MIT OpenCourseWarehttp://ocw.mit.edu 18.102 Introduction to Functional Analysis Spring 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.4 LECTURE NOTES FOR 18.102, SPRING 2009 Lecture 1. Tuesday, 3 Feb. Linear spaces, metric spaces, normed spaces. Banach spaces. Examples – Eu-clidean spaces, continuous functions on a closed interval – C0([0, 1]) with supremum norm. The (Riemannian) L1 norm, outline that this is not complete on C0([0, 1]). Brief description of l2 – This is Hilbert space, but not explained. What is it all for? Main aims:- To be able to carry out ‘standard’ constructions in (linear) functional analysis: Abstract Hilbert space – one in each dimension Concrete Hilbert space – Many, such as L2([0, 1]) Example of a theorem:- The Dirichlet problem. Let V : [0, 1] −→ R be a real-valued function. We are interested in ‘oscillating modes’ on the interval; something like this arises in quantum mechanics for instance. Namely we want to know about functions u(x) – twice continuously differentiable on [0, 1] which satisfy the differential equation d2u(1.1) − dx2 (x) + V (x)u(x) = λu(x) where λ is an ‘unknown’ constant – that is we want to know which λ’s can occur. Well, of course all λ’s can occur with u ≡ 0 but this is the ‘trivial solution’ which will always be there for such an equation. What other solutions are there? Well, there is an infinite sequence of λ’s for which there is a non-trivial solution of (1.1) λj ∈ R – they are all real no non-real complex λ’s can occur. For each of these there is at least one (and maybe more) ‘independent’ solution uj . We can say a lot more about everything here but one main aim of this course is to get at least to this point. Now, in fact (1.1) is just the eigenvalue equation. What we are dealing with here is an ‘infinite matrix’. This is not obvious, and in fact is not a very good way of looking at things (there was such a matrix approach to quantum mechanics in the early days but it was replaced by the sort of ‘operator’ theory on Hilbert space that we will use here.) Still, we are in some sense dealing with infinite dimensional matrices. One of the crucial differences between infinite and finite dimensional settings is that topology is encountered. This is enshrined here in the notion of a normed linear space Linear space:- Should I break out the axioms? It is a space V in which we can add elements and multiply by scalars with rules very similar to the basic examples of Rn or Cn . Note that for us the ‘scalars’ are either the real numbers of the complex numbers – usually the latter. Let’s be neutral and denote by K either R or C but of course consistently. Then our set V – the set of vectors with which we will deal, comes with two ‘laws’. These are maps (1.2) + : V × V −→ V, : K × V −→ V. · which we denote not by +(v, w) and (s, v) but by v + w and sv. Then we ·impose the axioms of a vector space – look them up! These are commutative group axioms for + axioms for the action of K and the distributive law. Our examples: The ‘trivial’ case of a finite dimensional vector space. The lp spaces. These are spaces of sequences – the first problem set is all about them. Thus l2 – which is a Hilbert space – consists of all the sequences� 5 LECTURE NOTES FOR 18.102, SPRING 2009 a : N −→ C also denoted {aj }∞where aj = a(j), such that j=1 ∞(1.3) |aj |2 < ∞. j=1 Seriously non-trivial examples such as C([0, 1]) the space of continuous func-tions on [0, 1] (say with complex values). All these vector spaces carry norms, and that is what we want to talk about for most of the semester. Definition 1. A norm on a vector space V is a function (1.4) � · � : V −→ [0, ∞) which satisfies the three properties: (1) �v� = 0 iff v = 0. (2) �tv� = |t|�v� for all t ∈ K and v ∈ V. (3) Triangle inequality. �v + w� ≤ �v� + �w� for all v, w ∈ V. Then show that d(v, w) = �v − w� is a metric on V. It follows that all the notions from 18.100 come into play – open sets, balls, closed sets, convergence of sequences, compact sets, connected sets, complete metric spaces. We will use them all! Definition 2. A normed space which is complete with respect to the induced metric is called a Banach space. I then discussed the supremum norm on C([0, 1]); (1.5) = sup u(x) .�u�∞ x∈[0,1] | |I said, but did not prove, that it this gives a Banach space. I also disussed the L1 norm on C([0, 1]) : � 1 (1.6) �u�L1 = |u(x)|dx 0 and indicated why it is not complete. This is basically why we need to study the Lebesgue integral. One might ask ‘is the space of Riemann integrable functions on [0, 1] complete with respect to the L1 norm’ – indeed someone did. The answer is that �u�L1 is not even a norm on the space of Riemann integable functions. Namely it is only a ‘seminorm’ – the second two conditions are fine but there are non-zero functions with integral zero. So, the question is not quite precise, which is why I did not talk about it. One the other hand one can fiddle with the space (take a quotient) so that one gets a norm and then it is not complete. So the morally correct answer is NO, it is not a Banach space and this is true for a better (or worse) reason than it simply not being a normed space in the first place! We will get to this.� � � 6 LECTURE NOTES FOR 18.102, SPRING 2009 Problem set 1, Due 11AM Tuesday 10 Feb. Full marks will be given to anyone who makes a good faith attempt to answer each question. The first four problems concern the ‘little L p’ spaces lp. Note that you have the choice of doing everything for p = 2 or for all 1 ≤ p < ∞. Problem 1.1 Write out a proof (you can steal it from one of many places but at least write it out in your own hand) either for p = 2 or for each p with 1 ≤ p < ∞that ∞lp = {a : N −→ C; |aj |p < ∞, aj = a(j)}j=1 is a normed space with the norm ⎛ ⎞ 1 ∞p p�a�p = ⎝ |aj |⎠ . j=1 This means writing out the proof that this is a linear space and that the three conditions required of a norm hold. Problem 1.2 The ‘tricky’ part in Problem 1.1 is the triangle inequality.