$ C 0 $-Sequentially Equicontinuous Semigroups: Theory and Applications
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Surjective Isometries on Grassmann Spaces 11
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repository of the Academy's Library SURJECTIVE ISOMETRIES ON GRASSMANN SPACES FERNANDA BOTELHO, JAMES JAMISON, AND LAJOS MOLNAR´ Abstract. Let H be a complex Hilbert space, n a given positive integer and let Pn(H) be the set of all projections on H with rank n. Under the condition dim H ≥ 4n, we describe the surjective isometries of Pn(H) with respect to the gap metric (the metric induced by the operator norm). 1. Introduction The study of isometries of function spaces or operator algebras is an important research area in functional analysis with history dating back to the 1930's. The most classical results in the area are the Banach-Stone theorem describing the surjective linear isometries between Banach spaces of complex- valued continuous functions on compact Hausdorff spaces and its noncommutative extension for general C∗-algebras due to Kadison. This has the surprising and remarkable consequence that the surjective linear isometries between C∗-algebras are closely related to algebra isomorphisms. More precisely, every such surjective linear isometry is a Jordan *-isomorphism followed by multiplication by a fixed unitary element. We point out that in the aforementioned fundamental results the underlying structures are algebras and the isometries are assumed to be linear. However, descriptions of isometries of non-linear structures also play important roles in several areas. We only mention one example which is closely related with the subject matter of this paper. This is the famous Wigner's theorem that describes the structure of quantum mechanical symmetry transformations and plays a fundamental role in the probabilistic aspects of quantum theory. -
1 Lecture 09
Notes for Functional Analysis Wang Zuoqin (typed by Xiyu Zhai) September 29, 2015 1 Lecture 09 1.1 Equicontinuity First let's recall the conception of \equicontinuity" for family of functions that we learned in classical analysis: A family of continuous functions defined on a region Ω, Λ = ffαg ⊂ C(Ω); is called an equicontinuous family if 8 > 0; 9δ > 0 such that for any x1; x2 2 Ω with jx1 − x2j < δ; and for any fα 2 Λ, we have jfα(x1) − fα(x2)j < . This conception of equicontinuity can be easily generalized to maps between topological vector spaces. For simplicity we only consider linear maps between topological vector space , in which case the continuity (and in fact the uniform continuity) at an arbitrary point is reduced to the continuity at 0. Definition 1.1. Let X; Y be topological vector spaces, and Λ be a family of continuous linear operators. We say Λ is an equicontinuous family if for any neighborhood V of 0 in Y , there is a neighborhood U of 0 in X, such that L(U) ⊂ V for all L 2 Λ. Remark 1.2. Equivalently, this means if x1; x2 2 X and x1 − x2 2 U, then L(x1) − L(x2) 2 V for all L 2 Λ. Remark 1.3. If Λ = fLg contains only one continuous linear operator, then it is always equicontinuous. 1 If Λ is an equicontinuous family, then each L 2 Λ is continuous and thus bounded. In fact, this boundedness is uniform: Proposition 1.4. Let X,Y be topological vector spaces, and Λ be an equicontinuous family of linear operators from X to Y . -
Functional Analysis Solutions to Exercise Sheet 6
Institut für Analysis WS2019/20 Prof. Dr. Dorothee Frey 21.11.2019 M.Sc. Bas Nieraeth Functional Analysis Solutions to exercise sheet 6 Exercise 1: Properties of compact operators. (a) Let X and Y be normed spaces and let T 2 K(X; Y ). Show that R(T ) is separable. (b) Let X, Y , and Z be Banach spaces. Let T 2 K(X; Y ) and S 2 L(Y; Z), where S is injective. Prove that for every " > 0 there is a constant C ≥ 0 such that kT xk ≤ "kxk + CkST xk for all x 2 X. Solution: (a) Since bounded sets have a relatively compact image under T , for each n 2 N the sets T (nBX ) are totally bounded, where nBX := fx 2 X : kxk ≤ ng. Then it follows from Exercise 1 of Exercise sheet 3 that T (nBX ) is separable. But then ! [ [ T (nBX ) = T nBX = T (X) = R(T ) n2N n2N is also separable, as it is a countable union of separable sets. The assertion follows. (b) First note that by using a homogeneity argument, one finds that this result is true, ifand only it is true for x 2 X with kxk = 1. Thus, it remains to prove the result for x 2 X with kxk = 1. We argue by contradiction. If the result does not hold, then there is an " > 0 such that for all C ≥ 0 there exists and x 2 X with kxk = 1 such that kT xk > " + CkST xk. By choosing C = n for n 2 N, we find a sequence (xn)n2N in X with kxnk = 1 such that kT xnk > " + nkST xnk: (1) Since (xn)n2N is bounded and T is compact, there exists a subsequence (T xnj )j2N with limit y 2 Y . -
