CHAPTER 5. COMPLETENESS 5.1. There Are Three Basic Properties

Total Page:16

File Type:pdf, Size:1020Kb

CHAPTER 5. COMPLETENESS 5.1. There Are Three Basic Properties CHAPTER 5. COMPLETENESS 5.1. There are three basic properties about metric spaces which can be called infor- mally as three ‘C’s. They are completeness, compactness and connectedness. They will be the focus of the rest of this book. In the present chapter we study the first C: complete- ness. Let us briefly recall the following definitions from Chapter 3: a sequence xn in a { } metric space (X, ρ) is called a Cauchy sequence if, for each ε > 0, there exists N such that, for all m, n > N, ρ(xn, xm) < ε. or, according to our ad hoc definition, if there is a sequence of positive numbers αn decreasing to zero such that ρ(xn, xm) < αn for all m, n with m > n. A metric space is complete if all Cauchy sequences in it converge. According to Cauchy’s theorem, the real line R under the metric ρ(x, y) = x y | − | is complete. We have also seen that, more generally, the Euclidean space Rd is complete under the metric 2 2 ρ(x, y) = (x y ) + + (xd yd) 1 − 1 ··· − d for x = (x1, . , xd) and y = (y1, . , yd) in R . We will see more examples of complete metric spaces (actually, many of them are Banach spaces) in the future. Anyway, here we describe a “cheap way” to get a substantial supply of quick examples. Let (X, ρ) be an arbitrary metric space and let Y be any nonempty subset of X. Then we can equip Y with the metric ρY inherited from X: for all x, y Y , let ρY (x, y) = ρ(x, y); that is, ρY is ∈ just the restriction of ρ to Y Y . It is clear that ρY is a metric for Y , which is called the × metric on Y induced by ρ. The metric space (Y, ρY ) is called a subspace of X. Fact 1. With the above notation, if (X, ρ) is a complete metric space and if Y is a closed subset of X, then (Y, ρY ) is also a complete metric space. Proof: Let yn be a Cauchy sequence in Y . Then certainly it is a Cauchy sequence { } in X. Hence it has a limit in X. Since Y is a closed subset of X, this limit is in Y . This shows the convergence in Y of the Cauchy sequence yn . Q.E.D. { } Take any complete metric space, e.g. Rd. Then any nonempty closed set in it gives us an example of complete metric space. Notice the following strong form of converse to Fact 1; (it is strong because we do not have to assume the completeness of X.) Fact 2. A set Y of a metric space (X, ρ) is closed if (Y, ρY ) is complete. 1 The proof of this fact is left to you as an exercise (Exercise 4). 5.2. What can completeness do for you? In the present section we present the so called Banach’s principle for contractive mappings which hinges upon the completeness assumption. First we give: Definition. A mapping φ from a metric space (X, ρ) into itself is said to be a contractive mapping or simply a contraction if there exists a positive number α strictly less than 1 such that, for all x, y X, ρ(φ(x), φ(y)) αρ(x, y). ∈ ≤ Given a contractive mapping φ on X and take any point x X, we can define 0 ∈ a sequence xn in X by iteration: x = φ(x ), x = φ(x ) = φ(φ(x )), x = φ(x ) = { } 1 0 2 1 0 3 2 φ(φ(φ(x0))), etc. The question is, does this sequence converge? The answer is Yes, provided that the metric space X is complete. In that case it is easy to check that the limit z of this sequence is a fixed point of φ in the sense that φ(z) = z. Theorem (Banach’s Principle of Contractive Mappings). A contractive map- ping φ from a complete metric space (X, ρ) into itself has a unique fixed point in X, that is, a point z X satisfying φ(z) = z. Furthermore, for each x X, the sequence xn ∈ 0 ∈ { } defined iteratively by xn = φ(xn) (n 0) tends to this unique fixed point. + 1 ≥ Proof: Let x be an arbitrary point in X and define the sequence xn iteratively by 0 { } n 2 xn+ 1 = φ(xn). Denote by φ the composite of φ with itself n times. Thus φ (x) = φ(φ(x)), φ3(x) = φ(φ(φ(x))) etc. and in general φn+ 1(x) = φ(φn(x)). By induction, it is easy to show that, for all x, y X and