FUNCTIONAL ANALYSIS CHRISTIAN REMLING Contents 1. Metric and topological spaces 2 2. Banach spaces 12 3. Consequences of Baire's Theorem 30 4. Dual spaces and weak topologies 34 5. Hilbert spaces 50 6. Operators in Hilbert spaces 61 7. Banach algebras 67 8. Commutative Banach algebras 78 9. C∗-algebras 87 10. The Spectral Theorem 105 These are lecture notes that have evolved over time. Center stage is given to the Spectral Theorem for (bounded, in this first part) normal operators on Hilbert spaces; this is approached through the Gelfand representation of commutative C∗-algebras. Banach space topics are ruthlessly reduced to the mere basics (some would argue, less than that); topological vector spaces aren't mentioned at all. 1 2 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 defines a metric on X. Properties 1, 2 are clear from the definition, but if p > 1, then the verification of the triangle inequality is not completely straightforward here. We leave the matter at that for the time being, but will return to this example later. An additional metric on X is given by d1(x; y) = max jxj − yjj j=1;:::;n FUNCTIONAL ANALYSIS 3 Exercise 1.2. (a) Show that (X; d1) is a metric space. (b) Show that limp!1 dp(x; y) = d1(x; y) for fixed x; y 2 X. Example 1.4. Similar metrics can be introduced on function spaces. For example, we can take X = C[a; b] = ff :[a; b] ! C : f continuous g and define, for 1 ≤ p < 1, Z b 1=p p dp(f; g) = jf(x) − g(x)j dx a and d1(f; g) = max jf(x) − g(x)j: a≤x≤b Again, the proof of the triangle inequality requires some care if 1 < p < 1. We will discuss this later. Exercise 1.3. Prove that (X; d1) is a metric space. Actually, we will see later that it is often advantageous to use the spaces Z b p p Xp = L (a; b) = ff :[a; b] ! C : f measurable, jf(x)j dx < 1g a instead of X if we want to work with these metrics. We will discuss these issues in much greater detail in Section 2. On a metric space, we can define convergence in a natural way. We just interpret \d(x; y) small" as \x close to y", and we are then natu- rally led to make the following definition. Definition 1.2. Let (X; d) be a metric space, and xn; x 2 X. We say that xn converges to x (in symbols: xn ! x or lim xn = x, as usual) if d(xn; x) ! 0. Similarly, we call xn a Cauchy sequence if for every > 0, there exists an N = N() 2 N so that d(xm; xn) < for all m; n ≥ N. We can make some quick remarks on this. First of all, if a sequence xn is convergent, then the limit is unique because if xn ! x and xn ! y, then, by the triangle inequality, d(x; y) ≤ d(x; xn) + d(xn; y) ! 0; so d(x; y) = 0 and thus x = y. Furthermore, a convergent sequence is a Cauchy sequence: If xn ! x and > 0 is given, then we can find an N 2 N so that d(xn; x) < /2 if n ≥ N. But then we also have that d(x ; x ) ≤ d(x ; x) + d(x; x ) < + = (m; n ≥ N); m n n m 2 2 4 CHRISTIAN REMLING so xn is a Cauchy sequence, as claimed. The converse is wrong in general metric spaces. Consider for example X = Q with the metric d(x; y) = jx − yj from Example 1.2. Pick a sequence xn 2 Q thatp converges in R (that is, in the traditional sense) to an irrational limit ( 2, say). Then xn is a Cauchy sequence in (X; d) because it is convergent in the bigger space (R; d), so, as just observed, xn must be a Cauchy sequence in (R; d). But then xn is also a Cauchy sequence in (Q; d) because this is actually the exact same condition (only the distances d(xm; xn) matter, we don't need to know how big the total space is). However, xn can not converge in (Q; d) because then it would have to converge to the same limit in the bigger space (R; d), but by construction, in this space, it converges to a limit that was not in Q. Please make sure you understand exactly how this example works. There's nothing mysterious about this divergent Cauchy sequence. The sequence really wants to converge, but, unfortunately, the putative limit fails to lie in the space. Spaces where Cauchy sequences do always converge are so important that they deserve a special name. Definition 1.3. Let X be a metric space. X is called complete if every Cauchy sequence converges. The mechanism from the previous example is in fact the only possible reason why spaces can fail to be complete. Moreover, it is always possible to complete a given metric space by including the would-be limits of Cauchy sequences. The bigger space obtained in this way is called the completion of X. We will have no need to apply this construction, so I don't want to discuss the (somewhat technical) details here. In most cases, the completion is what you think it should be; for example, the completion of (Q; d) is (R; d). Exercise 1.4. Show that (C[−1; 1]; d1) is not complete. Suggestion: Consider the sequence 8 −1 −1 ≤ x < −1=n <> fn(x) = nx −1=n ≤ x ≤ 1=n : :>1 1=n < x ≤ 1 A more general concept is that of a topological space. By definition, a topological space X is a non-empty set together with a collection T of distinguished subsets of X (called open sets) with the following properties: FUNCTIONAL ANALYSIS 5 (1) ;;X 2 T S (2) If Uα 2 T , then also Uα 2 T . (3) If U1;:::;UN 2 T , then U1 \ ::: \ UN 2 T . This structure allows us to introduce some notion of closeness also, but things are fuzzier than on a metric space. We can zoom in on points, but there is no notion of one point being closer to a given point than another point. We call V ⊂ X a neighborhood of x 2 X if x 2 V and V 2 T . (Warning: This is sometimes called an open neighborhood, and it is also possible to define a more general notion of not necessarily open neighborhoods. We will always work with open neighborhoods here.) We can then say that xn converges to x if for every neighborhood V of x, there exists an N 2 N so that xn 2 V for all n ≥ N. However, on general topological spaces, sequences are not particularly useful; for example, if T = f;;Xg, then (obviously, by just unwrapping the definitions) every sequence converges to every limit. Here are some additional basic notions for topological spaces. Please make sure you're thoroughly familiar with these (the good news is that we won't need much beyond these definitions). Definition 1.4. Let X be a topological space. (a) A ⊂ X is called closed if Ac is open. (b) For an arbitrary subset B ⊂ X, the closure of B ⊂ X is defined as \ B = A; A⊃B;A closed this is the smallest closed set that contains B (in particular, there always is such a set). (c) The interior of B ⊂ X is the biggest open subset of B (such a set exists). Equivalently, the complement of the interior is the closure of the complement. (d) K ⊂ X is called compact if every open cover of K contains a finite subcover. (e) B ⊂ T is called a neighborhood base of X if for every neighborhood V of some x 2 X, there exists a B 2 B with x 2 B ⊂ V .