Math 61CM - Solutions to homework 6

Size: px
Start display at page:

Download "Math 61CM - Solutions to homework 6"

Transcription

1 Math 61CM - Solutions to homework 6 Cédric De Groote November 5 th, 2018 Problem 1: (i) Give an example of a metric space X such that not all Cauchy sequences in X are convergent. (ii) Let X be a metric space, and K a compact subset of X. Let x n be a Cauchy sequence such that each x n K. Show that x n is a convergent sequence. Solution: (i) We can take X to be the open interval (0, 1), with the usual Euclidean distance d E (x, y) = x y. Then, the sequence x n = 1/n is Cauchy (because it converges in R), but is not convergent in X. (ii) Since K is compact, we know that there exists a subsequence x jn of x n that converges in K; call the limit x. We will use the fact that x n is Cauchy to show that, in fact, the full sequence x n converges to x. Let ε > 0. Since x n is Cauchy, we know that there exists N 1 N such that x m x n < ε/2 if m, n N 1. Since x jn converges to x, we know that there exists N 2 N such that x jn x < ε/2 if n N 2. Let N = max(n 1, N 2 ). Let n N. Then, pick k such that k N (which implies j k N, since these are the indices of a subsequence). Then, we have where we x n x x n x jk + x jk x < ε/2 + ε/2 = ε, used the fact that j k N 1 and n N 1, along with the Cauchyness of x n to bound the first term, and used the fact that k N 2, along with the fact that x jn converges to x to bound the second term. This means that x n converges to x. 1

2 Problem 2: Let f n (x) be a sequence of functions on [0, 1]. We say that the sequence f n converges pointwise to a function f if f n (x) f(x) as n + for each x fixed. We say that f n converges uniformly to a function f if sup f n (x) f(x) 0 as n. x [0,1] (i) Show that f n converges to f uniformly if and only if for any ε > 0 there exists N so that f n (x) f(x) < ε for all n N and x [0, 1]. (ii) Give an example of a sequence of functions f n (x) and a function f(x) such that f n converges to f pointwise but not uniformly. (iii) Show that if all f n are continuous functions on [0, 1] and f n converges uniformly to a function f then the function f is also continuous on [0, 1]. (iv) Give an example of a sequence f n of continuous functions on [0, 1] that converges pointwise to a function f that is discontinuous at some point x [0, 1]. Solution: (i) The fact that sup x [0,1] f n (x) f(x) converges to 0 means that for any ε > 0, there exists N N such that sup x [0,1] f n (x) f(x) < ε for all n N (here we use that this supremum is nonnegative, as it s a supremum of a family of non-negative numbers, to get rid of the absolute value). In particular, for any given x, we have f n (x) f(x) sup f n (x) f(x) < ε x [0,1] whenever n N, since the supremum of a set is in particular an upper bound for that set. (ii) Take f n (x) = { 1/(nx) if x 0 0 else and f to be identically 0. Then, given any x 0, the sequence f n (x) = 1/(nx) converges to f(x) = 0, and also f n (0) = 0 converges to f(0) = 0; this shows that f n (x) converges pointwise to the function f. However, taking ε = 1, there can not exist N such that f n (x) f(x) < ε for all n N and x [0, 1]. Indeed, assume that N exists. Then, take x = 1/2N; then we have f N (x) = 2 which is larger than 1; contradiction. Therefore, f n does not converge uniformly to f. (iii) Let ε > 0, and a [0, 1]. Pick N N such that f N (x) f(x) < ε/3 for all x [0, 1]; this exists since we assume that f n converges uniformly to f. Since f N is continuous at a, there exists δ > 0 such that f N (x) f N (a) < ε/3 whenever x a < δ. Therefore, we have f(x) f(a) f(x) f N (x) + f N (x) f N (a) + f N (a) f(x) < ε/3 + ε/3 + ε/3 = ε, whenever x a < δ. This shows that f is continuous at a. Since a was arbitrary, we conclude that f is continuous., 2

3 (iv) Take f n (x) = x n, and f(x) = { 0 if x 1 1 if x = 1. Then it is clear that f n converges pointwise fo f. However, the function f is not continuous, therefore by the point (iii) the sequence f n can not converge uniformly, since it consists of continuous functions. Problem 3: (i) Let a n > 0 be a decreasing sequence of positive numbers such that lim n a n = 0. Show that the series ( 1)n+1 a n converges. Hint: use the Cauchy criterion. (ii) We say that a series c n is a rearrangement of a series b n if there exists a one-to-one map J : N N such that for each n we have c J(n) = b n. Let b n be a series that converges but not absolutely. Show that for any s R we can find a rearrangement c n of the series bn such that n c n = s. Hint: start with showing that the series that consists only of the positive b n > 0 diverges, as does the series consisting only of the negative b n. Keep in mind what lim b n is. Solution: (i) (Solution without using the Cauchy criterion) Write s k = k ( 1)n+1 a n the sequence of partial sums. Notice that 2k+2 s 2k+2 s 2k = ( 1) n+1 a n 2k ( 1) n+1 a n = a 2k+2 + a 2k+1 0 2k+3 2k+1 s 2k+3 s 2k+1 = ( 1) n+1 a n ( 1) n+1 a n = a 2k+3 a 2k+2 0 since the sequence a n is decreasing. It follows that the sequence (s 2k ) k of even partial sums is increasing, while the sequence (s 2k+1 ) k of odd partial sums is decreasing. Furthermore, we have s 2k = s 2k+1 a 2k+1 s 2k+1 s 1, where we used a n > 0 and the decreasingness of the sequence of odd partial sums. Similarly, s 2k+1 = s 2k + a 2k+1 s 2k s 2, where we used a n > 0 and the increasingness of the sequence of even partial sums. From this, it follows that the sequence (s 2k ) k is increasing and bounded, and therefore converges to some limit t. Similarly, the sequence (s 2k+1 ) k is decreasing and bounded, and therefore converges to some limit s. Let us show that s = t: s t = lim k s 2k+1 s 2k = lim k a 2k+1 = 0. 3

