Problem Set. Problem Set #1. Math 5322, Fall March 4, 2002 ANSWERS

Size: px
Start display at page:

Download "Problem Set. Problem Set #1. Math 5322, Fall March 4, 2002 ANSWERS"

Transcription

1 Problem Set Problem Set #1 Math 5322, Fall 2001 March 4, 2002 ANSWRS

2 i

3 All of the problems are from Chapter 3 of the text. Problem 1. [Problem 2, page 88] If ν is a signed measure, is ν-null iff ν () 0. Also, if ν and µ are signed measures, ν µ iff ν µ iff ν + µ and ν µ For the first part of the problem, let P N be a Hahn decomposition of with respect to ν. Thus, we have ν + () ν( P ) ν () ν( N) ν ν + + ν Assume that is ν-null. This means that for all measurable F, ν(f ) 0. But then P, so ν + () 0 and N, so ν () 0. Then we have ν () ν + () + ν () 0 Conversely, suppose that ν () 0. Let F be measurable. Then 0 ν + (F ) + ν (F ) ν (F ) ν () 0, so ν + (F ) 0 and ν (F ) 0. But then ν(f ) ν + (F ) ν (F ) 0. Thus, we can conclude that is ν-null. For the second part of the problem, we want to show that the following conditions are equivalent. (1) ν µ (2) ν + µ and ν µ (3) ν µ Let s first show that (1) (2). Since ν µ, we can decompose into a disjoint union of measurable sets A and B so that A is µ-null and B is ν-null. We can also find an Hahn decomposition P N with respect to ν, as above. Since B in ν-null, we have ν + (B) ν(p B) 0 and ν (B) ν(b N) 0. Thus, B is ν + -null and ν -null. Since A is still µ-null, we conclude that ν + µ and ν µ. Next, we show that (2) (3). Since ν + µ, we can find disjoint measurable sets A 1 and B 1 so that A 1 B 1, ν + (B 1 ) 0 and A 1 is µ-null. Similarly, we can write A 2 B 2 (disjoint union) where ν (B 2 ) 0 and A 2 is µ-null. We can then partition as (A 1 A 2 ) (A 1 B 2 ) (A 2 B 1 ) (B 1 B 2 ) Now, (A 1 A 2 ) is contained in the µ-null set A 1, and so is µ-null. Similarly, A 2 B 2 and A 2 B 1 are µ-null. We have 0 ν + (B 1 B 2 ) ν + (B 1 ) 0, so ν + (B 1 B 2 ) 0. Similarly, ν (B 1 B 2 ) 0, and thus ν (B 1 B 2 ) 0. We thus have a disjoint measurable decomposition A 3 B 3 where A 3 (A 1 A 2 ) (A 1 B 2 ) (A 2 B 1 ) B 3 B 1 B 2. 1

4 The set A 3 is µ-null and B 3 is ν -null. This shows that ν µ. Finally we show that (3) (1). So, suppose that ν µ. The we have a measurable decomposition A B where ν (B) 0 and A is µ-null. However, ν (B) 0 implies that B is ν-null (as we proved above), so we have ν µ. Problem 2. [Problem 3, page 88] Let ν be a signed measure on (, M). a. L 1 (ν) L 1 ( ν ). b. If f L 1 (ν), f dν f d ν. c. If M, { ν () sup } f dν f 1 According to the definition on page 88 of the text, the definition of L 1 (ν) is L 1 (ν) L 1 (ν + ) L 1 (ν ) and if f L 1 (ν), we define f dν f dν + f dν. So, consider part (a.) of the problem. If f L 1 (ν), then, by definition the integrals ( ) f dν +, f dν are finite. On the other hand, ν is defined by ν () ν + ()+ν (). Hence, the equation χ d ν χ dν + + χ dν holds by definition. Thus, we can apply the usual argument to show that ( ) g d ν g dν + + g dν holds for all measurable g : [0, ]: This equations holds for characteristic functions, hence (by linearity) for simple functions and hence (by the monotone convergence theorem) for nonnegative functions. Thus, if the integrals in ( ) are finite, the integral f d ν is finite, so f L 1 ( ν ). Conversely, if f L 1 ( ν ), the integral f d ν is finite, and so by ( ), the integrals in ( ) are finite, so f L 1 (ν). 2

5 Next consider part (b.) of the problem. If f L 1 (ν), then by definition f dν f dν + f dν f + dν + f dν + f + dν + f dν, where all of the integrals on the right are positive numbers. Hence, by the triangle inequality, f dν f + dν + + f dν + + f + dν + f dν (f + + f ) dν + + (f + + f ) dν f dν + + f dν f d ν Finally, consider part (c.) of the problem. If f 1, then for any M, f χ χ. Thus, we have f dν f d ν f χ d ν χ d ν ν (). Thus, the sup in part (c.) is ν (). To get the reverse inequality, let P N be a Hahn decomposition of with respect to ν, and recall the description of ν + and ν in terms of P and N from the last problem. Then we have ν () ν( P ) ν( N) (χ P χ N ) dν. But, χ P χ N 1 (P and N are disjoint). This shows that ν () is an element of the set we re supping over, so ν () the sup. Problem 3. [Problem 4, page 88] If ν is a signed measure and λ, µ are positive measures such that ν λ µ, then λ ν + and µ ν. Let P N be a Hahn decomposition of with respect to ν. Then for any measurable set, we have ( ) ν + () ν( P ) λ( P ) µ( P ). 3