The Banach-Alaoglu Theorem for Topological Vector Spaces
The Banach-Alaoglu theorem for topological vector spaces Christiaan van den Brink a thesis submitted to the Department of Mathematics at Utrecht University in partial fulfillment of the requirements for the degree of Bachelor in Mathematics Supervisor: Fabian Ziltener date of submission 06-06-2019 Abstract In this thesis we generalize the Banach-Alaoglu theorem to topological vector spaces. the theorem then states that the polar, which lies in the dual space, of a neighbourhood around zero is weak* compact. We give motivation for the non-triviality of this theorem in this more general case. Later on, we show that the polar is sequentially compact if the space is separable. If our space is normed, then we show that the polar of the unit ball is the closed unit ball in the dual space. Finally, we introduce the notion of nets and we use these to prove the main theorem. i ii Acknowledgments A huge thanks goes out to my supervisor Fabian Ziltener for guiding me through the process of writing a bachelor thesis. I would also like to thank my girlfriend, family and my pet who have supported me all the way. iii iv Contents 1 Introduction 1 1.1 Motivation and main result . .1 1.2 Remarks and related works . .2 1.3 Organization of this thesis . .2 2 Introduction to Topological vector spaces 4 2.1 Topological vector spaces . .4 2.1.1 Definition of topological vector space . .4 2.1.2 The topology of a TVS . .6 2.2 Dual spaces . .9 2.2.1 Continuous functionals . -
ARZEL`A-ASCOLI's THEOREM in UNIFORM SPACES Mateusz Krukowski 1. Introduction. Around 1883, Cesare Arzel`
DISCRETE AND CONTINUOUS doi:10.3934/dcdsb.2018020 DYNAMICAL SYSTEMS SERIES B Volume 23, Number 1, January 2018 pp. 283{294 ARZELA-ASCOLI'S` THEOREM IN UNIFORM SPACES Mateusz Krukowski∗ L´od´zUniversity of Technology, Institute of Mathematics W´olcza´nska 215, 90-924L´od´z, Poland Abstract. In the paper, we generalize the Arzel`a-Ascoli'stheorem in the setting of uniform spaces. At first, we recall the Arzel`a-Ascolitheorem for functions with locally compact domains and images in uniform spaces, coming from monographs of Kelley and Willard. The main part of the paper intro- duces the notion of the extension property which, similarly as equicontinuity, equates different topologies on C(X; Y ). This property enables us to prove the Arzel`a-Ascoli'stheorem for uniform convergence. The paper culminates with applications, which are motivated by Schwartz's distribution theory. Using the Banach-Alaoglu-Bourbaki's theorem, we establish the relative compactness of 0 n subfamily of C(R; D (R )). 1. Introduction. Around 1883, Cesare Arzel`aand Giulio Ascoli provided the nec- essary and sufficient conditions under which every sequence of a given family of real-valued continuous functions, defined on a closed and bounded interval, has a uniformly convergent subsequence. Since then, numerous generalizations of this result have been obtained. For instance, in [10], p. 278 the compact families of C(X; R), with X a compact space, are exactly those which are equibounded and equicontinuous. The space C(X; R) is given with the standard norm kfk := sup jf(x)j; x2X where f 2 C(X; R). -
Bounded Operator - Wikipedia, the Free Encyclopedia
Bounded operator - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Bounded_operator Bounded operator From Wikipedia, the free encyclopedia In functional analysis, a branch of mathematics, a bounded linear operator is a linear transformation L between normed vector spaces X and Y for which the ratio of the norm of L(v) to that of v is bounded by the same number, over all non-zero vectors v in X. In other words, there exists some M > 0 such that for all v in X The smallest such M is called the operator norm of L. A bounded linear operator is generally not a bounded function; the latter would require that the norm of L(v) be bounded for all v, which is not possible unless Y is the zero vector space. Rather, a bounded linear operator is a locally bounded function. A linear operator on a metrizable vector space is bounded if and only if it is continuous. Contents 1 Examples 2 Equivalence of boundedness and continuity 3 Linearity and boundedness 4 Further properties 5 Properties of the space of bounded linear operators 6 Topological vector spaces 7 See also 8 References Examples Any linear operator between two finite-dimensional normed spaces is bounded, and such an operator may be viewed as multiplication by some fixed matrix. Many integral transforms are bounded linear operators. For instance, if is a continuous function, then the operator defined on the space of continuous functions on endowed with the uniform norm and with values in the space with given by the formula is bounded. -