4 It is now easy to conclude that (s n ) n converges to s = t. Let ε > 0. Since (s 2k ) k converges to s, there exists N 1 N such that s s 2k < ε when k N 1. Since (s 2k+1 ) k converges to s, there exists N 2 N such that s s 2k+1 < ε. when k N 2. It follows that for every n max(2n 1, 2N 2 + 1), we have s n s < ε. (i) (Solution using the Cauchy criterion) Given n > m 1, note that any sums ( 1) m+1 a m + + ( 1) n+1 a n is always in the interval whose bounds are ( 1) m+1 a m and ( 1) m+2 a m+1. This is clear from a picture, and also follows from the same kind of arguments as in the first half of the proof above (but it s annoying to write down). Notice that the length of this interval is a m a m+1. Let ε > 0. Choose N 1 such that a m a m+1 < ε/2 for all m N 1 ; this exists since the sequence a n is Cauchy (as it converges to 0). Choose N 2 such that a m < ε/2 for m N 2 ; this exists since a n converges to 0. Let N = max(n 1, N 2 ). Then, for any m and n larger than N, we have that ( 1) m+1 a m + + ( 1) n+1 a n a m + am ( 1) m+1 a m + + ( 1) n+1 a n a m + a m a m+1 ε/2 + ε/2 = ε. The first inequality is the triangle inequality, and the second one follows from that fact that the distance between a point in an interval and an extremity of the interval is always smaller than the length of the interval. This shows that the Cauchy criterion is satisfied, hence our series converges. (ii) Outline: We first prove that both the series of the positive terms, and the series of the negative terms, must diverge. This is done by using the fact that the series converges but not absolutely, by passing to finite sums. Then, given any s R (that we assume non-negative for simplicity; the other case is similar), add the positive terms until we get something larger than s. Then add the negative terms until we get something smaller than s. Then add the positive terms until we get something larger than s. Keep going this way; it is always possible because both the series of positive terms and of negative terms diverge. Since lim b n = 0, at some point we only add b n very small, so we will stay in a very small interval around s, proving that the series obtained this way converges to s. Let b + n = max(b n, 0) and b n = min(b n, 0). Notice that b n = b + n + b n and that b n = b + n b n. Also, b n = b + n if it is positive, and b n = b n if it is negative. Lemma 0.1 Both n b+ n and n b n diverge. Proof. Suppose that they both converge, say to b + and b. Then M b n = M M b + n b n b + b would also converge, which contradicts the fact that n b n does not converge absolutely. If n b+ n converges but not n b n, then M b n = M M b + n + b n, 4

5 contradicting the convergence of n b n. Similarly, if n b n converges but not n b+ n, then M b n = contradicting the convergence of n b n. M M b + n + b n, In particular, the series b + n and b n both have infinitely many non-zero terms. Denote x n the n th term in the sequence b + n that is non-negative, and y n the absolute value of the n th term in the sequence b n that is negative. Notice that this defines a bijection between {b n n N} and {x n n N} {y n n N}, and also b + n = x n and b n = y n. n n n Therefore, n x n and n y n also both diverge. n Now, let µ 1 be the smallest element of N such that µ 1 x n > s; this exists since n x n diverges (if s 0, then set µ 1 = 0). Let ν 1 be the smallest element of N such that µ 1 x n ν 1 y n < s; again, it exists because n y n diverges. Let µ 2 be the smallest element of N such that µ 2 x n ν 1 y n > s; this exists since n x n diverges. Let ν 2 be the smallest element of N such that µ 2 x n ν 2 y n < s; again, it exists because n y n diverges. Here is the general step. Suppose that µ 1,, µ k, ν 1,, ν k have been chosen such that µ k is the least element of N such that µ k x n ν k 1 y n > s and ν k is the least element of N such that µk x n ν k y n < s, and such that µ k > µ k 1 and ν k > ν k 1. Let µ k+1 be the least element of N such that µ k+1 x n ν k y n > s and let ν k be the least element of N such that µ k+1 x n ν k+1 y n < s. It is straightworward that µ k+1 > µ k and ν k+1 > ν k. Now, define c n as the n th term of the sequence x 1,, x µ1, y 1,, y ν1, x µ1 +1,, x µ2, y ν1 +1,, y ν2, x µ2 +1, (again, if s < 0 this sequence starts with y 1 ). By construction, since the sequence (µ k ) k and (ν k ) k are increasing and since there is a bijection between {b n n N} and {x n n N} {y n n N}, this sequence c n is a rearrangement of the sequence b n. Notice in particular that this rearrangement keeps the relative order of the non-negative terms of (b n ) n, as well as the relative order of the negative terms of (b n ) n. It remains to show that n c n = s; this is where we use the fact that lim n b n = 0, which follows from the convergence of n b n. Let ε > 0. Since the sequence c n must alternate between terms from the x n s and terms from the y n s, and because lim n b n = 0, there exists N N such that c n < ε for n N. Notice that K c n differs from s by at most the last x n we added if this sum is larger than s, and by at most the last y n we added if this sum is smaller than s. Therefore, s r c n < ε as soon as r is larger than the first µ k or ν k that is larger than N. 5

6 Problem 4: A ball that falls from height h bounces back up to height qh, with some fixed 0 < q < 1. Find the time until it comes to rest, and the total distance it travels through the air. Hint: yes, you should use equations of motion for a ball falling through vacuum. Solution: First, we compute the total distance it travels. From the time it is dropped to the first time it touches the ground, it travels distance h. From the first time it touches the ground to the second time, it travels from height 0 to height qh, back to height 0, therefore travelling 2qh. Generalizing, from the k th time it touches the ground to the (k + 1) st time, it will travel from height 0 to height q k h, and back to 0, therefore travelling a distance of 2q k h. Therefore, the total distance travelled is h + 2q k h = 2h q k h = k=1 k=0 2h 1 q h, using the fact that if q < 1, we have k=0 qk = 1/(1 q). Now, let us compute the total time it takes. Recall that the height at time t of a ball that is dropped from height h 0 at time t = 0, without initial velocity, on earth, is h 0 t2 g 2. So, the time t for which this is equal to 0 (i.e. the time it touches the ground for the first time) is 2h0 g. If it starts at height qk h, then this time would be equal to 2q k h g. Similarly, since the laws of mechanics are symmetric under reversal of time, this is also the time it takes from the time the ball rebounds to the time it reaches height q k h with 0 velocity. So, the total time this process takes is 2h g + 2q 2 k h 2h = 2 ( q) k 2h g g g = 2 2h 2h g(1 q) g. k=1 k=0 Problem 5: (i) If S is the 2-dimensional subspace of R 4 spanned by the vectors (1, 1, 0, 0), (0, 0, 1, 1), find an orthonormal basis for S and find the matrix of the orthogonal projection of R 4 onto S. (ii) If S is the subspace of R 4 spanned by the vectors (1, 0, 0, 1), (1, 1, 0, 0), (0, 0, 1, 1), find an orthonormal basis for S, and find the matrix of the orthogonal projection of R 4 onto S. Solution: We recall the Gram-Schmidt process, which is explained in the Lemmas 5.2 and 5.3 of Simon s book (and also here: Given a set of vectors v 1,, v n in an inner product space V, we always can create an orthonormal set of vector u 1,, u n whose span is the same as the span of v 1,, v n. Notice in particular that if v 1,, v n form a basis of V, then the u 1,, u n must generate V (since they have the same span), and by dimension they must also form a basis. Here is the algorithm: i=1 ũ 1 = v 1, k 1 ũ i, v k ũ k = v k ũ i, ũ i ũi, k 2. 6

