# An introduction to some aspects of functional analysis

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 An introduction to some aspects of functional analysis Stephen Semmes Rice University Abstract These informal notes deal with some very basic objects in functional analysis, including norms and seminorms on vector spaces, bounded linear operators, and dual spaces of bounded linear functionals in particular. Contents 1 Norms on vector spaces 3 2 Convexity 4 3 Inner product spaces 5 4 A few simple estimates 7 5 Summable functions 8 6 p-summable functions 9 7 p = Bounded continuous functions 11 9 Integral norms Completeness Bounded linear mappings Continuous extensions Bounded linear mappings, Bounded linear functionals 16 1

2 15 l 1 (E) c 0 (E) Hölder s inequality l p (E) Separability An extension lemma The Hahn Banach theorem Complex vector spaces The weak topology Seminorms The weak topology, The Banach Alaoglu theorem The weak topology Seminorms, Convergent sequences Uniform boundedness Examples An embedding Induced mappings Continuity of norms Infinite series Some examples Quotient spaces Quotients and duality Dual mappings 39 2

3 40 Second duals Continuous functions Bounded sets Hahn Banach, revisited 43 References 44 1 Norms on vector spaces Let V be a vector space over the real numbers R or the complex numbers C. A norm on V is a nonnegative real-valued function v defined for v V such that (1.1) v = 0 if and only if v = 0, (1.2) t v = t v for every v V and t R or C, as appropriate, and (1.3) v + w v + w for every v, w V. Here t is the absolute value of t when t R, and the modulus of t when t C. For example, if V is the one-dimensional vector space R or C, then the absolute value or modulus defines a norm on V, and every norm on V is a positive multiple of this norm. More generally, let n be a positive integer, and let V be the space R n or C n of n-tuples v = (v 1,..., v n ) of real or complex numbers. As usual, this is a vector space with respect to coordinatewise addition and scalar multiplication. The standard Euclidean norm on V is given by (1.4) ( n v = v j 2) 1/2. It is well-known that this is a norm, although the triangle inequality (1.3) is not immediately obvious. A couple of ways to show the triangle inequality in this case will be reviewed in the next sections. It is easy to see directly that (1.5) n v 1 = v j satisfies the triangle inequality. Hence v 1 defines a norm on R n, C n. Similarly, (1.6) v = max( v 1,..., v n ) 3

4 defines a norm on R n, C n. The standard Euclidean norm on R n, C n is sometimes denoted v 2. If v is a norm on a real or complex vector space V, then (1.7) d(v, w) = v w defines a metric on V. For instance, the standard Euclidean metric on R n or C n is the same as the metric associated to the standard Euclidean norm in this way. The metrics on R n or C n associated to the norms v 1, v determine the same topology as the standard Euclidean metric, and we shall say more about this soon. The main point is that v 1, v 2, and v are equivalent norms on R n and C n for each positive integer n, in the sense that each is bounded by constant multiples of the others. 2 Convexity Let V be a real or complex vector space. As usual, a set E V is said to be convex if for every x, y E and real number t with 0 < t < 1, we have that (2.1) t x + (1 t)y is also an element of E. If v is a norm on V, then the closed unit ball (2.2) B 1 = {v V : v 1} is a convex set in V. Similarly, the open unit ball (2.3) {v V : v < 1} is also a convex set in V. Conversely, suppose that v is a nonnegative real-valued function on V which is positive when v 0 and satisfies the homogeneity condition (1.2). If the corresponding closed unit ball B 1 is convex, then one can check that v satisfies the triangle inequality, and is thus a norm. For if v, w are nonzero vectors in V, then (2.4) v = v v, w = w w have norm 1, and the convexity of B 1 implies that (2.5) v w v + w v + v + w w has norm less than or equal to 1. Equivalently, (2.6) v + w v + w B 1, which implies (1.3). 4

5 Let n be a positive integer, let p be a positive real number, and consider (2.7) ( n v p = v j p) 1/p for v R n or C n. This is the same as the standard Euclidean norm when p = 2, and is easily seen to be a norm when p = 1, as in the previous section. Let us check that the closed unit ball associated to v p is convex when p > 1, so that v p defines a norm in this case. This does not work for p < 1, even when n = 2. Suppose that v, w R n or C n satisfy v p, w p 1, which is the same as (2.8) n v j p, n w j p 1. If p 1 and 0 < t < 1, then (2.9) t v j + (1 t)w j p (t v j + (1 t) w j ) p t v j p + (1 t) w j p for each j, because of the convexity of the function f(x) = x p on the positive real numbers. Summing over j, we get that (2.10) n n n t v j + (1 t)w j p t v j p + (1 t) w j p 1. This implies that (2.11) as desired. t v + (1 t)w p 1, 3 Inner product spaces An inner product on a real or complex vector space V is a real or complex-valued function v, w defined for v, w V, as appropriate, which satisfies the following conditions. First, v, w is linear as a function of v for each w V. Second, (3.1) w, v = v, w for every v, w V in the real case, while (3.2) w, v = v, w in the complex case. Here z denotes the complex conjugate of z C, defined by (3.3) z = x y i 5

6 when z = x + y i and x, y R. It follows from this symmetry condition that v, w is linear in w in the real case, and conjugate-linear in w in the complex case. It also follows that (3.4) v, v R for every v V, even in the complex case. The third requirement of an inner product is that (3.5) v, v > 0 for every v V with v 0, and of course the inner product is equal to 0 when v = 0. If v, w is an inner product on V, then the Cauchy Schwarz inequality states that (3.6) v, w v, v 1/2 w, w 1/2 for every v, w V. Because of homogeneity, it suffices to show that (3.7) v, w 1 when (3.8) v, v = w, w = 1, since the inequality is trivial when v or w is 0. To do this, one can use the fact that (3.9) v + t w, v + t w 0 for every t R or C, and expand the inner product to estimate v, w. The norm on V associated to the inner product is defined by (3.10) v = v, v 1/2. This clearly satisfies the positivity and homogeneity requirements of a norm. In order to verify the triangle inequality, one can use the Cauchy Schwarz inequality to get that (3.11) v + w 2 = v + w, v + w = v, v + v, w + w, v + w, w v v w + w 2 = ( v + w ) 2 for every v, w V. The standard inner product on R n is defined by (3.12) v, w = n v j w j. Similarly, the standard inner product on C n is defined by (3.13) v, w = n v j w j. The corresponding norms are the standard Euclidean norms. 6

7 4 A few simple estimates Observe that (4.1) v v p for each v R n or C n and p > 0. If 0 < p < q <, then n n (4.2) v j q v q p v j p for every v R n or C n, and hence (4.3) v q v 1 (p/q) v p/q p v p. Thus v p is monotone decreasing in p. In the other direction, (4.4) v p n 1/p v for each v R n or C n and p > 0. If 0 < p < q <, then (4.5) v p n (1/p) (1/q) v q. To see this, remember that f(x) = x r is a convex function of x 0 when r 1, so that ( 1 n ) r 1 n (4.6) a j a r j n n for any nonnegative real numbers a 1,...,a n. Applying this with r = q/p, we get that ( 1 n v j p) q/p 1 n (4.7) v j q, n n or (4.8) as desired. The fact that (4.9) ( 1 n n v j p) 1/p ( 1 n v 1 v p n v j q) 1/q, when 0 < p < 1 and n = 2 can be re-expressed as (4.10) for every a, b 0. It follows that n (4.11) v j + w j p (a + b) p a p + b p n v j p + n w j p for every v, w R n or C n when 0 < p < 1, and hence (4.12) v + w p p v p p + w p p. This is a natural substitute for the triangle inequality in this case. 7

8 5 Summable functions Let E be a nonempty set, and let f(x) be a nonnegative real-valued function on E. If E has only finitely many elements, then the sum (5.1) f(x) x E is defined in the usual way. Otherwise, (5.1) is defined to be the supremum of (5.2) f(x) x E 1 over all finite subsets E 1 of E, which may be +. If there is a finite upper bound for the subsums (5.2), so that (5.1) is finite, then we say that f is summable on E. Note that f is summable on E if there is a nonnegative summable function F on E such that f(x) F(x) for every x E. If f 1, f 2 are nonnegative functions on E, then (5.3) 1 (x) + f 2 (x)) = x E(f f 1 (x) + f 2 (x). x E x E This is easy to see, with the standard convention that (5.4) a + = + a =, 0 a. In particular, f 1 + f 2 is summable on E when f 1 and f 2 are summable on E. Similarly, if f is a nonnegative function on E and t 0, then (5.5) t f(x) = t f(x). x E x E This uses the conventions that t = when t > 0, and 0 = 0. A real or complex-valued function f on E is said to be summable on E if f(x) is summable on E. It follows from the previous remarks that the summable functions on E form a linear subspace of the vector space of real or complex-valued functions on E, with respect to pointwise addition and scalar multiplication. If f is a summable function on E and ǫ > 0, then the set (5.6) E ǫ (f) = {x E : f(x) ǫ} has only finitely many elements, and hence (5.7) E(f) = {x E : f(x) 0} has only finitely or countably many elements. More precisely, E ǫ (f) 1 (5.8) f(x), ǫ 8 x E