for all positive integer n, ∈ ρ(φn(x), φn(y)) αnρ(x, y). ≤ Thus, for all positive integers m and n with m > n, we have n m ρ(xn, xm) = ρ(φ (x0), φ (x0)) ρ(φn(x ), φn+ 1(x )) + ρ(φn+ 1(x ), φn+ 2(x )) + + ρ(φm−1(x ), φm(x )) ≤ 0 0 0 0 ··· 0 0 n n+ 1 m−1 α ρ(x , φ(x )) + α ρ(x , φ(x )) + + α ρ(x , φ(x )) βn ≤ 0 0 0 0 ··· 0 0 ≤ ∞ k where βn ρ(x0, x1) α are positive numbers decreasing to zero as n . This ≡ k= n → ∞ shows that xn is a Cauchy sequence in X. The completeness of X tells us that xn { } { } converges, say to z. A contractive mapping is clearly a continuous mapping and hence we are allowed to let n in the identity φ(xn) = xn to conclude φ(z) = z. → ∞ + 1 2 To show the “uniqueness” part, let z1 and z2 be fixed points of the contraction φ: φ(z1) = z1 and φ(z2) = z2. Then ρ(z , z ) = ρ(φ(z ), φ(z )) αρ(z , z ). 1 2 1 2 ≤ 1 2 Since 0 < α < 1, we must have ρ(z1, z2) = 0 and hence z1 = z2. Q.E.D. 5.3. Banach’s contraction principle gives applications to local solvability of initial value problem in ordinary differential equations under the Lipschitz condition, the inverse function theorem in mathematical analysis of smooth functions with several variables, and a basic fact for constructing fractals, such as the Cantor set. These applications need substantial background for their descriptions. Here we briefly present an application to justify Newton’s method in a special case as well as an application to solvability of Bellman’s equation in dynamical programming. Example. Let a be any positive number greater than 1 and let X = [√a, ) x R: x √a , ∞ ≡ { ∈ ≥ } which is a closed subset R of the real line R and hence is a complete metric space in its own right, (according to Fact 2 in 5.1). Consider the map φ on X given by § 1 a φ(x) = x + . 2 x Here we ask the reader to check that φ indeed maps X into X itself, as well as the inequality φ(x) φ(y) α x y for x, y X, where α = 1/2. So, by Banach’s | − | ≤ | − | ∈ contraction principle, φ has a unique fixed point, say z. We can find this unique fixed point of φ, say z. By solving the equation φ(z) = z, we can find this fixed point, which turns out to be z = √a. Take any convenient number x0 in X, for example, x0 = a. Then the iteration xn = φ(xn) with x = a will give us a sequence xn of numbers + 1 0 { } approximating √a. When a = 2, xn are rational numbers approximating the irrational number √2. In this example the recipe for the contractive map φ comes from Newton’s method by applying f(x) φ(x) = x − f ′(x) to the function f(x) = x2 a. The reader is asked to check this. − 3 By using Lagrange’s mean value theorem and Banach’s principle of contractive map- pings, we can prove the following: Proposition. If φ is a differentiable function on a closed interval I such that φ(x) I ∈ for all x I and there exists a positive α < 1 such that φ′(x) α, then there is a unique ∈ | | ≤ point a in I such that φ(a) = a. We leave the proof of the above proposition to those readers who know Lagrange’s mean value theorem. Notice that, for the function φ given in the above example, we have 1 a φ′(x) = 1 2 − x2 and hence 0 φ′(x) 1/2 for all x [√a, ). ≤ ≤ ∈ ∞ 5.4. Recall that, given a metric space X, b(X) stands for the space of all bounded, C continuous, real-valued functions on X, and the norm f of f b(X) is given by ∈ C f = sup ∈ f(x) . Also, the recipe ρ(f, g) = f g defined a metric ρ on b(X). x X | | − C Next we recall that, given any nonempty set X (not necessary a metric space), the space consisting of all bounded functions on X is denoted by ℓ∞ (X). Proposition. With the notation as above, we have: (a) as a metric space, ℓ∞ (X) is complete, and (b) b(X) is closed in ℓ∞ (X) and hence is a complete space by itself. C Proof: Part (b) is just a repetition of a proposition in 4.6, as well as Fact 1 in our § previous section. So it is enough to establish part (a). Let fn be a Cauchy sequence { } in ℓ∞ (X). Then there is a sequence of positive numbers αn decreasing to zero such that ρ(fn, fm) fn fm ∞ αn for all n, m with m n.
