROFESSOR: JOHN QUIGG SEMESTER: FALL 204 Contents. Sets 2 2. Functions 5 3. Countability 7 4. Axiom of choice 8 5. Equivalence relations 9 6. Real numbers 9 7. Extended

More information

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set Analysis Finite and Infinite Sets Definition. An initial segment is {n N n n 0 }. Definition. A finite set can be put into one-to-one correspondence with an initial segment. The empty set is also considered

More information

CHAPTER VIII HILBERT SPACES

CHAPTER VIII HILBERT SPACES CHAPTER VIII HILBERT SPACES DEFINITION Let X and Y be two complex vector spaces. A map T : X Y is called a conjugate-linear transformation if it is a reallinear transformation from X into Y, and if T (λx)

More information

(U) =, if 0 U, 1 U, (U) = X, if 0 U, and 1 U. (U) = E, if 0 U, but 1 U. (U) = X \ E if 0 U, but 1 U. n=1 A n, then A M.

(U) =, if 0 U, 1 U, (U) = X, if 0 U, and 1 U. (U) = E, if 0 U, but 1 U. (U) = X \ E if 0 U, but 1 U. n=1 A n, then A M. 1. Abstract Integration The main reference for this section is Rudin s Real and Complex Analysis. The purpose of developing an abstract theory of integration is to emphasize the difference between the

More information

Banach Spaces V: A Closer Look at the w- and the w -Topologies

Banach Spaces V: A Closer Look at the w- and the w -Topologies BS V c Gabriel Nagy Banach Spaces V: A Closer Look at the w- and the w -Topologies Notes from the Functional Analysis Course (Fall 07 - Spring 08) In this section we discuss two important, but highly non-trivial,

More information

Lebesgue Integration on R n

Lebesgue Integration on R n Lebesgue Integration on R n The treatment here is based loosely on that of Jones, Lebesgue Integration on Euclidean Space We give an overview from the perspective of a user of the theory Riemann integration

More information

Spectral theorems for bounded self-adjoint operators on a Hilbert space

Spectral theorems for bounded self-adjoint operators on a Hilbert space Chapter 10 Spectral theorems for bounded self-adjoint operators on a Hilbert space Let H be a Hilbert space. For a bounded operator A : H H its Hilbert space adjoint is an operator A : H H such that Ax,

More information

Riesz Representation Theorems

Riesz Representation Theorems Chapter 6 Riesz Representation Theorems 6.1 Dual Spaces Definition 6.1.1. Let V and W be vector spaces over R. We let L(V, W ) = {T : V W T is linear}. The space L(V, R) is denoted by V and elements of

More information

Integral Jensen inequality

Integral Jensen inequality Integral Jensen inequality Let us consider a convex set R d, and a convex function f : (, + ]. For any x,..., x n and λ,..., λ n with n λ i =, we have () f( n λ ix i ) n λ if(x i ). For a R d, let δ a

More information

Another Riesz Representation Theorem

Another Riesz Representation Theorem Another Riesz Representation Theorem In these notes we prove (one version of) a theorem known as the Riesz Representation Theorem. Some people also call it the Riesz Markov Theorem. It expresses positive

More information

1 Inner Product Space

1 Inner Product Space Ch - Hilbert Space 1 4 Hilbert Space 1 Inner Product Space Let E be a complex vector space, a mapping (, ) : E E C is called an inner product on E if i) (x, x) 0 x E and (x, x) = 0 if and only if x = 0;

More information

1 Topology Definition of a topology Basis (Base) of a topology The subspace topology & the product topology on X Y 3

1 Topology Definition of a topology Basis (Base) of a topology The subspace topology & the product topology on X Y 3 Index Page 1 Topology 2 1.1 Definition of a topology 2 1.2 Basis (Base) of a topology 2 1.3 The subspace topology & the product topology on X Y 3 1.4 Basic topology concepts: limit points, closed sets,

More information

Notes for Functional Analysis

Notes for Functional Analysis Notes for Functional Analysis Wang Zuoqin (typed by Xiyu Zhai) November 6, 2015 1 Lecture 18 1.1 The convex hull Let X be any vector space, and E X a subset. Definition 1.1. The convex hull of E is the

More information

van Rooij, Schikhof: A Second Course on Real Functions

van Rooij, Schikhof: A Second Course on Real Functions vanrooijschikhof.tex April 25, 2018 van Rooij, Schikhof: A Second Course on Real Functions Notes from [vrs]. Introduction A monotone function is Riemann integrable. A continuous function is Riemann integrable.

More information

Lecture 5 Theorems of Fubini-Tonelli and Radon-Nikodym

Lecture 5 Theorems of Fubini-Tonelli and Radon-Nikodym Lecture 5: Fubini-onelli and Radon-Nikodym 1 of 13 Course: heory of Probability I erm: Fall 2013 Instructor: Gordan Zitkovic Lecture 5 heorems of Fubini-onelli and Radon-Nikodym Products of measure spaces

More information

MEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich

MEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich MEASURE AND INTEGRATION Dietmar A. Salamon ETH Zürich 9 September 2016 ii Preface This book is based on notes for the lecture course Measure and Integration held at ETH Zürich in the spring semester 2014.

More information

Finite-dimensional spaces. C n is the space of n-tuples x = (x 1,..., x n ) of complex numbers. It is a Hilbert space with the inner product

Finite-dimensional spaces. C n is the space of n-tuples x = (x 1,..., x n ) of complex numbers. It is a Hilbert space with the inner product Chapter 4 Hilbert Spaces 4.1 Inner Product Spaces Inner Product Space. A complex vector space E is called an inner product space (or a pre-hilbert space, or a unitary space) if there is a mapping (, )

More information

CHOQUET LIKE SETS IN FUNCTION SPACES

CHOQUET LIKE SETS IN FUNCTION SPACES CHOQUET LIKE SETS IN FUNCTION SPACES JAROSLAV LUKEŠ, TOMÁŠ MOCEK, MICHAEL SMRČKA, AND JIŘÍ SPURNÝ Abstract. In convex analysis when studying function spaces of continuous affine functions, notions of a

More information

Three hours THE UNIVERSITY OF MANCHESTER. 24th January

Three hours THE UNIVERSITY OF MANCHESTER. 24th January Three hours MATH41011 THE UNIVERSITY OF MANCHESTER FOURIER ANALYSIS AND LEBESGUE INTEGRATION 24th January 2013 9.45 12.45 Answer ALL SIX questions in Section A (25 marks in total). Answer THREE of the

More information