On a Semigroup Problem II
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Functional Analysis Lecture Notes Chapter 3. Banach
FUNCTIONAL ANALYSIS LECTURE NOTES CHAPTER 3. BANACH SPACES CHRISTOPHER HEIL 1. Elementary Properties and Examples Notation 1.1. Throughout, F will denote either the real line R or the complex plane C. All vector spaces are assumed to be over the field F. Definition 1.2. Let X be a vector space over the field F. Then a semi-norm on X is a function k · k: X ! R such that (a) kxk ≥ 0 for all x 2 X, (b) kαxk = jαj kxk for all x 2 X and α 2 F, (c) Triangle Inequality: kx + yk ≤ kxk + kyk for all x, y 2 X. A norm on X is a semi-norm which also satisfies: (d) kxk = 0 =) x = 0. A vector space X together with a norm k · k is called a normed linear space, a normed vector space, or simply a normed space. Definition 1.3. Let I be a finite or countable index set (for example, I = f1; : : : ; Ng if finite, or I = N or Z if infinite). Let w : I ! [0; 1). Given a sequence of scalars x = (xi)i2I , set 1=p jx jp w(i)p ; 0 < p < 1; 8 i kxkp;w = > Xi2I <> sup jxij w(i); p = 1; i2I > where these quantities could be infinite.:> Then we set p `w(I) = x = (xi)i2I : kxkp < 1 : n o p p p We call `w(I) a weighted ` space, and often denote it just by `w (especially if I = N). If p p w(i) = 1 for all i, then we simply call this space ` (I) or ` and write k · kp instead of k · kp;w. -
Elements of Linear Algebra, Topology, and Calculus
00˙AMS September 23, 2007 © Copyright, Princeton University Press. No part of this book may be distributed, posted, or reproduced in any form by digital or mechanical means without prior written permission of the publisher. Appendix A Elements of Linear Algebra, Topology, and Calculus A.1 LINEAR ALGEBRA We follow the usual conventions of matrix computations. Rn×p is the set of all n p real matrices (m rows and p columns). Rn is the set Rn×1 of column vectors× with n real entries. A(i, j) denotes the i, j entry (ith row, jth column) of the matrix A. Given A Rm×n and B Rn×p, the matrix product Rm×p ∈ n ∈ AB is defined by (AB)(i, j) = k=1 A(i, k)B(k, j), i = 1, . , m, j = 1∈, . , p. AT is the transpose of the matrix A:(AT )(i, j) = A(j, i). The entries A(i, i) form the diagonal of A. A Pmatrix is square if is has the same number of rows and columns. When A and B are square matrices of the same dimension, [A, B] = AB BA is termed the commutator of A and B. A matrix A is symmetric if −AT = A and skew-symmetric if AT = A. The commutator of two symmetric matrices or two skew-symmetric matrices− is symmetric, and the commutator of a symmetric and a skew-symmetric matrix is skew-symmetric. The trace of A is the sum of the diagonal elements of A, min(n,p ) tr(A) = A(i, i). i=1 X We have the following properties (assuming that A and B have adequate dimensions) tr(A) = tr(AT ), (A.1a) tr(AB) = tr(BA), (A.1b) tr([A, B]) = 0, (A.1c) tr(B) = 0 if BT = B, (A.1d) − tr(AB) = 0 if AT = A and BT = B. -
Linear Spaces
Chapter 2 Linear Spaces Contents FieldofScalars ........................................ ............. 2.2 VectorSpaces ........................................ .............. 2.3 Subspaces .......................................... .............. 2.5 Sumofsubsets........................................ .............. 2.5 Linearcombinations..................................... .............. 2.6 Linearindependence................................... ................ 2.7 BasisandDimension ..................................... ............. 2.7 Convexity ............................................ ............ 2.8 Normedlinearspaces ................................... ............... 2.9 The `p and Lp spaces ............................................. 2.10 Topologicalconcepts ................................... ............... 2.12 Opensets ............................................ ............ 2.13 Closedsets........................................... ............. 2.14 Boundedsets......................................... .............. 2.15 Convergence of sequences . ................... 2.16 Series .............................................. ............ 2.17 Cauchysequences .................................... ................ 2.18 Banachspaces....................................... ............... 2.19 Completesubsets ....................................... ............. 2.19 Transformations ...................................... ............... 2.21 Lineartransformations.................................. ................ 2.21 -
Arxiv:Math/0605475V1 [Math.FA] 17 May 2006 Td Lse Fsprbebnc Pcsi B2 O] Eproved He Bo3]
