Weekly Notices 3. September 26, October 2 J. Hoffmann-Jørgensen September 26, 2005

Size: px
Start display at page:

Download "Weekly Notices 3. September 26, October 2 J. Hoffmann-Jørgensen September 26, 2005"

Transcription

1 Weekly Notices 3 September 26, October 2 J. Hoffmann-Jørgensen September 26, 2005 Last lectures: Lectures: Last time I introduce the complex numbers and the n-dimensional space R n. I introduced the notion of a limits of sequences of real or complex numbers or vectors and I introduced notions of limits and continuity of vector functions. Friday September 21 (13:15 15:00): Metric spaces: limits and Cauchy sequences (Rudin Chap.3 p.45 55) 1. Cauchy sequence Augustin Louis Cauchy ( ) was a French mathematician teaching mathematical analysis at the University of Paris. In his famous book Cours d analyse from 1820 he observes that every convergent sequence (an) of real numbers have the following property (called the Cauchy property): the distance between an and ak becomes arbitraly small when n and k are sufficiently large and then he writes le contraire et évidente ; that is, every sequence with the Cauchy property is convergent to some real number c 2 R. Cauchy did not prove that statement actually he was not able to tdo it, due to the lack of a precise definition of limits and real numbers. Weierstrass rephrased the Cauchy property as follows. If (an) is a sequence of real numbers, then we say that (an) is a Cauchy sequence if and only if (*) 8 ">0 9 n" 1 so that jak 0 anj <" 8 n; k n" If (an) is a sequence of complex numbers or a sequence of vectors in R k, we say that (an) is a Cauchy sequence if and only if (**) holds. With this rephrasing, Cauchy s stament: (an) is convergent to some c 2 R if and only if (an) is Cauchy sequence becomes true. However, to proved the statement we the following notions: The limes superior and the limes inferior: Let a 1 ;a 2 ;... 2 R be a given sequence of real mumbers. Then we define the limes superior, and the limes inferior of (an) as follows: lim sup n!1 a n := inf sup an ; lim inf k1 nk n!1 a n := sup k1 inf nk a n Suppose that an denote the hight of landscape at n for every n 1. If you stand at k, then sup nk a n is the height of the heighest mountain ahead of you, and inf nk an is the depth of the deepest valley ahead of you. Hence, we see that lim sup an is the 1

2 height of the heighest mountain infinitely far way, and that lim inf a n the deepest valley infinitely far way. In particular, we have is the depth of (1) inf a n nk lim inf n!1 a n lim sup n!1 a n sup a n 8 k 1 nk NB: Rudin (p.56) gives an different definition of lim sup and lim inf. However, the two definitions are equivalent and the definition chosen here is more convenient for our purpose. Theorem 1.1: (The Cauchy-Weierstrass theorem) Let a 1 ;a 2 ;... 2 R be a given sequence of real numbers and let c 2 R be a real number. Then we have (i) c = lim n!1 a n, c = lim sup n!1 a n = lim inf n!1 a n (ii) (a n ) is convergent to some number b 2 R if and only if (a n ) is a Cauchy sequence Remark: Suppose that we want to show that (a n ) is convevergent. If use the definition (*), we have to specify the limit c. The importance of (ii) lies in the fact that we can prove convergence by verifying (**) without specifying the limit c. The fact that Cauchy observed (ii) without to be able to proof it, shows his ingenious insight in mathematics. Let me illustrate (ii) by an example: Euler s constant: Let R x 1 log x := dy denote the natural logarithm of x for 1 y all x 1 and let us define a n := log n 8 n =1; 2; 3;... n It can be shown that (a n ) is a Cauchy sequence (see Rudin Exc.9 p.197) and so by (ii) we have that (a n ) converges to some number wich is called Euler s constant and is denoted. Euler s constant owes it existence to Cauchy property and it is still today unknown if is rational or irrational (but it is generraly believed that is irrational). Euler s constant can be computed to any given precision; for instance, we have = 0: Proof: (i): Let us define a 3 := lim sup n!1 a n and a3 := lim inf n!1 a n. Suppose that c = lim n!1 a n and let ">0 be given. Then there exists n " 1 such that jc 0 a n j < " for all n n". Let n n" be given integers. Since jc 0 anj <",we have 0" <c0 an and an 0 c<" and since an = c +(an 0 c) =c 0 (c 0 an), we have c 0 "<an <c+ ". So by (1), we have c 0 " inf nn " an a3 a 3 sup nn " an c + " Since this holds for every ">0, we see that a3 = a 3 = c. Conversely, suppose that a3 = a 3 = c and let ">0 be given. By the definition of lim sup, there exists 2

