FIXED POINT THEOREMS Econ 2010 - Fall 2013
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Uniform Boundedness Principle for Unbounded Operators
UNIFORM BOUNDEDNESS PRINCIPLE FOR UNBOUNDED OPERATORS C. GANESA MOORTHY and CT. RAMASAMY Abstract. A uniform boundedness principle for unbounded operators is derived. A particular case is: Suppose fTigi2I is a family of linear mappings of a Banach space X into a normed space Y such that fTix : i 2 Ig is bounded for each x 2 X; then there exists a dense subset A of the open unit ball in X such that fTix : i 2 I; x 2 Ag is bounded. A closed graph theorem and a bounded inverse theorem are obtained for families of linear mappings as consequences of this principle. Some applications of this principle are also obtained. 1. Introduction There are many forms for uniform boundedness principle. There is no known evidence for this principle for unbounded operators which generalizes classical uniform boundedness principle for bounded operators. The second section presents a uniform boundedness principle for unbounded operators. An application to derive Hellinger-Toeplitz theorem is also obtained in this section. A JJ J I II closed graph theorem and a bounded inverse theorem are obtained for families of linear mappings in the third section as consequences of this principle. Go back Let us assume the following: Every vector space X is over R or C. An α-seminorm (0 < α ≤ 1) is a mapping p: X ! [0; 1) such that p(x + y) ≤ p(x) + p(y), p(ax) ≤ jajαp(x) for all x; y 2 X Full Screen Close Received November 7, 2013. 2010 Mathematics Subject Classification. Primary 46A32, 47L60. Key words and phrases. -
On the Boundary Between Mereology and Topology
On the Boundary Between Mereology and Topology Achille C. Varzi Istituto per la Ricerca Scientifica e Tecnologica, I-38100 Trento, Italy [email protected] (Final version published in Roberto Casati, Barry Smith, and Graham White (eds.), Philosophy and the Cognitive Sciences. Proceedings of the 16th International Wittgenstein Symposium, Vienna, Hölder-Pichler-Tempsky, 1994, pp. 423–442.) 1. Introduction Much recent work aimed at providing a formal ontology for the common-sense world has emphasized the need for a mereological account to be supplemented with topological concepts and principles. There are at least two reasons under- lying this view. The first is truly metaphysical and relates to the task of charac- terizing individual integrity or organic unity: since the notion of connectedness runs afoul of plain mereology, a theory of parts and wholes really needs to in- corporate a topological machinery of some sort. The second reason has been stressed mainly in connection with applications to certain areas of artificial in- telligence, most notably naive physics and qualitative reasoning about space and time: here mereology proves useful to account for certain basic relation- ships among things or events; but one needs topology to account for the fact that, say, two events can be continuous with each other, or that something can be inside, outside, abutting, or surrounding something else. These motivations (at times combined with others, e.g., semantic transpar- ency or computational efficiency) have led to the development of theories in which both mereological and topological notions play a pivotal role. How ex- actly these notions are related, however, and how the underlying principles should interact with one another, is still a rather unexplored issue. -
Lectures on Some Fixed Point Theorems of Functional Analysis
Lectures On Some Fixed Point Theorems Of Functional Analysis By F.F. Bonsall Tata Institute Of Fundamental Research, Bombay 1962 Lectures On Some Fixed Point Theorems Of Functional Analysis By F.F. Bonsall Notes by K.B. Vedak No part of this book may be reproduced in any form by print, microfilm or any other means with- out written permission from the Tata Institute of Fundamental Research, Colaba, Bombay 5 Tata Institute of Fundamental Research Bombay 1962 Introduction These lectures do not constitute a systematic account of fixed point the- orems. I have said nothing about these theorems where the interest is essentially topological, and in particular have nowhere introduced the important concept of mapping degree. The lectures have been con- cerned with the application of a variety of methods to both non-linear (fixed point) problems and linear (eigenvalue) problems in infinite di- mensional spaces. A wide choice of techniques is available for linear problems, and I have usually chosen to use those that give something more than existence theorems, or at least a promise of something more. That is, I have been interested not merely in existence theorems, but also in the construction of eigenvectors and eigenvalues. For this reason, I have chosen elementary rather than elegant methods. I would like to draw special attention to the Appendix in which I give the solution due to B. V. Singbal of a problem that I raised in the course of the lectures. I am grateful to Miss K. B. Vedak for preparing these notes and seeing to their publication. -