SOME STRONGLY BOUNDED CLASSES OF BANACH SPACES PANDELIS DODOS AND VALENTIN FERENCZI Abstract. We show that the classes of separable reflexive Banach spaces and of spaces with separable dual are strongly bounded. This gives a new proof of a recent result of E. Odell and Th. Schlumprecht, asserting that there exists a separable reflexive Ba- nach space containing isomorphic copies of every separable uni- formly convex Banach spaces. 1. Introduction A Banach space X is said to be universal for a class C of Banach spaces when every space in C embeds isomorphically into X. It is complementably universal when the embeddings are complemented. According to the classical Mazur Theorem, the space C(2N) is uni- versal for separable Banach spaces. By [JS], there does not exist a sep- arable Banach space which is complementably universal for the class of separable Banach spaces. However, A. Pe lczy´nski [P] constructed a space U with a Schauder basis which is complementably universal for the class of spaces with a Schauder basis (and even for the class of spaces with the Bounded Approximation Property). There is also an unconditional version of U, i.e. a space with an unconditional ba- sis which is complementably universal for the class of spaces with an unconditional basis. In 1968, W. Szlenk proved that there does not exist a Banach space arXiv:math/0605475v1 [math.FA] 17 May 2006 with separable dual which is universal for the class of reflexive Banach spaces [Sz]. His proof is based on the definition of the Szlenk index which is a transfinite measure of the separability of the dual of a space. -
On the Hypercyclicity Criterion for Operators of Read's Type
ON THE HYPERCYCLICITY CRITERION FOR OPERATORS OF READ’S TYPE by Sophie Grivaux Abstract. — Let T be a so-called operator of Read’s type on a (real or complex) separable Banach space, having no non-trivial invariant subset. We prove in this note that T ⊕ T is then hypercyclic, i.e. that T satisfies the Hypercyclicity Criterion. 1. The Invariant Subspace Problem Given a (real or complex) infinite-dimensional separable Banach space X, the Invariant Subspace Problem for X asks whether every bounded operator T on X admits a non- trivial invariant subspace, i.e. a closed subspace M of X with M 6= {0} and M 6= X such that T (M) ⊆ M. It was answered in the negative in the 80’s, first by Enflo [11] and then by Read [24], who constructed examples of separable Banach spaces supporting operators without non-trivial closed invariant subspace. One of the most famous open questions in modern operator theory is the Hilbertian version of the Invariant Subspace Problem, but it is also widely open in the reflexive setting: to this day, all the known examples of operators without non-trivial invariant subspace live on non-reflexive Banach spaces. Read provided several classes of operators on ℓ1(N) having no non-trivial invariant subspace [25], [26], [29]. In the work [28], he gave examples of such operators on c0(N) and X = Lℓ2 J, the ℓ2-sum of countably many copies of the James space J; since J is quasi reflexive (i.e. has codimension 1 in its bidual J ∗∗), the space X has the property that X∗∗/X is separable. -
Basic Differentiable Calculus Review
Basic Differentiable Calculus Review Jimmie Lawson Department of Mathematics Louisiana State University Spring, 2003 1 Introduction Basic facts about the multivariable differentiable calculus are needed as back- ground for differentiable geometry and its applications. The purpose of these notes is to recall some of these facts. We state the results in the general context of Banach spaces, although we are specifically concerned with the finite-dimensional setting, specifically that of Rn. Let U be an open set of Rn. A function f : U ! R is said to be a Cr map for 0 ≤ r ≤ 1 if all partial derivatives up through order r exist for all points of U and are continuous. In the extreme cases C0 means that f is continuous and C1 means that all partials of all orders exists and are continuous on m r r U. A function f : U ! R is a C map if fi := πif is C for i = 1; : : : ; m, m th where πi : R ! R is the i projection map defined by πi(x1; : : : ; xm) = xi. It is a standard result that mixed partials of degree less than or equal to r and of the same type up to interchanges of order are equal for a C r-function (sometimes called Clairaut's Theorem). We can consider a category with objects nonempty open subsets of Rn for various n and morphisms Cr-maps. This is indeed a category, since the composition of Cr maps is again a Cr map. 1 2 Normed Spaces and Bounded Linear Oper- ators At the heart of the differential calculus is the notion of a differentiable func- tion. -
Embedding Banach Spaces