9 where E ǫ (f) denotes the number of elements of E ǫ (f). If f is a real or complex-valued summable function on E, then the sum (5.1) can be defined in various equivalent ways. One way is to express f as a linear combination of nonnegative summable functions, and then use the definition of the sum in that case. Another way is to enumerate the elements of E(f) in a sequence, and treat the sum as an infinite series. A basic property of the sum is that (5.9) f(x) f(x) x E x E for any summable function f on E. As usual, the sum of a summable function is also linear in the function. 6 p-summable functions Let E be a nonempty set, and let p be a positive real number. A real or complexvalued function f on E is said to be p-summable if f(x) p is a summable function on E. Thus p-summability is the same as summability when p = 1. One can check that the spaces (6.1) l p (E,R), l p (E,C) of p-summable real and complex-valued functions on E are linear subspaces of the vector spaces of all such functions on E. This can be extended to p = by taking l (E,R), l (E,C) to be the spaces of bounded real and complex-valued functions on E. If f is a p-summable function on E for some p, 0 < p <, then we put (6.2) ( f p = f(x) p) 1/p. x E Here the sum over E is defined to be the supremum over all finite subsums, as in the previous section. Similarly, if f is a bounded function on E, then we put (6.3) f = sup{ f(x) : x E}. It is easy to see that (6.4) f 1 + f 2 p f 1 p + f 2 p when f 1, f 2 l p (E,R) or l p (E,C) and 1 p, so that f p defines a norm on these spaces in this case. This is quite straightforward when p = 1,, and can be obtained from the corresponding statement for finite sums when 1 < p <. If 0 < p < 1 and f 1, f 2 are p-summable functions on E, then (6.5) f 1 + f 2 p p f 1 p p + f 2 p p. This can be derived using the same argument as for finite sums. If f is p-summable, 0 < p <, then f is bounded, and (6.6) f f p. 9

10 Moreover, f is q-summable for each q > p, and (6.7) f q f p. This can be shown in the same way as for finite sums. A function f on E is said to vanish at infinity if for each ǫ > 0 there are only finitely many x E such that f(x) ǫ. The spaces (6.8) c 0 (E,R), c 0 (E,C) of real and complex-valued functions vanishing at infinity on E are linear subspaces of the corresponding spaces of bounded functions on E. These are proper linear subspaces when E has infinitely many elements, and otherwise every function on E is bounded and vanishes at infinity when E has only finitely many elements. As in the previous section, p-summable functions vanish at infinity for each 0 < p <. 7 p = 2 It is well known that (7.1) for every a, b 0, since (7.2) a b a2 + b (a b) 2 = a 2 2 a b + b 2. If f 1, f 2 are square-summable functions on a nonempty set E, then it follows that f 1 (x) f 2 (x) f 1(x) 2 + f 2(x) 2 (7.3) 2 2 for every x E, and hence (7.4) f 1 (x) f 2 (x) 1 f 1 (x) f 2 (x) 2 <. 2 2 x E x E This implies that the product f 1 (x)f 2 (x) is summable on E. Alternatively, one can get the same conclusion using the Cauchy Schwarz inequality for finite sums to show that ( f 1 (x) f 2 (x) f 1 (x) 2) 1/2 ( f 2 (x) 2) 1/2 (7.5). x E x E x E x E At any rate, this permits us to define an inner product on l 2 (E,R) by (7.6) f 1, f 2 = x E f 1 (x)f 2 (x), 10

11 and on l 2 (E,C) by (7.7) f 1, f 2 = x E f 1 (x)f 2 (x). These are basically the same as the standard inner products on R n and C n when E is a finite set with n elements. The norms associated to these inner products are the same as f 2 defined in the previous section. 8 Bounded continuous functions Let X be a topological space, and let (8.1) C b (X,R), C b (X,C) be the spaces of bounded continuous real or complex-valued functions on X. As usual, these are vector spaces with respect to pointwise addition and scalar multiplication. If X is compact, then every continuous function on X is bounded, and we may drop the subscript b. The supremum norm of a bounded continuous function f is defined by (8.2) f = sup{ f(x) : x X}. It is easy to see that this defines a norm on these vector spaces. Of course, constant functions on X are automatically bounded and continuous. If X is equipped with the discrete topology, then every function on X is continuous. Locally compact Hausdorff spaces form a nice class of topological spaces. If X is such a space, K X is compact, and V X is an open set with K V, then it is well known that there is a continuous real-valued function f on X such that (8.3) f(x) = 1 when x K, (8.4) when x X\V, and (8.5) f(x) = 0 0 f(x) 1 for every x X. In particular, there are plenty of bounded continuous functions on X in this case. A continuous function f on X is said to vanish at infinity if for every ǫ > 0 there is a compact set K X such that (8.6) f(x) < ǫ for every x X\K. If X is equipped with the discrete topology, then the compact subsets of X are the same as the finite sets, and this definition reduces to the previous one in the context of summable functions. For any X, the space 11

12 of real or complex-valued continuous functions on X that vanish at infinity may be denoted (8.7) C 0 (X,R), C 0 (X,C). It is easy to see that these are linear subspaces of the corresponding spaces of bounded continuous functions on X, since continuous functions are bounded on compact sets. If X is compact, then every continuous function on X vanishes at infinity trivially. The support of a continuous function f on X is the closure of the set where f 0, i.e., (8.8) suppf = {x X : f(x) 0}. The spaces of real or complex-valued continuous functions on X with compact support may be denoted (8.9) C 00 (X,R), C 00 (X,C). These are linear subspaces of the corresponding spaces of continuous functions on X that vanish at infinity, and are the same as the spaces of all continuous functions on X when X is compact. There are a lot of continuous functions with compact support on locally compact Hausdorff spaces, as before. 9 Integral norms Let f be a continuous real or complex-valued function on the closed unit interval [0, 1] in the real line. For each positive real number p, put (9.1) ( 1 1/p. f p = f(x) dx) p 0 This obviously satisfies the triangle inequality when p = 1, and one can show that it also holds for p > 1 using the same argument as for finite sums. Thus f p defines a norm on the vector space of continuous functions on [0, 1] when p 1. If 0 < p < 1, then (9.2) f 1 + f 2 p p f 1 p p + f 2 p p for all continuous functions f 1, f 2 on [0, 1]. We also have that (9.3) f p f for each continuous function f on [0, 1] and every p > 0, where f is the supremum norm of f, as in the previous section. If 0 < p < q <, then one can check that (9.4) f p f q, using convexity as in Section 4. 12

13 Now let f be a continuous real or complex-valued function on the real line with compact support. For each p > 0, consider (9.5) ( 1/p. f p = f(x) dx) p More precisely, the integral here can be reduced to one on a bounded interval, since f has compact support. As before, this defines a norm on the vector space of continuous functions on R with compact support when p 1, and satisfies the usual substitute for the triangle inequality when 0 < p < 1. However, f p is normally neither monotone increasing nor decreasing in p in this case. If p = 2, then these norms associated to suitable inner products. On the unit interval, these inner products are given by (9.6) in the real case, and (9.7) f 1, f 2 = f 1, f 2 = f 1 (x)f 2 (x)dx f 1 (x)f 2 (x) dx in the complex case. The inner products on continuous functions with compact support on R are defined similarly. Of course, there are analogous norms and inner products for other situations in which integration is defined. This includes sums over discrete sets as another special case. 10 Completeness Remember that a metric space (M, d(x, y)) is said to be complete if every Cauchy sequence in M converges to an element of M. If M is complete and N M, then N is complete with respect to the restriction of d(x, y) to x, y N if and only if N is a closed set in M. Let V be a real or complex vector space equipped with a norm and hence a metric, as in Section 1. If V is complete with respect to this metric, then V is said to be a Banach space. If the norm is also associated to an inner product, then V is said to be a Hilbert space. It is well known that the real line is complete with respect to the standard metric, and that consequently R n and C n are complete for each n. One can use this to show that the l p (E) spaces are complete with respect to the p-norm f p for every nonempty set E and 1 p. For if {f j } is a Cauchy sequence of functions on E with respect to the p-norm for some p, then {f j (x)} is a Cauchy sequence of real or complex numbers for each x E. Hence {f j } converges pointwise to a function f on E, and one can use the fact that the norms f j p are uniformly bounded in j to show that f l p (E). Using the Cauchy condition for {f j } in lp (E), one can also show that {f j } converges 13

14 to f in l p (E). Similarly, l p (E) is complete when 0 < p < 1, with respect to the metric (10.1) f 1 f 2 p p. In addition, one can check that c 0 (E) is a closed subspace of l (E), and is thus complete with respect to the supremum norm. The space C b (X) of bounded continuous functions on any topological space X is complete with respect to the supremum norm. As in the previous paragraph, one can first show that a Cauchy sequence in C b (X) converges pointwise, and then use the Cauchy condition with respect to the supremum norm to check that the sequence converges uniformly, and that the limit is also bounded and continuous. The space C 0 (X) of continuous functions that vanish at infinity on X is a closed subspace of C b (X), and hence also complete with respect to the supremum norm. However, the space of continuous functions on the unit interval is not complete with respect to the integral norm f p for any p, 1 p <, nor with respect to the metric f 1 f 2 p p when 0 < p < 1. The completions of these spaces can be described in terms of the Lebesgue integral. 11 Bounded linear mappings Let V and W be vector spaces, both real or both complex, and equipped with norms V, W, respectively. A linear mapping T : V W is said to be bounded if there is an A 0 such that (11.1) T(v) W A v V for every v V. It is easy to see that every linear mapping is bounded when V is R n or C n for some positive integer n. If V = W with the same norm, then the identity mapping I(v) = v is bounded, with A = 1. If T is a linear mapping from V into W that satisfies (11.1), then (11.2) T(v 1 ) T(v 2 ) W A v 1 v 2 V for every v 1, v 2 V, and it follows that T : V W is uniformly continuous. Conversely, if a linear mapping T : V W is continuous at the origin, then it is bounded. For if there is a δ > 0 such that (11.3) T(v) W < 1 when v V satisfies v V < δ, then (11.1) holds with A = 1/δ. A linear mapping from a real or complex vector space V into the real or complex numbers, as appropriate, is also known as a linear functional on V. A linear functional on a vector space V with a norm V is bounded if it is bounded with respect to the standard norm on R or C. Remember that the kernel of a linear mapping T : V W is the linear subspace of V consisting of the v V such that T(v) = 0. By continuity, the kernel of a bounded linear mapping is a closed subspace of V. Conversely, one can show that a linear functional on V is bounded when its kernel is closed. 14