Recommended publications
  • FUNCTIONAL ANALYSIS 1. Metric and Topological Spaces A
    FUNCTIONAL ANALYSIS CHRISTIAN REMLING 1. Metric and topological spaces A metric space is a set on which we can measure distances. More precisely, we proceed as follows: let X 6= ; be a set, and let d : X×X ! [0; 1) be a map. Definition 1.1. (X; d) is called a metric space if d has the following properties, for arbitrary x; y; z 2 X: (1) d(x; y) = 0 () x = y (2) d(x; y) = d(y; x) (3) d(x; y) ≤ d(x; z) + d(z; y) Property 3 is called the triangle inequality. It says that a detour via z will not give a shortcut when going from x to y. The notion of a metric space is very flexible and general, and there are many different examples. We now compile a preliminary list of metric spaces. Example 1.1. If X 6= ; is an arbitrary set, then ( 0 x = y d(x; y) = 1 x 6= y defines a metric on X. Exercise 1.1. Check this. This example does not look particularly interesting, but it does sat- isfy the requirements from Definition 1.1. Example 1.2. X = C with the metric d(x; y) = jx−yj is a metric space. X can also be an arbitrary non-empty subset of C, for example X = R. In fact, this works in complete generality: If (X; d) is a metric space and Y ⊆ X, then Y with the same metric is a metric space also. Example 1.3. Let X = Cn or X = Rn. For each p ≥ 1, n !1=p X p dp(x; y) = jxj − yjj j=1 1 2 CHRISTIAN REMLING defines a metric on X.
    [Show full text]
  • Course 221: Michaelmas Term 2006 Section 3: Complete Metric Spaces, Normed Vector Spaces and Banach Spaces
    Course 221: Michaelmas Term 2006 Section 3: Complete Metric Spaces, Normed Vector Spaces and Banach Spaces David R. Wilkins Copyright c David R. Wilkins 1997–2006 Contents 3 Complete Metric Spaces, Normed Vector Spaces and Banach Spaces 2 3.1 The Least Upper Bound Principle . 2 3.2 Monotonic Sequences of Real Numbers . 2 3.3 Upper and Lower Limits of Bounded Sequences of Real Numbers 3 3.4 Convergence of Sequences in Euclidean Space . 5 3.5 Cauchy’s Criterion for Convergence . 5 3.6 The Bolzano-Weierstrass Theorem . 7 3.7 Complete Metric Spaces . 9 3.8 Normed Vector Spaces . 11 3.9 Bounded Linear Transformations . 15 3.10 Spaces of Bounded Continuous Functions on a Metric Space . 19 3.11 The Contraction Mapping Theorem and Picard’s Theorem . 20 3.12 The Completion of a Metric Space . 23 1 3 Complete Metric Spaces, Normed Vector Spaces and Banach Spaces 3.1 The Least Upper Bound Principle A set S of real numbers is said to be bounded above if there exists some real number B such x ≤ B for all x ∈ S. Similarly a set S of real numbers is said to be bounded below if there exists some real number A such that x ≥ A for all x ∈ S. A set S of real numbers is said to be bounded if it is bounded above and below. Thus a set S of real numbers is bounded if and only if there exist real numbers A and B such that A ≤ x ≤ B for all x ∈ S.
