Bases in Banach spaces

Size: px
Start display at page:

Download "Bases in Banach spaces"

Transcription

1 Chapter 3 Bases in Banach spaces Like every vector space a Banach space X admits an algebraic or Hamel basis, i.e. a subset B X, so that every x X is in a unique way the (finite) linear combination of elements in B. This definition does not take into account that we can take infinite sums in Banach spaces and that we might want to represent elements x X as converging series. Hamel bases are also not very useful for Banach spaces, since (see Exercise 1), the coordinate functionals might not be continuous. 3.1 Schauder Bases Definition [Schauder bases of Banach spaces] Let X be an infinite dimensional Banach space. A sequence (e n ) X is called Schauder basis of X, or simply a basis of X, if for every x X, there is a unique sequence of scalars (a n ) K so that Examples For n N let x = a n e n. n=1 e n = ( 0,... 0, 1, 0,...) K N }{{} n 1 times Then (e n ) is a basis of l p,1 p< and c 0. We call (e n ) the unit vector of l p and c 0, respectively. Remarks. Assume that X is a Banach space and (e n ) a basis of X. 51

2 52 CHAPTER 3. BASES IN BANACH SPACES a) (e n ) is linear independent. b) span(e n : n N) is dense in X, in particular X is separable. c) Every element x is uniquely determined by the sequence (a n ) so that x = a ne n. So we can identify X with a space of sequences in K N. Proposition Let X be a normed linear space and assume that (e n ) X has the property that each x X can be uniquely represented as a series x = a n e n, with (a n ) K n=1 (we could call (e n ) Schauder basis of X but we want to reserve this term only for Banach spaces). For n N and x X define e n(x) K to be the unique element in K, so that x = e n(x)e n. Then e n : X K is linear. For n N let n=1 P n : X span(e j : j n),x e n(x)e n. Then P n : X X are linear projections onto span(e j : j n) and the following properties hold: a) dim(p n (X)) = n, b) P n P m = P m P n = P min(m.n), for m, n N, c) lim n P n (x) =x, for every x X. Conversely if (P n : n N) is a sequence of linear projections satisfying (a), (b), and (c), and moreover are bounded, and if e 1 P 1 (X) \{0} and e n P n (X) N (P n 1 ), with e n 0, if n>1, then each x X can be uniquely represented as a series x = a n e n, with (a n ) K, n=1 so in particular (e n ) is a Schauder basis of X in case X is a Banach space.

3 3.1. SCHAUDER BASES 53 Proof. The linearity of e n follows from the unique representation of every x X as x = e n(x)e n, which implies that for x and y in X and α, β K, αx + βy = lim α e n j(x)e j + β = lim n and, on the other hand αx + βy = e j(y)e j (αe j(x)+βe j(y))e j = e j(αx + βy)e j, (αe j(x)+βe j(y))e j, thus, by uniqueness, e j (αx + βy) =αe j (x) +βe j (y), for all j N. The conditions (a), (b) and (c) are clear. Conversely assume that (P n ) is a sequence of bounded and linear projections satisfying (a), (b), and (c), and if e 1 P 1 (X) \{0} and e n P n (X) N (P n 1 ), if n>1, then for x X, by (b) P n 1 (P n (x) P n 1 (x)) = P n 1 (x) P n 1 (x) = 0, and P n (x) P n 1 (x) =P n (P n (x) P n 1 (x)) P n (X), and therefore P n (x) P n 1 (x) P n (X) N (P n 1 ). Thus we can write P n (x) P n 1 (x) =a n e n, for n N, and it follows from (c) that (letting P 0 = 0) x = lim P n(x) = lim n n P j (x) P j 1 (x) = lim n a j e j = a j e j. In order to show uniqueness of this representation of x assume x = b je j. From the continuity of P m P m 1, for m N it follows that ( ) a m e m =(P m P m 1 )(x) = lim (P m P m 1 ) b j e j = b m e m, n and thus a m = b m.

4 54 CHAPTER 3. BASES IN BANACH SPACES Definition [Canonical Projections and Coordinate functionals] Let X be a normed space and assume that (e i ) satisfies the assumptions of Proposition The linear functionals (e n) as defined in Proposition are called the Coordinate Functionals for (e n ) and the projections P n, n N, are called the Canonical Projections for (e n ). Proposition If X is a normed linear space and (e i ) assume that (e n ) X has the property that each x X can be uniquely represented as a series x = a n e n, with (a n ) K. n=1 If the canonical projections are bounded, and, moreover, sup n N P n <, then (e i ) is a Schauder basis of its completion X. Proof. Let P n : X X, n N, be the (by Proposition and Exercise 1 in Section 1.2 uniquely existing) extensions of P n. Since P n has finite range it follows that P n ( X) =P n (X) = span(e j : j n) is finite dimensional and, thus, closed. ( P n ) satisfies therefore (a) of Proposition Since the P n are continuous, and satisfy the equalities in (b) of Proposition on a dense subset of X, (b) is satisfied on all of X. Finally, (c) of Proposition is satisfied on a dense subset of X, and we deduce for x X, x = lim k x k, with x k X, for k N, that x P n ( x) x x k + sup P j x x k + x k P n (x k ) j N and, since (P n ) is uniformly bounded, we can find for given ε> 0, k large enough so that the first two summands do not exceed ε, and then we choose n N large enough so that the third summand is smaller than ε. It follows therefore that also (c) is satisfied on all of X. Thus, our claim follows from the second part of Proposition applied to X. We will now show that if (e n ) is a basis for a Banach space X the coordinate functionals, and thus the canonical projections are bounded, and moreover the canonical projections are uniformly bounded. Theorem Let X be a Banach space with a basis (e n ) and let (e n) be the corresponding coordinate functionals and (P n ) the canonical projections. Then P n is bounded for every n N and b = sup P n L(X,X) <, n N