7 Then, we simply do u k = ũk ũ k, k 1, unless ũ k is 0, in which case we define u k = 0. What the first part with the ũ i does, is the following. It keeps the first vector v 1. Then, it projects v 2 onto the orthogonal complement of the span of v 1, making sure that ũ 1 and ũ 2 are orthogonal. Then, it keeps doing so: it projects the k th vector onto the orthogonal complement of the k 1 first vectors, ensuring that everything is orthogonal. Then, the second part simply normalizes each vector, so that they have length 1. Remark 0.2 A few remarks: 1. You can change the order of the vectors, if you notice it will make things simpler (this is what I ll do in (ii) below). 2. If you notice that the vectors are already orthogonal to each other, then you can simply normalize them, and it will give an orthonormal set (this is what I ll do in (i) below). 3. If we throw away all the u k s which are 0, we always get a set of vectors which are linearly independent. Now, we explain the solution to the problem. (i) Notice that these two vectors are already orthogonal, so all we need to do is normalize them. Since they both have norm 2, an orthonormal basis of S is given by ( ) ( ) u 1 = 2,, 0, 0, u 2 = 0, 0,, To find the matrix of the orthogonal projection, we use the Lemma 5.2 in Simon s book, which says that the orthogonal projection of a vector x onto S is P S (x) = x, u 1 u 1 + x, u 2 u 2. Applying it to the basis vectors e 1, e 2, e 3, e 4 of R 4, we get that the matrix of the orthogonal projection onto S is 1/2 1/ /2 1/ /2 1/ /2 1/2 (ii) Here, since the last two vectors are already orthogonal, we will change their order to (1, 1, 0, 0), (0, 0, 1, 1), (1, 0, 0, 1). Of course it does not change anything, except that the first step of the Gram-Schmidt process leaves the first two vectors unchanged now, instead of messing everything around. For the third one: (1, 1, 0, 0) (1, 0, 0, 1) 0, 1, 1) (1, 0, 0, 1) ũ 3 = (1, 1, 0, 0) (1, 1, 0, 0) (0, (0, 0, 1, 1) = (1/2, 1/2, 1/2, 1/2). (1, 1, 0, 0) (1, 1, 0, 0) (0, 0, 1, 1) (0, 0, 1, 1) 7

8 Finally, we just need to normalize them. For the first two it s as before. For the third one, it already has norm 1, so it remains unchanged. Therefore, we get the following orthonormal basis for S: ( ) ( ) ( u 1 = 2,, 0, 0, u 2 = 0, 0,,, u 3 = , 1 2, 1 ) 2, 1. 2 To find the matrix of the projection onto S, one can use the same formula as in the previous part of the problem. But, out of laziness, I ll just use part (ii) of Problem 1 of homework 5. Since S is 3-dimensional, it suffices to find a vector orthogonal to S with norm 1, and then we can use the result in the previous homework. Notice that all three vectors u 1, u 2, u 3 satisfy x 1 x 2 +x 3 x 4 = 0, so they all lie in the hyperplan defined by this equation. A vector orthogonal to that plane is given by the coefficients of the variables in that equation, so the vector (1, 1, 1, 1) will do it. We divide it by its norm (which is 2) to make it have length 1: we obtain v := (1/2, 1/2, 1/2, 1/2). The the projection of R 4 onto S is the projection of R 4 onto span (v), which is the following by problem 1.(ii) of homework 5: 3/4 1/4 1/4 1/4 1/4 3/4 1/4 1/4 1/4 1/4 3/4 1/4 1/4 1/4 1/4 3/4 Problem 6: Showing all row operations, calculate the determinant of Solution: We start by substracting the first column from the second, third, and fourth ones: det Then we substract the second row from the fifth one: det Now, we can develop with respect to the fifth row. The only element will be the coming coming 8

9 from the 3 in row-column (5, 1). The associated sign is ( 1) 1+5 = +1. So, this determinant is equal to det Now, substract the second row from the first one: det We develop with respect to the first row. Again, the only non-zero term is the one corresponding to the 5 in row-column (1, 4), coming with sign ( 1) 1+4 = 1. So, this determinant is equal to det Now, add the second colum to the third one: det Develop with respect to the third row; the only nonzero term is the one corresponding to the 1 in row-column (1, 3), coming with sign ( 1) 3+1 = +1: ( ) det. 1 2 Now, add twice the second row to the first one: ( ) det. 1 2 Develop with respect to the first row: the only nonzero term is the one corresponding to the 1 in row-column (1, 2), coming with sign ( 1) 1+2 = 1: 15 det( 1). By linearity of the determinant, det( 1) = ( 1) det(1), and det(1) = 1 by the normalization property of the determinant. Therefore, this determinant is equal to 15. 9

10 Problem 7: This problem asks you to prove the factor theorem. (i) Suppose f(x) = a d x d + a d 1 x d a 1 x + a 0 is a degree d polynomial (so a d 0) with coefficients in a field F. Prove that the number of zeros (elements x F with f(x) = 0) is at most d. (ii) Conversely, if E F with E d, then there is a degree d polynomial that vanishes on E, i.e., f(x) = 0 for all x E. Solution: We start by proving a classical lemma: Lemma 0.3 If f(a) = 0 for some a F, then (x a) divides f(x). Proof. Let g(x) = f(x+a). Then g(0) = f(a) = 0, hence g does not have independent term. So, g(x) = b d x d + +b 1 x for some b 1,, b d F. Since f(x) = g(x a), we have f(x) = b d (x a) d + +b 1 (x a), which is clearly divisble by (x a). (i) Suppose that f(x) has k distinct zeroes, which we call x 1,, x k. Then, by the Lemma above, the polynomial (x x 1 )(x x 2 ) (x x k ) must divide f(x). So, f(x) must have degree at least k. Therefore, if f has degree d, we must have d k. (ii) Write E = {e 1,, e k } with k d. Consider f(x) = (x e 1 )(x e 2 ) (x e k ); clearly it vanishes on E. In case k < d, it does not quite have degree d, so let s artificially modify its degree by taking for example f(x) = (x e 1 ) d k+1 (x e 2 ) (x e k ). Problem 8: Suppose f(x) = a d x d + a d 1 x d a 1 x + a 0 is a degree d polynomial with coefficients in R. Prove that f(x) has a nonzero multiple in which all the exponents are prime numbers. For instance, such a multiple of f(x) = x 2 x + 5 is x 5 + 4x 3 + 5x 2 = (x 3 + x 2 )(x 2 x + 5). You may assume that that there are infinitely many prime numbers. Solution: Pick d + 1 different prime numbers p 1,, p d+1. Using the long division for polynomials, write x p i = q i (x) f(x) + r i (x), (1) for polynomials q i (x) and r i (x) such that the degree of r i (x) is smaller or equal to d 1. Then the collection {r 1,, r d+1 } is a collection of d+1 polynomials in the space of polynomials of degree smaller or equal to d 1, which has dimension d. It follows that {r 1,, r d+1 } must be linearly dependent, so there exists c 1,, c d+1 R such that c 1 r 1 (x) + + c d+1 r d+1 (x) = 0. Then, multiplying the Equation (1) by c i and summing over i, we get d+1 c i x p i = i=1 ( d+1 ) d+1 c i q i (x) f(x) + c i r i (x), i=1 which shows that d+1 i=1 c ix p i is a multiple of f(x). 10 i=1 } {{ } =0