    [Show full text]
  • General Topology
    General Topology Tom Leinster 2014{15 Contents A Topological spaces2 A1 Review of metric spaces.......................2 A2 The definition of topological space.................8 A3 Metrics versus topologies....................... 13 A4 Continuous maps........................... 17 A5 When are two spaces homeomorphic?................ 22 A6 Topological properties........................ 26 A7 Bases................................. 28 A8 Closure and interior......................... 31 A9 Subspaces (new spaces from old, 1)................. 35 A10 Products (new spaces from old, 2)................. 39 A11 Quotients (new spaces from old, 3)................. 43 A12 Review of ChapterA......................... 48 B Compactness 51 B1 The definition of compactness.................... 51 B2 Closed bounded intervals are compact............... 55 B3 Compactness and subspaces..................... 56 B4 Compactness and products..................... 58 B5 The compact subsets of Rn ..................... 59 B6 Compactness and quotients (and images)............. 61 B7 Compact metric spaces........................ 64 C Connectedness 68 C1 The definition of connectedness................... 68 C2 Connected subsets of the real line.................. 72 C3 Path-connectedness.......................... 76 C4 Connected-components and path-components........... 80 1 Chapter A Topological spaces A1 Review of metric spaces For the lecture of Thursday, 18 September 2014 Almost everything in this section should have been covered in Honours Analysis, with the possible exception of some of the examples. For that reason, this lecture is longer than usual. Definition A1.1 Let X be a set. A metric on X is a function d: X × X ! [0; 1) with the following three properties: • d(x; y) = 0 () x = y, for x; y 2 X; • d(x; y) + d(y; z) ≥ d(x; z) for all x; y; z 2 X (triangle inequality); • d(x; y) = d(y; x) for all x; y 2 X (symmetry).
    [Show full text]
  • Be a Metric Space
    2 The University of Sydney show that Z is closed in R. The complement of Z in R is the union of all the Pure Mathematics 3901 open intervals (n, n + 1), where n runs through all of Z, and this is open since every union of open sets is open. So Z is closed. Metric Spaces 2000 Alternatively, let (an) be a Cauchy sequence in Z. Choose an integer N such that d(xn, xm) < 1 for all n ≥ N. Put x = xN . Then for all n ≥ N we have Tutorial 5 |xn − x| = d(xn, xN ) < 1. But xn, x ∈ Z, and since two distinct integers always differ by at least 1 it follows that xn = x. This holds for all n > N. 1. Let X = (X, d) be a metric space. Let (xn) and (yn) be two sequences in X So xn → x as n → ∞ (since for all ε > 0 we have 0 = d(xn, x) < ε for all such that (yn) is a Cauchy sequence and d(xn, yn) → 0 as n → ∞. Prove that n > N). (i)(xn) is a Cauchy sequence in X, and 4. (i) Show that if D is a metric on the set X and f: Y → X is an injective (ii)(xn) converges to a limit x if and only if (yn) also converges to x. function then the formula d(a, b) = D(f(a), f(b)) defines a metric d on Y , and use this to show that d(m, n) = |m−1 − n−1| defines a metric Solution.