3 n 1 1 such that sup kn 1 ak <a3 + " = c + " and by the definition of lim inf, there exists n 2 1 such that sup kn 2 ak >a3 0 " = c 0 ". Let us define n" := n 1 + n 2 and let n n" be given. Since n n 1 and n n 2, we have c 0 "< sup kn 2 ak an sup kn 1 ak <c0 " 8 n n" Hence, we see that an 2 N"(c) for all n n" and so by Thm.4.1.(ii) in Weekly Notices 2, we conclude that c = lim n!1 an which completes the proof of (i). (ii): Suppose that (an) converges to some number b 2 R and let " >0 be given. Applying (3) with " replaced by ", there exists and integer 2 n " 1 such that an 2 N " (c) for all n n ". Let n; k n" be given integers. Then we 2 have an 2 N " (c) and a k 2 N " (c) and since N " (c) =(c0 " ;c+ " ) is an open 2 interval of lenght ", we have jak 0 anj < ". Hence, se that (an) is a Cauchy sequence. Conversely, suppose that (an) is a Cauchy sequence and let us define a 3 := lim sup n!1 a n and a3 := lim inf n!1 an. Let ">0 be given. By (*), there exists an integer n" such that jak 0 anj <" for all n; k n". Let n n" be given. Since jan " 0 anj <", we have 0" <an " 0 an and an 0 an " <" and since an = an " +(an 0 an " )=an " 0 (an " 0 an), we have an " 0 " 2 <a n <an " + ". Since inf nk a n a3 a 3 sup nk for all k 1, we have an " 0 " inf nn " an a3 a 3 sup nn " an an " + " In particular, we see that a3 and 3 a are finite and that 0 3 a 0 a3 < 2 ". Since this holds for all ">0, we see that a3 3 = a and that b := a3 2 R. So by (i) we see that b = lim n!1 an which completes the proof of (ii). 2. Metric spaces Observe that the all the definitons and theorems on limits and continuity from Weekly Notices 2 only depend on the following properties of the distance (a): jx 0 xj =0 for all x ; (b): 0 < jx 0 yj = jy 0 xj for all x 6= y ; (c): jx 0 yj jx 0 zj + jz 0 yj for all x; y; z. This observation suggests the following generalization: Let X be an arbitrary set. Then a metric (also call a distance function) on X is a function d : X 2 X! [0; 1) satisfying (A) d(x; x) =0 8 x 2 X and 0 <d(x; y) =d(y; x) 8 x 6= y ; x; y 2 X (B) d(x; y) d(x; z) +d(z; y) 8 x; y; z 2 X Let a 1 ;a 2 ;... 2 X be a given sequence and let a 2 X be a given element. Then we say that (a n ) is convergent with limit a if and only if (*) 8 ">0 9 n " 1 so that d(a; a n ) <" 8 n n " 3

4 and we say that (a n ) is a Cauchy sequence if and only if (**) 8 ">0 9 n " 1 so that d(a k ;a n ) <" 8 n; k n " If a 2 X and r 0, we let N r (a) := fx 2 X j d(x; a) < rg and N r [a] := fx 2 X j d(x; a) rg denote the open and closed d-balls with center a and radius r, and we let N (a) :=N r r(a) nfag and N [a] :=N r r[a] nfag denote the open and closed punctuated d-balls with center a and radius r. Let A X be a given set and let a 2 X be a given elements. Then we introduce the following definitions: (1) We say that a is an interior point of A if and only if there exists a positive number r>0 satisfying N r (a) A. The set of all interior points of A is denoted A and is called the interior of A. Note that A A (2) We say that A is open if and only if all points in A are interior poins; that is, if and only if A A and if and only if A = A (3) We say that a is a limit point of A if and only if A \ N (a) 6= ; for all r>0. r The set of all limits of A is denoted A 0 and the set A := A [ A 0 is called the closure of A. Note that A A (4) We say that A is closed if and only if every limit point of A belongs to A ; that is, if and only if A 0 A and if and only if A A and if and only if A = A (5) We say that a is an isolated point of A if and only if a 2 A and a is not a limit point of A ; that is, if and only if a 2 A n A 0 and if and only if there exist a positive number r>0 satisfying A \ N r (a) =fag (6) A is perfect if every element in A is a limit point of A ; that is, if and only if A A 0 and if and only if A has no isolated points (7) We say that A is dense in X if and only if A = X (8) We say that A is bounded if and only if there exist q 2 X and r > 0 satisfying A N r (q) (9) If r 0 is a given non-negative number, we say that A is r-dicrete if and only if d(x; y) >r for all x; y 2 A satisfying x 6= y. Note that every set A X is 0-discrete The standard metric on R : Let x; y 2 R be real numbers. Then jx 0 yj is the lenght of the interval connecting x and y and d(x; y) :=jx 0 yj is a metric on R called the standard metric on R. Let :[0; 1)! R be a concave increasing function satisfying (0) = 0 and (x) > 0 for all x > 0. By concavity, we obtain the following inequality (u + v) (u) +(v) for all u; v 0, and if we define 4

5 p d (x; y) :=(jx 0 yj) for all x; y 2 R, then d is metric on R. For instance, we have that jx 0 yj and log (1 + jx 0 yj) are metrics on R. The Euclidian metric on R 2 : Let A =(x; y) and B =(u; v) be two points in the plane R 2. By Phytagoras theorem we have that ja0bj := p(u 0 x) 2 +(v 0 y) 2 is the distance between the points A and B and by Euclid s famous triangle theorem we have that ja 0 Bj is a metric on R 2 which is called the Euclidian metric; see the figure below: B=(u,v) A-B v-y A=(x,y) u-x The taxi metric on R 2 : The Euclidian metric, is the shortest rute between the points A and B provided that you actually are able to move along the straight line between A and B. However, if you are a taxidriver in New York city you have to follow the streets and if so, then the shortest route becomes ja0bj 1 := ju0xj+jv0yj. It is easily verified that jja 0 Bjj 1 is a metric on R 2 which is called the taxi metric. Metric on R n : Let n 1 be a given integer and let A =(x 1 ;...;x n ) and B =(u 1 ;...;u n ) be two vectors in R 2. In analogy with the previous examples we define the Euclidian metric and the taxi metric as follows ja 0 Bj := q (x 1 0 u 1 ) (x n 0 u n ) 2 ja 0 Bj 1 := jx 1 0 u 1 j jx n 0 u n j and we define the maximum metric as follows: ja 0 Bj1 := maxfjx 1 0 u 1 j ;...; jx n 0 u n jg The maximum metric is used in connection with election methods. Let p>0 be a given number, the we define the `p-metric as follows ja 0 Bj p := jx 1 0 u 1 j p jx n 0 u n j p if 0 <p 1 ja 0 Bj p := 8jx 1 0 u 1 j p jx n 0 u n j p 9 1 p if p>1 Then ja 0 Bj p is a metric on R n for every fixed number 0 <p1, and we have ja 0 Bj = ja 0 Bj 2. Metrics on function spaces: Let I = [a; b] be a closed interval and let f; g : I! R be two continuous functions. In analogy with the example above 5