11 Problem 9: Let σ be the permutation which sends the ordered 5-tuple (12345) to (54321). (a) Prove that if we write σ as the product of adjacent transpositions, then the minimum possible number of such adjacent transpositions is 10. (b) Prove that if we write σ as the product of transpositions, then the minimum possible number of such transpositions is 2. Solution: (a) We can certainly do it in 10 adjacent transpositions: we bring 5 in the first spot using 4 adjacent transpositions, then 4 (which is now in the fifth spot) on the second spot using 3 adjacent transpositions, then 3 (which is now in the fifth spot) on the third spot using 2 adjacent transpositions, then 2 (which is now in the fifth spot) on the fourth spot using 1 adjacent transpositions. So we used = 10 adjacent transpositions. Now, let s show that we can not do it in less than 10 adjacent transpositions. The idea comes from the first part of the proof of the Lemma 1.5, page 63 of Simon s book (in particular, the sixth line before the end): an adjacent transposition can only change the number of inversions by 1 (this is defined on page 62 of Simon s book). It is proven carefully in the book, but the idea is that if we switch two adjacent elements in a transpositions, only the relative order of these two will be affected, so the number of transpositions can only increase or decrease by 1. Since (54321) has 10 inversions and (12345) has 0 inversions, we need at least ten adjacent transpositions to go from one to the other. (b) A transposition of a set of n elements has n 2 fixed points. Since σ has only 1 fixed point, it can not be written as a single transposition. But we can write it as τ 1,5 τ 2,4, proving that we can write it as the product of 2 transpositions, which is furthermore the minimal number. 11

Math 61CM - Quick answer key to section problems Fall 2018

Math 61CM - Quick answer key to section problems Fall 2018 Math 6CM - Quick answer key to section problems Fall 08 Cédric De Groote These are NOT complete solutions! You are always expected to write down all the details and justify everything. This document is

More information

MATH 51H Section 4. October 16, Recall what it means for a function between metric spaces to be continuous:

MATH 51H Section 4. October 16, Recall what it means for a function between metric spaces to be continuous: MATH 51H Section 4 October 16, 2015 1 Continuity Recall what it means for a function between metric spaces to be continuous: Definition. Let (X, d X ), (Y, d Y ) be metric spaces. A function f : X Y is

More information

Math 328 Course Notes

Math 328 Course Notes Math 328 Course Notes Ian Robertson March 3, 2006 3 Properties of C[0, 1]: Sup-norm and Completeness In this chapter we are going to examine the vector space of all continuous functions defined on the

More information

Finite-dimensional spaces. C n is the space of n-tuples x = (x 1,..., x n ) of complex numbers. It is a Hilbert space with the inner product

Finite-dimensional spaces. C n is the space of n-tuples x = (x 1,..., x n ) of complex numbers. It is a Hilbert space with the inner product Chapter 4 Hilbert Spaces 4.1 Inner Product Spaces Inner Product Space. A complex vector space E is called an inner product space (or a pre-hilbert space, or a unitary space) if there is a mapping (, )

More information

Math 61CM - Solutions to homework 2

Math 61CM - Solutions to homework 2 Math 61CM - Solutions to homework 2 Cédric De Groote October 5 th, 2018 Problem 1: Let V be the vector space of polynomials of degree at most 5, with coefficients in a field F Let U be the subspace of

More information

Immerse Metric Space Homework

Immerse Metric Space Homework Immerse Metric Space Homework (Exercises -2). In R n, define d(x, y) = x y +... + x n y n. Show that d is a metric that induces the usual topology. Sketch the basis elements when n = 2. Solution: Steps

More information

DS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra.

DS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra. DS-GA 1002 Lecture notes 0 Fall 2016 Linear Algebra These notes provide a review of basic concepts in linear algebra. 1 Vector spaces You are no doubt familiar with vectors in R 2 or R 3, i.e. [ ] 1.1

More information

Your first day at work MATH 806 (Fall 2015)

Your first day at work MATH 806 (Fall 2015) Your first day at work MATH 806 (Fall 2015) 1. Let X be a set (with no particular algebraic structure). A function d : X X R is called a metric on X (and then X is called a metric space) when d satisfies

More information

Maths 212: Homework Solutions

Maths 212: Homework Solutions Maths 212: Homework Solutions 1. The definition of A ensures that x π for all x A, so π is an upper bound of A. To show it is the least upper bound, suppose x < π and consider two cases. If x < 1, then

More information

l(y j ) = 0 for all y j (1)

l(y j ) = 0 for all y j (1) Problem 1. The closed linear span of a subset {y j } of a normed vector space is defined as the intersection of all closed subspaces containing all y j and thus the smallest such subspace. 1 Show that

More information

Solutions Final Exam May. 14, 2014

Solutions Final Exam May. 14, 2014 Solutions Final Exam May. 14, 2014 1. Determine whether the following statements are true or false. Justify your answer (i.e., prove the claim, derive a contradiction or give a counter-example). (a) (10

More information

Your first day at work MATH 806 (Fall 2015)

Your first day at work MATH 806 (Fall 2015) Your first day at work MATH 806 (Fall 2015) 1. Let X be a set (with no particular algebraic structure). A function d : X X R is called a metric on X (and then X is called a metric space) when d satisfies

More information

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9 MAT 570 REAL ANALYSIS LECTURE NOTES PROFESSOR: JOHN QUIGG SEMESTER: FALL 204 Contents. Sets 2 2. Functions 5 3. Countability 7 4. Axiom of choice 8 5. Equivalence relations 9 6. Real numbers 9 7. Extended

More information

Functional Analysis HW #5

Functional Analysis HW #5 Functional Analysis HW #5 Sangchul Lee October 29, 2015 Contents 1 Solutions........................................ 1 1 Solutions Exercise 3.4. Show that C([0, 1]) is not a Hilbert space, that is, there

More information

Hilbert Spaces. Hilbert space is a vector space with some extra structure. We start with formal (axiomatic) definition of a vector space.