Chapter 6 Linear Transformations and Operators
Chapter 6 Linear Transformations and Operators 6.1 The Algebra of Linear Transformations Theorem 6.1.1. Let V and W be vector spaces over the field F . Let T and U be two linear transformations from V into W . The function (T + U) defined pointwise by (T + U)(v) = T v + Uv is a linear transformation from V into W . Furthermore, if s F , the function (sT ) ∈ defined by (sT )(v) = s (T v) is also a linear transformation from V into W . The set of all linear transformation from V into W , together with the addition and scalar multiplication defined above, is a vector space over the field F . Proof. Suppose that T and U are linear transformation from V into W . For (T +U) defined above, we have (T + U)(sv + w) = T (sv + w) + U (sv + w) = s (T v) + T w + s (Uv) + Uw = s (T v + Uv) + (T w + Uw) = s(T + U)v + (T + U)w, 127 128 CHAPTER 6. LINEAR TRANSFORMATIONS AND OPERATORS which shows that (T + U) is a linear transformation. Similarly, we have (rT )(sv + w) = r (T (sv + w)) = r (s (T v) + (T w)) = rs (T v) + r (T w) = s (r (T v)) + rT (w) = s ((rT ) v) + (rT ) w which shows that (rT ) is a linear transformation. To verify that the set of linear transformations from V into W together with the operations defined above is a vector space, one must directly check the conditions of Definition 3.3.1. These are straightforward to verify, and we leave this exercise to the reader. -
REVIEW on SPACES of LINEAR OPERATORS Throughout, We Let X, Y Be Complex Banach Spaces. Definition 1. B(X, Y ) Denotes the Vector
REVIEW ON SPACES OF LINEAR OPERATORS Throughout, we let X; Y be complex Banach spaces. Definition 1. B(X; Y ) denotes the vector space of bounded linear maps from X to Y . • For T 2 B(X; Y ), let kT k be smallest constant C such that kT xk ≤ Ckxk, or equivalently kT k = sup kT xk : kxk=1 Lemma 2. kT k is a norm on B(X; Y ), and makes B(X; Y ) into a Banach space. Proof. That kT k is a norm is easy, so we check completeness of B(X; Y ). 1 Suppose fTjgj=1 is a Cauchy sequence, i.e. kTi − Tjk ! 0 as i; j ! 1 : For each x 2 X, Tix is Cauchy in Y , since kTix − Tjxk ≤ kTi − Tjk kxk : Thus lim Tix ≡ T x exists by completeness of Y: i!1 Linearity of T follows immediately. And for kxk = 1, kTix − T xk = lim kTix − Tjxk ≤ sup kTi − Tjk j!1 j>i goes to 0 as i ! 1, so kTi − T k ! 0. Important cases ∗ • Y = C: we denote B(X; C) = X , the \dual space of X". It is a Banach space, and kvkX∗ = sup jv(x)j : kxk=1 • If T 2 B(X; Y ), define T ∗ 2 B(Y ∗;X∗) by the rule (T ∗v)(x) = v(T x) : Then j(T ∗v)(x)j = jv(T x)j ≤ kT k kvk kxk, so kT ∗vk ≤ kT k kvk. Thus ∗ kT kB(Y ∗;X∗) ≤ kT kB(X;Y ) : • If X = H is a Hilbert space, we saw we can identify X∗ and H by the relation v $ y where v(x) = hx; yi : Then T ∗ is defined on H by hx; T ∗yi = hT x; yi : Note the identification is a conjugate linear map between X∗ and H: cv(x) = chx; yi = hx; cy¯ i : 1 2 526/556 LECTURE NOTES • The case of greatest interest is when X = Y ; we denote B(X) ≡ B(X; X). -
Linear Algebra Review
1. Some Basics from Linear Algebra With these notes, I will try and clarify certain topics that I only quickly mention in class. First and foremost, I will assume that you are familiar with many basic facts about real and complex numbers. In particular, both R and C are fields; they satisfy the field axioms. For p z = x + iy 2 C, the modulus, i.e. jzj = x2 + y2 ≥ 0, represents the distance from z to the origin in the complex plane. (As such, it coincides with the absolute value for real z.) For z = x + iy 2 C, complex conjugation, i.e. z = x − iy, represents reflection about the x-axis in the complex plane. It will also be important that both R and C are complete, as metric spaces, when equipped with the metric d(z; w) = jz − wj. 1.1. Vector Spaces. One of the most important notions in this course is that of a vector space. Although vector spaces can be defined over any field, we will (by and large) restrict our attention to fields F 2 fR; Cg. The following definition is fundamental. Definition 1.1. Let F be a field. A vector space V over F is a non-empty set V (the elements of V are called vectors) over a field F (the elements of F are called scalars) equipped with two operations: i) To each pair u; v 2 V , there exists a unique element u + v 2 V . This operation is called vector addition. ii) To each u 2 V and α 2 F, there exists a unique element αu 2 V . -