6 we define the L p -metric as follows: kf 0 gk p := kf 0 gk p := kf 0 gk1 := Z b a Z b a jf (x) 0 g(x)j p dx if 0 <p 1 sup axb jf (x) 0 g(x)j p jf (x) 0 g(x)j p 1 p dx if 1 <p<1 Then jjf 0 gjj p is a metric on the set of all continuous functions on I for every fixed number 0 <p1. 3. Covering and density numbers If M is a given set, we let # M denote the number of elements in M if M is finite and define # M := 1 if M is infinite. Let (X; d) be a metric space and let A X be a given set and let r 0 be a non-negative number. Then we define the covering number C r (A) and the density number D r (A) as follows: (1) C r (A) :=0 if A = ; and if A 6= ;, we let C r (A) denote the smallest number of closed balls of radius r needed to cover the set A, with the convention C r (A) =1 if we cannot cover the set A with finitely many balls (2) D r (A) :=supf # D j D A and D is r-discrete g Note that C 0 (A) =D 0 (A) =#A and that C r (;) =D r (;) for all r 0. We say that A is totally bounded if C r (A) is finite for every positive number r>0. Example: Let us consider the real line R with its standard metric jx 0 yj. If A = [0; 1], we have C r (A) =1 if r 1 2 ; C r(a) =2 if 1 4 r<1 2 ; C r(a) =3 if 1 6 r<1 4 C r (A) =4 if 1 8 r< 1 6 etc.: C r(a) =n if n 2 and 1 2n r< 1 2(n01) In particular, we see that A is totally bounded. Theorem 3.1: Let (X; d) be a metric space and let A X is a given set, then we have (i) Every open ball is open and every closed ball is closed (ii) If A is bounded, then for every p 2 X there exists a number > 0 such that A N [p] (iii) If there exists a number r 0 such that C r (A) < 1, then A is bounded 6

7 (iv) If A is totally bounded, then A is bounded (v) C r (A) D r (A) C r 2 (A) 8 r 0 (vi) A is totally bounded if and only if D r (A) < 1 for all r >0 Proof: (i): Let p 2 X and r 0 be given. If r = 0, we have N0(p) =; which is open by convention. Suppose that r > 0 and let y 2 N r (p) be given. Then d(y; p) < r and := r 0 d(y; p) is strictly positive. Let x 2 N (y) be given. Then we have d(x; y) < and by the triangle inequality we have d(x; p) d(x; y) + d(y; p) < r+ d(y; q) =. Since that holds for all x 2 N (y), we have N (y) N r (p). Hence, we see that N r (p) is open. To show that N r [p] is closed we shall consider the complement N r [p] c := fx 2 X j d(x; p) > rg. Let y 2 N r [p] c be given. Then d(y; p) >r and := d(y; p) 0 r is strictly positive. Let x 2 N (y) be given. Then we have d(x; y) < and by the triangle inequality we have + r = d(y; p) d(y; p) +d(p; x) <+ d(x; p). Hence, we have d(x; p) >r and consequently, we have x 2 N r [p] c. Since that holds for all x 2 N (y), we have N (y) N r [p] c. Hence, we see that N r [p] c is open and so by Thm.2.23 in Rudin p.34, we conclude that N r [p] is closed. (ii): Let p 2 X be given. Since A is bounded, there exists q 2 X and r > 0 such that A N r [q]. Let us define := r + d(p; q) and let x 2 A be given. Since x 2 A N r [q], we have d(x; q) r and by the triangle inequality we have d(x; p) d(x; q) +d(q; p) r + d(p; q) =. Since this holds for all x 2 A, we see that A N [p] which completes the proof of (ii). (iii): Suppose that n := C r (A) < 1. Then there P exist p1;...;p n 2 X such that n A N r [p1] [111[ N r [p n ]. Let us define := r + i=1 d(p 1;p i ) and let x 2 A be given. Since A N r [p1] [111[N r [p n ], there exists and integer 1 i n such that x 2 N r [p i ]. Hence, we have d(x; p i ) r and by the triangle inequality we have d(x; p1) d(x; p i )+d(p i ;p1) r + d(p i ;p1) =. Since this holds for all x 2 A, we see that A N [p1] which completes the proof of (iii). (iv): Immediate consequence of (iii). (v): Let me first show that C r (A) D r (A). If D r (A) =1, this is evident. So suppose that d := D r (A) < 1. Then there exists an r-discrete set D A with exactly d elements. Then I claim that A [ u2d N r [u]. So let x 2 A be given. If x 2 D, we have x 2 N r [x] [ u2d N r [u]. If x 2 A n D, we see that D [fxg has d +1 elements and since d +1 > D r (A), we see that D [fxg is not r-discret. Since D is r-discrete, there exists v 2 D such that d(v; x) r or equivalently x 2 N r [v] [ u2d N r [u]. Hence, we have A [ u2d N r [u] and since D has d = D r (A) elements, we see that C r (A) D r (A). Suppose that D r (A) >C r=2 (A). Then we have n := C r=2 (A) < 1 and there exist closed balls N r=2 (u 1 );...;N r=2 (u n ) satisfying A N r=2 (u 1 )[111[N r=2 (u n ). Since D r (A) >n, we have D r (A) n +1 ans so there exists an r-discrete set D A with n +1 elements. Since D A N r=2 (u 1 )[111[N r=2 (u n ), there exist an integer 1 n and elements x; y 2 D such that x 6= y and x; y 2 N r=2 (u ). Hence, we have 7