    [Show full text]
  • DEFINITIONS and THEOREMS in GENERAL TOPOLOGY 1. Basic
    DEFINITIONS AND THEOREMS IN GENERAL TOPOLOGY 1. Basic definitions. A topology on a set X is defined by a family O of subsets of X, the open sets of the topology, satisfying the axioms: (i) ; and X are in O; (ii) the intersection of finitely many sets in O is in O; (iii) arbitrary unions of sets in O are in O. Alternatively, a topology may be defined by the neighborhoods U(p) of an arbitrary point p 2 X, where p 2 U(p) and, in addition: (i) If U1;U2 are neighborhoods of p, there exists U3 neighborhood of p, such that U3 ⊂ U1 \ U2; (ii) If U is a neighborhood of p and q 2 U, there exists a neighborhood V of q so that V ⊂ U. A topology is Hausdorff if any distinct points p 6= q admit disjoint neigh- borhoods. This is almost always assumed. A set C ⊂ X is closed if its complement is open. The closure A¯ of a set A ⊂ X is the intersection of all closed sets containing X. A subset A ⊂ X is dense in X if A¯ = X. A point x 2 X is a cluster point of a subset A ⊂ X if any neighborhood of x contains a point of A distinct from x. If A0 denotes the set of cluster points, then A¯ = A [ A0: A map f : X ! Y of topological spaces is continuous at p 2 X if for any open neighborhood V ⊂ Y of f(p), there exists an open neighborhood U ⊂ X of p so that f(U) ⊂ V .
    [Show full text]
  • Chapter 7. Complete Metric Spaces and Function Spaces
    43. Complete Metric Spaces 1 Chapter 7. Complete Metric Spaces and Function Spaces Note. Recall from your Analysis 1 (MATH 4217/5217) class that the real numbers R are a “complete ordered field” (in fact, up to isomorphism there is only one such structure; see my online notes at http://faculty.etsu.edu/gardnerr/4217/ notes/1-3.pdf). The Axiom of Completeness in this setting requires that ev- ery set of real numbers with an upper bound have a least upper bound. But this idea (which dates from the mid 19th century and the work of Richard Dedekind) depends on the ordering of R (as evidenced by the use of the terms “upper” and “least”). In a metric space, there is no such ordering and so the completeness idea (which is fundamental to all of analysis) must be dealt with in an alternate way. Munkres makes a nice comment on page 263 declaring that“completeness is a metric property rather than a topological one.” Section 43. Complete Metric Spaces Note. In this section, we define Cauchy sequences and use them to define complete- ness. The motivation for these ideas comes from the fact that a sequence of real numbers is Cauchy if and only if it is convergent (see my online notes for Analysis 1 [MATH 4217/5217] http://faculty.etsu.edu/gardnerr/4217/notes/2-3.pdf; notice Exercise 2.3.13). 43. Complete Metric Spaces 2 Definition. Let (X, d) be a metric space. A sequence (xn) of points of X is a Cauchy sequence on (X, d) if for all ε > 0 there is N N such that if m, n N ∈ ≥ then d(xn, xm) < ε.
    [Show full text]
  • Chapter 2 Metric Spaces and Topology
    2.1. METRIC SPACES 29 Definition 2.1.29. The function f is called uniformly continuous if it is continu- ous and, for all > 0, the δ > 0 can be chosen independently of x0. In precise mathematical notation, one has ( > 0)( δ > 0)( x X) ∀ ∃ ∀ 0 ∈ ( x x0 X d (x , x0) < δ ), d (f(x ), f(x)) < . ∀ ∈ { ∈ | X 0 } Y 0 Definition 2.1.30. A function f : X Y is called Lipschitz continuous on A X → ⊆ if there is a constant L R such that dY (f(x), f(y)) LdX (x, y) for all x, y A. ∈ ≤ ∈ Let fA denote the restriction of f to A X defined by fA : A Y with ⊆ → f (x) = f(x) for all x A. It is easy to verify that, if f is Lipschitz continuous on A ∈ A, then fA is uniformly continuous. Problem 2.1.31. Let (X, d) be a metric space and define f : X R by f(x) = → d(x, x ) for some fixed x X. Show that f is Lipschitz continuous with L = 1. 0 0 ∈ 2.1.3 Completeness Suppose (X, d) is a metric space. From Definition 2.1.8, we know that a sequence x , x ,... of points in X converges to x X if, for every δ > 0, there exists an 1 2 ∈ integer N such that d(x , x) < δ for all i N. i ≥ 1 n = 2 n = 4 0.8 n = 8 ) 0.6 t ( n f 0.4 0.2 0 1 0.5 0 0.5 1 − − t Figure 2.1: The sequence of continuous functions in Example 2.1.32 satisfies the Cauchy criterion.
