Norm-Attaining Operators
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On Quasi Norm Attaining Operators Between Banach Spaces
ON QUASI NORM ATTAINING OPERATORS BETWEEN BANACH SPACES GEUNSU CHOI, YUN SUNG CHOI, MINGU JUNG, AND MIGUEL MART´IN Abstract. We provide a characterization of the Radon-Nikod´ymproperty in terms of the denseness of bounded linear operators which attain their norm in a weak sense, which complement the one given by Bourgain and Huff in the 1970's. To this end, we introduce the following notion: an operator T : X ÝÑ Y between the Banach spaces X and Y is quasi norm attaining if there is a sequence pxnq of norm one elements in X such that pT xnq converges to some u P Y with }u}“}T }. Norm attaining operators in the usual (or strong) sense (i.e. operators for which there is a point in the unit ball where the norm of its image equals the norm of the operator) and also compact operators satisfy this definition. We prove that strong Radon-Nikod´ymoperators can be approximated by quasi norm attaining operators, a result which does not hold for norm attaining operators in the strong sense. This shows that this new notion of quasi norm attainment allows to characterize the Radon-Nikod´ymproperty in terms of denseness of quasi norm attaining operators for both domain and range spaces, completing thus a characterization by Bourgain and Huff in terms of norm attaining operators which is only valid for domain spaces and it is actually false for range spaces (due to a celebrated example by Gowers of 1990). A number of other related results are also included in the paper: we give some positive results on the denseness of norm attaining Lipschitz maps, norm attaining multilinear maps and norm attaining polynomials, characterize both finite dimensionality and reflexivity in terms of quasi norm attaining operators, discuss conditions to obtain that quasi norm attaining operators are actually norm attaining, study the relationship with the norm attainment of the adjoint operator and, finally, present some stability results. -
On Fixed Points of One Class of Operators
⥬ â¨ç÷ âã¤÷ù. .7, ü2 Matematychni Studii. V.7, No.2 517.988 ON FIXED POINTS OF ONE CLASS OF OPERATORS Yu.F. Korobe˘ınik Yu. Korobeinik. On xed points of one class of operators, Matematychni Studii, 7(1997) 187{192. Let T : Q ! Q be a selfmapping of some subset Q of a Banach space E. The operator T does not increase the distance between Q and the origin (the null element E) if 8x 2 Q kT xk ≤ kxk (such operator is called a NID operator). In this paper some sucient conditions for the existence of a non-trivial xed point of a NID operator are obtained. These conditions enable in turn to prove some new results on xed points of nonexpanding operators. Let Q be a subset of a linear normed space (E; k · k). An operator T : Q ! E is said to be nonexpanding or NE operator if 8x1; x2 2 Q kT x1 − T x2k ≤ kx1 − x2k: It is evident that each nonexpanding mapping of Q is continuous in Q. The problem of existence of xed points of nonexpanding operators was investi- gated in many papers (see [1]{[9]). The monograph [10] contains a comparatively detailed survey of essential results on xed points of NE operators obtained in the beginning of the seventies. In particular, it was proved that each NE operator in a closed bounded convex subset of a uniformly convex space has at least one xed point in Q (for the de nition of a uniformly convex space see [10], ch.2, x4 or [11], ch.V, x12). -
The Nonstandard Theory of Topological Vector Spaces
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 172, October 1972 THE NONSTANDARDTHEORY OF TOPOLOGICAL VECTOR SPACES BY C. WARD HENSON AND L. C. MOORE, JR. ABSTRACT. In this paper the nonstandard theory of topological vector spaces is developed, with three main objectives: (1) creation of the basic nonstandard concepts and tools; (2) use of these tools to give nonstandard treatments of some major standard theorems ; (3) construction of the nonstandard hull of an arbitrary topological vector space, and the beginning of the study of the class of spaces which tesults. Introduction. Let Ml be a set theoretical structure and let *JR be an enlarge- ment of M. Let (E, 0) be a topological vector space in M. §§1 and 2 of this paper are devoted to the elementary nonstandard theory of (F, 0). In particular, in §1 the concept of 0-finiteness for elements of *E is introduced and the nonstandard hull of (E, 0) (relative to *3R) is defined. §2 introduces the concept of 0-bounded- ness for elements of *E. In §5 the elementary nonstandard theory of locally convex spaces is developed by investigating the mapping in *JK which corresponds to a given pairing. In §§6 and 7 we make use of this theory by providing nonstandard treatments of two aspects of the existing standard theory. In §6, Luxemburg's characterization of the pre-nearstandard elements of *E for a normed space (E, p) is extended to Hausdorff locally convex spaces (E, 8). This characterization is used to prove the theorem of Grothendieck which gives a criterion for the completeness of a Hausdorff locally convex space. -