8 d(x; y) >r and d(u ;x) r and d(u 2 ;y) r 2 have r<d(x; y) d(x; u )+d(u ;y) r that D r (A) N r=2 which completes the proof of (v). (vi): Immediate consequence of (v).. So by the triangle inequality we which is impossible. Thus, we conclude 8

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

Definition 2.1. A metric (or distance function) defined on a non-empty set X is a function d: X X R that satisfies: For all x, y, and z in X :

Definition 2.1. A metric (or distance function) defined on a non-empty set X is a function d: X X R that satisfies: For all x, y, and z in X : MATH 337 Metric Spaces Dr. Neal, WKU Let X be a non-empty set. The elements of X shall be called points. We shall define the general means of determining the distance between two points. Throughout we

More information

Lecture 3. Econ August 12

Lecture 3. Econ August 12 Lecture 3 Econ 2001 2015 August 12 Lecture 3 Outline 1 Metric and Metric Spaces 2 Norm and Normed Spaces 3 Sequences and Subsequences 4 Convergence 5 Monotone and Bounded Sequences Announcements: - Friday

More information

Econ Lecture 3. Outline. 1. Metric Spaces and Normed Spaces 2. Convergence of Sequences in Metric Spaces 3. Sequences in R and R n

Econ Lecture 3. Outline. 1. Metric Spaces and Normed Spaces 2. Convergence of Sequences in Metric Spaces 3. Sequences in R and R n Econ 204 2011 Lecture 3 Outline 1. Metric Spaces and Normed Spaces 2. Convergence of Sequences in Metric Spaces 3. Sequences in R and R n 1 Metric Spaces and Metrics Generalize distance and length notions

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

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

Mid Term-1 : Practice problems

Mid Term-1 : Practice problems Mid Term-1 : Practice problems These problems are meant only to provide practice; they do not necessarily reflect the difficulty level of the problems in the exam. The actual exam problems are likely to

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

Math 4317 : Real Analysis I Mid-Term Exam 1 25 September 2012

Math 4317 : Real Analysis I Mid-Term Exam 1 25 September 2012 Instructions: Answer all of the problems. Math 4317 : Real Analysis I Mid-Term Exam 1 25 September 2012 Definitions (2 points each) 1. State the definition of a metric space. A metric space (X, d) is set

More information

2 Topology of a Metric Space

2 Topology of a Metric Space 2 Topology of a Metric Space The real number system has two types of properties. The first type are algebraic properties, dealing with addition, multiplication and so on. The other type, called topological

More information

Problem Set 2: Solutions Math 201A: Fall 2016

Problem Set 2: Solutions Math 201A: Fall 2016 Problem Set 2: s Math 201A: Fall 2016 Problem 1. (a) Prove that a closed subset of a complete metric space is complete. (b) Prove that a closed subset of a compact metric space is compact. (c) Prove that

More information

Week 2: Sequences and Series

Week 2: Sequences and Series QF0: Quantitative Finance August 29, 207 Week 2: Sequences and Series Facilitator: Christopher Ting AY 207/208 Mathematicians have tried in vain to this day to discover some order in the sequence of prime

More information

2. Metric Spaces. 2.1 Definitions etc.

2. Metric Spaces. 2.1 Definitions etc. 2. Metric Spaces 2.1 Definitions etc. The procedure in Section for regarding R as a topological space may be generalized to many other sets in which there is some kind of distance (formally, sets with

More information

Continuity. Matt Rosenzweig

Continuity. Matt Rosenzweig Continuity Matt Rosenzweig Contents 1 Continuity 1 1.1 Rudin Chapter 4 Exercises........................................ 1 1.1.1 Exercise 1............................................. 1 1.1.2 Exercise

More information

Lecture 4: Completion of a Metric Space

Lecture 4: Completion of a Metric Space 15 Lecture 4: Completion of a Metric Space Closure vs. Completeness. Recall the statement of Lemma??(b): A subspace M of a metric space X is closed if and only if every convergent sequence {x n } X satisfying

More information

Math 5210, Definitions and Theorems on Metric Spaces

Math 5210, Definitions and Theorems on Metric Spaces Math 5210, Definitions and Theorems on Metric Spaces Let (X, d) be a metric space. We will use the following definitions (see Rudin, chap 2, particularly 2.18) 1. Let p X and r R, r > 0, The ball of radius