6 If ν + (), this equation shows λ( P ), and so λ(). Thus, ν + () λ() is true in this case. If ν + () <, then both numbers on the right of ( ) must be finite, and ( ) gives ν + () + µ( P ) λ( P ). Thus, we have ν + () ν + () + µ( P ) λ( P ) λ(). The argument for the other inequality is similar. We have ν () ν( N) λ( N) + µ( N) If ν (), then we must have µ() µ( N), so µ() ν (). Otherwise, every thing must be finite and ν () ν () + λ( N) µ( N) µ(). Problem 4. [Problem 5, page 88] If ν 1 and ν 2 are signed measures that both omitted the value + or, then ν 1 + ν 2 ν 1 + ν 2. (Use xercise 4.) The hypothesis that ± is omitted assures that the signed measure ν 1 + ν 2 is defined. We have v 1 ν 1 + ν 1 and ν 2 ν 2 + ν 2. Adding these equations gives ν 1 + ν 2 (ν ν+ 2 ) (ν 1 + ν 2 ), where the expressions in parentheses are positive measures. Thus, by xercise 4, we have (ν 1 + ν 2 ) + ν ν+ 2 (ν 1 + ν 2 ) ν 1 + ν 2 and adding these inequalities gives ν 1 + ν 2 ν 1 + ν 2. Problem 5. [Problem 12, page 92] For j 1, 2, let µ j, ν j be σ-finite measures on ( j, M j ) such that ν j µ j. then ν 1 ν 2 µ 1 µ 2 and d(ν 1 ν 2 ) d(µ 1 µ 2 ) (x 1, x 2 ) dν 1 dµ 1 (x 1 ) dν 2 dµ 2 (x 2 ). We first want to show that ν 1 ν 2 µ 1 µ 2. So, suppose that M 1 M 2 and (µ 1 µ 2 )() 0. Recall that the x 1 -section of is defined by x1 { x 2 2 (x 1, x 2 ) }. 4

7 By Theorem 2.36 on page 66 of the text, the function x 1 µ 2 ( x1 ) is measurable and 0 (µ 1 µ 2 )() µ 2 ( x1 ) dµ 1 (x 1 ) 1 Thus, µ 2 ( x1 ) 0 for µ 1 -almost all x 1. In other words, there is a µ 1 -null set N 1 so that µ 2 ( x1 ) 0 if x 1 / N. If x 1 / N, we must have ν 2 ( x1 ) 0, since ν 2 µ 2. We also have ν 1 (N) 0, since ν 1 µ 1. Thus, x 1 ν 2 ( x1 ) is zero almost everywhere with respect to ν 1. Thus, we have (ν 1 ν 2 )() ν 2 ( x1 ) dν 1 (x 1 ) 0. 1 Thus, (µ 1 µ 2 )() 0 (ν 1 ν 2 )() 0, so ν 1 ν 2 µ 1 µ 2. For notational simplicity, let f d(ν 1 ν 2 ) d(µ 1 µ 2 ), f 1 dν 1 dµ 1, f 2 dν 2 dµ 2. The, we have ( ) (ν 1 ν 2 )() f(x 1, x 2 ) d(µ 1 µ 2 )(x 1, x 2 ) and f is the unique (up to equality almost everywhere) function with this property. Of course we have ν 1 (A) f 1 (x 1 ) dµ 1 (x 1 ) A ν 2 (B) f 2 (x 2 ) dµ 2 (x 2 ) B if A 1 and B 2 are measurable. On the other hand, the functions (x 1, x 2 ) f 1 (x 1 ) and (x 1, x 2 ) f 2 (x 2 ) are measurable, so we can define a measure λ on 1 2 by ( ) λ() f 1 (x 1 )f 2 (x 2 ) d(µ 1 µ 2 )(x 1, x 2 ). Suppose that A 1 1 and A 2 2. Then, we can use Tonelli s Theorem to 5

8 calculate λ(a 1 A 2 ) as follows: λ(a 1 A 2 ) f 1 (x 1 )f 2 (x 2 ) d(µ 1 µ 2 )(x 1, x 2 ) A 1 A 2 χ A1 A 2 (x 1, x 2 )f 1 (x 1 )f 2 (x 2 ) d(µ 1 µ 2 )(x 1, x 2 ) 1 2 χ A1 (x 1 )χ A2 (x 2 )f 1 (x 1 )f 2 (x 2 ) d(µ 1 µ 2 )(x 1, x 2 ) 1 2 χ A1 (x 1 )χ A2 (x 2 )f 1 (x 1 )f 2 (x 2 ) dµ 2 (x 2 ) dµ 1 (x 1 ) 1 2 χ A1 (x 1 )f 1 (x 1 ) χ A2 (x 2 )f 2 (x 2 ) dµ 2 (x 2 ) dµ 1 (x 1 ) 1 2 χ A1 (x 1 )f 1 (x 1 )ν 2 (A 2 ) dµ 1 (x 1 ) 1 ν 2 (A 2 ) χ A1 (x 1 )f 1 (x 1 ) dµ 1 (x 1 ) 1 ν 1 (A 1 )ν 2 (A 2 ). Thus we have λ(a 1 A 2 ) ν 1 (A 1 )ν 2 (A 2 ) (ν 1 ν 2 )(A 1 A 2 ). for all measurable rectangles A 1 A 2. Since our measures are σ-finite, this shows that λ ν 1 ν 2, see the remark at the bottom of page 64 in the text. Comparing ( ) and ( ) and using the uniqueness in ( ) then shows that f(x 1, x 2 ) f 1 (x 1 )f 2 (x 2 ) Problem 6. [Problem 16, page 92] Suppose that µ, ν are measures on (, M) with ν µ and let λ µ + ν. If f dν/dλ, then 0 f < 1 µ-a.e. and dν/dµ f/(1 f). It s clear that ν λ, so f dν/dλ makes sense. We want to show 0 f < 1 µ-a.e. Suppose, for a contradiction, that the is a set with µ() > 0 and f 1 on. On the on hand, we have ν() < ν() + µ() λ(). On the other hand, fχ χ, so ν() f dλ χ f dλ χ dλ λ(), a contradiction. If is measurable, we have 1 dν ν() f dλ 6 f dµ + f dν,