Hilbert Spaces. Hilbert space is a vector space with some extra structure. We start with formal (axiomatic) definition of a vector space. Hilbert Spaces Hilbert space is a vector space with some extra structure. We start with formal (axiomatic) definition of a vector space. Vector Space. Vector space, ν, over the field of complex numbers,

More information

MATH 23a, FALL 2002 THEORETICAL LINEAR ALGEBRA AND MULTIVARIABLE CALCULUS Solutions to Final Exam (in-class portion) January 22, 2003

MATH 23a, FALL 2002 THEORETICAL LINEAR ALGEBRA AND MULTIVARIABLE CALCULUS Solutions to Final Exam (in-class portion) January 22, 2003 MATH 23a, FALL 2002 THEORETICAL LINEAR ALGEBRA AND MULTIVARIABLE CALCULUS Solutions to Final Exam (in-class portion) January 22, 2003 1. True or False (28 points, 2 each) T or F If V is a vector space

More information

Mathematics Department Stanford University Math 61CM/DM Inner products

Mathematics Department Stanford University Math 61CM/DM Inner products Mathematics Department Stanford University Math 61CM/DM Inner products Recall the definition of an inner product space; see Appendix A.8 of the textbook. Definition 1 An inner product space V is a vector

More information

Recall that any inner product space V has an associated norm defined by

Recall that any inner product space V has an associated norm defined by Hilbert Spaces Recall that any inner product space V has an associated norm defined by v = v v. Thus an inner product space can be viewed as a special kind of normed vector space. In particular every inner

More information

h(x) lim H(x) = lim Since h is nondecreasing then h(x) 0 for all x, and if h is discontinuous at a point x then H(x) > 0. Denote

h(x) lim H(x) = lim Since h is nondecreasing then h(x) 0 for all x, and if h is discontinuous at a point x then H(x) > 0. Denote Real Variables, Fall 4 Problem set 4 Solution suggestions Exercise. Let f be of bounded variation on [a, b]. Show that for each c (a, b), lim x c f(x) and lim x c f(x) exist. Prove that a monotone function

More information

Applied Analysis (APPM 5440): Final exam 1:30pm 4:00pm, Dec. 14, Closed books.

Applied Analysis (APPM 5440): Final exam 1:30pm 4:00pm, Dec. 14, Closed books. Applied Analysis APPM 44: Final exam 1:3pm 4:pm, Dec. 14, 29. Closed books. Problem 1: 2p Set I = [, 1]. Prove that there is a continuous function u on I such that 1 ux 1 x sin ut 2 dt = cosx, x I. Define

More information

Vector Spaces. Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms.

Vector Spaces. Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms. Vector Spaces Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms. For each two vectors a, b ν there exists a summation procedure: a +

More information

08a. Operators on Hilbert spaces. 1. Boundedness, continuity, operator norms

08a. Operators on Hilbert spaces. 1. Boundedness, continuity, operator norms (February 24, 2017) 08a. Operators on Hilbert spaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ [This document is http://www.math.umn.edu/ garrett/m/real/notes 2016-17/08a-ops

More information

Introduction to Real Analysis Alternative Chapter 1

Introduction to Real Analysis Alternative Chapter 1 Christopher Heil Introduction to Real Analysis Alternative Chapter 1 A Primer on Norms and Banach Spaces Last Updated: March 10, 2018 c 2018 by Christopher Heil Chapter 1 A Primer on Norms and Banach Spaces

More information

Assignment-10. (Due 11/21) Solution: Any continuous function on a compact set is uniformly continuous.

Assignment-10. (Due 11/21) Solution: Any continuous function on a compact set is uniformly continuous. Assignment-1 (Due 11/21) 1. Consider the sequence of functions f n (x) = x n on [, 1]. (a) Show that each function f n is uniformly continuous on [, 1]. Solution: Any continuous function on a compact set

More information

Linear Algebra Massoud Malek

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

More information

INNER PRODUCT SPACE. Definition 1

INNER PRODUCT SPACE. Definition 1 INNER PRODUCT SPACE Definition 1 Suppose u, v and w are all vectors in vector space V and c is any scalar. An inner product space on the vectors space V is a function that associates with each pair of

More information

[Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty.]

[Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty.] Math 43 Review Notes [Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty Dot Product If v (v, v, v 3 and w (w, w, w 3, then the

More information

Introductory Analysis I Fall 2014 Homework #9 Due: Wednesday, November 19

Introductory Analysis I Fall 2014 Homework #9 Due: Wednesday, November 19 Introductory Analysis I Fall 204 Homework #9 Due: Wednesday, November 9 Here is an easy one, to serve as warmup Assume M is a compact metric space and N is a metric space Assume that f n : M N for each

More information

There are two things that are particularly nice about the first basis

There are two things that are particularly nice about the first basis Orthogonality and the Gram-Schmidt Process In Chapter 4, we spent a great deal of time studying the problem of finding a basis for a vector space We know that a basis for a vector space can potentially

More information

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 1. I. Foundational material

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 1. I. Foundational material SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 1 Fall 2014 I. Foundational material I.1 : Basic set theory Problems from Munkres, 9, p. 64 2. (a (c For each of the first three parts, choose a 1 1 correspondence

More information

1. Continuous Functions between Euclidean spaces

1. Continuous Functions between Euclidean spaces Math 441 Topology Fall 2012 Metric Spaces by John M. Lee This handout should be read between Chapters 1 and 2 of the text. It incorporates material from notes originally prepared by Steve Mitchell and

More information

Exercise Solutions to Functional Analysis

Exercise Solutions to Functional Analysis Exercise Solutions to Functional Analysis Note: References refer to M. Schechter, Principles of Functional Analysis Exersize that. Let φ,..., φ n be an orthonormal set in a Hilbert space H. Show n f n

More information

Thus f is continuous at x 0. Matthew Straughn Math 402 Homework 6

Thus f is continuous at x 0. Matthew Straughn Math 402 Homework 6 Matthew Straughn Math 402 Homework 6 Homework 6 (p. 452) 14.3.3, 14.3.4, 14.3.5, 14.3.8 (p. 455) 14.4.3* (p. 458) 14.5.3 (p. 460) 14.6.1 (p. 472) 14.7.2* Lemma 1. If (f (n) ) converges uniformly to some

More information

An introduction to some aspects of functional analysis

An introduction to some aspects of functional analysis 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

More information

Homework 3 Solutions, Math 55

Homework 3 Solutions, Math 55 Homework 3 Solutions, Math 55 1.8.4. There are three cases: that a is minimal, that b is minimal, and that c is minimal. If a is minimal, then a b and a c, so a min{b, c}, so then Also a b, so min{a, b}

More information

Some Background Material

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

More information

Exercises to Applied Functional Analysis

Exercises to Applied Functional Analysis Exercises to Applied Functional Analysis Exercises to Lecture 1 Here are some exercises about metric spaces. Some of the solutions can be found in my own additional lecture notes on Blackboard, as the