Embedding Banach spaces. Daniel Freeman with Edward Odell, Thomas Schlumprecht, Richard Haydon, and Andras Zsak January 24, 2009 Daniel Freeman with Edward Odell, Thomas Schlumprecht, Richard Haydon, andEmbedding Andras Zsak Banach spaces. Definition (Banach space) A normed vector space (X ; k · k) is called a Banach space if X is complete in the norm topology. Definition (Schauder basis) 1 A sequence (xi )i=1 ⊂ X is called a basis for X if for every x 2 X there exists a 1 P1 unique sequence of scalars (ai )i=1 such that x = i=1 ai xi . Question (Mazur '36) Does every separable Banach space have a basis? Theorem (Enflo '72) No. Background Daniel Freeman with Edward Odell, Thomas Schlumprecht, Richard Haydon, andEmbedding Andras Zsak Banach spaces. Definition (Schauder basis) 1 A sequence (xi )i=1 ⊂ X is called a basis for X if for every x 2 X there exists a 1 P1 unique sequence of scalars (ai )i=1 such that x = i=1 ai xi . Question (Mazur '36) Does every separable Banach space have a basis? Theorem (Enflo '72) No. Background Definition (Banach space) A normed vector space (X ; k · k) is called a Banach space if X is complete in the norm topology. Daniel Freeman with Edward Odell, Thomas Schlumprecht, Richard Haydon, andEmbedding Andras Zsak Banach spaces. Question (Mazur '36) Does every separable Banach space have a basis? Theorem (Enflo '72) No. Background Definition (Banach space) A normed vector space (X ; k · k) is called a Banach space if X is complete in the norm topology. Definition (Schauder basis) 1 A sequence (xi )i=1 ⊂ X is called a basis for X if for every x 2 X there exists a 1 P1 unique sequence of scalars (ai )i=1 such that x = i=1 ai xi . -
Nonlinear Spectral Theory Henning Wunderlich
Nonlinear Spectral Theory Henning Wunderlich Dr. Henning Wunderlich, Frankfurt, Germany E-mail address: [email protected] 1991 Mathematics Subject Classification. 46-XX,47-XX I would like to sincerely thank Prof. Dr. Delio Mugnolo for his valuable advice and support during the preparation of this thesis. Contents Introduction 5 Chapter 1. Spaces 7 1. Topological Spaces 7 2. Uniform Spaces 11 3. Metric Spaces 14 4. Vector Spaces 15 5. Topological Vector Spaces 18 6. Locally-Convex Spaces 21 7. Bornological Spaces 23 8. Barreled Spaces 23 9. Metric Vector Spaces 29 10. Normed Vector Spaces 30 11. Inner-Product Vector Spaces 31 12. Examples 31 Chapter 2. Fixed Points 39 1. Schauder-Tychonoff 39 2. Monotonic Operators 43 3. Dugundji and Quasi-Extensions 51 4. Measures of Noncompactness 53 5. Michael Selection 58 Chapter 3. Existing Spectra 59 1. Linear Spectrum and Properties 59 2. Spectra Under Consideration 63 3. Restriction to Linear Operators 76 4. Nonemptyness 77 5. Closedness 78 6. Boundedness 82 7. Upper Semicontinuity 84 Chapter 4. Applications 87 1. Nemyckii Operator 87 2. p-Laplace Operator 88 3. Navier-Stokes Equations 92 Bibliography 97 Index 101 3 Introduction The term Spectral Theory was coined by David Hilbert in his studies of qua- dratic forms in infinitely-many variables. This theory evolved into a beautiful blend of Linear Algebra and Analysis, with striking applications in different fields of sci- ence. For example, the formulation of the calculus of Quantum Mechanics (e.g., POVM) would not have been possible without such a (Linear) Spectral Theory at hand. -
A Representation Theorem for Schauder Bases in Hilbert Space
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 126, Number 2, February 1998, Pages 553{560 S 0002-9939(98)04168-9 A REPRESENTATION THEOREM FOR SCHAUDER BASES IN HILBERT SPACE STEPHANE JAFFARD AND ROBERT M. YOUNG (Communicated by Dale Alspach) Abstract. A sequence of vectors f1;f2;f3;::: in a separable Hilbert space f g H is said to be a Schauder basis for H if every element f H has a unique norm-convergent expansion 2 f = cnfn: If, in addition, there exist positive constantsX A and B such that 2 2 2 A cn cnfn B cn ; j j ≤ ≤ j j then we call f1;f2;fX3;::: a Riesz X basis. In theX first half of this paper, we f g show that every Schauder basis for H can be obtained from an orthonormal basis by means of a (possibly unbounded) one-to-one positive self adjoint oper- ator. In the second half, we use this result to extend and clarify a remarkable theorem due to Duffin and Eachus characterizing the class of Riesz bases in Hilbert space. 1. The main theorem A sequence of vectors f1;f2;f3;::: in a separable Hilbert space H is said to be a Schauder basis for H{ if every element} f H has a unique norm-convergent expansion ∈ f = cnfn: If, in addition, there exist positive constantsX A and B such that 2 A c 2 c f B c 2; | n| ≤ n n ≤ | n| then we call f1;f2;f3;:::X a Riesz basis.X Riesz basesX have been studied intensively ever since Paley{ and Wiener} first recognized the possibility of nonharmonic Fourier expansions iλnt f = cne for functions f in L2( π, π) (see, for example,X [2] and [5] and the references therein). -