9 which yields or, in other words, (1 f) dν f dµ, χ (1 f) dν χ f dµ. By the usual procedure, we can extend this equation from characteristic functions to nonnegative functions. Thus, we have g(1 f) dν gf dµ for all nonnegative measurable functions g. Hence, for any measurable set, we have ( ) g(1 f) dν gf dµ for all nonnegative measurable functions g. Since 0 f < 1 µ-a.e. the function 1/(1 f) is defined and nonnegative µ-a.e. Since ν µ, 1/(1 f) is also defined and nonnegative ν-a.e. Thus, we can set g 1/(1 f) in ( ). This gives f ν() 1 f dµ, for all measurable, whence dν/dµ f/(1 f). Problem 7. [Problem 18, page 94] Prove Proposition 3.13c. In other words, suppose that ν is a complex measure on (, M). L 1 (ν) L 1 ( ν ) and if f L 1 (ν), f dν f d ν. Then I seem to have been very confused the day I tried to do this in class! Let z x + iy be a complex number, where x, y are real. We have x 2, y 2 x 2 + y 2 z 2 so taking square roots gives x, y z. One the other hand, we have z 2 x 2 + y 2 x 2 + y 2 x x y + y 2 [ x + y ] 2, and so z x + y. 7

10 Now lets begin the solution of the problem. By the definition on page 93 of the text, L 1 (ν) is defined to be L 1 (ν r ) L 1 (ν i ) and for f L 1 (ν), we define f dν f dν r + i f dν i. On the other hand, we know from xercise 3 on page 88 of the text (one of the problems in this set) that for a signed measure λ, L 1 (λ) L 1 ( λ ) and for f L 1 (λ), f dλ f d λ. Hence in our present situation, we know L 1 (ν) L 1 ( ν r ) L 1 ( ν i ). Let s recall the reasoning of part b. of Proposition We can find some finite positive measure µ such that ν r and ν i are absolutely continuous with respect to µ (µ ν r + ν i will do). Then, by the Lebesgue-Radon-Nikodym Theorem, there are real-valued measurable functions f and g such that dν r f dµ and dν i g dµ. Then, we have ( ) ν() h dµ where h f + ig. From this equation, we have d ν h dµ, by definition. Since f, g h we have ν r () f dµ h dµ ν () ν i () g dµ h dµ ν () so we certainly have ν r, ν i ν. Thus, once again, we can find real-valued functions ϕ and η such that dν r ϕ d ν and dν i η d ν. If ϕ ψ + iη, then ν() ϕ d ν. But then, comparing with ( ), we have ϕ h dµ h dµ. By the uniqueness part of the Lebesgue-Radon-Nikodym Theorem, ϕ h h, µ-a.e., and hence ν -a.e. On the other hand if Z is the set where h 0, then ν (Z) h dµ 0 dµ 0 Z so h 0, ν -a.e. This shows that ϕ 1, ν -a.e., so we can conclude that ψ, η ϕ 1, ν -a.e., and 1 ϕ ψ + η, ν -a.e. Finally, we have d ν r ψ d ν and d ν i η d ν. Z 8

11 Now, suppose that f L 1 ( ν ). Then f d ν < Since f ψ f, ν -a.e., we have f ψ d ν f d ν < but the integral on the left is f d ν r, so f L 1 ( ν r ). Similarly, f L 1 ( ν i ) Conversely, suppose that f L 1 ( ν r ) L 1 ( ν i ) Then we have f ψ d ν f d ν r < f η d ν f d ν i <. and so we have f [ ψ + η ] d ν <. However, we have 1 ψ + η, ν -a.e., so f d ν f [ ψ + η ] d ν <. and so f L 1 ( ν ). Finally, suppose that f L 1 (ν) L 1 ( ν ), we then have f dν f dν r + i f dν i by definition fψ d ν + i fη d ν f[ψ + iη] d ν fϕ d ν f ϕ d ν f d ν, since ϕ 1, ν -a.e. This completes the proof. 9

12 Problem 8. [Problem 20, page 94] If ν is a complex measure on (, M) and ν() ν (), then ν ν. Let f dν/d ν, so we have ( ) ν() f d ν for all measurable sets, and we know from Proposition 3.13 on page 94 of the text that f 1 ν -a.e. We may as well suppose that f 1 everywhere. We can write the complex function f as f g + ih, where g and h are real-valued. We can also write ν ν r + iν i for finite signed measures ν r and ν i. Thus, we have ν r () + iν i () ν() f d ν g d ν + i h d ν. Comparing real and imaginary parts, we get ν r () g d ν ν i () h d ν. By hypothesis, we have ν() ν (), so g d ν + i h d ν ν (). Since the right-hand side is real, the imaginary part of the left-hand side must be zero, and we have g d ν ν () 1 d ν, and so ( ) 0 Since g is the real part of f, we have (1 g) d ν. g g g + ih f 1 so 1 g 0. But then ( ) shows that 1 g 0 a.e., so g 1 a.e. We then have 1 f 2 g 2 + h h 2 a.e., so h 0 a.e. and hence f g 1 a.e. Putting this in ( ) shows that ν ν. 10