More information

In class midterm Exam - Answer key

In class midterm Exam - Answer key Fall 2013 In class midterm Exam - Answer key ARE211 Problem 1 (20 points). Metrics: Let B be the set of all sequences x = (x 1,x 2,...). Define d(x,y) = sup{ x i y i : i = 1,2,...}. a) Prove that d is

More information

Chapter 3 Transformations

Chapter 3 Transformations Chapter 3 Transformations An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Linear Transformations A function is called a linear transformation if 1. for every and 2. for every If we fix the bases

More information

Inner products. Theorem (basic properties): Given vectors u, v, w in an inner product space V, and a scalar k, the following properties hold:

Inner products. Theorem (basic properties): Given vectors u, v, w in an inner product space V, and a scalar k, the following properties hold: Inner products Definition: An inner product on a real vector space V is an operation (function) that assigns to each pair of vectors ( u, v) in V a scalar u, v satisfying the following axioms: 1. u, v

More information

Contents Ordered Fields... 2 Ordered sets and fields... 2 Construction of the Reals 1: Dedekind Cuts... 2 Metric Spaces... 3

Contents Ordered Fields... 2 Ordered sets and fields... 2 Construction of the Reals 1: Dedekind Cuts... 2 Metric Spaces... 3 Analysis Math Notes Study Guide Real Analysis Contents Ordered Fields 2 Ordered sets and fields 2 Construction of the Reals 1: Dedekind Cuts 2 Metric Spaces 3 Metric Spaces 3 Definitions 4 Separability

More information

McGill University Math 354: Honors Analysis 3

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

More information

Math 320-2: Midterm 2 Practice Solutions Northwestern University, Winter 2015

Math 320-2: Midterm 2 Practice Solutions Northwestern University, Winter 2015 Math 30-: Midterm Practice Solutions Northwestern University, Winter 015 1. Give an example of each of the following. No justification is needed. (a) A metric on R with respect to which R is bounded. (b)

More information

MATH 131A: REAL ANALYSIS (BIG IDEAS)

MATH 131A: REAL ANALYSIS (BIG IDEAS) MATH 131A: REAL ANALYSIS (BIG IDEAS) Theorem 1 (The Triangle Inequality). For all x, y R we have x + y x + y. Proposition 2 (The Archimedean property). For each x R there exists an n N such that n > x.

More information

HOMEWORK 2 - RIEMANNIAN GEOMETRY. 1. Problems In what follows (M, g) will always denote a Riemannian manifold with a Levi-Civita connection.

HOMEWORK 2 - RIEMANNIAN GEOMETRY. 1. Problems In what follows (M, g) will always denote a Riemannian manifold with a Levi-Civita connection. HOMEWORK 2 - RIEMANNIAN GEOMETRY ANDRÉ NEVES 1. Problems In what follows (M, g will always denote a Riemannian manifold with a Levi-Civita connection. 1 Let X, Y, Z be vector fields on M so that X(p Z(p

More information

MATH 202B - Problem Set 5

MATH 202B - Problem Set 5 MATH 202B - Problem Set 5 Walid Krichene (23265217) March 6, 2013 (5.1) Show that there exists a continuous function F : [0, 1] R which is monotonic on no interval of positive length. proof We know there

More information

Solutions to Homework #3, Math 116

Solutions to Homework #3, Math 116 Solutions to Homework #, Math 6 Keziah Cook and Michael McElroy November 5, Problem Goroff) In each of the following cases, determine the supremum of f over its domain D. If there are points x D for which

More information

Math 421, Homework #9 Solutions

Math 421, Homework #9 Solutions Math 41, Homework #9 Solutions (1) (a) A set E R n is said to be path connected if for any pair of points x E and y E there exists a continuous function γ : [0, 1] R n satisfying γ(0) = x, γ(1) = y, and

More information

The Completion of a Metric Space

The Completion of a Metric Space The Completion of a Metric Space Let (X, d) be a metric space. The goal of these notes is to construct a complete metric space which contains X as a subspace and which is the smallest space with respect

More information

Theorems. Theorem 1.11: Greatest-Lower-Bound Property. Theorem 1.20: The Archimedean property of. Theorem 1.21: -th Root of Real Numbers

Theorems. Theorem 1.11: Greatest-Lower-Bound Property. Theorem 1.20: The Archimedean property of. Theorem 1.21: -th Root of Real Numbers Page 1 Theorems Wednesday, May 9, 2018 12:53 AM Theorem 1.11: Greatest-Lower-Bound Property Suppose is an ordered set with the least-upper-bound property Suppose, and is bounded below be the set of lower

More information

Functional Analysis F3/F4/NVP (2005) Homework assignment 2

Functional Analysis F3/F4/NVP (2005) Homework assignment 2 Functional Analysis F3/F4/NVP (25) Homework assignment 2 All students should solve the following problems:. Section 2.7: Problem 8. 2. Let x (t) = t 2 e t/2, x 2 (t) = te t/2 and x 3 (t) = e t/2. Orthonormalize

More information

David Hilbert was old and partly deaf in the nineteen thirties. Yet being a diligent

David Hilbert was old and partly deaf in the nineteen thirties. Yet being a diligent Chapter 5 ddddd dddddd dddddddd ddddddd dddddddd ddddddd Hilbert Space The Euclidean norm is special among all norms defined in R n for being induced by the Euclidean inner product (the dot product). A

More information

Course 212: Academic Year Section 1: Metric Spaces

Course 212: Academic Year Section 1: Metric Spaces Course 212: Academic Year 1991-2 Section 1: Metric Spaces D. R. Wilkins Contents 1 Metric Spaces 3 1.1 Distance Functions and Metric Spaces............. 3 1.2 Convergence and Continuity in Metric Spaces.........

More information

7: FOURIER SERIES STEVEN HEILMAN

7: FOURIER SERIES STEVEN HEILMAN 7: FOURIER SERIES STEVE HEILMA Contents 1. Review 1 2. Introduction 1 3. Periodic Functions 2 4. Inner Products on Periodic Functions 3 5. Trigonometric Polynomials 5 6. Periodic Convolutions 7 7. Fourier

More information

REAL AND COMPLEX ANALYSIS

REAL AND COMPLEX ANALYSIS REAL AND COMPLE ANALYSIS Third Edition Walter Rudin Professor of Mathematics University of Wisconsin, Madison Version 1.1 No rights reserved. Any part of this work can be reproduced or transmitted in any

More information

Similar to sequence, note that a series converges if and only if its tail converges, that is, r 1 r ( 1 < r < 1), ( 1) k k. r k =

Similar to sequence, note that a series converges if and only if its tail converges, that is, r 1 r ( 1 < r < 1), ( 1) k k. r k = Infinite Series We say an infinite series a k converges to s if its sequence of initial sums converges to s, that is, lim( n a k : n N) = s. Similar to sequence, note that a series converges if and only

More information

Math 118B Solutions. Charles Martin. March 6, d i (x i, y i ) + d i (y i, z i ) = d(x, y) + d(y, z). i=1