5 3.1. SCHAUDER BASES 55 and thus e n X and e n X = P n P n 1 e n 2b e n. We call b the basis constant of (e j ). If b =1we say that (e i ) is a monotone basis. Furthermore there is an equivalent renorming of (X, ) for which (e n ) is a monotone basis for (X, ). Proof. For x X we define x = sup P n (x), n N since x = lim n P n (x), it follows that x x < for x X. It is clear that is a norm on X. Note that for n N P n = sup P n (x) x X, x 1 = sup x X, x 1 m N = sup x X, x 1 m N sup P m P n (x) sup P min(m,n) (x) 1. Thus the projections P n are uniformly bounded on (X, ). We also note that the P n satisfy the conditions (a), (b) and (c) of Proposition Indeed (a) and (b) are purely algebraic properties which are satisfied by the first part of Proposition Moreover for x X then (3.1) x P n (x) = sup P m (x) P min(m,n) (x) m N = sup P m (x) P n (x) 0 if n, m n which verifies conditionn (c). Thus, it follows therefore from the second part of Proposition 3.1.3, the above proven fact that P n 1, for n N, and Proposition 3.1.5, that (e n ) is a Schauder basis of the completion of (X, ) which we denote by ( X, ). We will now show that actually X = X, and thus that, (X, ) is aleady complete. Then it would follow from Corollary of the Closed Graph Theorem that is an equivalent norm, an thus that C = sup sup P n (x) = sup x <. x B X x B X n N

6 56 CHAPTER 3. BASES IN BANACH SPACES So, let x X and write it (uniquely) as x = a je j, since, and since X is complete the series a je j with respect to in X to say x X. But now (3.1) that P n (x) = n a je j also converges in to x. But this means that x = x, which finishes our proof. After reading the proof of Theorem one might ask whether the last part couldn t be generalized and whether the following could be true: If and are two norms on the same linear space X, so that, and so that (,X) is complete, does it then follow that (X, ) is also complete (and thus and are equivalent norms). The answer is negative, as the following example shows. Example Let X =l 2 with its usual norm 2 and let (b γ :γ Γ) S l2 be a Hamel basis of Γ (by Exercise 2, Γ is necessarily uncountable). For x l 2 define x, x = x γ, γ Γ where x = γ Γ x γb γ is the unique representation of x as a finite linear combination of elements of (b γ : γ Γ). Since b γ 2, for γ Γ, it follows for x = γ Γ x γb γ l 2 from the triangle inequality that x = x γ = x γ b γ 2 2 x γ b γ = x 2. γ Γ γ Γ γ Γ Finally both norms and, cannot be equivalent. Indeed, for arbitrary ε> 0, there is an uncountable set Γ Γ, so that b γ b γ 2 <ε, γ, γ Γ, (Γ is uncountable but S l2 is in the 2 -norm separable). for any two different elements γ, γ Γ it follows that b γ b γ <ε< 2= b γ b γ. Since ε> 0 was arbitrary this proves that and cannot be equivalent. Definition [Basic Sequences] Let X be a Banach space. A sequence (x n ) X \{0} is called basic sequence if it is a basis for span(x n : n N). If (e j ) and (f j ) are two basic sequences (in possibly two different Banach spaces X and Y ). We say that (e j ) and (f j ) are isomorphically equivalent if the map T : span(e j : j N) span(f j : j N), a j e j a j f j,

7 3.1. SCHAUDER BASES 57 extends to an isomorphism between the Banach spaces between span(e j : j N) and span(f j : j N). Note that this is equivalent with saying that there are constants 0 <c C so that any n N and any sequence of scalars (λ j ) n it follows that c. λ j e j λ j f j C λ j e j Proposition Let X be Banach space and (x n : n N) X \{0}. The (x n ) is a basic sequence if and only if there is a constant K 1, so that for all m<nand all scalars (a j ) n K we have (3.2) m. a i x i K a i x i i=1 In that case the basis constant is the smallest of all K 1 so that (3.2) holds. Proof. ( Follows from Theorem 3.1.6, since K := sup n N P n < and P n m i=1 a ) ix i = m i=1 a ix m, if m n and (a i ) n i=1 K. Assume that there is a constant K 1 so that for all m<nand all scalars (a j ) n K we have i=1 m. a i x i K a i x i i=1 We first note that this implies that (x n ) is linear independent. Indeed, if we assume that n a jx j = 0, for some choice of n N and (a j ) n K, and not all of the a j are vanishing, we first observe that at least two of a j s cannot be equal to 0 (since x j 0, for j N), thus if we let m := min{j : a j 0}, it follows that m a jx j 0, but n a jx j = 0, which contradicts our assumption. It follows therefore that (x n ) is a Hamel basis for (the vector space) span(x j : j N), which implies that the projections P n are well defined on span(x j : j N), and satisfy (a), (b), and (c) of Proposition Moreover, it follows from our assumption that i=1 { m P m = sup a j x j : n N, (aj ) n K, Thus, our claim follows from Proposition } a j x j 1 K.