A Note on Bi-Contractive Projections on Spaces of Vector Valued Continuous
Concr. Oper. 2018; 5: 42–49 Concrete Operators Research Article Fernanda Botelho* and T.S.S.R.K. Rao A note on bi-contractive projections on spaces of vector valued continuous functions https://doi.org/10.1515/conop-2018-0005 Received October 10, 2018; accepted December 12, 2018. Abstract: This paper concerns the analysis of the structure of bi-contractive projections on spaces of vector valued continuous functions and presents results that extend the characterization of bi-contractive projec- tions given by the rst author. It also includes a partial generalization of these results to ane and vector valued continuous functions from a Choquet simplex into a Hilbert space. Keywords: Contractive projections, Bi-contractive projections, Vector measures, Spaces of vector valued functions MSC: 47B38, 46B04, 46E40 1 Introduction We denote by C(Ω, E) the space of all continuous functions dened on a compact Hausdor topological space Ω with values in a Banach space E, endowed with the norm YfY∞ = maxx∈Ω Yf(x)YE. The continuity of a function f ∈ C(Ω, E) is understood to be “in the strong sense", meaning that for every w0 ∈ Ω and > 0 there exists O, an open subset of Ω, containing w0 such that Yf (w) − f(w0)YE < , for every w ∈ O, see [9] or [16]. A contractive projection P ∶ C(Ω, E) → C(Ω, E) is an idempotent bounded linear operator of norm 1. ⊥ A contractive projection is called bi-contractive if its complementary projection P (= I − P) is contractive. In this paper, we extend the characterization given in [6], of bi-contractive projections on C(Ω, E), with Ω a compact metric space, E a Hilbert space that satisfy an additional support disjointness property. -
Semi-Indefinite-Inner-Product and Generalized Minkowski Spaces
Semi-indefinite-inner-product and generalized Minkowski spaces A.G.Horv´ath´ Department of Geometry, Budapest University of Technology and Economics, H-1521 Budapest, Hungary Nov. 3, 2008 Abstract In this paper we parallelly build up the theories of normed linear spaces and of linear spaces with indefinite metric, called also Minkowski spaces for finite dimensions in the literature. In the first part of this paper we collect the common properties of the semi- and indefinite-inner-products and define the semi-indefinite- inner-product and the corresponding structure, the semi-indefinite-inner- product space. We give a generalized concept of Minkowski space embed- ded in a semi-indefinite-inner-product space using the concept of a new product, that contains the classical cases as special ones. In the second part of this paper we investigate the real, finite dimen- sional generalized Minkowski space and its sphere of radius i. We prove that it can be regarded as a so-called Minkowski-Finsler space and if it is homogeneous one with respect to linear isometries, then the Minkowski- Finsler distance its points can be determined by the Minkowski-product. MSC(2000):46C50, 46C20, 53B40 Keywords: normed linear space, indefinite and semi-definite inner product, arXiv:0901.4872v1 [math.MG] 30 Jan 2009 orthogonality, Finsler space, group of isometries 1 Introduction 1.1 Notation and Terminology concepts without definition: real and complex vector spaces, basis, dimen- sion, direct sum of subspaces, linear and bilinear mapping, quadratic forms, inner (scalar) product, hyperboloid, ellipsoid, hyperbolic space and hyperbolic metric, kernel and rank of a linear mapping. -
BS 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 important features. Sub-section B collects the five so-called Principles of Banach space theory, which were already presented earlier. Convention. Throughout this note K will be one of the fields R or C, and all vector spaces are over K. A. Banach Spaces Definition. A normed vector space (X , k . k), which is complete in the norm topology, is called a Banach space. When there is no danger of confusion, the norm will be omitted from the notation. Comment. Banach spaces are particular cases of Frechet spaces, which were defined as locally convex (F)-spaces. Therefore, the results from TVS IV and LCVS III will all be relevant here. In particular, when one checks completeness, the condition that a net (xλ)λ∈Λ ⊂ X is Cauchy, is equivalent to the metric condition: (mc) for every ε > 0, there exists λε ∈ Λ, such that kxλ − xµk < ε, ∀ λ, µ λε. As pointed out in TVS IV, this condition is actually independent of the norm, that is, if one norm k . k satisfies (mc), then so does any norm equivalent to k . k. The following criteria have already been proved in TVS IV. Proposition 1. For a normed vector space (X , k . k), the following are equivalent. (i) X is a Banach space. (ii) Every Cauchy net in X is convergent. (iii) Every Cauchy sequence in X is convergent. -