Baire Category Theorem and Uniform Boundedness Principle)
Functional Analysis (WS 19/20), Problem Set 3 (Baire Category Theorem and Uniform Boundedness Principle) Baire Category Theorem B1. Let (X; k·kX ) be an innite dimensional Banach space. Prove that X has uncountable Hamel basis. Note: This is Problem A2 from Problem Set 1. B2. Consider subset of bounded sequences 1 A = fx 2 l : only nitely many xk are nonzerog : Can one dene a norm on A so that it becomes a Banach space? Consider the same question with the set of polynomials dened on interval [0; 1]. B3. Prove that the set L2(0; 1) has empty interior as the subset of Banach space L1(0; 1). B4. Let f : [0; 1) ! [0; 1) be a continuous function such that for every x 2 [0; 1), f(kx) ! 0 as k ! 1. Prove that f(x) ! 0 as x ! 1. B5.( Uniform Boundedness Principle) Let (X; k · kX ) be a Banach space and (Y; k · kY ) be a normed space. Let fTαgα2A be a family of bounded linear operators between X and Y . Suppose that for any x 2 X, sup kTαxkY < 1: α2A Prove that . supα2A kTαk < 1 Uniform Boundedness Principle U1. Let F be a normed space C[0; 1] with L2(0; 1) norm. Check that the formula 1 Z n 'n(f) = n f(t) dt 0 denes a bounded linear functional on . Verify that for every , F f 2 F supn2N j'n(f)j < 1 but . Why Uniform Boundedness Principle is not satised in this case? supn2N k'nk = 1 U2.( pointwise convergence of operators) Let (X; k · kX ) be a Banach space and (Y; k · kY ) be a normed space. -
FUNCTIONAL ANALYSIS 1. Banach and Hilbert Spaces in What
FUNCTIONAL ANALYSIS PIOTR HAJLASZ 1. Banach and Hilbert spaces In what follows K will denote R of C. Definition. A normed space is a pair (X, k · k), where X is a linear space over K and k · k : X → [0, ∞) is a function, called a norm, such that (1) kx + yk ≤ kxk + kyk for all x, y ∈ X; (2) kαxk = |α|kxk for all x ∈ X and α ∈ K; (3) kxk = 0 if and only if x = 0. Since kx − yk ≤ kx − zk + kz − yk for all x, y, z ∈ X, d(x, y) = kx − yk defines a metric in a normed space. In what follows normed paces will always be regarded as metric spaces with respect to the metric d. A normed space is called a Banach space if it is complete with respect to the metric d. Definition. Let X be a linear space over K (=R or C). The inner product (scalar product) is a function h·, ·i : X × X → K such that (1) hx, xi ≥ 0; (2) hx, xi = 0 if and only if x = 0; (3) hαx, yi = αhx, yi; (4) hx1 + x2, yi = hx1, yi + hx2, yi; (5) hx, yi = hy, xi, for all x, x1, x2, y ∈ X and all α ∈ K. As an obvious corollary we obtain hx, y1 + y2i = hx, y1i + hx, y2i, hx, αyi = αhx, yi , Date: February 12, 2009. 1 2 PIOTR HAJLASZ for all x, y1, y2 ∈ X and α ∈ K. For a space with an inner product we define kxk = phx, xi . Lemma 1.1 (Schwarz inequality). -
A Study of Optimization in Hilbert Space
California State University, San Bernardino CSUSB ScholarWorks Theses Digitization Project John M. Pfau Library 1998 A study of optimization in Hilbert Space John Awunganyi Follow this and additional works at: https://scholarworks.lib.csusb.edu/etd-project Part of the Mathematics Commons Recommended Citation Awunganyi, John, "A study of optimization in Hilbert Space" (1998). Theses Digitization Project. 1459. https://scholarworks.lib.csusb.edu/etd-project/1459 This Project is brought to you for free and open access by the John M. Pfau Library at CSUSB ScholarWorks. It has been accepted for inclusion in Theses Digitization Project by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. A STUDY OF OPTIMIZATIONIN HELBERT SPACE A Project Presented to the Faculty of California State University, San Bernardino In Partial Fulfillment ofthe Requirements for the Degree Master ofArts in Mathematics by John Awunganyi June 1998 A STUDY OF OPTIMIZATION IN HILBERT SPACE A Project Presented to the Faculty of California State University, San Bernardino by John Awunganyi June 1998 Approved by; Dr. Chetan Prakash, Chair, Mathematics Date r. Wenxang Wang Dr. Christopher Freiling Dr. Terry Hallett, Graduate Coordinator Dr. Peter Williams, Department Chair ABSTRACT The primary objective ofthis project is to demonstrate that a certain field ofoptimization can be effectively unified by afew geometric principles ofcomplete normed linear space theory. By employing these principles, important and complex finite - dimensional problems can be interpreted and solved by methods springing from geometric insight. Concepts such as distance, orthogonality, and convexity play a fundamental and indispensable role in this development. Viewed in these terms, seemingly diverse problems and techniques often are found to be closely related. -