8 58 CHAPTER 3. BASES IN BANACH SPACES Also note that the proof of implies that the smallest constant so that 3.2 is at most as big as the basis constant, and the proof of yielded that it is at least as large as the basis constant. Exercises 1. Let (e γ : γ Γ) be a Hamel basis of an infinite dimensional Banach space X.Show that some of the coordinate functionals associated with that basis are not continuous. Hint: pick a sequence (γ n ) Γ of pairwise different elements of Γ and consider x = n=1 2 n e γ n e γn. 2. Show that the Hamel basis of an infinite dimensional Banach space X must be uncountable. 3. For n N define in c 0 s n = e j = (1, 1,... 1, 0,...). }{{} n times Prove that (s n ) is a basis for c 0, but that one can reorder (s n ) so that it is not a basis of c 0. (s n ) is called the summing basis of c 0. Hint: Play around with alternating series of (s n ). 4. Let 1 <p< and assume that (x n ) is a weakly null sequence in l p with inf n N x n > 0. Show that (x n ) has a subsequence which is isomorphically equivalent to the unit vector basis of l p. And then deduce form this: Let T : l p l q with 1 < q < p <, be a bounded linear operator. Show that T is compact., meaning that T (B lp ) is relatively compact in l q.

9 3.2. BASES OF C[0, 1] AND L P [0, 1] Bases of C[0, 1] and L p [0, 1] In the previous section we introduced the unit vector bases of l p and c 0. Less obvious is it to find bases of function spaces like C[0, 1] and L p [0, 1] Example [The Spline Basis of C[0, 1]] Let (t n ) [0, 1] be a dense sequence in [0, 1], and assume that t 1 = 0, t 2 = 1. It follows that (3.3) mesh(t 1,t 2,... t n ) 0, if n where mesh(t 1,t 2,... t n ) = max { t i t j : i, j {1,..., n}, i j }. For f C[0, 1] we let P 1 (f) to be the constant function taking the value f(0), and for n 2 we let P n (f) be the piecewise linear function which interpolates the f at the point t 1,t 2,... t n. More precisely, let 0 = s 1 <s 2 <... s n =1 be the increasing reordering of {t 1,t 2,... t n }, then define P n (f) by P n (f) : [0, 1] K, with P n (f)(s) = s j s f(s j 1 )+ s s j 1 f(s j ), for s [s j 1,s j ]. s j s j 1 s j s j 1 We note that P n : C[0, 1] C[0, 1] is a linear projection and that P n = 1, and that (a), (b), (c) of Proposition are satisfied. Indeed, the image of P n (C[0, 1]] is generated by the functions f 1 1, f 2 (s) =s, for s [0, 1], and for n 2, f n (s) is the functions with the property f(t n ) = 1, f(t j ) = 0, j {1, 2...} \ {t n }, and is linear between any t j and and the next bigger t i. Thus dim(p n (C[0, 1])) = n. Property (b) is clear, and property (c) follows form the fact that elements of C[0, 1] are uniformly continuous, and condition (3.3). Also note that for n>1 it follows that f n P n (C[0, 1]) N (P n 1 ) \{0} and thus it follows from Proposition that (f n ) is a monotone basis of C[0, 1]. Now we define a basis of L p [0, 1], the Haar basis of L p [0, 1]. Let T = {(n, j) :n N 0,j =1, 2..., 2 n } { 0}. We partially order the elements of T as follows (n 1,j 1 ) < (n 2,j 2 ) [(j 2 1)2 n 2,j 2 2 n 2 ] [(j 1 1)2 n 1,j 1 2 n 1 ] (j 1 1)2 n 1 (j 2 1)2 n 2 <j 2 2 n 2 j 1 2 n 1, and n 1 <n 2

10 60 CHAPTER 3. BASES IN BANACH SPACES and whenever (n 1,j 1 ), (n 2,j 2 ) T 0 < (n, j), whenever (n, j) T \{0} Let 1 p< be fixed. We define the Haar basis (h t ) t T and the in L p normalized Haar basis (h (p) t ) t T as follows. h 0 = h (p) 0 1 on [0, 1] and for n N 0 and j =1, n we put and we let h (n,j) =1 [(j 1)2 n,(j 1 2 )2 n ) 1 [(j 1 2 )2 n ),j2 n )). (n,j) = supp(h (n,j) )= [ (j 1)2 n,j2 n), + (n,j) = [ (j 1)2 n, (j 1 2 )2 n) (n,j) = [ (j 1 2 )2 n,j2 n). We let h ( ) (n,j) = h (n,j). And for 1 p< h (p) (n,j) = h (n,j) =2 n/p( 1 h (n,j) [(j 1)2 n,(j 1 p 2 )2 n ) 1 ) [(j 1 2 )2 n ),j2 n ). Note that h t p = 1 for all t T and that supp(h t ) supp(h s ) if and only if s t. Theorem If one orders (h (p) t ) t T linearly in any order compatible with the order on T then (h (p) t ) is a monotone basis of L p [0, 1] for all 1 p<. Remark. a linear order compatible with the order on T is for example the lexicographical order h 0,h (0,1),h (1,1),h (1,2),h (2,1),h (2,2),.... Important observation: if (h t : t T ) is linearly ordered into h 0,h 1,..., which is compatible with the partial order of T, then the following is true:

11 3.2. BASES OF C[0, 1] AND L P [0, 1] 61 If n N and and if n 1 h = a j h j, is any linear combination of the first n 1 elements, then h is constant on the support of h n 1. Moreover h can be written as a step function h = N b j 1 [sj 1,s j ), with 0 = s 0 <s 1 <... s N, so that sj s j 1 h n (t)dt =0. As we will see later, if 1 < p <, any linear ordering of (h t : t T ) is a basis of L p [0, 1], but not necessarily a monotone one. Proof of Theorem First note that the indicator functions on all dyadic intervals are in span(h t : t T ). Indeed: 1 [0,1/2) =(h 0 + h (0,1) )/2, 1 (1/2,1] = (h 0 h (0,1) )/2, 1 [0,1/4) =1/2(1 [0,1/2) h (1,1) ), etc. Since the indicator functions on all dyadic intervals are dense in L p [0, 1] it follows that span(h t : t T ). Let (h n ) be a linear ordering of (h (p) t ) t T which is compatible with the ordering of T. Let n N and (a i ) n i=1 a scalar sequence. We need to show that n 1. a i h i a i h i i=1 As noted above, on the set A = supp(h n ) the function f = n 1 i=1 a ih i is constant, say f(x) =a, for x A, therefore we can write i=1 1 A (f + a n h n ) = 1 A +(a + a n )+1 A (a a n ), where A + is the first half of interval A and A the second half. From the convexity of [0, ) r r p, we deduce that 1[ a + an p + a a n p ] a p, 2

12 62 CHAPTER 3. BASES IN BANACH SPACES and thus f + a n h n p dx = f p dx + a + a n p 1 A + + a a n p 1 A dx A c A = f p dx + 1 A c 2 m(a)[ a + a n p + a a n p] f p dx + m(a) a p = A c f p dx which implies our claim. Proposition Since for 1 p<, and 1 <q, with 1 p + 1 q easy to see that for s, t T it is (3.4) h (p) s,h (q) t = δ(s, t), we deduce that (h (q) t ) t T are the coordinate functionals of (h (p) t ) t T. Exercise 1. Decide whether or not the monomial 1, x, x 2,... are a Schauder basis of C[0, 1]. 2. Show that the Haar basis in L 1 [0, 1] can be reordered in such a way that it is not a a Schauder basis anymore.

3.2 Bases of C[0, 1] and L p [0, 1]

3.2 Bases of C[0, 1] and L p [0, 1] 70 CHAPTER 3. BASES IN BANACH SPACES 3.2 Bases of C[0, ] and L p [0, ] In the previous section we introduced the unit vector bases of `p and c 0. Less obvious is it to find bases of function spaces like

More information

The Threshold Algorithm

The Threshold Algorithm Chapter The Threshold Algorithm. Greedy and Quasi Greedy Bases We start with the Threshold Algorithm: Definition... Let be a separable Banach space with a normalized M- basis (e i,e i ):i2n ; we mean by

More information

Weak Topologies, Reflexivity, Adjoint operators

Weak Topologies, Reflexivity, Adjoint operators Chapter 2 Weak Topologies, Reflexivity, Adjoint operators 2.1 Topological vector spaces and locally convex spaces Definition 2.1.1. [Topological Vector Spaces and Locally convex Spaces] Let E be a vector

More information

Math 328 Course Notes

Math 328 Course Notes Math 328 Course Notes Ian Robertson March 3, 2006 3 Properties of C[0, 1]: Sup-norm and Completeness In this chapter we are going to examine the vector space of all continuous functions defined on the

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

4 Hilbert spaces. The proof of the Hilbert basis theorem is not mathematics, it is theology. Camille Jordan

4 Hilbert spaces. The proof of the Hilbert basis theorem is not mathematics, it is theology. Camille Jordan The proof of the Hilbert basis theorem is not mathematics, it is theology. Camille Jordan Wir müssen wissen, wir werden wissen. David Hilbert We now continue to study a special class of Banach spaces,

More information

Functional Analysis. Franck Sueur Metric spaces Definitions Completeness Compactness Separability...

Functional Analysis. Franck Sueur Metric spaces Definitions Completeness Compactness Separability... Functional Analysis Franck Sueur 2018-2019 Contents 1 Metric spaces 1 1.1 Definitions........................................ 1 1.2 Completeness...................................... 3 1.3 Compactness......................................