13 Problem 9. [Problem 20, page 94] Let ν be a complex measure on (, M). If M, define n n (A) µ 1 () sup ν( j ) n N, 1,..., n disjoint, j, (B) µ 2 () sup ν( j ) 1, 2,... disjoint, j, { } (C) µ 3 () sup f dν f 1. Then µ 1 µ 2 µ 3 ν. Although the author didn t explicitly say so, the sets j in (A) and (B) should, of course, be measurable and the functions f in (C) should be measurable. Also note that the book has a typo in (C) ( dµ instead of dν). As suggested in the book, we first prove that µ 1 µ 2 µ 3 µ 1 and then that µ 3 ν. We have µ 1 µ 2, because the sums in (A) are among the sums in (B): given a finite sequence 1,..., n of disjoint sets whose union is, just set j for j > n. Then 1, 2,... is an infinite sequence of disjoint sets whose union is and n ν( j ) ν( j ). Thus, the set of numbers in (A) is a subset of the set of numbers in (B). Since the sup over a larger set is larger, µ 1 µ 2. To see that µ 2 µ 3, let 1, 2,... be a sequence of disjoint sets whose union is. Now each ν( j ) is a complex number, so there is a complex number ω j such that ω j 1 and ω j ν( j ) ν( j ). Indeed, we can say { ν(j) ν( ω j, ν( j) j) 0 1, ν( j ) 0. Consider the function f defined by f ω j χ j. If x /, f(x) 0. If x, it is in exactly one j and then f(x) ω j 1. Thus, f 1 and the partial sums n f n ω j χ j 11

14 satisfy f n 1 by the same reasoning. We then have f n dν f dν (f n f) dν f n f d ν 0 by the dominated convergence theorem, since f n f pointwise and f n f is bounded above by the constant function 2, which is integrable with respect to the finite measure ν. We then have f dν lim f n dν n ω j χ j dν ω j ν( j ) ν( j ). since f dν is positive, we have f dν ν( j ). Thus, the set of numbers in (B) is a subset of the set in (C), so µ 2 µ 3. Now we want to show that µ 3 µ 1. First, we use Proposition 3.13 (and xercise 18) to show that µ 3 is finite. If f 1, then f d ν 1 d ν ν () <, so f L 1 ( ν ) L 1 (ν) and f dν f d ν 1 d ν ν (). Thus, ν () is an upper bound for the set of numbers in (C), so µ 3 () ν () ν () <. To show that µ 3 µ 1, let ε > 0 be given. Then we can find some f with f 1 such that µ 3 () ε < f dν, which implies ( ) µ 3 () 12 f dν + ε.

15 We approximate f by a simple function as follows. Let D { z C z 1 } be the closed unit disk in the complex plane. Since f 1, all the values of f are in D. The collection of balls { B(ε, z) z D } is an open cover of D. Since D is compact, there is a finite subcover, say { B(ε, z j ) j 1,..., m }. Define B j f 1 (B(ε, z j )), which is measurable since f is measurable. The union of the sets B j is. The sets B j won t be disjoint, but we can use the usual trick and define A 1 B 1, A j B j \ to get a disjoint collection of sets with A j B j and j A j. Define a simple function ϕ by m ϕ z j χ Aj. This function takes on the values z j, which are in D, so ϕ 1. If x it is in exactly one of the sets A j. But then f(x) B(ε, z j ), so j 1 k1 B k f(x) ϕ(x) f(x) z j < ε. Thus, f ϕ < ε. We then have f dν ϕ dν f dν ϕ dν f dν ϕ dν f ϕ d ν ε d ν ε ν (). Thus, we have f dν ϕ dν + ε ν (). 13

16 Substituting this is ( ) gives ( ) µ 3 () ϕ dν + ε + ε ν (). Now define j A j, j 1,..., m. Then the j s are a finite sequence of disjoint sets whose union is and we have Substituting this into ( ) gives ϕ dν [ m ] z j χ Aj dν m z j χ Aj dν m z j χ χ Aj dν m z j χ j dν m z j ν( j ) m z j ν( j ) m ν( j ) µ 1 (). µ 3 () µ 1 () + [1 + ν ()]ε. Since ε > 0 was arbitrary, we conclude that µ 3 () µ 1 (). We ve now shown that µ 1 µ 2 µ 3. The last step is to show that µ 3 ν. We ve already shown above that µ 3 () ν (). To get the reverse inequality, let g dν/d ν. We know that g 1 ν -a.e., and we may as well assume that g 1 everywhere (by modifying it on a set of measure zero). Then the conjugate ḡ of g satisfies ḡ 1 1 and so is one of the functions in the definition of µ 3, so ḡ dν µ 3(). 14

17 But, from the definition of g, ḡ dν ḡg d ν g 2 d ν 1 d ν ν (). Thus, ν () µ 3 (), so µ 3 ν, and we re done. 15

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

Real Analysis, 2nd Edition, G.B.Folland Signed Measures and Differentiation

Real Analysis, 2nd Edition, G.B.Folland Signed Measures and Differentiation Real Analysis, 2nd dition, G.B.Folland Chapter 3 Signed Measures and Differentiation Yung-Hsiang Huang 3. Signed Measures. Proof. The first part is proved by using addivitiy and consider F j = j j, 0 =.

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

Exercise 1. Show that the Radon-Nikodym theorem for a finite measure implies the theorem for a σ-finite measure.