Dual-Norm Least-Squares Finite Element Methods for Hyperbolic Problems
Dual-Norm Least-Squares Finite Element Methods for Hyperbolic Problems by Delyan Zhelev Kalchev Bachelor, Sofia University, Bulgaria, 2009 Master, Sofia University, Bulgaria, 2012 M.S., University of Colorado at Boulder, 2015 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Applied Mathematics 2018 This thesis entitled: Dual-Norm Least-Squares Finite Element Methods for Hyperbolic Problems written by Delyan Zhelev Kalchev has been approved for the Department of Applied Mathematics Thomas A. Manteuffel Stephen Becker Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Kalchev, Delyan Zhelev (Ph.D., Applied Mathematics) Dual-Norm Least-Squares Finite Element Methods for Hyperbolic Problems Thesis directed by Professor Thomas A. Manteuffel Least-squares finite element discretizations of first-order hyperbolic partial differential equations (PDEs) are proposed and studied. Hyperbolic problems are notorious for possessing solutions with jump discontinuities, like contact discontinuities and shocks, and steep exponential layers. Furthermore, nonlinear equations can have rarefaction waves as solutions. All these contribute to the challenges in the numerical treatment of hyperbolic PDEs. The approach here is to obtain appropriate least-squares formulations based on suitable mini- mization principles. Typically, such formulations can be reduced to one or more (e.g., by employing a Newton-type linearization procedure) quadratic minimization problems. Both theory and numer- ical results are presented. -
On the Duality of Strong Convexity and Strong Smoothness: Learning Applications and Matrix Regularization
On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari Toyota Technological Institute—Chicago, USA fsham,shai,[email protected] Abstract strictly convex functions). Here, the notion of strong con- vexity provides one means to characterize this gap in terms We show that a function is strongly convex with of some general norm (rather than just Euclidean). respect to some norm if and only if its conjugate This work examines the notion of strong convexity from function is strongly smooth with respect to the a duality perspective. We show a function is strongly convex dual norm. This result has already been found with respect to some norm if and only if its (Fenchel) con- to be a key component in deriving and analyz- jugate function is strongly smooth with respect to its dual ing several learning algorithms. Utilizing this du- norm. Roughly speaking, this notion of smoothness (defined ality, we isolate a single inequality which seam- precisely later) provides a second order upper bound of the lessly implies both generalization bounds and on- conjugate function, which has already been found to be a key line regret bounds; and we show how to construct component in deriving and analyzing several learning algo- strongly convex functions over matrices based on rithms. Using this relationship, we are able to characterize strongly convex functions over vectors. The newly a number of matrix based penalty functions, of recent in- constructed functions (over matrices) inherit the terest, as being strongly convex functions, which allows us strong convexity properties of the underlying vec- to immediately derive online algorithms and generalization tor functions. -
An Introduction to Some Aspects of Functional Analysis, 7: Convergence of Operators
An introduction to some aspects of functional analysis, 7: Convergence of operators Stephen Semmes Rice University Abstract Here we look at strong and weak operator topologies on spaces of bounded linear mappings, and convergence of sequences of operators with respect to these topologies in particular. Contents I The strong operator topology 2 1 Seminorms 2 2 Bounded linear mappings 4 3 The strong operator topology 5 4 Shift operators 6 5 Multiplication operators 7 6 Dense sets 9 7 Shift operators, 2 10 8 Other operators 11 9 Unitary operators 12 10 Measure-preserving transformations 13 II The weak operator topology 15 11 Definitions 15 1 12 Multiplication operators, 2 16 13 Dual linear mappings 17 14 Shift operators, 3 19 15 Uniform boundedness 21 16 Continuous linear functionals 22 17 Bilinear functionals 23 18 Compactness 24 19 Other operators, 2 26 20 Composition operators 27 21 Continuity properties 30 References 32 Part I The strong operator topology 1 Seminorms Let V be a vector space over the real or complex numbers. A nonnegative real-valued function N(v) on V is said to be a seminorm on V if (1.1) N(tv) = |t| N(v) for every v ∈ V and t ∈ R or C, as appropriate, and (1.2) N(v + w) ≤ N(v) + N(w) for every v, w ∈ V . Here |t| denotes the absolute value of a real number t, or the modulus of a complex number t. If N(v) > 0 when v 6= 0, then N(v) is a norm on V , and (1.3) d(v, w) = N(v − w) defines a metric on V . -
November 7, 2013 1 Introduction