Functional Analysis
Functional Analysis Lecture Notes of Winter Semester 2017/18 These lecture notes are based on my course from winter semester 2017/18. I kept the results discussed in the lectures (except for minor corrections and improvements) and most of their numbering. Typi- cally, the proofs and calculations in the notes are a bit shorter than those given in class. The drawings and many additional oral remarks from the lectures are omitted here. On the other hand, the notes con- tain a couple of proofs (mostly for peripheral statements) and very few results not presented during the course. With `Analysis 1{4' I refer to the class notes of my lectures from 2015{17 which can be found on my webpage. Occasionally, I use concepts, notation and standard results of these courses without further notice. I am happy to thank Bernhard Konrad, J¨orgB¨auerle and Johannes Eilinghoff for their careful proof reading of my lecture notes from 2009 and 2011. Karlsruhe, May 25, 2020 Roland Schnaubelt Contents Chapter 1. Banach spaces2 1.1. Basic properties of Banach and metric spaces2 1.2. More examples of Banach spaces 20 1.3. Compactness and separability 28 Chapter 2. Continuous linear operators 38 2.1. Basic properties and examples of linear operators 38 2.2. Standard constructions 47 2.3. The interpolation theorem of Riesz and Thorin 54 Chapter 3. Hilbert spaces 59 3.1. Basic properties and orthogonality 59 3.2. Orthonormal bases 64 Chapter 4. Two main theorems on bounded linear operators 69 4.1. The principle of uniform boundedness and strong convergence 69 4.2. -
U(T)\\ for All S, Te CD(H) (See [8, P. 197])
proceedings of the american mathematical society Volume 110, Number 3, November 1990 ON SOME EQUIVALENT METRICS FOR BOUNDED OPERATORS ON HILBERT SPACE FUAD KITTANEH (Communicated by Palle E. T. Jorgensen) Abstract. Several operator norm inequalities concerning the equivalence of some metrics for bounded linear operators on Hilbert space are given. In addi- tion, some related inequalities for the Hilbert-Schmidt norm are presented. 1. Introduction Let H be a complex Hilbert space and let CD(H) denote the family of closed, densely defined linear operators on H, and B(H) the bounded members of CD(H). For each T G CD(H), let 11(F) denote the orthogonal projection of H @ H onto the graph of F, and let a(T) denote the pure contraction defined by a(T) = T(l + T*T)~ ' . The gap metric on CD(H) is defined by d(S, T) = \\U(S) - U(T)\\ for all S, Te CD(H) (see [8, p. 197]). In [11], W. E. Kaufman introduced a metric ô on CD(H) defined by ô(S, T) = \\a(S)-a(T)\\ for all S, TeCD(H) and he showed that this metric is stronger than the gap metric d and not equivalent to it. He also stated that on B(H) the gap metric d is equivalent to the metric generated by the usual operator norm and he proved that the metric ô has this property. The purpose of this paper is to present quantitative estimates to this effect. In §2, we present several operator norm inequalities to compare the metric ô, the gap metric d, and the usual operator norm metric. -
On the Approximability of Injective Tensor Norm Vijay Bhattiprolu
On the Approximability of Injective Tensor Norm Vijay Bhattiprolu CMU-CS-19-115 June 28, 2019 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Venkatesan Guruswami, Chair Anupam Gupta David Woodruff Madhur Tulsiani, TTIC Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright c 2019 Vijay Bhattiprolu This research was sponsored by the National Science Foundation under award CCF-1526092, and by the National Science Foundation under award 1422045. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: Injective Tensor Norm, Operator Norm, Hypercontractive Norms, Sum of Squares Hierarchy, Convex Programming, Continuous Optimization, Optimization over the Sphere, Approximation Algorithms, Hardness of Approximation Abstract The theory of approximation algorithms has had great success with combinatorial opti- mization, where it is known that for a variety of problems, algorithms based on semidef- inite programming are optimal under the unique games conjecture. In contrast, the ap- proximability of most continuous optimization problems remains unresolved. In this thesis we aim to extend the theory of approximation algorithms to a wide class of continuous optimization problems captured by the injective tensor norm framework. Given an order-d tensor T, and symmetric convex sets C1,... Cd, the injective tensor norm of T is defined as sup hT , x1 ⊗ · · · ⊗ xdi, i x 2Ci Injective tensor norm has manifestations across several branches of computer science, optimization and analysis.