Exercise 1. Show that the Radon-Nikodym theorem for a finite measure implies the theorem for a σ-finite measure. Real Variables, Fall 2014 Problem set 8 Solution suggestions xercise 1. Show that the Radon-Nikodym theorem for a finite measure implies the theorem for a σ-finite measure. nswer: ssume that the Radon-Nikodym

More information

Real Analysis Chapter 3 Solutions Jonathan Conder. ν(f n ) = lim

Real Analysis Chapter 3 Solutions Jonathan Conder. ν(f n ) = lim . Suppose ( n ) n is an increasing sequence in M. For each n N define F n : n \ n (with 0 : ). Clearly ν( n n ) ν( nf n ) ν(f n ) lim n If ( n ) n is a decreasing sequence in M and ν( )

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

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

2 Lebesgue integration

2 Lebesgue integration 2 Lebesgue integration 1. Let (, A, µ) be a measure space. We will always assume that µ is complete, otherwise we first take its completion. The example to have in mind is the Lebesgue measure on R n,

More information

Chapter 6. Integration. 1. Integrals of Nonnegative Functions. a j µ(e j ) (ca j )µ(e j ) = c X. and ψ =

Chapter 6. Integration. 1. Integrals of Nonnegative Functions. a j µ(e j ) (ca j )µ(e j ) = c X. and ψ = Chapter 6. Integration 1. Integrals of Nonnegative Functions Let (, S, µ) be a measure space. We denote by L + the set of all measurable functions from to [0, ]. Let φ be a simple function in L +. Suppose

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

FUNDAMENTALS OF REAL ANALYSIS by. IV.1. Differentiation of Monotonic Functions

FUNDAMENTALS OF REAL ANALYSIS by. IV.1. Differentiation of Monotonic Functions FUNDAMNTALS OF RAL ANALYSIS by Doğan Çömez IV. DIFFRNTIATION AND SIGND MASURS IV.1. Differentiation of Monotonic Functions Question: Can we calculate f easily? More eplicitly, can we hope for a result

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

Signed Measures. Chapter Basic Properties of Signed Measures. 4.2 Jordan and Hahn Decompositions

Signed Measures. Chapter Basic Properties of Signed Measures. 4.2 Jordan and Hahn Decompositions Chapter 4 Signed Measures Up until now our measures have always assumed values that were greater than or equal to 0. In this chapter we will extend our definition to allow for both positive negative values.

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

Lebesgue-Radon-Nikodym Theorem

Lebesgue-Radon-Nikodym Theorem Lebesgue-Radon-Nikodym Theorem Matt Rosenzweig 1 Lebesgue-Radon-Nikodym Theorem In what follows, (, A) will denote a measurable space. We begin with a review of signed measures. 1.1 Signed Measures Definition

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

Chapter 5. Measurable Functions

Chapter 5. Measurable Functions Chapter 5. Measurable Functions 1. Measurable Functions Let X be a nonempty set, and let S be a σ-algebra of subsets of X. Then (X, S) is a measurable space. A subset E of X is said to be measurable if

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

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

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

Lebesgue s Differentiation Theorem via Maximal Functions

Lebesgue s Differentiation Theorem via Maximal Functions Lebesgue s Differentiation Theorem via Maximal Functions Parth Soneji LMU München Hütteseminar, December 2013 Parth Soneji Lebesgue s Differentiation Theorem via Maximal Functions 1/12 Philosophy behind

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

Exercise 1. Let f be a nonnegative measurable function. Show that. where ϕ is taken over all simple functions with ϕ f. k 1.

Exercise 1. Let f be a nonnegative measurable function. Show that. where ϕ is taken over all simple functions with ϕ f. k 1. Real Variables, Fall 2014 Problem set 3 Solution suggestions xercise 1. Let f be a nonnegative measurable function. Show that f = sup ϕ, where ϕ is taken over all simple functions with ϕ f. For each n

More information

Defining the Integral

Defining the Integral Defining the Integral In these notes we provide a careful definition of the Lebesgue integral and we prove each of the three main convergence theorems. For the duration of these notes, let (, M, µ) be

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

+ 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

Section The Radon-Nikodym Theorem

Section The Radon-Nikodym Theorem 18.4. The Radon-Nikodym Theorem 1 Section 18.4. The Radon-Nikodym Theorem Note. For (X, M,µ) a measure space and f a nonnegative function on X that is measurable with respect to M, the set function ν on

More information

MATH 418: Lectures on Conditional Expectation

MATH 418: Lectures on Conditional Expectation MATH 418: Lectures on Conditional Expectation Instructor: r. Ed Perkins, Notes taken by Adrian She Conditional expectation is one of the most useful tools of probability. The Radon-Nikodym theorem enables

More information

2 Measure Theory. 2.1 Measures

2 Measure Theory. 2.1 Measures 2 Measure Theory 2.1 Measures A lot of this exposition is motivated by Folland s wonderful text, Real Analysis: Modern Techniques and Their Applications. Perhaps the most ubiquitous measure in our lives

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

Integration on Measure Spaces

Integration on Measure Spaces Chapter 3 Integration on Measure Spaces In this chapter we introduce the general notion of a measure on a space X, define the class of measurable functions, and define the integral, first on a class of

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

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

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

Homework 11. Solutions

Homework 11. Solutions Homework 11. Solutions Problem 2.3.2. Let f n : R R be 1/n times the characteristic function of the interval (0, n). Show that f n 0 uniformly and f n µ L = 1. Why isn t it a counterexample to the Lebesgue