More information

Set, functions and Euclidean space. Seungjin Han

Set, functions and Euclidean space. Seungjin Han Set, functions and Euclidean space Seungjin Han September, 2018 1 Some Basics LOGIC A is necessary for B : If B holds, then A holds. B A A B is the contraposition of B A. A is sufficient for B: If A holds,

More information

Metric Spaces and Topology

Metric Spaces and Topology Chapter 2 Metric Spaces and Topology From an engineering perspective, the most important way to construct a topology on a set is to define the topology in terms of a metric on the set. This approach underlies

More information

A Course in Real Analysis. Saeed Zakeri

A Course in Real Analysis. Saeed Zakeri A Course in Real Analysis Saeed Zakeri Contents Chapter 1. Topology of Metric Spaces I 5 1.1. Basic definitions 5 1.2. Convergence 11 1.3. Continuity 12 1.4. Completeness 14 1.5. Compactness 17 1.6. Connectedness

More information

By (a), B ε (x) is a closed subset (which

By (a), B ε (x) is a closed subset (which Solutions to Homework #3. 1. Given a metric space (X, d), a point x X and ε > 0, define B ε (x) = {y X : d(y, x) ε}, called the closed ball of radius ε centered at x. (a) Prove that B ε (x) is always a

More information

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.

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

More information

Fourth Week: Lectures 10-12

Fourth Week: Lectures 10-12 Fourth Week: Lectures 10-12 Lecture 10 The fact that a power series p of positive radius of convergence defines a function inside its disc of convergence via substitution is something that we cannot ignore

More information

MATH 140B - HW 5 SOLUTIONS

MATH 140B - HW 5 SOLUTIONS MATH 140B - HW 5 SOLUTIONS Problem 1 (WR Ch 7 #8). If I (x) = { 0 (x 0), 1 (x > 0), if {x n } is a sequence of distinct points of (a,b), and if c n converges, prove that the series f (x) = c n I (x x n

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

*Room 3.13 in Herschel Building

*Room 3.13 in Herschel Building MAS3706: Topology Dr. Zinaida Lykova School of Mathematics, Statistics and Physics Newcastle University *Room 3.13 in Herschel Building These lectures concern metric and topological spaces and continuous

More information

a) Let x,y be Cauchy sequences in some metric space. Define d(x, y) = lim n d (x n, y n ). Show that this function is well-defined.

a) Let x,y be Cauchy sequences in some metric space. Define d(x, y) = lim n d (x n, y n ). Show that this function is well-defined. Problem 3) Remark: for this problem, if I write the notation lim x n, it should always be assumed that I mean lim n x n, and similarly if I write the notation lim x nk it should always be assumed that

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

Lecture 9 Metric spaces. The contraction fixed point theorem. The implicit function theorem. The existence of solutions to differenti. equations.

Lecture 9 Metric spaces. The contraction fixed point theorem. The implicit function theorem. The existence of solutions to differenti. equations. Lecture 9 Metric spaces. The contraction fixed point theorem. The implicit function theorem. The existence of solutions to differential equations. 1 Metric spaces 2 Completeness and completion. 3 The contraction

More information

Solutions Manual for Homework Sets Math 401. Dr Vignon S. Oussa

Solutions Manual for Homework Sets Math 401. Dr Vignon S. Oussa 1 Solutions Manual for Homework Sets Math 401 Dr Vignon S. Oussa Solutions Homework Set 0 Math 401 Fall 2015 1. (Direct Proof) Assume that x and y are odd integers. Then there exist integers u and v such

More information

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set Analysis Finite and Infinite Sets Definition. An initial segment is {n N n n 0 }. Definition. A finite set can be put into one-to-one correspondence with an initial segment. The empty set is also considered

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

Numerical Sequences and Series

Numerical Sequences and Series Numerical Sequences and Series Written by Men-Gen Tsai email: b89902089@ntu.edu.tw. Prove that the convergence of {s n } implies convergence of { s n }. Is the converse true? Solution: Since {s n } is

More information

That is, there is an element

That is, there is an element Section 3.1: Mathematical Induction Let N denote the set of natural numbers (positive integers). N = {1, 2, 3, 4, } Axiom: If S is a nonempty subset of N, then S has a least element. That is, there is

More information

ECARES Université Libre de Bruxelles MATH CAMP Basic Topology

ECARES Université Libre de Bruxelles MATH CAMP Basic Topology ECARES Université Libre de Bruxelles MATH CAMP 03 Basic Topology Marjorie Gassner Contents: - Subsets, Cartesian products, de Morgan laws - Ordered sets, bounds, supremum, infimum - Functions, image, preimage,

More information

The Heine-Borel and Arzela-Ascoli Theorems

The Heine-Borel and Arzela-Ascoli Theorems The Heine-Borel and Arzela-Ascoli Theorems David Jekel October 29, 2016 This paper explains two important results about compactness, the Heine- Borel theorem and the Arzela-Ascoli theorem. We prove them

More information

Near convexity, metric convexity, and convexity

Near convexity, metric convexity, and convexity Near convexity, metric convexity, and convexity Fred Richman Florida Atlantic University Boca Raton, FL 33431 28 February 2005 Abstract It is shown that a subset of a uniformly convex normed space is nearly

More information

Mathematics 324 Riemann Zeta Function August 5, 2005

Mathematics 324 Riemann Zeta Function August 5, 2005 Mathematics 324 Riemann Zeta Function August 5, 25 In this note we give an introduction to the Riemann zeta function, which connects the ideas of real analysis with the arithmetic of the integers. Define

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

Math 117: Infinite Sequences

Math 117: Infinite Sequences Math 7: Infinite Sequences John Douglas Moore November, 008 The three main theorems in the theory of infinite sequences are the Monotone Convergence Theorem, the Cauchy Sequence Theorem and the Subsequence

More information

FINAL EXAM Math 25 Temple-F06

FINAL EXAM Math 25 Temple-F06 FINAL EXAM Math 25 Temple-F06 Write solutions on the paper provided. Put your name on this exam sheet, and staple it to the front of your finished exam. Do Not Write On This Exam Sheet. Problem 1. (Short

More information

MAS3706 Topology. Revision Lectures, May I do not answer enquiries as to what material will be in the exam.