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

I teach myself... Hilbert spaces

I teach myself... Hilbert spaces I teach myself... Hilbert spaces by F.J.Sayas, for MATH 806 November 4, 2015 This document will be growing with the semester. Every in red is for you to justify. Even if we start with the basic definition

More information

FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES. 1. Compact Sets

FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES. 1. Compact Sets FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES CHRISTOPHER HEIL 1. Compact Sets Definition 1.1 (Compact and Totally Bounded Sets). Let X be a metric space, and let E X be

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

Functional Analysis. Martin Brokate. 1 Normed Spaces 2. 2 Hilbert Spaces The Principle of Uniform Boundedness 32

Functional Analysis. Martin Brokate. 1 Normed Spaces 2. 2 Hilbert Spaces The Principle of Uniform Boundedness 32 Functional Analysis Martin Brokate Contents 1 Normed Spaces 2 2 Hilbert Spaces 2 3 The Principle of Uniform Boundedness 32 4 Extension, Reflexivity, Separation 37 5 Compact subsets of C and L p 46 6 Weak

More information

Analysis II Lecture notes

Analysis II Lecture notes Analysis II Lecture notes Christoph Thiele (lectures 11,12 by Roland Donninger lecture 22 by Diogo Oliveira e Silva) Summer term 2015 Universität Bonn July 5, 2016 Contents 1 Analysis in several variables

More information

Functional Analysis Exercise Class

Functional Analysis Exercise Class Functional Analysis Exercise Class Week 2 November 6 November Deadline to hand in the homeworks: your exercise class on week 9 November 13 November Exercises (1) Let X be the following space of piecewise

More information

Introduction to Bases in Banach Spaces

Introduction to Bases in Banach Spaces Introduction to Bases in Banach Spaces Matt Daws June 5, 2005 Abstract We introduce the notion of Schauder bases in Banach spaces, aiming to be able to give a statement of, and make sense of, the Gowers

More information

Notions such as convergent sequence and Cauchy sequence make sense for any metric space. Convergent Sequences are Cauchy

Notions such as convergent sequence and Cauchy sequence make sense for any metric space. Convergent Sequences are Cauchy Banach Spaces These notes provide an introduction to Banach spaces, which are complete normed vector spaces. For the purposes of these notes, all vector spaces are assumed to be over the real numbers.

More information

2.2 Annihilators, complemented subspaces

2.2 Annihilators, complemented subspaces 34CHAPTER 2. WEAK TOPOLOGIES, REFLEXIVITY, ADJOINT OPERATORS 2.2 Annihilators, complemented subspaces Definition 2.2.1. [Annihilators, Pre-Annihilators] Assume X is a Banach space. Let M X and N X. We

More information

Examples of Dual Spaces from Measure Theory

Examples of Dual Spaces from Measure Theory Chapter 9 Examples of Dual Spaces from Measure Theory We have seen that L (, A, µ) is a Banach space for any measure space (, A, µ). We will extend that concept in the following section to identify an

More information

(c) For each α R \ {0}, the mapping x αx is a homeomorphism of X.

(c) For each α R \ {0}, the mapping x αx is a homeomorphism of X. A short account of topological vector spaces Normed spaces, and especially Banach spaces, are basic ambient spaces in Infinite- Dimensional Analysis. However, there are situations in which it is necessary

More information

Functional Analysis Exercise Class

Functional Analysis Exercise Class Functional Analysis Exercise Class Week: December 4 8 Deadline to hand in the homework: your exercise class on week January 5. Exercises with solutions ) Let H, K be Hilbert spaces, and A : H K be a linear

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

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

Chapter 1. Preliminaries. The purpose of this chapter is to provide some basic background information. Linear Space. Hilbert Space.

Chapter 1. Preliminaries. The purpose of this chapter is to provide some basic background information. Linear Space. Hilbert Space. Chapter 1 Preliminaries The purpose of this chapter is to provide some basic background information. Linear Space Hilbert Space Basic Principles 1 2 Preliminaries Linear Space The notion of linear space

More information

Combinatorics in Banach space theory Lecture 12

Combinatorics in Banach space theory Lecture 12 Combinatorics in Banach space theory Lecture The next lemma considerably strengthens the assertion of Lemma.6(b). Lemma.9. For every Banach space X and any n N, either all the numbers n b n (X), c n (X)

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

Lecture 5 - Hausdorff and Gromov-Hausdorff Distance

Lecture 5 - Hausdorff and Gromov-Hausdorff Distance Lecture 5 - Hausdorff and Gromov-Hausdorff Distance August 1, 2011 1 Definition and Basic Properties Given a metric space X, the set of closed sets of X supports a metric, the Hausdorff metric. If A is

More information

Applied Analysis (APPM 5440): Final exam 1:30pm 4:00pm, Dec. 14, Closed books.

Applied Analysis (APPM 5440): Final exam 1:30pm 4:00pm, Dec. 14, Closed books. Applied Analysis APPM 44: Final exam 1:3pm 4:pm, Dec. 14, 29. Closed books. Problem 1: 2p Set I = [, 1]. Prove that there is a continuous function u on I such that 1 ux 1 x sin ut 2 dt = cosx, x I. Define

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

MAT 771 FUNCTIONAL ANALYSIS HOMEWORK 3. (1) Let V be the vector space of all bounded or unbounded sequences of complex numbers.