15 12 Continuous extensions Let (M, d(x, y)) and (N, ρ(u, v)) be metric spaces. If N is complete, then every uniformly continuous mapping from a dense subset of M into N has a unique extension to a uniformly continuous mapping from M into N. Ordinary continuity is sufficient for uniqueness of the extension, but uniform continuity is needed for its existence. Now let V and W be vector spaces, both real or both complex, and equipped with norms. If W is complete, then every bounded linear mapping from a dense linear subspace of V into W has a unique extension to a bounded linear mapping from V into W. Bounded linear mappings are automatically uniformly continuous, as in the previous section, and the remaining point to check is that the extension is also linear in this case. One can use the same constant A as in (11.1) for the extension as for the initial mapping too. For example, let E be an infinite set, and let V be the space of real or complex-valued summable functions on E. It is easy to see that the space of functions f on E such that f(x) 0 for only finitely many x E is dense in l 1 (E) with respect to the l 1 norm. For each f in this subspace, the sum (12.1) f(x) x E can be defined in the obvious way, and satisfies (12.2) f(x) f(x) = f 1. x E x E Hence there is a unique extension of (12.1) to a bounded linear functional on l 1 (E). This gives another approach to the definition of the sum of a summable function, as in Section Bounded linear mappings, 2 Let V and W be vector spaces, both real or both complex. The space L(V, W) of linear mappings from V into W is also a vector space in a natural way, since the sum of two linear mappings from V into W is also a linear mapping, as is the product of a linear mapping with a real or complex number, as appropriate. If V and W are equipped with norms V and W, respectively, then one can check that the space BL(V, W) of bounded linear mappings from V into W is a linear subspace of L(V, W). The operator norm of a bounded linear mapping T : V W is defined by (13.1) Equivalently, (13.2) T op = sup{ T(v) W : v V, v V 1}. T(v) W T op v V 15

16 for every v V, and T op is the smallest number with this property, which is to say the smallest value of A for which (11.1) holds. It is easy to see that the operator norm defines a norm on the vector space BL(V, W). For example, if V = W with the same norm, then the identity mapping I(v) = v has operator norm equal to 1. Suppose that V 1, V 2, and V 3 are vector spaces, all real or all complex, and equipped with norms. If T 1 : V 1 V 2 and T 2 : V 2 V 3 are bounded linear mappings, then their composition T 2 T 1 : V 1 V 3, defined by (13.3) (T 2 T 1 )(v) = T 2 (T 1 (v)), v V, is also a bounded linear mapping. Moreover, (13.4) T 2 T 1 op,13 T 1 op,12 T 2 op,23, where the subscripts indicate which spaces are involved in the operator norms. If W is complete, then one can show that the space of bounded linear mappings from V into W is complete with respect to the operator norm. For if {T j } is a Cauchy sequence in BL(V, W), then {T j(v)} is a Cauchy sequence in W for each v V, and hence converges in W. The limit determines a linear mapping T : V W, and one can use the boundedness of the operator norms of the T j s to show that T is bounded too. Using the Cauchy condition again, one can show that {T j } converges to T in the operator norm, as desired. 14 Bounded linear functionals Let V be a real or complex vector space equipped with a norm. As in Section 11, a bounded linear functional on V is a bounded linear mapping from V into the real or complex numbers, as appropriate. The dual space V of bounded linear functionals is thus the same as BL(V,R) or BL(V,C), and the dual norm on V is defined to be the corresponding operator norm, using the standard norm on R or C. In particular, V is automatically complete with respect to the dual norm, as in the previous section. Suppose that the norm v on V is associated to an inner product v, w. Each w V determines a linear functional λ w on V, defined by (14.1) λ w (v) = v, w. The Cauchy Schwarz inequality implies that λ w is a bounded linear functional on V, and that (14.2) λ w = w. If V has finite dimension, then every linear functional on V is of the form λ w for some w V. If V is a Hilbert space, then every bounded linear functional on V is of this form. 16

17 To see this, let λ be a bounded linear functional on V, which we may as well suppose is not identically equal to 0. Thus λ(z) = 1 for some z V, and we can put (14.3) ρ = inf{ z : z V, λ(z) = 1}. Let {z j } be a sequence of elements of V such that λ(z j) = 1 for each j and (14.4) lim z j = ρ. j We would like to show that {z j } is a Cauchy sequence in V, and hence converges when V is a Hilbert space. Because the norm is associated to an inner product, we have the parallelogram law v + w 2 + v w (14.5) 2 = v 2 + w for every v, w V. We want to apply this to terms of the sequence in order to show that (14.6) z j z l 0 as j, l. For each j, l 1, λ(z j ) = λ(z l ) = 1, which implies that ( zj + z ) l (14.7) λ = 1, 2 and hence z j + z l (14.8) ρ. 2 Using the parallelogram law, this implies in turn that ρ 2 + z j z l 2 z j 2 + z l 2 (14.9) It follows that {z j } is a Cauchy sequence, since z j, z l ρ as j, l. Assuming that V is a Hilbert space, we get that {z j } converges to an element z of V. By construction, λ(z) = 1, and z = ρ is minimal among vectors with this property. If v V is in the kernel of λ, so that λ(v) = 0, then v is orthogonal to z, which is to say that (14.10) v, z = 0. This is because (14.11) λ(z + t v) = 1 for every t R or C, as appropriate, and hence (14.12) z + t v z by minimality, which implies orthogonality, as in calculus. If w = z/ z 2, then it follows that (14.13) λ(v) = v, w = λ w (v) for every v V, as desired. 17

18 15 l 1 (E) Let E be a nonempty set, and let h be a bounded real or complex-valued function on E. If f is a summable function on E, then f h is a summable function on E, and we can put (15.1) λ h (f) = f(x)h(x). x E This defines a bounded linear functional on l 1 (E), since (15.2) λ h (f) x E f(x) h(x) h f 1. For each y E, let δ y be the function on E defined by δ y (x) = 0 when x y and δ y (y) = 1. Thus δ y 1 = 1 and (15.3) λ h (δ y ) = h(y) for each y E, and one can use this to show that (15.4) λ h 1, = h, where 1, denotes the dual norm with respect to the norm f 1 on l 1 (E). Conversely, suppose that λ is a bounded linear functional on l 1 (E). For each y E, put (15.5) h(y) = λ(δ y ). Thus (15.6) h(y) λ 1, for each y E, since δ y 1 = 1. Equivalently, h is bounded on E, and (15.7) h λ 1,. It remains to show that λ = λ h as linear functionals on l 1 (E). More precisely, we want to show that for each f l 1 (E), (15.8) λ(f) = λ h (f) If f(x) 0 for only finitely many x E, then f is a linear combination of finitely many δ y s, and this holds by linearity. Because these functions are dense in l 1 (E), the same conclusion holds for every f l 1 (E), by continuity. 16 c 0 (E) Let E be a nonempty set, and let h be a summable real or complex-valued function on E. If f is a bounded function on E, then f h is a summable function on E, and we can put (16.1) λ h (f) = f(x)h(x). x E 18

19 This defines a bounded linear functional on l (E), since (16.2) λ h (f) x E f(x) h(x) h 1 f. If we choose f so that f = 1 and (16.3) f(x)h(x) = h(x) for each x E, then (16.4) λ h (f) = h 1, at it follows the dual norm of λ h on l (E) is equal to h 1. We can also restrict λ h to a bounded linear functional on c 0 (E) with dual norm less than or equal to h 1. If E 1 E has only finitely many elements, then we can choose a function f on E such that f(x) = 0 on E\E 1, f = 1, and (16.3) holds on E 1. Thus f c 0 (E) and (16.5) λ h (f) = h(x), x E 1 and one can use this to show that the dual norm of the restriction of λ h to c 0 (E) is still equal to h 1, by taking the supremum over all finite subsets E 1 of E. Now let λ be any bounded linear functional on c 0 (E). As in the previous section, we can define a function h on E by (16.6) By construction, (16.7) h(y) = λ(δ y ). λ(f) = x E f(x)h(x) when f(x) 0 for only finitely many x E. If E 1 E has only finitely many elements, then we can choose f as in the preceding paragraph to get that (16.8) h(x) x E 1 is less than or equal to the dual norm of λ on c 0 (E). It follows that h is a summable function on E, and that h 1 is less than or equal to the dual norm of λ on c 0 (E). It remains to check that (16.9) λ(f) = λ h (f) for every f c 0 (E). We already know that this holds when f(x) 0 for only finitely many x E, and it holds for every f c 0 (E) by continuity, since these functions are dense in c 0 (E). 19