More information

I. ANALYSIS; PROBABILITY

I. ANALYSIS; PROBABILITY ma414l1.tex Lecture 1. 12.1.2012 I. NLYSIS; PROBBILITY 1. Lebesgue Measure and Integral We recall Lebesgue measure (M411 Probability and Measure) λ: defined on intervals (a, b] by λ((a, b]) := b a (so

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

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

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

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

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

THEOREMS, ETC., FOR MATH 516

THEOREMS, ETC., FOR MATH 516 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

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

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

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

Probability and Random Processes

Probability and Random Processes Probability and Random Processes Lecture 7 Conditional probability and expectation Decomposition of measures Mikael Skoglund, Probability and random processes 1/13 Conditional Probability A probability

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

Partial Solutions to Folland s Real Analysis: Part I

Partial Solutions to Folland s Real Analysis: Part I Partial Solutions to Folland s Real Analysis: Part I (Assigned Problems from MAT1000: Real Analysis I) Jonathan Mostovoy - 1002142665 University of Toronto January 20, 2018 Contents 1 Chapter 1 3 1.1 Folland

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

ERRATA TO REAL ANALYSIS, 2nd edition (6th and later printings) G. B. Folland

ERRATA TO REAL ANALYSIS, 2nd edition (6th and later printings) G. B. Folland ERRATA TO REAL ANALYSIS, 2nd edition (6th and later printings) G. B. Folland Last updated June 3, 208. Additional corrections will be gratefully received at folland@math.washington.edu. Page 7, line 2:

More information

The Lebesgue Integral

The Lebesgue Integral The Lebesgue Integral Having completed our study of Lebesgue measure, we are now ready to consider the Lebesgue integral. Before diving into the details of its construction, though, we would like to give

More information

Proof of Radon-Nikodym theorem

Proof of Radon-Nikodym theorem Measure theory class notes - 13 October 2010, class 20 1 Proof of Radon-Niodym theorem Theorem (Radon-Niodym). uppose Ω is a nonempty set and a σ-field on it. uppose µ and ν are σ-finite measures on (Ω,

More information

Solutions to Tutorial 11 (Week 12)

Solutions to Tutorial 11 (Week 12) THE UIVERSITY OF SYDEY SCHOOL OF MATHEMATICS AD STATISTICS Solutions to Tutorial 11 (Week 12) MATH3969: Measure Theory and Fourier Analysis (Advanced) Semester 2, 2017 Web Page: http://sydney.edu.au/science/maths/u/ug/sm/math3969/

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

Measure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond

Measure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond Measure Theory on Topological Spaces Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond May 22, 2011 Contents 1 Introduction 2 1.1 The Riemann Integral........................................ 2 1.2 Measurable..............................................

More information

MATH41011/MATH61011: FOURIER SERIES AND LEBESGUE INTEGRATION. Extra Reading Material for Level 4 and Level 6

MATH41011/MATH61011: FOURIER SERIES AND LEBESGUE INTEGRATION. Extra Reading Material for Level 4 and Level 6 MATH41011/MATH61011: FOURIER SERIES AND LEBESGUE INTEGRATION Extra Reading Material for Level 4 and Level 6 Part A: Construction of Lebesgue Measure The first part the extra material consists of the construction

More information

Disintegration into conditional measures: Rokhlin s theorem

Disintegration into conditional measures: Rokhlin s theorem Disintegration into conditional measures: Rokhlin s theorem Let Z be a compact metric space, µ be a Borel probability measure on Z, and P be a partition of Z into measurable subsets. Let π : Z P be the

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

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

Dual Space of L 1. C = {E P(I) : one of E or I \ E is countable}.

Dual Space of L 1. C = {E P(I) : one of E or I \ E is countable}. Dual Space of L 1 Note. The main theorem of this note is on page 5. The secondary theorem, describing the dual of L (µ) is on page 8. Let (X, M, µ) be a measure space. We consider the subsets of X which

More information

x 0 + f(x), exist as extended real numbers. Show that f is upper semicontinuous This shows ( ɛ, ɛ) B α. Thus

x 0 + f(x), exist as extended real numbers. Show that f is upper semicontinuous This shows ( ɛ, ɛ) B α. Thus Homework 3 Solutions, Real Analysis I, Fall, 2010. (9) Let f : (, ) [, ] be a function whose restriction to (, 0) (0, ) is continuous. Assume the one-sided limits p = lim x 0 f(x), q = lim x 0 + f(x) exist

More information

L p Spaces and Convexity

L p Spaces and Convexity L p Spaces and Convexity These notes largely follow the treatments in Royden, Real Analysis, and Rudin, Real & Complex Analysis. 1. Convex functions Let I R be an interval. For I open, we say a function

More information

212a1214Daniell s integration theory.

212a1214Daniell s integration theory. 212a1214 Daniell s integration theory. October 30, 2014 Daniell s idea was to take the axiomatic properties of the integral as the starting point and develop integration for broader and broader classes

More information

ANALYSIS QUALIFYING EXAM FALL 2016: SOLUTIONS. = lim. F n

ANALYSIS QUALIFYING EXAM FALL 2016: SOLUTIONS. = lim. F n ANALYSIS QUALIFYING EXAM FALL 206: SOLUTIONS Problem. Let m be Lebesgue measure on R. For a subset E R and r (0, ), define E r = { x R: dist(x, E) < r}. Let E R be compact. Prove that m(e) = lim m(e /n).