    [Show full text]
  • 3 Limits of Sequences and Filters
    3 Limits of Sequences and Filters The Axiom of Choice is obviously true, the well-ordering theorem is obviously false; and who can tell about Zorn’s Lemma? —Jerry Bona (Schechter, 1996) Introduction. Chapter 2 featured various properties of topological spaces and explored their interactions with a few categorical constructions. In this chapter we’ll again discuss some topological properties, this time with an eye toward more fine-grained ideas. As introduced early in a study of analysis, properties of nice topological spaces X can be detected by sequences of points in X. We’ll be interested in some of these properties and the extent to which sequences suffice to detect them. But take note of the adjective “nice” here. What if X is any topological space, not just a nice one? Unfortunately, sequences are not well suited for characterizing properties in arbitrary spaces. But all is not lost. A sequence can be replaced with a more general construction—a filter—which is much better suited for the task. In this chapter we introduce filters and highlight some of their strengths. Our goal is to spend a little time inside of spaces to discuss ideas that may be familiar from analysis. For this reason, this chapter contains less category theory than others. On the other hand, we’ll see in section 3.3 that filters are a bit like functors and hence like generalizations of points. This perspective thus gives us a coarse-grained approach to investigating fine-grained ideas. We’ll go through some of these basic ideas—closure, limit points, sequences, and more—rather quickly in sections 3.1 and 3.2.
    [Show full text]
  • Metric Spaces
    Chapter 1. Metric Spaces Definitions. A metric on a set M is a function d : M M R × → such that for all x, y, z M, Metric Spaces ∈ d(x, y) 0; and d(x, y)=0 if and only if x = y (d is positive) MA222 • ≥ d(x, y)=d(y, x) (d is symmetric) • d(x, z) d(x, y)+d(y, z) (d satisfies the triangle inequality) • ≤ David Preiss The pair (M, d) is called a metric space. [email protected] If there is no danger of confusion we speak about the metric space M and, if necessary, denote the distance by, for example, dM . The open ball centred at a M with radius r is the set Warwick University, Spring 2008/2009 ∈ B(a, r)= x M : d(x, a) < r { ∈ } the closed ball centred at a M with radius r is ∈ x M : d(x, a) r . { ∈ ≤ } A subset S of a metric space M is bounded if there are a M and ∈ r (0, ) so that S B(a, r). ∈ ∞ ⊂ MA222 – 2008/2009 – page 1.1 Normed linear spaces Examples Definition. A norm on a linear (vector) space V (over real or Example (Euclidean n spaces). Rn (or Cn) with the norm complex numbers) is a function : V R such that for all · → n n , x y V , x = x 2 so with metric d(x, y)= x y 2 ∈ | i | | i − i | x 0; and x = 0 if and only if x = 0(positive) i=1 i=1 • ≥ cx = c x for every c R (or c C)(homogeneous) • | | ∈ ∈ n n x + y x + y (satisfies the triangle inequality) Example (n spaces with p norm, p 1).