MAS3706 Topology. Revision Lectures, May I do not answer  enquiries as to what material will be in the exam. MAS3706 Topology Revision Lectures, May 208 Z.A.Lykova It is essential that you read and try to understand the lecture notes from the beginning to the end. Many questions from the exam paper will be similar

More information

MATH 31BH Homework 1 Solutions

MATH 31BH Homework 1 Solutions MATH 3BH Homework Solutions January 0, 04 Problem.5. (a) (x, y)-plane in R 3 is closed and not open. To see that this plane is not open, notice that any ball around the origin (0, 0, 0) will contain points

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

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

Characterisation of Accumulation Points. Convergence in Metric Spaces. Characterisation of Closed Sets. Characterisation of Closed Sets

Characterisation of Accumulation Points. Convergence in Metric Spaces. Characterisation of Closed Sets. Characterisation of Closed Sets Convergence in Metric Spaces Functional Analysis Lecture 3: Convergence and Continuity in Metric Spaces Bengt Ove Turesson September 4, 2016 Suppose that (X, d) is a metric space. A sequence (x n ) X is

More information

Analysis I. Classroom Notes. H.-D. Alber

Analysis I. Classroom Notes. H.-D. Alber Analysis I Classroom Notes H-D Alber Contents 1 Fundamental notions 1 11 Sets 1 12 Product sets, relations 5 13 Composition of statements 7 14 Quantifiers, negation of statements 9 2 Real numbers 11 21

More information

MA2223 Tutorial solutions Part 1. Metric spaces

MA2223 Tutorial solutions Part 1. Metric spaces MA2223 Tutorial solutions Part 1. Metric spaces T1 1. Show that the function d(,y) = y defines a metric on R. The given function is symmetric and non-negative with d(,y) = 0 if and only if = y. It remains

More information

Analysis III. Exam 1

Analysis III. Exam 1 Analysis III Math 414 Spring 27 Professor Ben Richert Exam 1 Solutions Problem 1 Let X be the set of all continuous real valued functions on [, 1], and let ρ : X X R be the function ρ(f, g) = sup f g (1)

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

Metric Spaces. DEF. If (X; d) is a metric space and E is a nonempty subset, then (E; d) is also a metric space, called a subspace of X:

Metric Spaces. DEF. If (X; d) is a metric space and E is a nonempty subset, then (E; d) is also a metric space, called a subspace of X: Metric Spaces DEF. A metric space X or (X; d) is a nonempty set X together with a function d : X X! [0; 1) such that for all x; y; and z in X : 1. d (x; y) 0 with equality i x = y 2. d (x; y) = d (y; x)

More information

1 Lecture 4: Set topology on metric spaces, 8/17/2012

1 Lecture 4: Set topology on metric spaces, 8/17/2012 Summer Jump-Start Program for Analysis, 01 Song-Ying Li 1 Lecture : Set topology on metric spaces, 8/17/01 Definition 1.1. Let (X, d) be a metric space; E is a subset of X. Then: (i) x E is an interior

More information

REVIEW OF ESSENTIAL MATH 346 TOPICS

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

More information

A LITTLE REAL ANALYSIS AND TOPOLOGY

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

More information

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

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,

More information

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

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

More information

Chapter 2 Metric Spaces

Chapter 2 Metric Spaces Chapter 2 Metric Spaces The purpose of this chapter is to present a summary of some basic properties of metric and topological spaces that play an important role in the main body of the book. 2.1 Metrics

More information

Topology, Math 581, Fall 2017 last updated: November 24, Topology 1, Math 581, Fall 2017: Notes and homework Krzysztof Chris Ciesielski

Topology, Math 581, Fall 2017 last updated: November 24, Topology 1, Math 581, Fall 2017: Notes and homework Krzysztof Chris Ciesielski Topology, Math 581, Fall 2017 last updated: November 24, 2017 1 Topology 1, Math 581, Fall 2017: Notes and homework Krzysztof Chris Ciesielski Class of August 17: Course and syllabus overview. Topology

More information

Math LM (24543) Lectures 01

Math LM (24543) Lectures 01 Math 32300 LM (24543) Lectures 01 Ethan Akin Office: NAC 6/287 Phone: 650-5136 Email: ethanakin@earthlink.net Spring, 2018 Contents Introduction, Ross Chapter 1 and Appendix The Natural Numbers N and The

More information

COMPLETE METRIC SPACES AND THE CONTRACTION MAPPING THEOREM

COMPLETE METRIC SPACES AND THE CONTRACTION MAPPING THEOREM COMPLETE METRIC SPACES AND THE CONTRACTION MAPPING THEOREM A metric space (M, d) is a set M with a metric d(x, y), x, y M that has the properties d(x, y) = d(y, x), x, y M d(x, y) d(x, z) + d(z, y), x,