Math 118B Solutions. Charles Martin. March 6, d i (x i, y i ) + d i (y i, z i ) = d(x, y) + d(y, z). i=1 Math 8B Solutions Charles Martin March 6, Homework Problems. Let (X i, d i ), i n, be finitely many metric spaces. Construct a metric on the product space X = X X n. Proof. Denote points in X as x = (x,

More information

Final Exam Review. 2. Let A = {, { }}. What is the cardinality of A? Is

Final Exam Review. 2. Let A = {, { }}. What is the cardinality of A? Is 1. Describe the elements of the set (Z Q) R N. Is this set countable or uncountable? Solution: The set is equal to {(x, y) x Z, y N} = Z N. Since the Cartesian product of two denumerable sets is denumerable,

More information

Convex Analysis and Economic Theory Winter 2018

Convex Analysis and Economic Theory Winter 2018 Division of the Humanities and Social Sciences Ec 181 KC Border Convex Analysis and Economic Theory Winter 2018 Topic 0: Vector spaces 0.1 Basic notation Here are some of the fundamental sets and spaces

More information

Methods of Mathematical Physics X1 Homework 2 - Solutions

Methods of Mathematical Physics X1 Homework 2 - Solutions Methods of Mathematical Physics - 556 X1 Homework - Solutions 1. Recall that we define the orthogonal complement as in class: If S is a vector space, and T is a subspace, then we define the orthogonal

More information

THE PROBLEMS FOR THE SECOND TEST FOR BRIEF SOLUTIONS

THE PROBLEMS FOR THE SECOND TEST FOR BRIEF SOLUTIONS THE PROBLEMS FOR THE SECOND TEST FOR 18.102 BRIEF SOLUTIONS RICHARD MELROSE Question.1 Show that a subset of a separable Hilbert space is compact if and only if it is closed and bounded and has the property

More information

Vectors in Function Spaces

Vectors in Function Spaces Jim Lambers MAT 66 Spring Semester 15-16 Lecture 18 Notes These notes correspond to Section 6.3 in the text. Vectors in Function Spaces We begin with some necessary terminology. A vector space V, also

More information

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi Real Analysis Math 3AH Rudin, Chapter # Dominique Abdi.. If r is rational (r 0) and x is irrational, prove that r + x and rx are irrational. Solution. Assume the contrary, that r+x and rx are rational.

More information

5 Compact linear operators

5 Compact linear operators 5 Compact linear operators One of the most important results of Linear Algebra is that for every selfadjoint linear map A on a finite-dimensional space, there exists a basis consisting of eigenvectors.

More information

Homework I, Solutions

Homework I, Solutions Homework I, Solutions I: (15 points) Exercise on lower semi-continuity: Let X be a normed space and f : X R be a function. We say that f is lower semi - continuous at x 0 if for every ε > 0 there exists

More information

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define HILBERT SPACES AND THE RADON-NIKODYM THEOREM STEVEN P. LALLEY 1. DEFINITIONS Definition 1. A real inner product space is a real vector space V together with a symmetric, bilinear, positive-definite mapping,

More information

Analysis-3 lecture schemes

Analysis-3 lecture schemes Analysis-3 lecture schemes (with Homeworks) 1 Csörgő István November, 2015 1 A jegyzet az ELTE Informatikai Kar 2015. évi Jegyzetpályázatának támogatásával készült Contents 1. Lesson 1 4 1.1. The Space

More information

96 CHAPTER 4. HILBERT SPACES. Spaces of square integrable functions. Take a Cauchy sequence f n in L 2 so that. f n f m 1 (b a) f n f m 2.

96 CHAPTER 4. HILBERT SPACES. Spaces of square integrable functions. Take a Cauchy sequence f n in L 2 so that. f n f m 1 (b a) f n f m 2. 96 CHAPTER 4. HILBERT SPACES 4.2 Hilbert Spaces Hilbert Space. An inner product space is called a Hilbert space if it is complete as a normed space. Examples. Spaces of sequences The space l 2 of square

More information

MATH 117 LECTURE NOTES

MATH 117 LECTURE NOTES MATH 117 LECTURE NOTES XIN ZHOU Abstract. This is the set of lecture notes for Math 117 during Fall quarter of 2017 at UC Santa Barbara. The lectures follow closely the textbook [1]. Contents 1. The set

More information

Homework Assignment #5 Due Wednesday, March 3rd.

Homework Assignment #5 Due Wednesday, March 3rd. Homework Assignment #5 Due Wednesday, March 3rd. 1. In this problem, X will be a separable Banach space. Let B be the closed unit ball in X. We want to work out a solution to E 2.5.3 in the text. Work

More information

2. Signal Space Concepts

2. Signal Space Concepts 2. Signal Space Concepts R.G. Gallager The signal-space viewpoint is one of the foundations of modern digital communications. Credit for popularizing this viewpoint is often given to the classic text of

More information

We are going to discuss what it means for a sequence to converge in three stages: First, we define what it means for a sequence to converge to zero

We are going to discuss what it means for a sequence to converge in three stages: First, we define what it means for a sequence to converge to zero Chapter Limits of Sequences Calculus Student: lim s n = 0 means the s n are getting closer and closer to zero but never gets there. Instructor: ARGHHHHH! Exercise. Think of a better response for the instructor.

More information

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define Homework, Real Analysis I, Fall, 2010. (1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define ρ(f, g) = 1 0 f(x) g(x) dx. Show that

More information

Functional Analysis MATH and MATH M6202

Functional Analysis MATH and MATH M6202 Functional Analysis MATH 36202 and MATH M6202 1 Inner Product Spaces and Normed Spaces Inner Product Spaces Functional analysis involves studying vector spaces where we additionally have the notion of

More information

REFLECTIONS IN A EUCLIDEAN SPACE

REFLECTIONS IN A EUCLIDEAN SPACE REFLECTIONS IN A EUCLIDEAN SPACE PHILIP BROCOUM Let V be a finite dimensional real linear space. Definition 1. A function, : V V R is a bilinear form in V if for all x 1, x, x, y 1, y, y V and all k R,

More information

I teach myself... Hilbert spaces

I teach myself... Hilbert spaces I teach myself... Hilbert spaces by F.J.Sayas, for MATH 806 November 4, 2015 This document will be growing with the semester. Every in red is for you to justify. Even if we start with the basic definition

More information

converges as well if x < 1. 1 x n x n 1 1 = 2 a nx n

converges as well if x < 1. 1 x n x n 1 1 = 2 a nx n Solve the following 6 problems. 1. Prove that if series n=1 a nx n converges for all x such that x < 1, then the series n=1 a n xn 1 x converges as well if x < 1. n For x < 1, x n 0 as n, so there exists

More information

Definition 6.1. A metric space (X, d) is complete if every Cauchy sequence tends to a limit in X.