MAT 771 FUNCTIONAL ANALYSIS HOMEWORK 3. (1) Let V be the vector space of all bounded or unbounded sequences of complex numbers. MAT 771 FUNCTIONAL ANALYSIS HOMEWORK 3 (1) Let V be the vector space of all bounded or unbounded sequences of complex numbers. (a) Define d : V V + {0} by d(x, y) = 1 ξ j η j 2 j 1 + ξ j η j. Show that

More information

Your first day at work MATH 806 (Fall 2015)

Your first day at work MATH 806 (Fall 2015) Your first day at work MATH 806 (Fall 2015) 1. Let X be a set (with no particular algebraic structure). A function d : X X R is called a metric on X (and then X is called a metric space) when d satisfies

More information

CHAPTER 1. Metric Spaces. 1. Definition and examples

CHAPTER 1. Metric Spaces. 1. Definition and examples CHAPTER Metric Spaces. Definition and examples Metric spaces generalize and clarify the notion of distance in the real line. The definitions will provide us with a useful tool for more general applications

More information

Normed Vector Spaces and Double Duals

Normed Vector Spaces and Double Duals Normed Vector Spaces and Double Duals Mathematics 481/525 In this note we look at a number of infinite-dimensional R-vector spaces that arise in analysis, and we consider their dual and double dual spaces

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) 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

HOMEWORK ASSIGNMENT 6

HOMEWORK ASSIGNMENT 6 HOMEWORK ASSIGNMENT 6 DUE 15 MARCH, 2016 1) Suppose f, g : A R are uniformly continuous on A. Show that f + g is uniformly continuous on A. Solution First we note: In order to show that f + g is uniformly

More information

Course Notes for Functional Analysis I, Math , Fall Th. Schlumprecht

Course Notes for Functional Analysis I, Math , Fall Th. Schlumprecht Course Notes for Functional Analysis I, Math 655-601, Fall 2011 Th. Schlumprecht December 13, 2011 2 Contents 1 Some Basic Background 5 1.1 Normed Linear Spaces, Banach Spaces............. 5 1.2 Operators

More information

FUNCTIONAL ANALYSIS-NORMED SPACE

FUNCTIONAL ANALYSIS-NORMED SPACE MAT641- MSC Mathematics, MNIT Jaipur FUNCTIONAL ANALYSIS-NORMED SPACE DR. RITU AGARWAL MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY JAIPUR 1. Normed space Norm generalizes the concept of length in an arbitrary

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

2.2 Some Consequences of the Completeness Axiom

2.2 Some Consequences of the Completeness Axiom 60 CHAPTER 2. IMPORTANT PROPERTIES OF R 2.2 Some Consequences of the Completeness Axiom In this section, we use the fact that R is complete to establish some important results. First, we will prove that

More information

Selçuk Demir WS 2017 Functional Analysis Homework Sheet

Selçuk Demir WS 2017 Functional Analysis Homework Sheet Selçuk Demir WS 2017 Functional Analysis Homework Sheet 1. Let M be a metric space. If A M is non-empty, we say that A is bounded iff diam(a) = sup{d(x, y) : x.y A} exists. Show that A is bounded iff there

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

Exercise Solutions to Functional Analysis

Exercise Solutions to Functional Analysis Exercise Solutions to Functional Analysis Note: References refer to M. Schechter, Principles of Functional Analysis Exersize that. Let φ,..., φ n be an orthonormal set in a Hilbert space H. Show n f n

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

MAT 449 : Problem Set 7

MAT 449 : Problem Set 7 MAT 449 : Problem Set 7 Due Thursday, November 8 Let be a topological group and (π, V ) be a unitary representation of. A matrix coefficient of π is a function C of the form x π(x)(v), w, with v, w V.

More information

Lecture Notes. Functional Analysis in Applied Mathematics and Engineering. by Klaus Engel. University of L Aquila Faculty of Engineering

Lecture Notes. Functional Analysis in Applied Mathematics and Engineering. by Klaus Engel. University of L Aquila Faculty of Engineering Lecture Notes Functional Analysis in Applied Mathematics and Engineering by Klaus Engel University of L Aquila Faculty of Engineering 2012-2013 http://univaq.it/~engel ( = %7E) (Preliminary Version of

More information

Part V. 17 Introduction: What are measures and why measurable sets. Lebesgue Integration Theory

Part V. 17 Introduction: What are measures and why measurable sets. Lebesgue Integration Theory Part V 7 Introduction: What are measures and why measurable sets Lebesgue Integration Theory Definition 7. (Preliminary). A measure on a set is a function :2 [ ] such that. () = 2. If { } = is a finite

More information

Lecture Course. Functional Analysis

Lecture Course. Functional Analysis MATHEMATISCHES INSTITUT PROF. DR. PETER MÜLLER Summer Term 2013 Lecture Course Functional Analysis Typesetting by Kilian Lieret and Marcel Schaub If you find mistakes, I would appreciate getting a short

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

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

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

Banach Spaces II: Elementary Banach Space Theory

Banach Spaces II: Elementary Banach Space Theory BS II c Gabriel Nagy Banach Spaces II: Elementary Banach Space Theory Notes from the Functional Analysis Course (Fall 07 - Spring 08) In this section we introduce Banach spaces and examine some of their

More information

David Hilbert was old and partly deaf in the nineteen thirties. Yet being a diligent

David Hilbert was old and partly deaf in the nineteen thirties. Yet being a diligent Chapter 5 ddddd dddddd dddddddd ddddddd dddddddd ddddddd Hilbert Space The Euclidean norm is special among all norms defined in R n for being induced by the Euclidean inner product (the dot product). A