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

Supplementary Notes for W. Rudin: Principles of Mathematical Analysis

Supplementary Notes for W. Rudin: Principles of Mathematical Analysis Supplementary Notes for W. Rudin: Principles of Mathematical Analysis SIGURDUR HELGASON In 8.00B it is customary to cover Chapters 7 in Rudin s book. Experience shows that this requires careful planning

More information

Solutions to Tutorial 3 (Week 4)

Solutions to Tutorial 3 (Week 4) The University of Sydney School of Mathematics and Statistics Solutions to Tutorial 3 (Week 4) MATH3961: Metric Spaces (Advanced) Semester 1, 2018 Web Page: http://www.maths.usyd.edu.au/u/ug/sm/math3961/

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

Advanced Calculus: MATH 410 Real Numbers Professor David Levermore 5 December 2010

Advanced Calculus: MATH 410 Real Numbers Professor David Levermore 5 December 2010 Advanced Calculus: MATH 410 Real Numbers Professor David Levermore 5 December 2010 1. Real Number System 1.1. Introduction. Numbers are at the heart of mathematics. By now you must be fairly familiar with

More information

2 Metric Spaces Definitions Exotic Examples... 3

2 Metric Spaces Definitions Exotic Examples... 3 Contents 1 Vector Spaces and Norms 1 2 Metric Spaces 2 2.1 Definitions.......................................... 2 2.2 Exotic Examples...................................... 3 3 Topologies 4 3.1 Open Sets..........................................

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

Tools from Lebesgue integration

Tools from Lebesgue integration Tools from Lebesgue integration E.P. van den Ban Fall 2005 Introduction In these notes we describe some of the basic tools from the theory of Lebesgue integration. Definitions and results will be given

More information

What to remember about metric spaces

What to remember about metric spaces Division of the Humanities and Social Sciences What to remember about metric spaces KC Border These notes are (I hope) a gentle introduction to the topological concepts used in economic theory. If the

More information

2.2 Some Consequences of the Completeness Axiom

2.2 Some Consequences of the Completeness Axiom 60 CHAPTER 2. IMPORTANT PROPERTIES OF R 2.2 Some Consequences of the Completeness Axiom In this section, we use the fact that R is complete to establish some important results. First, we will prove that

More information

Math 201 Topology I. Lecture notes of Prof. Hicham Gebran

Math 201 Topology I. Lecture notes of Prof. Hicham Gebran Math 201 Topology I Lecture notes of Prof. Hicham Gebran hicham.gebran@yahoo.com Lebanese University, Fanar, Fall 2015-2016 http://fs2.ul.edu.lb/math http://hichamgebran.wordpress.com 2 Introduction and

More information

2 2 + x =

2 2 + x = Lecture 30: Power series A Power Series is a series of the form c n = c 0 + c 1 x + c x + c 3 x 3 +... where x is a variable, the c n s are constants called the coefficients of the series. n = 1 + x +

More information

p-adic Analysis Compared to Real Lecture 1

p-adic Analysis Compared to Real Lecture 1 p-adic Analysis Compared to Real Lecture 1 Felix Hensel, Waltraud Lederle, Simone Montemezzani October 12, 2011 1 Normed Fields & non-archimedean Norms Definition 1.1. A metric on a non-empty set X is

More information

HW 4 SOLUTIONS. , x + x x 1 ) 2

HW 4 SOLUTIONS. , x + x x 1 ) 2 HW 4 SOLUTIONS The Way of Analysis p. 98: 1.) Suppose that A is open. Show that A minus a finite set is still open. This follows by induction as long as A minus one point x is still open. To see that A

More information

Convergence of a Generalized Midpoint Iteration

Convergence of a Generalized Midpoint Iteration J. Able, D. Bradley, A.S. Moon under the supervision of Dr. Xingping Sun REU Final Presentation July 31st, 2014 Preliminary Words O Rourke s conjecture We begin with a motivating question concerning the

More information

CHAPTER 1. Metric Spaces. 1. Definition and examples

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

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

Introduction to Dynamical Systems

Introduction to Dynamical Systems Introduction to Dynamical Systems France-Kosovo Undergraduate Research School of Mathematics March 2017 This introduction to dynamical systems was a course given at the march 2017 edition of the France

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

EC 521 MATHEMATICAL METHODS FOR ECONOMICS. Lecture 1: Preliminaries

EC 521 MATHEMATICAL METHODS FOR ECONOMICS. Lecture 1: Preliminaries EC 521 MATHEMATICAL METHODS FOR ECONOMICS Lecture 1: Preliminaries Murat YILMAZ Boğaziçi University In this lecture we provide some basic facts from both Linear Algebra and Real Analysis, which are going

More information

Problem List MATH 5143 Fall, 2013

Problem List MATH 5143 Fall, 2013 Problem List MATH 5143 Fall, 2013 On any problem you may use the result of any previous problem (even if you were not able to do it) and any information given in class up to the moment the problem was

More information

Functional Analysis. Franck Sueur Metric spaces Definitions Completeness Compactness Separability...

Functional Analysis. Franck Sueur Metric spaces Definitions Completeness Compactness Separability... Functional Analysis Franck Sueur 2018-2019 Contents 1 Metric spaces 1 1.1 Definitions........................................ 1 1.2 Completeness...................................... 3 1.3 Compactness......................................