20 17 Hölder s inequality Let 1 < p < be given, and let 1 < q < be the conjugate exponent, characterized by (17.1) 1 p + 1 q = 1. If a and b are nonnegative real numbers, then (17.2) a b ap p + bq q. This can be seen as a consequence of the convexity of the exponential function, and the p = q = 2 case is elementary, as in Section 7. Suppose that f, h are real or complex-valued functions on a nonempty set E. As in the previous paragraph, (17.3) f(x) h(x) f(x) p p + h(x) q q for every x E. If f is p-summable and h is q-summable, then it follows that f h is summable on E, with (17.4) Equivalently, (17.5) f(x) h(x) 1 f(x) p + 1 h(x) q. p q x E x E f h 1 f p p p If f p = h q = 1, then we get that + h q q. q x E (17.6) f h 1 1. This implies for any f l p (E) and h l q (E) that (17.7) f h 1 f p h q, by homogeneity. This is Hölder s inequality. The analogous statement also holds for p = 1 and q = 1, and was implicitly used in the previous two sections. 18 l p (E) Let 1 < p, q < be conjugate exponents again, and let h be a q-summable function on a nonempty set E. If f is a p-summable function on E, then f h is summable, and we can put (18.1) λ h (f) = x E f(x)h(x). 20

21 This defines a bounded linear functional on l p (E), since (18.2) λ h (f) h q f p, by Hölder s inequality. If we choose f so that (18.3) f(x)h(x) = h(x) q for every x E, and f(x) = 0 when h(x) = 0, then (18.4) f(x) = h(x) q 1, and (18.5) f(x) p = h(x) q, since p(q 1) = q. In this case, (18.6) λ h (f) = h q q = h q f p, and it follows that the dual norm of λ h on l p (E) is h q. Now let λ be any bounded linear functional on l p (E), and let h be the function on E defined by h(y) = λ(δ y ), so that (18.7) λ(f) = x E f(x)h(x) when f(x) 0 for only finitely many x E. If E 1 E has only finitely many elements, then we can choose f so that f(x) = 0 when x E\E 1 or h(x) = 0 and (18.3) holds for every x E 1, and we get that (18.8) We also have that (18.9) which implies that (18.10) λ h (f) = x E 1 h(x) q. ( f p = h(x) q x E 1 ( x E 1 h(x) q ) 1/q ) 1/p, is bounded by the dual norm of λ on l p (E). It follows that h is q-summable, and that h q is less than or equal to the dual norm of λ on l p (E). The remaining point is that λ(f) = λ h (f) for every f l p (E). This holds by construction when f(x) 0 for only finitely many x E, and it is easy to see that these functions are dense in l p (E). Thus λ(f) = λ h (f) for every f l p (E) by continuity. 21

22 19 Separability Remember that a metric space is said to be separable if it contains a dense set with only finitely or countably many elements. For example, the real line is separable, because the rationals are countable and dense. Similarly, R n and C n are separable for each positive integer n. Let V be a real or complex vector space equipped with a norm. If there is a set A V with only finitely or countably many elements whose span is dense in V, then V is separable. In the real case, the set of linear combinations of elements of A with rational coefficients is a countable dense set in V. In the complex case, one can use coefficients with rational real and imaginary parts. For example, l p (E) is separable when E is countable and 1 p <, because the linear span of the functions δ y, y E, is dense. The same argument applies to c 0 (E) equipped with the supremum norm, and to l p (E) with 0 < p < 1, using the metric f 1 f 2 p p. The space of continuous functions on the unit interval is separable with respect to the supremum norm, because polynomials are dense. One can show that the space of continuous functions on any compact metric space is separable with respect to the supremum norm. This uses the fact that continuous functions on compact metric spaces are automatically uniformly continuous. 20 An extension lemma Let V be a real vector space, let W be a linear subspace of codimension 1 in V, and let z be an element of V \W. Thus every v V can be expressed in a unique way as (20.1) v = w + t z, where w W and t R. If λ is a linear functional on W, then any extension of λ to a linear functional µ on V is uniquely determined by its value α R at z, since (20.2) µ(w + t z) = λ(w) + t α. Any choice of α R defines an extension µ of λ in this way. Suppose now that V is equipped with a norm, and that λ is a bounded linear functional with respect to the restriction of to W. Thus there is an L 0 such that (20.3) λ(w) L w for every w W. We would like to choose α R and hence µ so that (20.4) µ(v) L v for every v V, which is the same as (20.5) λ(w) + t α L w + t z 22

23 for every w W and t R. This holds by hypothesis when t = 0, and for t 0 it is enough to show that (20.6) λ(w) + α L w + z for every w W, since otherwise we can replace w by t w and then take out t from both sides by homogeneity. Equivalently, we would like to choose α R such that (20.7) L w + z λ(w) α L w + z λ(w) for every w W. To do this, it suffices to show that (20.8) L w 1 + z λ(w 1 ) L w 2 + z λ(w 2 ) for every w 1, w 2 W. By hypothesis, we know that (20.9) λ(w 2 w 1 ) L w 2 w 1 for every w 1, w 2 W, which implies that (20.10) λ(w 2 w 1 ) L w 2 + z + L w 1 + z, because of the triangle inequality. This gives the desired estimate. 21 The Hahn Banach theorem Let V be a real vector space equipped with a norm, and let W be a linear subspace of V. The Hahn Banach theorem asserts that every bounded linear functional λ on W has an extension to a bounded linear functional µ on V with the same norm. If W has finite codimension in V, then one can apply the procedure described in the previous section finitely many times. If V is separable, then one can apply this procedure repeatedly to extend λ to a dense linear subspace of V, and then to all of V by continuity. Otherwise, there is an argument based on the axiom of choice. As an application, it follows that for every v V there is a bounded linear functional µ on V with norm 1 such that (21.1) µ(v) = v. To see this, one can start with the linear functional on the 1-dimensional subspace spanned by v defined by (21.2) λ(r v) = r v, r R. This has norm 1, and a norm-1 extension to V has the desired properties. 23

24 Similarly, if W 0 is a closed linear subspace of V and v V \W 0, then there is a bounded linear functional µ on V such that (21.3) and (21.4) µ(v) 0 µ(w) = 0 for every w W 0. For it is easy to find a bounded linear functional on the span of W 0 and v with these properties, which can then be extended to V. 22 Complex vector spaces Let V be a complex vector space, which is then also a real vector space, since we can simply forget about multiplication by i. If λ is a linear functional on V as a complex vector space, then the real part of λ is a linear functional on V as a real vector space. The imaginary part of λ can be recovered from the real part, by the formula (22.1) Im λ(v) = i Re λ( i v). Similarly, any linear functional on V as a real vector space can be expressed as the real part of a linear functional on V as a complex vector space. Suppose that V is equipped with a norm, which is also a norm on V as a real vector space. If λ is a bounded linear functional on V as a complex vector space, then one can show that the norm of λ is equal to the norm of the real part of λ as a bounded linear functional on V as a real vector space. This uses the fact that the norm of any v V is equal to the norm of c v for every complex number c with c = 1. Also, the modulus of a complex number z is the same as the maximum of the real parts of the complex numbers c z with c C and c = 1. Every bounded linear functional on V as a real vector space is the real part of a bounded linear functional on V as a complex vector space, as in the previous paragraph. This permits questions about linear functionals on complex vector spaces to be reduced to questions about linear functionals on real vector spaces. In particular, the Hahn Banach theorem also applies to complex vector spaces. 23 The weak topology Let V be a real or complex vector space equipped with a norm v, and let V be the dual space of bounded linear functionals on V with the dual norm λ. As usual, the dual norm determines a metric on V, and hence a topology on V. We are also interested in another topology on V, known as the weak topology. A set U V is an open set in the weak topology if for every λ U there are finitely many vectors v 1,..., v n V and positive real numbers r 1,...,r n such that (23.1) {µ V : µ(v j ) λ(v j ) < r j, 1 j n} U. 24

25 Equivalently, (23.2) {µ V : µ(v j ) λ(v j ) < r j, 1 j n} is an open set in V with respect to the weak topology for every λ V, v 1,..., v n V, and r 1,...,r n > 0, and these sets form a base for the weak - topology. If U V is an open set with respect to the weak topology, then U is also an open set with respect to the topology determined by the dual norm. However, open balls in V with respect to the dual norm are not open sets in the weak topology, unless V is finite-dimensional. For each v V, (23.3) λ λ(v) defines a linear functional on V. More precisely, this is a bounded linear functional on V with respect to the dual norm, since (23.4) λ(v) λ v, by definition of the dual norm. The norm of this linear functional on V is actually equal to v, by the Hahn Banach theorem. These linear functionals are also continuous on V with respect to the weak topology, by definition of the weak topology. The weak topology can be described as the weakest topology on V in which these linear functionals are continuous. 24 Seminorms A nonnegative real-valued function N(w) on a real or complex vector space W is said to be a seminorm if (24.1) N(t w) = t N(w) for every w W and t R or C, as appropriate, and (24.2) N(w + z) N(w) + N(z) for every w, z W. These are the same conditions as for a norm, except that N(w) = 0 may occur when w 0. Suppose that N is a collection of seminorms on W which satisfies the positivity condition that for every w W with w 0, (24.3) N(w) > 0 for some N N. As in the case of a single norm, there is a nice topology on W determined by N, Specifically, U W is an open set in this topology if for every u U there are finitely many seminorms N 1,...,N l N and positive real numbers r 1,..., r l such that (24.4) {w W : N j (w u) < r j, 1 j l} U. 25