More information

NOTES ON VECTOR-VALUED INTEGRATION MATH 581, SPRING 2017

NOTES ON VECTOR-VALUED INTEGRATION MATH 581, SPRING 2017 NOTES ON VECTOR-VALUED INTEGRATION MATH 58, SPRING 207 Throughout, X will denote a Banach space. Definition 0.. Let ϕ(s) : X be a continuous function from a compact Jordan region R n to a Banach space

More information

Section Integration of Nonnegative Measurable Functions

Section Integration of Nonnegative Measurable Functions 18.2. Integration of Nonnegative Measurable Functions 1 Section 18.2. Integration of Nonnegative Measurable Functions Note. We now define integrals of measurable functions on measure spaces. Though similar

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

Lecture 6 Feb 5, The Lebesgue integral continued

Lecture 6 Feb 5, The Lebesgue integral continued CPSC 550: Machine Learning II 2008/9 Term 2 Lecture 6 Feb 5, 2009 Lecturer: Nando de Freitas Scribe: Kevin Swersky This lecture continues the discussion of the Lebesque integral and introduces the concepts

More information

for all x,y [a,b]. The Lipschitz constant of f is the infimum of constants C with this property.

for all x,y [a,b]. The Lipschitz constant of f is the infimum of constants C with this property. viii 3.A. FUNCTIONS 77 Appendix In this appendix, we describe without proof some results from real analysis which help to understand weak and distributional derivatives in the simplest context of functions

More information

9 Radon-Nikodym theorem and conditioning

9 Radon-Nikodym theorem and conditioning Tel Aviv University, 2015 Functions of real variables 93 9 Radon-Nikodym theorem and conditioning 9a Borel-Kolmogorov paradox............. 93 9b Radon-Nikodym theorem.............. 94 9c Conditioning.....................

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

Probability and Random Processes

Probability and Random Processes Probability and Random Processes Lecture 4 General integration theory Mikael Skoglund, Probability and random processes 1/15 Measurable xtended Real-valued Functions R = the extended real numbers; a subset

More information

ANALYSIS QUALIFYING EXAM FALL 2017: SOLUTIONS. 1 cos(nx) lim. n 2 x 2. g n (x) = 1 cos(nx) n 2 x 2. x 2.

ANALYSIS QUALIFYING EXAM FALL 2017: SOLUTIONS. 1 cos(nx) lim. n 2 x 2. g n (x) = 1 cos(nx) n 2 x 2. x 2. ANALYSIS QUALIFYING EXAM FALL 27: SOLUTIONS Problem. Determine, with justification, the it cos(nx) n 2 x 2 dx. Solution. For an integer n >, define g n : (, ) R by Also define g : (, ) R by g(x) = g n

More information

FACULTY OF SCIENCE FINAL EXAMINATION MATHEMATICS MATH 355. Analysis 4. Examiner: Professor S. W. Drury Date: Wednesday, April 18, 2007 INSTRUCTIONS

FACULTY OF SCIENCE FINAL EXAMINATION MATHEMATICS MATH 355. Analysis 4. Examiner: Professor S. W. Drury Date: Wednesday, April 18, 2007 INSTRUCTIONS FACULTY OF SCIENCE FINAL EXAMINATION MATHEMATICS MATH 355 Analysis 4 Examiner: Professor S. W. Drury Date: Wednesday, April 18, 27 Associate Examiner: Professor K. N. GowriSankaran Time: 2: pm. 5: pm.

More information

Functional Analysis HW #3

Functional Analysis HW #3 Functional Analysis HW #3 Sangchul Lee October 26, 2015 1 Solutions Exercise 2.1. Let D = { f C([0, 1]) : f C([0, 1])} and define f d = f + f. Show that D is a Banach algebra and that the Gelfand transform

More information

1/12/05: sec 3.1 and my article: How good is the Lebesgue measure?, Math. Intelligencer 11(2) (1989),

1/12/05: sec 3.1 and my article: How good is the Lebesgue measure?, Math. Intelligencer 11(2) (1989), Real Analysis 2, Math 651, Spring 2005 April 26, 2005 1 Real Analysis 2, Math 651, Spring 2005 Krzysztof Chris Ciesielski 1/12/05: sec 3.1 and my article: How good is the Lebesgue measure?, Math. Intelligencer

More information

REAL ANALYSIS I Spring 2016 Product Measures

REAL ANALYSIS I Spring 2016 Product Measures REAL ANALSIS I Spring 216 Product Measures We assume that (, M, µ), (, N, ν) are σ- finite measure spaces. We want to provide the Cartesian product with a measure space structure in which all sets of the

More information

Notes on Measure, Probability and Stochastic Processes. João Lopes Dias

Notes on Measure, Probability and Stochastic Processes. João Lopes Dias Notes on Measure, Probability and Stochastic Processes João Lopes Dias Departamento de Matemática, ISEG, Universidade de Lisboa, Rua do Quelhas 6, 1200-781 Lisboa, Portugal E-mail address: jldias@iseg.ulisboa.pt

More information

MA359 Measure Theory

MA359 Measure Theory A359 easure Theory Thomas Reddington Usman Qureshi April 8, 204 Contents Real Line 3. Cantor set.................................................. 5 2 General easures 2 2. Product spaces...............................................

More information

The optimal partial transport problem

The optimal partial transport problem The optimal partial transport problem Alessio Figalli Abstract Given two densities f and g, we consider the problem of transporting a fraction m [0, min{ f L 1, g L 1}] of the mass of f onto g minimizing

More information

6 Classical dualities and reflexivity

6 Classical dualities and reflexivity 6 Classical dualities and reflexivity 1. Classical dualities. Let (Ω, A, µ) be a measure space. We will describe the duals for the Banach spaces L p (Ω). First, notice that any f L p, 1 p, generates the

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

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

6.2 Fubini s Theorem. (µ ν)(c) = f C (x) dµ(x). (6.2) Proof. Note that (X Y, A B, µ ν) must be σ-finite as well, so that.