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

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

Math 127C, Spring 2006 Final Exam Solutions. x 2 ), g(y 1, y 2 ) = ( y 1 y 2, y1 2 + y2) 2. (g f) (0) = g (f(0))f (0).

Math 127C, Spring 2006 Final Exam Solutions. x 2 ), g(y 1, y 2 ) = ( y 1 y 2, y1 2 + y2) 2. (g f) (0) = g (f(0))f (0). Math 27C, Spring 26 Final Exam Solutions. Define f : R 2 R 2 and g : R 2 R 2 by f(x, x 2 (sin x 2 x, e x x 2, g(y, y 2 ( y y 2, y 2 + y2 2. Use the chain rule to compute the matrix of (g f (,. By the chain

More information

Math 117: Topology of the Real Numbers

Math 117: Topology of the Real Numbers Math 117: Topology of the Real Numbers John Douglas Moore November 10, 2008 The goal of these notes is to highlight the most important topics presented in Chapter 3 of the text [1] and to provide a few

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

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

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

Economics 204 Summer/Fall 2011 Lecture 5 Friday July 29, 2011

Economics 204 Summer/Fall 2011 Lecture 5 Friday July 29, 2011 Economics 204 Summer/Fall 2011 Lecture 5 Friday July 29, 2011 Section 2.6 (cont.) Properties of Real Functions Here we first study properties of functions from R to R, making use of the additional structure

More information

Introduction to Topology

Introduction to Topology Chapter 2 Introduction to Topology In this chapter, we will use the tools we developed concerning sequences and series to study two other mathematical objects: sets and functions. For definitions of set

More information

Week 5 Lectures 13-15

Week 5 Lectures 13-15 Week 5 Lectures 13-15 Lecture 13 Definition 29 Let Y be a subset X. A subset A Y is open in Y if there exists an open set U in X such that A = U Y. It is not difficult to show that the collection of all

More information

MATH 102 INTRODUCTION TO MATHEMATICAL ANALYSIS. 1. Some Fundamentals

MATH 102 INTRODUCTION TO MATHEMATICAL ANALYSIS. 1. Some Fundamentals MATH 02 INTRODUCTION TO MATHEMATICAL ANALYSIS Properties of Real Numbers Some Fundamentals The whole course will be based entirely on the study of sequence of numbers and functions defined on the real

More information

MA651 Topology. Lecture 10. Metric 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

More information

Cauchy Sequences. x n = 1 ( ) 2 1 1, . As you well know, k! n 1. 1 k! = e, = k! k=0. k = k=1

Cauchy Sequences. x n = 1 ( ) 2 1 1, . As you well know, k! n 1. 1 k! = e, = k! k=0. k = k=1 Cauchy Sequences The Definition. I will introduce the main idea by contrasting three sequences of rational numbers. In each case, the universal set of numbers will be the set Q of rational numbers; all

More information

From now on, we will represent a metric space with (X, d). Here are some examples: i=1 (x i y i ) p ) 1 p, p 1.

From now on, we will represent a metric space with (X, d). Here are some examples: i=1 (x i y i ) p ) 1 p, p 1. Chapter 1 Metric spaces 1.1 Metric and convergence We will begin with some basic concepts. Definition 1.1. (Metric space) Metric space is a set X, with a metric satisfying: 1. d(x, y) 0, d(x, y) = 0 x

More information

LECTURE 15: COMPLETENESS AND CONVEXITY

LECTURE 15: COMPLETENESS AND CONVEXITY LECTURE 15: COMPLETENESS AND CONVEXITY 1. The Hopf-Rinow Theorem Recall that a Riemannian manifold (M, g) is called geodesically complete if the maximal defining interval of any geodesic is R. On the other

More information

CLASS NOTES FOR APRIL 14, 2000

CLASS NOTES FOR APRIL 14, 2000 CLASS NOTES FOR APRIL 14, 2000 Announcement: Section 1.2, Questions 3,5 have been deferred from Assignment 1 to Assignment 2. Section 1.4, Question 5 has been dropped entirely. 1. Review of Wednesday class

More information

Chapter 1. Preliminaries. The purpose of this chapter is to provide some basic background information. Linear Space. Hilbert Space.

Chapter 1. Preliminaries. The purpose of this chapter is to provide some basic background information. Linear Space. Hilbert Space. Chapter 1 Preliminaries The purpose of this chapter is to provide some basic background information. Linear Space Hilbert Space Basic Principles 1 2 Preliminaries Linear Space The notion of linear space

More information

Problem Set 1: Solutions Math 201A: Fall Problem 1. Let (X, d) be a metric space. (a) Prove the reverse triangle inequality: for every x, y, z X

Problem Set 1: Solutions Math 201A: Fall Problem 1. Let (X, d) be a metric space. (a) Prove the reverse triangle inequality: for every x, y, z X Problem Set 1: s Math 201A: Fall 2016 Problem 1. Let (X, d) be a metric space. (a) Prove the reverse triangle inequality: for every x, y, z X d(x, y) d(x, z) d(z, y). (b) Prove that if x n x and y n y

More information

Logical Connectives and Quantifiers

Logical Connectives and Quantifiers Chapter 1 Logical Connectives and Quantifiers 1.1 Logical Connectives 1.2 Quantifiers 1.3 Techniques of Proof: I 1.4 Techniques of Proof: II Theorem 1. Let f be a continuous function. If 1 f(x)dx 0, then

More information