Chapter 3 the Contraction Mapping Principle
Chapter 3 The Contraction Mapping Principle The notion of a complete space is introduced in Section 1 where it is shown that every metric space can be enlarged to a complete one without further condition. The importance of complete metric spaces partly lies on the Contraction Mapping Principle, which is proved in Section 2. Two major applications of the Contraction Mapping Principle are subsequently given, first a proof of the Inverse Function Theorem in Section 3 and, second, a proof of the fundamental existence and uniqueness theorem for the initial value problem of differential equations in Section 4. 3.1 Complete Metric Space In Rn a basic property is that every Cauchy sequence converges. This property is called the completeness of the Euclidean space. The notion of a Cauchy sequence is well-defined in a metric space. Indeed, a sequence fxng in (X; d) is a Cauchy sequence if for every " > 0, there exists some n0 such that d(xn; xm) < ", for all n; m ≥ n0. A metric space (X; d) is complete if every Cauchy sequence in it converges. A subset E is complete if (E; d E×E) is complete, or, equivalently, every Cauchy sequence in E converges with limit in E. Proposition 3.1. Let (X; d) be a metric space. (a) Every complete set in X is closed. (b) Every closed set in a complete metric space is complete. In particular, this proposition shows that every subset in a complete metric space is complete if and only if it is closed. 1 2 CHAPTER 3. THE CONTRACTION MAPPING PRINCIPLE Proof. -
A Hilbert Space Theory of Generalized Graph Signal Processing
1 A Hilbert Space Theory of Generalized Graph Signal Processing Feng Ji and Wee Peng Tay, Senior Member, IEEE Abstract Graph signal processing (GSP) has become an important tool in many areas such as image pro- cessing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain. Our framework relies extensively on concepts and tools from functional analysis to generalize traditional GSP to graph signals in a separable Hilbert space with infinite dimensions. We develop a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals. Index Terms Graph signal proceesing, Hilbert space, generalized graph signals, F-transform, filtering, sampling I. INTRODUCTION Since its emergence, the theory and applications of graph signal processing (GSP) have rapidly developed (see for example, [1]–[10]). Traditional GSP theory is essentially based on a change of orthonormal basis in a finite dimensional vector space. Suppose G = (V; E) is a weighted, arXiv:1904.11655v2 [eess.SP] 16 Sep 2019 undirected graph with V the vertex set of size n and E the set of edges. Recall that a graph signal f assigns a complex number to each vertex, and hence f can be regarded as an element of n C , where C is the set of complex numbers. The heart of the theory is a shift operator AG that is usually defined using a property of the graph. -
HILBERT SPACES in MODELLING of SYSTEMS Jean Claude Dutailly
HILBERT SPACES IN MODELLING OF SYSTEMS Jean Claude Dutailly To cite this version: Jean Claude Dutailly. HILBERT SPACES IN MODELLING OF SYSTEMS. 2014. hal-00974251 HAL Id: hal-00974251 https://hal.archives-ouvertes.fr/hal-00974251 Preprint submitted on 5 Apr 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HILBERT SPACES IN MODELLING OF SYSTEMS Jean Claude Dutailly Paris April 4, 2014 Abstract This paper studies the quantitative models which are used to represent systems, whatever the field (Physics, Economics, Finances,...). We prove that models which meet some very general conditions can be associated to Hilbert vector spaces : the state of the system is then identified with a unique vector. The measure of this state is done through observables, whose properties are studied and we show that they can take only values which are eigen vectors of the observables, and a probability law can be associated to the occurence of these measures. The effect of a change of variables in a model is related to a unitary operator. This leads to general laws for the evolution of systems, and to powerful tools for the study of transitions of phases in a system. -
Normal Families of Holomorphic Functions and Mappings on a Banach Space