6.2 Fubini s Theorem. (µ ν)(c) = f C (x) dµ(x). (6.2) Proof. Note that (X Y, A B, µ ν) must be σ-finite as well, so that. 6.2 Fubini s Theorem Theorem 6.2.1. (Fubini s theorem - first form) Let (, A, µ) and (, B, ν) be complete σ-finite measure spaces. Let C = A B. Then for each µ ν- measurable set C C the section x C is

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 172 HW 1 Solutions

Math 172 HW 1 Solutions Math 172 HW 1 Solutions Joey Zou April 15, 2017 Problem 1: Prove that the Cantor set C constructed in the text is totally disconnected and perfect. In other words, given two distinct points x, y C, there

More information

Advanced Analysis Qualifying Examination Department of Mathematics and Statistics University of Massachusetts. Tuesday, January 16th, 2018

Advanced Analysis Qualifying Examination Department of Mathematics and Statistics University of Massachusetts. Tuesday, January 16th, 2018 NAME: Advanced Analysis Qualifying Examination Department of Mathematics and Statistics University of Massachusetts Tuesday, January 16th, 2018 Instructions 1. This exam consists of eight (8) problems

More information

MATH 202B - Problem Set 5

MATH 202B - Problem Set 5 MATH 202B - Problem Set 5 Walid Krichene (23265217) March 6, 2013 (5.1) Show that there exists a continuous function F : [0, 1] R which is monotonic on no interval of positive length. proof We know there

More information

Chapter 4. The dominated convergence theorem and applications

Chapter 4. The dominated convergence theorem and applications Chapter 4. The dominated convergence theorem and applications The Monotone Covergence theorem is one of a number of key theorems alllowing one to exchange limits and [Lebesgue] integrals (or derivatives

More information

g(x) = P (y) Proof. This is true for n = 0. Assume by the inductive hypothesis that g (n) (0) = 0 for some n. Compute g (n) (h) g (n) (0)

g(x) = P (y) Proof. This is true for n = 0. Assume by the inductive hypothesis that g (n) (0) = 0 for some n. Compute g (n) (h) g (n) (0) Mollifiers and Smooth Functions We say a function f from C is C (or simply smooth) if all its derivatives to every order exist at every point of. For f : C, we say f is C if all partial derivatives to

More information

L p Functions. Given a measure space (X, µ) and a real number p [1, ), recall that the L p -norm of a measurable function f : X R is defined by

L p Functions. Given a measure space (X, µ) and a real number p [1, ), recall that the L p -norm of a measurable function f : X R is defined by L p Functions Given a measure space (, µ) and a real number p [, ), recall that the L p -norm of a measurable function f : R is defined by f p = ( ) /p f p dµ Note that the L p -norm of a function f may

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

Compendium and Solutions to exercises TMA4225 Foundation of analysis

Compendium and Solutions to exercises TMA4225 Foundation of analysis Compendium and Solutions to exercises TMA4225 Foundation of analysis Ruben Spaans December 6, 2010 1 Introduction This compendium contains a lexicon over definitions and exercises with solutions. Throughout

More information

RADON-NIKODÝM THEOREMS

RADON-NIKODÝM THEOREMS RADON-NIKODÝM THEOREMS D. CANDELORO and A. VOLČIČ 1 Introduction Suppose λ is the Lebesgue measure on the real line and f is an integrable function. Then the measure ν defined for all the Lebesgue measurable

More information

Chapter 1: Probability Theory Lecture 1: Measure space, measurable function, and integration

Chapter 1: Probability Theory Lecture 1: Measure space, measurable function, and integration Chapter 1: Probability Theory Lecture 1: Measure space, measurable function, and integration Random experiment: uncertainty in outcomes Ω: sample space: a set containing all possible outcomes Definition

More information

Math 118B Solutions. Charles Martin. March 6, d i (x i, y i ) + d i (y i, z i ) = d(x, y) + d(y, z). i=1

Math 118B Solutions. Charles Martin. March 6, d i (x i, y i ) + d i (y i, z i ) = d(x, y) + d(y, z). i=1 Math 8B Solutions Charles Martin March 6, Homework Problems. Let (X i, d i ), i n, be finitely many metric spaces. Construct a metric on the product space X = X X n. Proof. Denote points in X as x = (x,

More information

6. Duals of L p spaces

6. Duals of L p spaces 6 Duals of L p spaces This section deals with the problem if identifying the duals of L p spaces, p [1, ) There are essentially two cases of this problem: (i) p = 1; (ii) 1 < p < The major difference between

More information

Analysis of Probabilistic Systems

Analysis of Probabilistic Systems Analysis of Probabilistic Systems Bootcamp Lecture 2: Measure and Integration Prakash Panangaden 1 1 School of Computer Science McGill University Fall 2016, Simons Institute Panangaden (McGill) Analysis

More information

Chapter 1: Probability Theory Lecture 1: Measure space and measurable function

Chapter 1: Probability Theory Lecture 1: Measure space and measurable function Chapter 1: Probability Theory Lecture 1: Measure space and measurable function Random experiment: uncertainty in outcomes Ω: sample space: a set containing all possible outcomes Definition 1.1 A collection

More information

4 Integration 4.1 Integration of non-negative simple functions

4 Integration 4.1 Integration of non-negative simple functions 4 Integration 4.1 Integration of non-negative simple functions Throughout we are in a measure space (X, F, µ). Definition Let s be a non-negative F-measurable simple function so that s a i χ Ai, with disjoint

More information