26 Equivalently, (24.5) {w W : N j (w u) < r j, 1 j l} is an open set in W with respect to this topology for every u W, N 1,..., N l in N, and r 1,..., r l > 0, and these sets form a base for the topology. The positivity condition ensures that this topology is Hausdorff. For example, if µ is a linear functional on W, then (24.6) N(w) = µ(w) is a seminorm on W. The weak topology on the dual V of a vector space V corresponds to the family of seminorms (24.7) N v (λ) = λ(v), v V. If N consists of only finitely many seminorms N 1,...,N k on W, then their maximum (24.8) N(w) = max(n 1 (w),..., N k (w)) is a norm on W, and the topology on W corresponding to N is the same as the usual one determined by N. If N consists of an infinite sequence N 1, N 2,... of seminorms on W, then the corresponding topology on W can be described by a metric. For each j 1, one can check that (24.9) ρ j (u, w) = min(n j (u w), 1/j) is a semimetric on W. This means that ρ j (u, w) satisfies the same conditions as a metric, except that ρ j (u, w) = 0 can occur when u w. One can also check that (24.10) ρ(u, w) = max{ρ j (u, w) : j 1} is a metric on W, and that the topology on W determined by this metric is the same as the topology corresponding to the sequence of seminorms as before. 25 The weak topology, 2 Let V be a real or complex vector space with a norm v, and let V be the dual space of bounded linear functionals on V with the dual norm λ. Also let (25.1) B = {λ V : λ 1} be the closed unit ball in V. As in the previous section, the weak topology on V is the same as the topology corresponding to the family of seminorms (25.2) N v (λ) = λ(v), v V. For A V, consider the family of seminorms N A on V consisting of N v with v A. If the linear span of A is dense in V, then the topology on B induced 26

27 by the weak topology on V is the same as the topology induced by the one corresponding to N A. This is easy to see, and it works just as well for any subset of V which is bounded with respect to the dual norm. If V is separable, then we can take A to be a countable set. As in the previous section, the topology corresponding to N A is then determined by a metric. Thus the topology on B or other bounded subsets of V induced by the weak topology can be described by a metric in this case. Technically, the term weak topology on V should perhaps only be used when V is complete. If V is not complete, then V can be isometrically embedded as a dense linear subspace of a Banach space. Although V and its completion have the same dual space, the additional elements of the completion can affect the weak topology on the dual. However, this does not matter for the topology on bounded subsets of the dual induced by the weak topology, by the earlier remarks. 26 The Banach Alaoglu theorem Let V be a real or complex vector space equipped with a norm v, and let V be the dual space with dual norm λ. The Banach Alaoglu theorem states that the closed unit ball B in V is compact with respect to the weak topology. As usual, each v V determines a linear functional (26.1) λ λ(v) on V. This defines a mapping from V into the Cartesian product of copies of R or C, as appropriate, parameterized by v V. One can check that this mapping is a homeomorphism from V onto its image in the Cartesian product with respect to the topology induced by the product topology, because of the way that the weak topology is defined. One can also check that the image of B under this mapping is a closed set with respect to the product topology. The image of B is also contained in the product of closed intervals or disks, as appropriate, which implies compactness. If V is separable, then one can argue more directly that every sequence {λ j } in B has a subsequence that converges to an element of B in the weak topology. For each v V, one can use the compactness of closed and bounded subsets of R or C to get a subsequence {λ jl } l=1 of {λ j} such that {λ jl (v)} converges. By standard arguments, this can be refined to show that there is a subsequence which converges pointwise on a countable subset of V. Hence there is a subsequence that converges on a dense set in V when V is separable. Using the fact that these linear functionals all have norm less than or equal to 1, one can show that this subsequence actually converges pointwise everywhere on V, and that the limit is also a linear functional with norm less than or equal to 1. 27

28 27 The weak topology Let V be a real or complex vector space equipped with a norm v. As in Section 24 each bounded linear functional λ on V determines a seminorm on V, given by (27.1) N λ (v) = λ(v). The topology on V defined by the family of seminorms N λ, λ V, is called the weak topology. Note that for each v V there is a λ V such that λ(v) 0 and hence N λ (v) > 0, by the Hahn Banach theorem, so that this family of seminorms satisfies the usual positivity condition. If U V is an open set with respect to the weak topology, then U is also an open set with respect to the topology associated to the norm. However, an open ball in V is not an open set with respect to the weak topology, unless V has finite dimension. If W is a linear subspace of V which is closed in the norm topology, then W is also closed in the weak topology, by the Hahn Banach theorem. Each bounded linear functional on V is also continuous with respect to the weak topology, and the weak topology is the weakest topology on V with this property. The weak topology can also be defined on the dual V of V, using bounded linear functionals on V with respect to the dual norm. As in Section 23, (27.2) λ λ(v) defines a bounded linear functional on V for each v V, which implies that every open set in V with respect to the weak topology is also an open set in the weak topology on V. If every bounded linear functional on V is given by evaluation at an element of V in this way, then V is said to be reflexive. Thus the weak and weak topologies on V are the same when V is reflexive. Hilbert spaces are automatically reflexive, because of the way that they can be identified with their dual spaces. If E is a nonempty set and 1 < p <, then l p (E) is also reflexive. As in Section 18, the dual of l p (E) can be identified with l q (E), where q is the conjugate exponent corresponding to p. For the same reasons, the dual of l q (E) can be identified with l p (E). As in Sections 15 and 16, the dual of c 0 (E) can be identified with l 1 (E) and the dual of l 1 (E) can be identified with l (E), which is not the same as c 0 (E) when E has infinitely many elements. 28 Seminorms, 2 Let W be a real or complex vector space, and let N be a family of seminorms on W that satisfies the positivity condition that for each w W there is an N N such that (28.1) N(w) > 0. As in Section 24, this leads to a nice topology on W. Let us say that a linear functional ψ on W is bounded with respect to N if there are finitely many 28

29 seminorms N 1,..., N l N and A 0 such that (28.2) ψ(w) A max(n 1 (w),..., N l (w)) for each w W. This implies that (28.3) ψ(u) ψ(w) A max(n 1 (u w),..., N l (u w)) for every u, w W, and it is easy to see that ψ is therefore continuous with respect to the topology on W corresponding to N. Conversely, suppose that ψ is a linear functional on W which is continuous with respect to the topology corresponding to N. This implies that (28.4) U = {w w : ψ(w) < 1} is an open set in this topology. Since 0 U, this means that there are finitely many seminorms N 1,...,N l N and positive real numbers r 1,..., r l > 0 such that (28.5) {w w : N j (w) < r j, 1 j l} U, by definition of the topology on W corresponding to N. Equivalently, ψ(w) < 1 when (28.6) N 1 (w) < r 1,...,N l (w) < r l, which implies that (28.7) ψ(w) max(r1 1 N 1 (w),..., r 1 N l (w)), l and hence that ψ is bounded with respect to N. For example, let Φ be a collection of linear functionals on W with the property that for each w W there is a φ Φ such that φ(w) 0. Let N Φ be the family of seminorms (28.8) N φ (w) = φ(w) on W, φ Φ, which satisfies the positivity condition by hypothesis. Each φ Φ is a continuous linear functional on W with respect to the topology corresponding to N Φ, as well as any finite linear combination of elements of Φ. Conversely, any linear functional ψ on W which is continuous with respect to the topology associated to N Φ can be expressed as a linear combination of finitely many elements of Φ. For if ψ is continuous, then it is bounded with respect to N Φ, and there are φ 1,..., φ l Φ and A 0 such that (28.9) ψ(w) A max( φ 1 (w),..., φ l (w) ) for each w W. In particular, ψ(w) = 0 when (28.10) φ 1 (w) = = φ l (w) = 0. It follows from standard arguments in linear algebra that ψ is a linear combination of φ 1,..., φ l, as desired. 29

30 29 Convergent sequences Let (M, d(, )) be a metric space. By definition, a sequence {x j } of elements of M converges to x M if and only if (29.1) lim d(x j, x) = 0 j as a sequence of real numbers. In particular, if W is a vector space with a norm W, then a sequence of vectors {w j } converges to w W with respect to the norm if and only if (29.2) lim w j w W = 0. j If instead the topology on W is determined by a family N of seminorms on W, then one can check that {w j } converges to w if and only if (29.3) lim N(w j w) = 0 j for each N N. Let V be a real or complex vector space equipped with a norm, and let V be the dual space of bounded linear functionals on V equipped with the dual norm. A sequence {v j } of vectors in V converges to v V in the weak topology if and only if (29.4) lim λ(v j) = λ(v) j as a sequence of real or complex numbers for each λ V. Similarly, a sequence {λ j } of bounded linear functionals on V converges to λ V in the weak topology if and only if (29.5) lim λ j(v) = λ(v) j as a sequence of real or complex numbers for each v V. In a metric space M, the topology is determined by convergence of sequences, because closed subsets of M can be characterized in terms of convergent sequences. This also works in topological spaces which satisfy the first countability condition, for which there is a countable local base for the topology at every point, but it does not work in arbitrary topological spaces. In some circumstances, the induced topology on interesting subsets of a topological space X may satisfy the first countability condition, even if X does not enjoy this property itself. For example, this is the case for bounded subsets of the dual V of a separable vector space V with a norm, with respect to the weak topology on V. Suppose that {λ j } is a sequence of bounded linear functionals on a vector space V with a norm which is uniformly bounded in the sense that (29.6) λ j L for some L 0 and each j 1. If {λ j (v)} converges as a sequence of real or complex numbers for a set of v V whose linear span is dense in V, then one 30