More information

Normed and Banach spaces

Normed and Banach spaces Normed and Banach spaces László Erdős Nov 11, 2006 1 Norms We recall that the norm is a function on a vectorspace V, : V R +, satisfying the following properties x + y x + y cx = c x x = 0 x = 0 We always

More information

FUNCTIONAL ANALYSIS: NOTES AND PROBLEMS

FUNCTIONAL ANALYSIS: NOTES AND PROBLEMS FUNCTIONAL ANALYSIS: NOTES AND PROBLEMS Abstract. These are the notes prepared for the course MTH 405 to be offered to graduate students at IIT Kanpur. Contents 1. Basic Inequalities 1 2. Normed Linear

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

Lecture 4 Lebesgue spaces and inequalities

Lecture 4 Lebesgue spaces and inequalities Lecture 4: Lebesgue spaces and inequalities 1 of 10 Course: Theory of Probability I Term: Fall 2013 Instructor: Gordan Zitkovic Lecture 4 Lebesgue spaces and inequalities Lebesgue spaces We have seen how

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

5 Compact linear operators

5 Compact linear operators 5 Compact linear operators One of the most important results of Linear Algebra is that for every selfadjoint linear map A on a finite-dimensional space, there exists a basis consisting of eigenvectors.

More information

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi Real Analysis Math 3AH Rudin, Chapter # Dominique Abdi.. If r is rational (r 0) and x is irrational, prove that r + x and rx are irrational. Solution. Assume the contrary, that r+x and rx are rational.

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

MATH 4200 HW: PROBLEM SET FOUR: METRIC SPACES

MATH 4200 HW: PROBLEM SET FOUR: METRIC SPACES MATH 4200 HW: PROBLEM SET FOUR: METRIC SPACES PETE L. CLARK 4. Metric Spaces (no more lulz) Directions: This week, please solve any seven problems. Next week, please solve seven more. Starred parts of

More information

FUNCTIONAL ANALYSIS HAHN-BANACH THEOREM. F (m 2 ) + α m 2 + x 0

FUNCTIONAL ANALYSIS HAHN-BANACH THEOREM. F (m 2 ) + α m 2 + x 0 FUNCTIONAL ANALYSIS HAHN-BANACH THEOREM If M is a linear subspace of a normal linear space X and if F is a bounded linear functional on M then F can be extended to M + [x 0 ] without changing its norm.

More information

3 Orthogonality and Fourier series

3 Orthogonality and Fourier series 3 Orthogonality and Fourier series We now turn to the concept of orthogonality which is a key concept in inner product spaces and Hilbert spaces. We start with some basic definitions. Definition 3.1. Let

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

Course 212: Academic Year Section 1: Metric Spaces

Course 212: Academic Year Section 1: Metric Spaces Course 212: Academic Year 1991-2 Section 1: Metric Spaces D. R. Wilkins Contents 1 Metric Spaces 3 1.1 Distance Functions and Metric Spaces............. 3 1.2 Convergence and Continuity in Metric Spaces.........

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

MTH 503: Functional Analysis

MTH 503: Functional Analysis MTH 53: Functional Analysis Semester 1, 215-216 Dr. Prahlad Vaidyanathan Contents I. Normed Linear Spaces 4 1. Review of Linear Algebra........................... 4 2. Definition and Examples...........................

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

MAA6617 COURSE NOTES SPRING 2014

MAA6617 COURSE NOTES SPRING 2014 MAA6617 COURSE NOTES SPRING 2014 19. Normed vector spaces Let X be a vector space over a field K (in this course we always have either K = R or K = C). Definition 19.1. A norm on X is a function : X K

More information

S chauder Theory. x 2. = log( x 1 + x 2 ) + 1 ( x 1 + x 2 ) 2. ( 5) x 1 + x 2 x 1 + x 2. 2 = 2 x 1. x 1 x 2. 1 x 1.

S chauder Theory. x 2. = log( x 1 + x 2 ) + 1 ( x 1 + x 2 ) 2. ( 5) x 1 + x 2 x 1 + x 2. 2 = 2 x 1. x 1 x 2. 1 x 1. Sep. 1 9 Intuitively, the solution u to the Poisson equation S chauder Theory u = f 1 should have better regularity than the right hand side f. In particular one expects u to be twice more differentiable

More information

FUNCTIONAL ANALYSIS CHRISTIAN REMLING

FUNCTIONAL ANALYSIS CHRISTIAN REMLING FUNCTIONAL ANALYSIS CHRISTIAN REMLING Contents 1. Metric and topological spaces 2 2. Banach spaces 12 3. Consequences of Baire s Theorem 30 4. Dual spaces and weak topologies 34 5. Hilbert spaces 50 6.

More information

************************************* Applied Analysis I - (Advanced PDE I) (Math 940, Fall 2014) Baisheng Yan

************************************* Applied Analysis I - (Advanced PDE I) (Math 940, Fall 2014) Baisheng Yan ************************************* Applied Analysis I - (Advanced PDE I) (Math 94, Fall 214) by Baisheng Yan Department of Mathematics Michigan State University yan@math.msu.edu Contents Chapter 1.