    [Show full text]
  • Metric Spaces
    Empirical Processes: Lecture 06 Spring, 2010 Introduction to Empirical Processes and Semiparametric Inference Lecture 06: Metric Spaces Michael R. Kosorok, Ph.D. Professor and Chair of Biostatistics Professor of Statistics and Operations Research University of North Carolina-Chapel Hill 1 Empirical Processes: Lecture 06 Spring, 2010 §Introduction to Part II ¤ ¦ ¥ The goal of Part II is to provide an in depth coverage of the basics of empirical process techniques which are useful in statistics: Chapter 6: mathematical background, metric spaces, outer • expectation, linear operators and functional differentiation. Chapter 7: stochastic convergence, weak convergence, other modes • of convergence. Chapter 8: empirical process techniques, maximal inequalities, • symmetrization, Glivenk-Canteli results, Donsker results. Chapter 9: entropy calculations, VC classes, Glivenk-Canteli and • Donsker preservation. Chapter 10: empirical process bootstrap. • 2 Empirical Processes: Lecture 06 Spring, 2010 Chapter 11: additional empirical process results. • Chapter 12: the functional delta method. • Chapter 13: Z-estimators. • Chapter 14: M-estimators. • Chapter 15: Case-studies II. • 3 Empirical Processes: Lecture 06 Spring, 2010 §Topological Spaces ¤ ¦ ¥ A collection of subsets of a set X is a topology in X if: O (i) and X , where is the empty set; ; 2 O 2 O ; (ii) If U for j = 1; : : : ; m, then U ; j 2 O j=1;:::;m j 2 O (iii) If U is an arbitrary collection of Tmembers of (finite, countable or f αg O uncountable), then U . α α 2 O S When is a topology in X, then X (or the pair (X; )) is a topological O O space, and the members of are called the open sets in X.
    [Show full text]
  • Banach and Fréchet Spaces of Functions 1. Function Spaces C K[A, B]
    (March 15, 2014) Banach and Fr´echetspaces of functions Paul Garrett [email protected] http:=/www.math.umn.edu/egarrett/ [This document is http://www.math.umn.edu/~garrett/m/fun/notes 2012-13/02 spaces fcns.pdf] Many familiar and useful spaces of continuous or differentiable functions, such as Ck[a; b], have natural metric structures, and are complete. Often, the metric d(; ) comes from a norm jj· jj, on the functions, meaning that d(f; g) = jjf − gjj where the norm itself has 8 jjfjj ≥ 0; with jjfjj = 0 only for f = 0 (positivity) > <> jjf + gjj ≤ jjfjj + jjgjj (triangle inequality) > :> jjα · fjj = jαj · jjfjj for α 2 C (homogeneity) A vector space with complete metric coming from a norm is a Banach space. Natural Banach spaces of functions are many of the most natural function spaces. Other natural function spaces, such as C1[a; b] and Co(R), are not Banach, but still have a metric topology and are complete: these are Fr´echetspaces, appearing as limits [1] of Banach spaces. These lack some of the conveniences of Banach spaces, but their expressions as limits of Banach spaces is often sufficient. o Other important spaces, such as compactly-supported continuous functions Cc (R) on R, or compactly- 1 supported smooth functions Cc (R) on R, are not reasonably metrizable at all. Some of these important spaces are expressible as colimits [2] of Banach or Fr´echet spaces, and such descriptions suffice for many applications. First, we look at some naturally occurring Banach and Fr´echet spaces. Our main point will be to prove completeness with the natural metrics.
    [Show full text]
  • Metric, Normed, and Topological Spaces
    Chapter 13 Metric, Normed, and Topological Spaces A metric space is a set X that has a notion of the distance d(x; y) between every pair of points x; y 2 X. A fundamental example is R with the absolute-value metric d(x; y) = jx − yj, and nearly all of the concepts we discuss below for metric spaces are natural generalizations of the corresponding concepts for R. A special type of metric space that is particularly important in analysis is a normed space, which is a vector space whose metric is derived from a norm. On the other hand, every metric space is a special type of topological space, which is a set with the notion of an open set but not necessarily a distance. The concepts of metric, normed, and topological spaces clarify our previous discussion of the analysis of real functions, and they provide the foundation for wide-ranging developments in analysis. The aim of this chapter is to introduce these spaces and give some examples, but their theory is too extensive to describe here in any detail. 13.1. Metric spaces A metric on a set is a function that satisfies the minimal properties we might expect of a distance. Definition 13.1. A metric d on a set X is a function d : X × X ! R such that for all x; y; z 2 X: (1) d(x; y) ≥ 0 and d(x; y) = 0 if and only if x = y (positivity); (2) d(x; y) = d(y; x) (symmetry); (3) d(x; y) ≤ d(x; z) + d(z; y) (triangle inequality).
    [Show full text]