### PROBLEMS. (b) (Polarization Identity) Show that in any inner product space

1 Professor Carl Cowen Math 54600 Fall 09 PROBLEMS 1. (Geometry in Inner Product Spaces) (a) (Parallelogram Law) Show that in any inner product space x + y 2 + x y 2 = 2( x 2 + y 2 ). (b) (Polarization

### CHAPTER VIII HILBERT SPACES

CHAPTER VIII HILBERT SPACES DEFINITION Let X and Y be two complex vector spaces. A map T : X Y is called a conjugate-linear transformation if it is a reallinear transformation from X into Y, and if T (λx)

### Hilbert spaces. 1. Cauchy-Schwarz-Bunyakowsky inequality

(October 29, 2016) Hilbert spaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ [This document is http://www.math.umn.edu/ garrett/m/fun/notes 2016-17/03 hsp.pdf] Hilbert spaces are

### Banach Spaces 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

### Examples of Dual Spaces from Measure Theory

Chapter 9 Examples of Dual Spaces from Measure Theory We have seen that L (, A, µ) is a Banach space for any measure space (, A, µ). We will extend that concept in the following section to identify an

### MATHS 730 FC Lecture Notes March 5, Introduction

1 INTRODUCTION MATHS 730 FC Lecture Notes March 5, 2014 1 Introduction Definition. If A, B are sets and there exists a bijection A B, they have the same cardinality, which we write as A, #A. If there exists

### THEOREMS, ETC., FOR MATH 515

THEOREMS, ETC., FOR MATH 515 Proposition 1 (=comment on page 17). If A is an algebra, then any finite union or finite intersection of sets in A is also in A. Proposition 2 (=Proposition 1.1). For every

### CHAPTER I THE RIESZ REPRESENTATION THEOREM

CHAPTER I THE RIESZ REPRESENTATION THEOREM We begin our study by identifying certain special kinds of linear functionals on certain special vector spaces of functions. We describe these linear functionals

### 2) Let X be a compact space. Prove that the space C(X) of continuous real-valued functions is a complete metric space.

University of Bergen General Functional Analysis Problems with solutions 6 ) Prove that is unique in any normed space. Solution of ) Let us suppose that there are 2 zeros and 2. Then = + 2 = 2 + = 2. 2)

### Math 209B Homework 2

Math 29B Homework 2 Edward Burkard Note: All vector spaces are over the field F = R or C 4.6. Two Compactness Theorems. 4. Point Set Topology Exercise 6 The product of countably many sequentally compact

### CHAPTER II THE HAHN-BANACH EXTENSION THEOREMS AND EXISTENCE OF LINEAR FUNCTIONALS

CHAPTER II THE HAHN-BANACH EXTENSION THEOREMS AND EXISTENCE OF LINEAR FUNCTIONALS In this chapter we deal with the problem of extending a linear functional on a subspace Y to a linear functional on the

### Lecture 4 Lebesgue spaces and inequalities

Lecture 4: Lebesgue spaces and inequalities 1 of 10 Course: Theory of Probability I Term: Fall 2013 Instructor: Gordan Zitkovic Lecture 4 Lebesgue spaces and inequalities Lebesgue spaces We have seen how

### Topological vectorspaces

(July 25, 2011) Topological vectorspaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ Natural non-fréchet spaces Topological vector spaces Quotients and linear maps More topological

### Real Analysis: Part II. William G. Faris

Real Analysis: Part II William G. Faris June 3, 2004 ii Contents 1 Function spaces 1 1.1 Spaces of continuous functions................... 1 1.2 Pseudometrics and seminorms.................... 2 1.3 L

### Notes on Integrable Functions and the Riesz Representation Theorem Math 8445, Winter 06, Professor J. Segert. f(x) = f + (x) + f (x).

References: Notes on Integrable Functions and the Riesz Representation Theorem Math 8445, Winter 06, Professor J. Segert Evans, Partial Differential Equations, Appendix 3 Reed and Simon, Functional Analysis,

### Linear Algebra Massoud Malek

CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product

### Some Background Material

Chapter 1 Some Background Material In the first chapter, we present a quick review of elementary - but important - material as a way of dipping our toes in the water. This chapter also introduces important

### 1 Topology Definition of a topology Basis (Base) of a topology The subspace topology & the product topology on X Y 3

Index Page 1 Topology 2 1.1 Definition of a topology 2 1.2 Basis (Base) of a topology 2 1.3 The subspace topology & the product topology on X Y 3 1.4 Basic topology concepts: limit points, closed sets,

### Continuity of convex functions in normed spaces

Continuity of convex functions in normed spaces In this chapter, we consider continuity properties of real-valued convex functions defined on open convex sets in normed spaces. Recall that every infinitedimensional

### ADJOINTS, ABSOLUTE VALUES AND POLAR DECOMPOSITIONS

J. OPERATOR THEORY 44(2000), 243 254 c Copyright by Theta, 2000 ADJOINTS, ABSOLUTE VALUES AND POLAR DECOMPOSITIONS DOUGLAS BRIDGES, FRED RICHMAN and PETER SCHUSTER Communicated by William B. Arveson Abstract.

### CHAPTER X THE SPECTRAL THEOREM OF GELFAND

CHAPTER X THE SPECTRAL THEOREM OF GELFAND DEFINITION A Banach algebra is a complex Banach space A on which there is defined an associative multiplication for which: (1) x (y + z) = x y + x z and (y + z)

### Contents. 2 Sequences and Series Approximation by Rational Numbers Sequences Basics on Sequences...

Contents 1 Real Numbers: The Basics... 1 1.1 Notation... 1 1.2 Natural Numbers... 4 1.3 Integers... 5 1.4 Fractions and Rational Numbers... 10 1.4.1 Introduction... 10 1.4.2 Powers and Radicals of Rational

### 16 1 Basic Facts from Functional Analysis and Banach Lattices

16 1 Basic Facts from Functional Analysis and Banach Lattices 1.2.3 Banach Steinhaus Theorem Another fundamental theorem of functional analysis is the Banach Steinhaus theorem, or the Uniform Boundedness

### 1.3.1 Definition and Basic Properties of Convolution

1.3 Convolution 15 1.3 Convolution Since L 1 (R) is a Banach space, we know that it has many useful properties. In particular the operations of addition and scalar multiplication are continuous. However,

### 2. The Concept of Convergence: Ultrafilters and Nets

2. The Concept of Convergence: Ultrafilters and Nets NOTE: AS OF 2008, SOME OF THIS STUFF IS A BIT OUT- DATED AND HAS A FEW TYPOS. I WILL REVISE THIS MATE- RIAL SOMETIME. In this lecture we discuss two

### INF-SUP CONDITION FOR OPERATOR EQUATIONS

INF-SUP CONDITION FOR OPERATOR EQUATIONS LONG CHEN We study the well-posedness of the operator equation (1) T u = f. where T is a linear and bounded operator between two linear vector spaces. We give equivalent

### Functional Analysis Exercise Class

Functional Analysis Exercise Class Wee November 30 Dec 4: Deadline to hand in the homewor: your exercise class on wee December 7 11 Exercises with solutions Recall that every normed space X can be isometrically

### 1. Let A R be a nonempty set that is bounded from above, and let a be the least upper bound of A. Show that there exists a sequence {a n } n N

Applied Analysis prelim July 15, 216, with solutions Solve 4 of the problems 1-5 and 2 of the problems 6-8. We will only grade the first 4 problems attempted from1-5 and the first 2 attempted from problems

### EXAMINATION SOLUTIONS

1 Marks & (a) If f is continuous on (, ), then xf is continuously differentiable 5 on (,) and F = (xf)/x as well (as the quotient of two continuously differentiable functions with the denominator never

### A Précis of Functional Analysis for Engineers DRAFT NOT FOR DISTRIBUTION. Jean-François Hiller and Klaus-Jürgen Bathe

A Précis of Functional Analysis for Engineers DRAFT NOT FOR DISTRIBUTION Jean-François Hiller and Klaus-Jürgen Bathe August 29, 22 1 Introduction The purpose of this précis is to review some classical

### 2 (Bonus). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure?

MA 645-4A (Real Analysis), Dr. Chernov Homework assignment 1 (Due 9/5). Prove that every countable set A is measurable and µ(a) = 0. 2 (Bonus). Let A consist of points (x, y) such that either x or y is

### MH 7500 THEOREMS. (iii) A = A; (iv) A B = A B. Theorem 5. If {A α : α Λ} is any collection of subsets of a space X, then

MH 7500 THEOREMS Definition. A topological space is an ordered pair (X, T ), where X is a set and T is a collection of subsets of X such that (i) T and X T ; (ii) U V T whenever U, V T ; (iii) U T whenever

### 1.2 Fundamental Theorems of Functional Analysis

1.2 Fundamental Theorems of Functional Analysis 15 Indeed, h = ω ψ ωdx is continuous compactly supported with R hdx = 0 R and thus it has a unique compactly supported primitive. Hence fφ dx = f(ω ψ ωdy)dx

### Eberlein-Šmulian theorem and some of its applications

Eberlein-Šmulian theorem and some of its applications Kristina Qarri Supervisors Trond Abrahamsen Associate professor, PhD University of Agder Norway Olav Nygaard Professor, PhD University of Agder Norway

### 4 Expectation & the Lebesgue Theorems

STA 205: Probability & Measure Theory Robert L. Wolpert 4 Expectation & the Lebesgue Theorems Let X and {X n : n N} be random variables on a probability space (Ω,F,P). If X n (ω) X(ω) for each ω Ω, does

### Lecture Notes in Advanced Calculus 1 (80315) Raz Kupferman Institute of Mathematics The Hebrew University

Lecture Notes in Advanced Calculus 1 (80315) Raz Kupferman Institute of Mathematics The Hebrew University February 7, 2007 2 Contents 1 Metric Spaces 1 1.1 Basic definitions...........................