CS 229r: Algorithms for Big Data Fall 2013 Lecture 20 | November 7, 2013 Prof. Jelani Nelson Scribe: Yun William Yu 1 Introduction Today we're going to go through the analysis of matrix completion. First though, let's go through the history of prior work on this problem. Recall the setup and model: • Matrix completion setup: n ×n { Want to recover M 2 R 1 2 , under the assumption that rank(M) = r, where r is small. { Only some small subset of the entries (Mij)ij2Ω are revealed, where Ω ⊂ [n1]×[n2]; jΩj = m n1; n2 • Model: { m times we sample i; j uniformly at random + insert into Ω (so Ω is a multiset). { Note that the same results hold if we choose m entries without replacement, but it's easier to analyze this way. In fact, if you can show that if recovery works with replacement, then that implies that recovery works without replacement, which makes sense because you'd only be seeing more information about M. • We recover M by Nuclear Norm Minimization (NNM): { Solve the program min kXk∗ s.t. 8i; j 2 Ω;Xij = Mij • [Recht, Fazel, Parrilo '10] [RFP10] was first to give some rigorous guarantees for NNM. { As you'll see on the pset, you can actually solve this in polynomial time since it's an instance of what's known as a semi-definite program. • [Cand´es,Recht, '09] [CR09] was the first paper to show provable guarantees for NNM applied to matrix completion. • There were some quantitative improvements (in the parameters) in two papers: [Cand´es, Tao '09] [CT10] and [Keshavan, Montanari, Oh '09] [KMO10] • Today we're going to cover an even newer analysis given in [Recht, 2011] [Rec11], which has a couple of advantages. -
Chebyshev Sets
CHEBYSHEV SETS JAMES FLETCHER and WARREN B. MOORS (October 9, 2014) Abstract A Chebyshev set is a subset of a normed linear space that admits unique best approximations. In the first part of this article we present some basic results concerning Chebyshev sets. In particular, we investigate properties of the metric projection map, sufficient conditions for a subset of a normed linear space to be a Chebyshev set, and sufficient conditions for a Chebyshev set to be convex. In the second half of the article we present a construction of a non-convex Chebyshev subset of an inner product space. 1. Introduction Given a normed linear space (X, ), we shall say that a subset of X is a ‘Chebyshev set’ if k·k every point outside of it admits a unique nearest point. In this article we will review some of the literature, and a little, a very little, of the history of the question of whether every Chebyshev set is convex (for a brief summary of the history of this problem, see [26, p.307]). The volume of research invested in this topic is vast and we will not even pretend to present, even a small amount, of what is known. Instead, we will be content to present some of the most basic results concerning Chebyshev sets (see Section 2) and then present what the authors believe are among some of the most interesting partial solutions to the following question, which from hence forth, will be referred to as the ‘Chebyshev set problem’: “Is every Chebyshev set in a Hilbert space convex?”. -
Discretization of Linear Problems in Banach Spaces: Residual Minimization, Nonlinear Petrov–Galerkin, and Monotone Mixed Methods
Discretization of Linear Problems in Banach Spaces: Residual Minimization, Nonlinear Petrov{Galerkin, and Monotone Mixed Methods I. Muga† · K.G. van der Zee‡ 5th June, 2018 Abstract This work presents a comprehensive discretization theory for abstract linear operator equations in Banach spaces. The fundamental starting point of the theory is the idea of residual minimization in dual norms, and its inex- act version using discrete dual norms. It is shown that this development, in the case of strictly-convex reflexive Banach spaces with strictly-convex dual, gives rise to a class of nonlinear Petrov{Galerkin methods and, equiv- alently, abstract mixed methods with monotone nonlinearity. Crucial in the formulation of these methods is the (nonlinear) bijective duality map. Under the Fortin condition, we prove discrete stability of the abstract inexact method, and subsequently carry out a complete error analysis. As part of our analysis, we prove new bounds for best-approximation projec- tors, which involve constants depending on the geometry of the underlying Banach space. The theory generalizes and extends the classical Petrov{ Galerkin method as well as existing residual-minimization approaches, such as the discontinuous Petrov{Galerkin method. Keywords Operators in Banach spaces · Residual minimization · Petrov{Galerkin discretization · Error analysis · Quasi-optimality · Duality mapping · Best approximation · Geometric constants Mathematics Subject Classification 41A65 · 65J05 · 46B20 · 65N12 · 65N15 arXiv:1511.04400v3 [math.NA] 29 Jul 2018 †Pontificia Univ. Cat´olicade Valpara´ıso,Instituto de Mathem´aticas,[email protected] ‡University of Nottingham, School of Mathematical Sciences, [email protected] 1 Contents 1 Introduction3 1.1 Petrov{Galerkin discretization and residual minimization .