Pre-Schauder Bases in Topological Vector Spaces
S S symmetry Article Pre-Schauder Bases in Topological Vector Spaces Francisco Javier García-Pacheco *,† and Francisco Javier Pérez-Fernández † Department of Mathematics, University of Cadiz, 11519 Puerto Real, Spain * Correspondence: [email protected] † These authors contributed equally to this work. Received: 17 June 2019; Accepted: 2 August 2019; Published: 9 August 2019 Abstract: A Schauder basis in a real or complex Banach space X is a sequence (en)n2N in X such ¥ that for every x 2 X there exists a unique sequence of scalars (ln)n2N satisfying that x = ∑n=1 lnen. Schauder bases were first introduced in the setting of real or complex Banach spaces but they have been transported to the scope of real or complex Hausdorff locally convex topological vector spaces. In this manuscript, we extend them to the setting of topological vector spaces over an absolutely valued division ring by redefining them as pre-Schauder bases. We first prove that, if a topological vector space admits a pre-Schauder basis, then the linear span of the basis is Hausdorff and the series linear span of the basis minus the linear span contains the intersection of all neighborhoods of 0. As a consequence, we conclude that the coefficient functionals are continuous if and only if the canonical projections are also continuous (this is a trivial fact in normed spaces but not in topological vector spaces). We also prove that, if a Hausdorff topological vector space admits a pre-Schauder basis and is w∗-strongly torsionless, then the biorthogonal system formed by the basis and its coefficient functionals is total. -
Functional Analysis Homework 4
Functional Analysis Homework 4 Due Tuesday, 13 February 2018 Problem 1. A sequence (en) of elements from a normed space X is said to be a Schauder basis if for every Pn x 2 X, there is a unique sequence (cn) of scalars such that i=1 cnen ! x. Show that if a normed space has a Schauder basis, then it is separable.[1] Proof. Suppose X is a normed space with a Schauder basis (en). Note that the scalar field F of X has a countable dense set which we will call G (for R it is Q, and for C it is Q + iQ). Let Y be the collection of all finite linear combinations of elements of the Schauder basis with coefficients in G; that is, ( n ) [ X Y := Y ; where Y := c e c 2 = span fe ; : : : ; e g: n n i i i G G 1 n n2N i=1 [2] n Note that Yn is countable because there is a natural bijective map G ! Yn given by (c1; : : : ; cn) ! Pn i=1 ciei. Since Y is a countable union of countable sets, it is countable. Now we claim that Y is dense in X. To this end, take any x 2 X. Since (en) is a Schauder basis, there Pn exists a sequence of scalars (cn) from F such that i=1 ciei ! x. Therefore, given " > 0, there is some N 2 N such that N X " c e − x < : i i 2 i=1 " Moreover, since is dense in , for each 1 ≤ i ≤ N, there is some ai 2 such that jai − cij ≤ . -
Lp Spaces in Isabelle
Lp spaces in Isabelle Sebastien Gouezel Abstract Lp is the space of functions whose p-th power is integrable. It is one of the most fundamental Banach spaces that is used in analysis and probability. We develop a framework for function spaces, and then im- plement the Lp spaces in this framework using the existing integration theory in Isabelle/HOL. Our development contains most fundamental properties of Lp spaces, notably the Hölder and Minkowski inequalities, completeness of Lp, duality, stability under almost sure convergence, multiplication of functions in Lp and Lq, stability under conditional expectation. Contents 1 Functions as a real vector space 2 2 Quasinorms on function spaces 4 2.1 Definition of quasinorms ..................... 5 2.2 The space and the zero space of a quasinorm ......... 7 2.3 An example: the ambient norm in a normed vector space .. 9 2.4 An example: the space of bounded continuous functions from a topological space to a normed real vector space ....... 10 2.5 Continuous inclusions between functional spaces ....... 10 2.6 The intersection and the sum of two functional spaces .... 12 2.7 Topology ............................. 14 3 Conjugate exponents 16 4 Convexity inequalities and integration 18 5 Lp spaces 19 5.1 L1 ................................. 20 5.2 Lp for 0 < p < 1 ......................... 21 5.3 Specialization to L1 ....................... 22 5.4 L0 ................................. 23 5.5 Basic results on Lp for general p ................ 24 5.6 Lp versions of the main theorems in integration theory .... 25 1 5.7 Completeness of Lp ........................ 26 5.8 Multiplication of functions, duality ............... 26 5.9 Conditional expectations and Lp ...............