### Semicontinuous functions and convexity

Semicontinuous functions and convexity Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto April 3, 2014 1 Lattices If (A, ) is a partially ordered set and S is a subset

### Real Analysis. Jesse Peterson

Real Analysis Jesse Peterson February 1, 2017 2 Contents 1 Preliminaries 7 1.1 Sets.................................. 7 1.1.1 Countability......................... 8 1.1.2 Transfinite induction.....................

### OPERATOR THEORY ON HILBERT SPACE. Class notes. John Petrovic

OPERATOR THEORY ON HILBERT SPACE Class notes John Petrovic Contents Chapter 1. Hilbert space 1 1.1. Definition and Properties 1 1.2. Orthogonality 3 1.3. Subspaces 7 1.4. Weak topology 9 Chapter 2. Operators

### 6. Duals of L p spaces

6 Duals of L p spaces This section deals with the problem if identifying the duals of L p spaces, p [1, ) There are essentially two cases of this problem: (i) p = 1; (ii) 1 < p < The major difference between

### ANALYSIS QUALIFYING EXAM FALL 2017: SOLUTIONS. 1 cos(nx) lim. n 2 x 2. g n (x) = 1 cos(nx) n 2 x 2. x 2.

ANALYSIS QUALIFYING EXAM FALL 27: SOLUTIONS Problem. Determine, with justification, the it cos(nx) n 2 x 2 dx. Solution. For an integer n >, define g n : (, ) R by Also define g : (, ) R by g(x) = g n

### SPRING 2006 PRELIMINARY EXAMINATION SOLUTIONS

SPRING 006 PRELIMINARY EXAMINATION SOLUTIONS 1A. Let G be the subgroup of the free abelian group Z 4 consisting of all integer vectors (x, y, z, w) such that x + 3y + 5z + 7w = 0. (a) Determine a linearly

### The Way of Analysis. Robert S. Strichartz. Jones and Bartlett Publishers. Mathematics Department Cornell University Ithaca, New York

The Way of Analysis Robert S. Strichartz Mathematics Department Cornell University Ithaca, New York Jones and Bartlett Publishers Boston London Contents Preface xiii 1 Preliminaries 1 1.1 The Logic of

### CHAPTER 7. Connectedness

CHAPTER 7 Connectedness 7.1. Connected topological spaces Definition 7.1. A topological space (X, T X ) is said to be connected if there is no continuous surjection f : X {0, 1} where the two point set

### THIRD SEMESTER M. Sc. DEGREE (MATHEMATICS) EXAMINATION (CUSS PG 2010) MODEL QUESTION PAPER MT3C11: COMPLEX ANALYSIS

THIRD SEMESTER M. Sc. DEGREE (MATHEMATICS) EXAMINATION (CUSS PG 2010) MODEL QUESTION PAPER MT3C11: COMPLEX ANALYSIS TIME:3 HOURS Maximum weightage:36 PART A (Short Answer Type Question 1-14) Answer All

### A LITTLE REAL ANALYSIS AND TOPOLOGY

A LITTLE REAL ANALYSIS AND TOPOLOGY 1. NOTATION Before we begin some notational definitions are useful. (1) Z = {, 3, 2, 1, 0, 1, 2, 3, }is the set of integers. (2) Q = { a b : aεz, bεz {0}} is the set

### Convexity in R n. The following lemma will be needed in a while. Lemma 1 Let x E, u R n. If τ I(x, u), τ 0, define. f(x + τu) f(x). τ.

Convexity in R n Let E be a convex subset of R n. A function f : E (, ] is convex iff f(tx + (1 t)y) (1 t)f(x) + tf(y) x, y E, t [0, 1]. A similar definition holds in any vector space. A topology is needed

### Stanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon Measures

2 1 Borel Regular Measures We now state and prove an important regularity property of Borel regular outer measures: Stanford Mathematics Department Math 205A Lecture Supplement #4 Borel Regular & Radon

### Lecture 5 - Hausdorff and Gromov-Hausdorff Distance

Lecture 5 - Hausdorff and Gromov-Hausdorff Distance August 1, 2011 1 Definition and Basic Properties Given a metric space X, the set of closed sets of X supports a metric, the Hausdorff metric. If A is

### Elements of Positive Definite Kernel and Reproducing Kernel Hilbert Space

Elements of Positive Definite Kernel and Reproducing Kernel Hilbert Space Statistical Inference with Reproducing Kernel Hilbert Space Kenji Fukumizu Institute of Statistical Mathematics, ROIS Department

### FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES. 1. Compact Sets

FUNCTIONAL ANALYSIS LECTURE NOTES: COMPACT SETS AND FINITE-DIMENSIONAL SPACES CHRISTOPHER HEIL 1. Compact Sets Definition 1.1 (Compact and Totally Bounded Sets). Let X be a metric space, and let E X be

### A note on some approximation theorems in measure theory

A note on some approximation theorems in measure theory S. Kesavan and M. T. Nair Department of Mathematics, Indian Institute of Technology, Madras, Chennai - 600 06 email: kesh@iitm.ac.in and mtnair@iitm.ac.in

### Optimization Theory. A Concise Introduction. Jiongmin Yong

October 11, 017 16:5 ws-book9x6 Book Title Optimization Theory 017-08-Lecture Notes page 1 1 Optimization Theory A Concise Introduction Jiongmin Yong Optimization Theory 017-08-Lecture Notes page Optimization

### A SHORT INTRODUCTION TO BANACH LATTICES AND

CHAPTER A SHORT INTRODUCTION TO BANACH LATTICES AND POSITIVE OPERATORS In tis capter we give a brief introduction to Banac lattices and positive operators. Most results of tis capter can be found, e.g.,

### Kernel Method: Data Analysis with Positive Definite Kernels

Kernel Method: Data Analysis with Positive Definite Kernels 2. Positive Definite Kernel and Reproducing Kernel Hilbert Space Kenji Fukumizu The Institute of Statistical Mathematics. Graduate University

### This chapter reviews some basic geometric facts that we will need during the course.

Chapter 1 Some Basic Geometry This chapter reviews some basic geometric facts that we will need during the course. 1.1 Affine Geometry We will assume that you are familiar with the basic notions of linear

### McGill University Math 354: Honors Analysis 3

Practice problems McGill University Math 354: Honors Analysis 3 not for credit Problem 1. Determine whether the family of F = {f n } functions f n (x) = x n is uniformly equicontinuous. 1st Solution: The

### Introduction to Topology

Introduction to Topology Randall R. Holmes Auburn University Typeset by AMS-TEX Chapter 1. Metric Spaces 1. Definition and Examples. As the course progresses we will need to review some basic notions about

### REVIEW OF ESSENTIAL MATH 346 TOPICS

REVIEW OF ESSENTIAL MATH 346 TOPICS 1. AXIOMATIC STRUCTURE OF R Doğan Çömez The real number system is a complete ordered field, i.e., it is a set R which is endowed with addition and multiplication operations

### GELFAND S THEOREM. Christopher McMurdie. Advisor: Arlo Caine. Spring 2004

GELFAND S THEOREM Christopher McMurdie Advisor: Arlo Caine Super Advisor: Dr Doug Pickrell Spring 004 This paper is a proof of the first part of Gelfand s Theorem, which states the following: Every compact,

### The Schwartz Space: Tools for Quantum Mechanics and Infinite Dimensional Analysis

Mathematics 2015, 3, 527-562; doi:10.3390/math3020527 Article OPEN ACCESS mathematics ISSN 2227-7390 www.mdpi.com/journal/mathematics The Schwartz Space: Tools for Quantum Mechanics and Infinite Dimensional

### Variational Formulations

Chapter 2 Variational Formulations In this chapter we will derive a variational (or weak) formulation of the elliptic boundary value problem (1.4). We will discuss all fundamental theoretical results that

### ANALYSIS TOOLS WITH APPLICATIONS

ANALYSIS TOOLS WITH APPLICATIONS ii BRUCE K. DRIVER Abstract. These are lecture notes from Math 24. Things to do: ) Exhibit a non-measurable null set and a non-borel measurable Riemann integrable function.

### Normed and Banach Spaces

(August 30, 2005) Normed and Banach Spaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ We have seen that many interesting spaces of functions have natural structures of Banach spaces:

### MA651 Topology. Lecture 10. Metric Spaces.

MA65 Topology. Lecture 0. Metric Spaces. This text is based on the following books: Topology by James Dugundgji Fundamental concepts of topology by Peter O Neil Linear Algebra and Analysis by Marc Zamansky

### 2 so Q[ 2] is closed under both additive and multiplicative inverses. a 2 2b 2 + b

. FINITE-DIMENSIONAL VECTOR SPACES.. Fields By now you ll have acquired a fair knowledge of matrices. These are a concrete embodiment of something rather more abstract. Sometimes it is easier to use matrices,

### MEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich

MEASURE AND INTEGRATION Dietmar A. Salamon ETH Zürich 9 September 2016 ii Preface This book is based on notes for the lecture course Measure and Integration held at ETH Zürich in the spring semester 2014.