EXPOSITIONES Expo. Math. 21 (2003): 193-218 © Urban & Fischer Verlag MATHEMATICAE www. urbanfischer.de/journals/expomath Normal Families of Holomorphic Functions and Mappings on a Banach Space Kang-Tae Kim 1 and Steven G. Krantz 2 1Pohang University of Science and Technology, Pohang 790-784, Korea 2 Department of Mathematics, Washington University, St. Louis, Missouri 63130, U.S.A. Abstract: The authors lay the foundations for the study of nor- mal families of holomorphic functions and mappings on an infinite- dimensional normed linear space. Characterizations of normal fami- lies, in terms of value distribution, spherical derivatives, and other geo- metric properties are derived. Montel-type theorems are established. A number of different topologies on spaces of holomorphic mappings are considered. Theorems about normal families are formulated and proved in the language of these various topologies. Normal functions are also introduced. Characterizations in terms of automorphisms and also in terms of invariant derivatives are presented. Keywords: holomorphic function, holomorphic mapping, Banach space, normal family, normal function. Introduction The theory of holomorphic functions of infinitely many complex variables is about forty years old. Pioneers of the subject were Nachbin [NAC1-NAC17], Gruman and Kiselman [GRK], and Mujica [MUJ]. After a quiet period of nearly two decades, the discipline is now enjoying a rebirth thanks to the ideas of Lempert [LEM1-LEM6]. Lempert has taught us that it is worthwhile to restrict attention to particular Banach spaces, and he has directed our efforts to especially fruitful questions. The work of the present paper is inspired by the results of [KIK]. That paper studied domains in a Hilbert space with an automorphism group orbit accumulating at a boundary point. -
Introduction to Fixed Point Methods
Math 951 Lecture Notes Chapter 4 { Introduction to Fixed Point Methods Mathew A. Johnson 1 Department of Mathematics, University of Kansas [email protected] Contents 1 Introduction1 2 Contraction Mapping Principle2 3 Example: Nonlinear Elliptic PDE4 4 Example: Nonlinear Reaction Diffusion7 5 The Chaffee-Infante Problem & Finite Time Blowup 10 6 Some Final Thoughts 13 7 Exercises 14 1 Introduction To this point, we have been using linear functional analytic tools (eg. Riesz Representation Theorem, etc.) to study the existence and properties of solutions to linear PDE. This has largely followed a well developed general theory which proceeded quite methodoligically and has been widely applicable. As we transition to nonlinear PDE theory, it is important to understand that there is essentially no widely developed, overaching theory that applies to all such equations. The closest thing I would way that exists is the famous Cauchy- Kovalyeskaya Theorem, which asserts quite generally the local existence of solutions to systems of partial differential equations equipped with initial conditions on a \noncharac- teristic" surface. However, this theorem requires the coefficients of the given PDE system, the initial data, and the surface where the IC is described to all be real analytic. While this is a very severe restriction, it turns out that it can not be removed. For this reason, and many more, the Cauchy-Kovalyeskaya Theorem is of little practical importance (although, it is obviously important from a historical context... hence why it is usually studied in Math 950). 1Copyright c 2020 by Mathew A. Johnson ([email protected]). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. -
Non-Linear Inner Structure of Topological Vector Spaces
mathematics Article Non-Linear Inner Structure of Topological Vector Spaces Francisco Javier García-Pacheco 1,*,† , Soledad Moreno-Pulido 1,† , Enrique Naranjo-Guerra 1,† and Alberto Sánchez-Alzola 2,† 1 Department of Mathematics, College of Engineering, University of Cadiz, 11519 Puerto Real, CA, Spain; [email protected] (S.M.-P.); [email protected] (E.N.-G.) 2 Department of Statistics and Operation Research, College of Engineering, University of Cadiz, 11519 Puerto Real (CA), Spain; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Inner structure appeared in the literature of topological vector spaces as a tool to charac- terize the extremal structure of convex sets. For instance, in recent years, inner structure has been used to provide a solution to The Faceless Problem and to characterize the finest locally convex vector topology on a real vector space. This manuscript goes one step further by settling the bases for studying the inner structure of non-convex sets. In first place, we observe that the well behaviour of the extremal structure of convex sets with respect to the inner structure does not transport to non-convex sets in the following sense: it has been already proved that if a face of a convex set intersects the inner points, then the face is the whole convex set; however, in the non-convex setting, we find an example of a non-convex set with a proper extremal subset that intersects the inner points. On the opposite, we prove that if a extremal subset of a non-necessarily convex set intersects the affine internal points, then the extremal subset coincides with the whole set.