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

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

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

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

d(x n, x) d(x n, x nk ) + d(x nk, x) where we chose any fixed k > N

d(x n, x) d(x n, x nk ) + d(x nk, x) where we chose any fixed k > N Problem 1. Let f : A R R have the property that for every x A, there exists ɛ > 0 such that f(t) > ɛ if t (x ɛ, x + ɛ) A. If the set A is compact, prove there exists c > 0 such that f(x) > c for all x

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

CHAPTER II HILBERT SPACES

CHAPTER II HILBERT SPACES CHAPTER II HILBERT SPACES 2.1 Geometry of Hilbert Spaces Definition 2.1.1. Let X be a complex linear space. An inner product on X is a function, : X X C which satisfies the following axioms : 1. y, x =

More information

From now on, we will represent a metric space with (X, d). Here are some examples: i=1 (x i y i ) p ) 1 p, p 1.

From now on, we will represent a metric space with (X, d). Here are some examples: i=1 (x i y i ) p ) 1 p, p 1. Chapter 1 Metric spaces 1.1 Metric and convergence We will begin with some basic concepts. Definition 1.1. (Metric space) Metric space is a set X, with a metric satisfying: 1. d(x, y) 0, d(x, y) = 0 x

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

RIESZ BASES AND UNCONDITIONAL BASES

RIESZ BASES AND UNCONDITIONAL BASES In this paper we give a brief introduction to adjoint operators on Hilbert spaces and a characterization of the dual space of a Hilbert space. We then introduce the notion of a Riesz basis and give some

More information

REAL RENORMINGS ON COMPLEX BANACH SPACES

REAL RENORMINGS ON COMPLEX BANACH SPACES REAL RENORMINGS ON COMPLEX BANACH SPACES F. J. GARCÍA PACHECO AND A. MIRALLES Abstract. In this paper we provide two ways of obtaining real Banach spaces that cannot come from complex spaces. In concrete

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

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

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

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

Supplementary Notes for W. Rudin: Principles of Mathematical Analysis

Supplementary Notes for W. Rudin: Principles of Mathematical Analysis Supplementary Notes for W. Rudin: Principles of Mathematical Analysis SIGURDUR HELGASON In 8.00B it is customary to cover Chapters 7 in Rudin s book. Experience shows that this requires careful planning

More information

Methods of constructing topological vector spaces

Methods of constructing topological vector spaces CHAPTER 2 Methods of constructing topological vector spaces In this chapter we consider projective limits (in particular, products) of families of topological vector spaces, inductive limits (in particular,

More information

2.4 Annihilators, Complemented Subspaces

2.4 Annihilators, Complemented Subspaces 40 CHAPTER 2. WEAK TOPOLOGIES AND REFLEXIVITY 2.4 Annihilators, Complemented Subspaces Definition 2.4.1. (Annihilators, Pre-Annihilators) Assume X is a Banach space. Let M X and N X. We call the annihilator

More information

MATH 117 LECTURE NOTES

MATH 117 LECTURE NOTES MATH 117 LECTURE NOTES XIN ZHOU Abstract. This is the set of lecture notes for Math 117 during Fall quarter of 2017 at UC Santa Barbara. The lectures follow closely the textbook [1]. Contents 1. The set

More information

RESTRICTED UNIFORM BOUNDEDNESS IN BANACH SPACES

RESTRICTED UNIFORM BOUNDEDNESS IN BANACH SPACES RESTRICTED UNIFORM BOUNDEDNESS IN BANACH SPACES OLAV NYGAARD AND MÄRT PÕLDVERE Abstract. Precise conditions for a subset A of a Banach space X are known in order that pointwise bounded on A sequences of

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

Homework I, Solutions

Homework I, Solutions Homework I, Solutions I: (15 points) Exercise on lower semi-continuity: Let X be a normed space and f : X R be a function. We say that f is lower semi - continuous at x 0 if for every ε > 0 there exists

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

Exercises from other sources REAL NUMBERS 2,...,

Exercises from other sources REAL NUMBERS 2,..., Exercises from other sources REAL NUMBERS 1. Find the supremum and infimum of the following sets: a) {1, b) c) 12, 13, 14, }, { 1 3, 4 9, 13 27, 40 } 81,, { 2, 2 + 2, 2 + 2 + } 2,..., d) {n N : n 2 < 10},

More information

Part III. 10 Topological Space Basics. Topological Spaces

Part III. 10 Topological Space Basics. Topological Spaces Part III 10 Topological Space Basics Topological Spaces Using the metric space results above as motivation we will axiomatize the notion of being an open set to more general settings. Definition 10.1.

More information

Continuity of convex functions in normed spaces

Continuity of convex functions in normed spaces Continuity of convex functions in normed spaces In this chapter, we consider continuity properties of real-valued convex functions defined on open convex sets in normed spaces. Recall that every infinitedimensional

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

Iowa State University. Instructor: Alex Roitershtein Summer Homework #5. Solutions

Iowa State University. Instructor: Alex Roitershtein Summer Homework #5. Solutions Math 50 Iowa State University Introduction to Real Analysis Department of Mathematics Instructor: Alex Roitershtein Summer 205 Homework #5 Solutions. Let α and c be real numbers, c > 0, and f is defined

More information