### L p Functions. Given a measure space (X, µ) and a real number p [1, ), recall that the L p -norm of a measurable function f : X R is defined by

L p Functions Given a measure space (, µ) and a real number p [, ), recall that the L p -norm of a measurable function f : R is defined by f p = ( ) /p f p dµ Note that the L p -norm of a function f may

### Metric Spaces. Exercises Fall 2017 Lecturer: Viveka Erlandsson. Written by M.van den Berg

Metric Spaces Exercises Fall 2017 Lecturer: Viveka Erlandsson Written by M.van den Berg School of Mathematics University of Bristol BS8 1TW Bristol, UK 1 Exercises. 1. Let X be a non-empty set, and suppose

### Peter Hochs. Strings JC, 11 June, C -algebras and K-theory. Peter Hochs. Introduction. C -algebras. Group. C -algebras.

and of and Strings JC, 11 June, 2013 and of 1 2 3 4 5 of and of and Idea of 1 Study locally compact Hausdorff topological spaces through their algebras of continuous functions. The product on this algebra

### Spheres with maximum inner diameter

Carnegie Mellon University Research Showcase @ CMU Department of Mathematical Sciences Mellon College of Science 1970 Spheres with maximum inner diameter Juan Jorge Schäffer Carnegie Mellon University,

### LINEAR CHAOS? Nathan S. Feldman

LINEAR CHAOS? Nathan S. Feldman In this article we hope to convience the reader that the dynamics of linear operators can be fantastically complex and that linear dynamics exhibits the same beauty and

### Continuity. Chapter 4

Chapter 4 Continuity Throughout this chapter D is a nonempty subset of the real numbers. We recall the definition of a function. Definition 4.1. A function from D into R, denoted f : D R, is a subset of

### Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm

Chapter 13 Radon Measures Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm (13.1) f = sup x X f(x). We want to identify

### Norm-induced partially ordered vector spaces

Stefan van Lent Norm-induced partially ordered vector spaces Bachelor thesis Thesis Supervisor: dr. O.W. van Gaans Date Bachelor Exam: 30th of June 016 Mathematisch Instituut, Universiteit Leiden Contents

### Real Analysis Problems

Real Analysis Problems Cristian E. Gutiérrez September 14, 29 1 1 CONTINUITY 1 Continuity Problem 1.1 Let r n be the sequence of rational numbers and Prove that f(x) = 1. f is continuous on the irrationals.

### NOTES (1) FOR MATH 375, FALL 2012

NOTES 1) FOR MATH 375, FALL 2012 1 Vector Spaces 11 Axioms Linear algebra grows out of the problem of solving simultaneous systems of linear equations such as 3x + 2y = 5, 111) x 3y = 9, or 2x + 3y z =

### An Introduction to Complex Analysis and Geometry John P. D Angelo, Pure and Applied Undergraduate Texts Volume 12, American Mathematical Society, 2010

An Introduction to Complex Analysis and Geometry John P. D Angelo, Pure and Applied Undergraduate Texts Volume 12, American Mathematical Society, 2010 John P. D Angelo, Univ. of Illinois, Urbana IL 61801.

### Normed & Inner Product Vector Spaces

Normed & Inner Product Vector Spaces ECE 174 Introduction to Linear & Nonlinear Optimization Ken Kreutz-Delgado ECE Department, UC San Diego Ken Kreutz-Delgado (UC San Diego) ECE 174 Fall 2016 1 / 27 Normed

### Distributions: Topology and Sequential Compactness

Distributions: Topology and Sequential Compactness Acknowledgement Distributions: Topology and Sequential Compactness Acknowledgement First of all I want to thank Professor Mouhot for setting this essay

### Metric Space Topology (Spring 2016) Selected Homework Solutions. HW1 Q1.2. Suppose that d is a metric on a set X. Prove that the inequality d(x, y)

Metric Space Topology (Spring 2016) Selected Homework Solutions HW1 Q1.2. Suppose that d is a metric on a set X. Prove that the inequality d(x, y) d(z, w) d(x, z) + d(y, w) holds for all w, x, y, z X.

### A SHORT NOTE ON STRASSEN S THEOREMS

DEPARTMENT OF MATHEMATICS TECHNICAL REPORT A SHORT NOTE ON STRASSEN S THEOREMS DR. MOTOYA MACHIDA OCTOBER 2004 No. 2004-6 TENNESSEE TECHNOLOGICAL UNIVERSITY Cookeville, TN 38505 A Short note on Strassen

### 1 Stochastic Dynamic Programming

1 Stochastic Dynamic Programming Formally, a stochastic dynamic program has the same components as a deterministic one; the only modification is to the state transition equation. When events in the future

### Introduction. Chapter 1. Contents. EECS 600 Function Space Methods in System Theory Lecture Notes J. Fessler 1.1

Chapter 1 Introduction Contents Motivation........................................................ 1.2 Applications (of optimization).............................................. 1.2 Main principles.....................................................

### REAL ANALYSIS II TAKE HOME EXAM. T. Tao s Lecture Notes Set 5

REAL ANALYSIS II TAKE HOME EXAM CİHAN BAHRAN T. Tao s Lecture Notes Set 5 1. Suppose that te 1, e 2, e 3,... u is a countable orthonormal system in a complex Hilbert space H, and c 1, c 2,... is a sequence

### Filters in Analysis and Topology

Filters in Analysis and Topology David MacIver July 1, 2004 Abstract The study of filters is a very natural way to talk about convergence in an arbitrary topological space, and carries over nicely into

### 2.2 Annihilators, complemented subspaces

34CHAPTER 2. WEAK TOPOLOGIES, REFLEXIVITY, ADJOINT OPERATORS 2.2 Annihilators, complemented subspaces Definition 2.2.1. [Annihilators, Pre-Annihilators] Assume X is a Banach space. Let M X and N X. We

### Notes on Geometry and Spacetime

Notes on Geometry and Spacetime Version 2.7, November 2009 David B. Malament Department of Logic and Philosophy of Science University of California, Irvine dmalamen@uci.edu Contents 1 Preface 2 2 Preliminary

### Elements of Convex Optimization Theory

Elements of Convex Optimization Theory Costis Skiadas August 2015 This is a revised and extended version of Appendix A of Skiadas (2009), providing a self-contained overview of elements of convex optimization

### Sequences. Chapter 3. n + 1 3n + 2 sin n n. 3. lim (ln(n + 1) ln n) 1. lim. 2. lim. 4. lim (1 + n)1/n. Answers: 1. 1/3; 2. 0; 3. 0; 4. 1.

Chapter 3 Sequences Both the main elements of calculus (differentiation and integration) require the notion of a limit. Sequences will play a central role when we work with limits. Definition 3.. A Sequence

### Math 123 Homework Assignment #2 Due Monday, April 21, 2008

Math 123 Homework Assignment #2 Due Monday, April 21, 2008 Part I: 1. Suppose that A is a C -algebra. (a) Suppose that e A satisfies xe = x for all x A. Show that e = e and that e = 1. Conclude that e

### Exercise 1. Let f be a nonnegative measurable function. Show that. where ϕ is taken over all simple functions with ϕ f. k 1.

Real Variables, Fall 2014 Problem set 3 Solution suggestions xercise 1. Let f be a nonnegative measurable function. Show that f = sup ϕ, where ϕ is taken over all simple functions with ϕ f. For each n

### c i x (i) =0 i=1 Otherwise, the vectors are linearly independent. 1

Hilbert Spaces Physics 195 Supplementary Notes 009 F. Porter These notes collect and remind you of several definitions, connected with the notion of a Hilbert space. Def: A (nonempty) set V is called a

### MATH 6210: SOLUTIONS TO PROBLEM SET #3

MATH 6210: SOLUTIONS TO PROBLEM SET #3 Rudin, Chater 4, Problem #3. The sace L (T) is searable since the trigonometric olynomials with comlex coefficients whose real and imaginary arts are rational form

### Let R be the line parameterized by x. Let f be a complex function on R that is integrable. The Fourier transform ˆf = F f is. e ikx f(x) dx. (1.

Chapter 1 Fourier transforms 1.1 Introduction Let R be the line parameterized by x. Let f be a complex function on R that is integrable. The Fourier transform ˆf = F f is ˆf(k) = e ikx f(x) dx. (1.1) It

### 4 Uniform convergence

4 Uniform convergence In the last few sections we have seen several functions which have been defined via series or integrals. We now want to develop tools that will allow us to show that these functions

### AN ELEMENTARY PROOF OF THE SPECTRAL RADIUS FORMULA FOR MATRICES

AN ELEMENTARY PROOF OF THE SPECTRAL RADIUS FORMULA FOR MATRICES JOEL A. TROPP Abstract. We present an elementary proof that the spectral radius of a matrix A may be obtained using the formula ρ(a) lim

### CHAPTER 1. Metric Spaces. 1. Definition and examples

CHAPTER Metric Spaces. Definition and examples Metric spaces generalize and clarify the notion of distance in the real line. The definitions will provide us with a useful tool for more general applications