Definition 6.1. A metric space (X, d) is complete if every Cauchy sequence tends to a limit in X. Chapter 6 Completeness Lecture 18 Recall from Definition 2.22 that a Cauchy sequence in (X, d) is a sequence whose terms get closer and closer together, without any limit being specified. In the Euclidean

More information

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 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

More information

Typical Problem: Compute.

Typical Problem: Compute. Math 2040 Chapter 6 Orhtogonality and Least Squares 6.1 and some of 6.7: Inner Product, Length and Orthogonality. Definition: If x, y R n, then x y = x 1 y 1 +... + x n y n is the dot product of x and

More information

d(x n, x) d(x n, x nk ) + d(x nk, x) where we chose any fixed k > N

d(x n, x) d(x n, x nk ) + d(x nk, x) where we chose any fixed k > N Problem 1. Let f : A R R have the property that for every x A, there exists ɛ > 0 such that f(t) > ɛ if t (x ɛ, x + ɛ) A. If the set A is compact, prove there exists c > 0 such that f(x) > c for all x

More information

Overview of normed linear spaces

Overview of normed linear spaces 20 Chapter 2 Overview of normed linear spaces Starting from this chapter, we begin examining linear spaces with at least one extra structure (topology or geometry). We assume linearity; this is a natural

More information

Notions such as convergent sequence and Cauchy sequence make sense for any metric space. Convergent Sequences are Cauchy

Notions such as convergent sequence and Cauchy sequence make sense for any metric space. Convergent Sequences are Cauchy Banach Spaces These notes provide an introduction to Banach spaces, which are complete normed vector spaces. For the purposes of these notes, all vector spaces are assumed to be over the real numbers.

More information

Review problems for MA 54, Fall 2004.

Review problems for MA 54, Fall 2004. Review problems for MA 54, Fall 2004. Below are the review problems for the final. They are mostly homework problems, or very similar. If you are comfortable doing these problems, you should be fine on

More information

Metric Spaces Math 413 Honors Project

Metric Spaces Math 413 Honors Project Metric Spaces Math 413 Honors Project 1 Metric Spaces Definition 1.1 Let X be a set. A metric on X is a function d : X X R such that for all x, y, z X: i) d(x, y) = d(y, x); ii) d(x, y) = 0 if and only

More information

Conceptual Questions for Review

Conceptual Questions for Review Conceptual Questions for Review Chapter 1 1.1 Which vectors are linear combinations of v = (3, 1) and w = (4, 3)? 1.2 Compare the dot product of v = (3, 1) and w = (4, 3) to the product of their lengths.

More information

Convex Analysis and Economic Theory Winter 2018

Convex Analysis and Economic Theory Winter 2018 Division of the Humanities and Social Sciences Ec 181 KC Border Convex Analysis and Economic Theory Winter 2018 Supplement A: Mathematical background A.1 Extended real numbers The extended real number

More information

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces.

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces. Math 350 Fall 2011 Notes about inner product spaces In this notes we state and prove some important properties of inner product spaces. First, recall the dot product on R n : if x, y R n, say x = (x 1,...,

More information

Several variables and partial derivatives

Several variables and partial derivatives Chapter 8 Several variables and partial derivatives 8.1 Vector spaces, linear mappings, and convexity Note: 2 3 lectures 8.1.1 Vector spaces The euclidean space R n has already made an appearance in the

More information

Normed and Banach spaces

Normed and Banach spaces Normed and Banach spaces László Erdős Nov 11, 2006 1 Norms We recall that the norm is a function on a vectorspace V, : V R +, satisfying the following properties x + y x + y cx = c x x = 0 x = 0 We always

More information

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

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)

More information

Real Analysis Problems

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.

More information

2.4 The Extreme Value Theorem and Some of its Consequences

2.4 The Extreme Value Theorem and Some of its Consequences 2.4 The Extreme Value Theorem and Some of its Consequences The Extreme Value Theorem deals with the question of when we can be sure that for a given function f, (1) the values f (x) don t get too big or

More information

Assignment 1: From the Definition of Convexity to Helley Theorem

Assignment 1: From the Definition of Convexity to Helley Theorem Assignment 1: From the Definition of Convexity to Helley Theorem Exercise 1 Mark in the following list the sets which are convex: 1. {x R 2 : x 1 + i 2 x 2 1, i = 1,..., 10} 2. {x R 2 : x 2 1 + 2ix 1x

More information

Iowa State University. Instructor: Alex Roitershtein Summer Homework #1. Solutions

Iowa State University. Instructor: Alex Roitershtein Summer Homework #1. Solutions Math 501 Iowa State University Introduction to Real Analysis Department of Mathematics Instructor: Alex Roitershtein Summer 015 EXERCISES FROM CHAPTER 1 Homework #1 Solutions The following version of the

More information

Measure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond

Measure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond Measure Theory on Topological Spaces Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond May 22, 2011 Contents 1 Introduction 2 1.1 The Riemann Integral........................................ 2 1.2 Measurable..............................................

More information

Analysis Comprehensive Exam Questions Fall F(x) = 1 x. f(t)dt. t 1 2. tf 2 (t)dt. and g(t, x) = 2 t. 2 t

Analysis Comprehensive Exam Questions Fall F(x) = 1 x. f(t)dt. t 1 2. tf 2 (t)dt. and g(t, x) = 2 t. 2 t Analysis Comprehensive Exam Questions Fall 2. Let f L 2 (, ) be given. (a) Prove that ( x 2 f(t) dt) 2 x x t f(t) 2 dt. (b) Given part (a), prove that F L 2 (, ) 2 f L 2 (, ), where F(x) = x (a) Using

More information

Real Analysis Notes. Thomas Goller

Real Analysis Notes. Thomas Goller Real Analysis Notes Thomas Goller September 4, 2011 Contents 1 Abstract Measure Spaces 2 1.1 Basic Definitions........................... 2 1.2 Measurable Functions........................ 2 1.3 Integration..............................

More information

Problem 1: Compactness (12 points, 2 points each)

Problem 1: Compactness (12 points, 2 points each) Final exam Selected Solutions APPM 5440 Fall 2014 Applied Analysis Date: Tuesday, Dec. 15 2014, 10:30 AM to 1 PM You may assume all vector spaces are over the real field unless otherwise specified. Your

More information

MIDTERM I LINEAR ALGEBRA. Friday February 16, Name PRACTICE EXAM SOLUTIONS

MIDTERM I LINEAR ALGEBRA. Friday February 16, Name PRACTICE EXAM SOLUTIONS MIDTERM I LIEAR ALGEBRA MATH 215 Friday February 16, 2018. ame PRACTICE EXAM SOLUTIOS Please answer the all of the questions, and show your work. You must explain your answers to get credit. You will be

More information