1 if 1 x 0 1 if 0 x 1
|
|
|
- Charla Owen
- 9 years ago
- Views:
Transcription
1 Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or is not continuous, it is often helpful to have more than one way of characterizing continuity and so we provide the original definition of continuity (which does not mention limits), the definition in terms of function limits and, finally, a definition in terms of sequences. Of course the definitions are equivalent but sometimes one of them is more convenient than the others in order to prove a given function is or is not continuous at a particular point. A function that is continuous on an interval has a number of special properties. Some of the important consequences of continuity include: the bounded range theorem (corollary 3.7) the extreme value theorem (corollary 3.8) the intermediate value theorem persistence of sign The notion of uniform continuity is also introduced. This notion will be of particular importance in connection with the discussion of integration in a later chapter. Finally we will introduce the related notions of injectivity and strict monotonicity for functions in connection with the discussion of existence and continuity of an inverse function. Functions A real valued function of a single real variable can be defined as a rule which assigns to each real number x in a subset D of the reals, a uniquely determined real number, y fx. The set D is called the domain of the function and the set of values y fx obtained as x varies over D is called the range of f. A function can also be defined as a set of ordered pairs x, y of reals such that no two distinct pairs have the same first element. Then the domain, D, is the set of all first entries for pairs in the set and the range of the function is the set of all second entries. The graph of the function is the set of all the pairs, x, fx, considered to be a set in the plane. Example Functions 1(a) The function fx x having domain D equal to the closed interval 0, assigns to each x in D, the real number x. The range of this function is then the set 0, 4. The graph of this function is the collection of points x, x, 0 x, in the plane. These points form a part of a parabola. Note that the domain D 0, is not the largest possible domain for this function but has been chosen arbitrarily for purposes of discussion in this example. 1(b) The function fx x 3 with domain D equal to the unbounded interval 3, has for its range the unbounded interval 0,. Note that for x 3, this function does not produce real values. The set x 3 is the largest possible domain for this function. 1(c) The function defined by fx 1 if 1 x 0 1 if 0 x 1 for x in the domain D 1, 1 has for its range the set consisting of the two points y 1 and y 1. The graph of this function is two unconnected horizontal line segments. Since the 1
2 function is not given by a single formula that applies over the entire domain, we say the function is piecewise defined. Continuity We begin with the first of several equivalent definitions for continuity: Definition A function fx with domain D is said to be continuous at x 0 D if, for every 0 there exists, x 0 0 such that y fx belongs to N fx 0 whenever x belongs to N x 0 D. If f is continuous at every point x 0 in D then we say f is continuous on D, or when there is no possibility of misunderstanding, we say f is continuous. A function that is not continuous is said to be discontinuous. Example Continuous Functions (a) For fixed constants, a 0 and b, consider the linear function fx ax b with domain D R. We can use the definition of continuity to show that this function is continuous at every point in its domain. To see this, let x 0 be fixed but arbitrary. Then fx fx 0 ax b ax 0 b a x x 0 and for any positive it is easy to see that fx fx 0 a x x 0 whenever x x 0 a. This is precisely the assertion that y fx belongs to N fx 0 whenever x belongs to N x 0 D, where is given by a. Since this argument did not require x 0 to have any particular value, it follows that fx ax b is continuous at every x 0 in the domain D R. (b) Consider the function fx x on D 1,. Note first that for x, x 0 two distinct points in D, we can write fx fx 0 x x 0 x x 0 x x 0 Now it is easy to see that x x 0 4 for all choices of x, x 0 D. Then to show that fx x is continuous on D, we simply write fx fx 0 x x 0 x x 0 4 x x 0 and it follows immediately that for all choices of x, x 0 D, x x 0 4. Since we can write, x 3 z 3 x zx zx z x 4 z 4 x zx 3 x zz x z 3 fx fx 0 whenever we can prove fx x 3 and fx x 4 are both continuous on D by a similar argument. In fact, fx x n is continuous for every n N on every compact interval a, b by essentially the same proof. (c) The following functions are continuous at every x R, Px a n x n a n1 x n1 a 1 x a 0 any polynomial Sinx, Cosx, e x
3 Each of the following functions is continuous at every point, x, where it is defined rx Px for polynomials P and Q Qx Tanx, Secx, Cotx, Cscx Logx Using the definition to prove the continuity (or discontinuity) for a function can be awkward. For that reason, we need alternative characterizations for continuity. Function limits and sequences are two notions that lead to useful alternative characterizations for continuity. Equivalent Definitions of Continuity We are in a position to use function limits and sequence limits to give alternative definitions for continuity. Theorem 3.4 (Limit definition of continuity) The function fx on domain D is continuous at the point x c in D if and only if lim xc fx fc. Note that this theorem makes several assertions: the limit point c belongs to D so that fc is defined the function fx tends to a limit, L, as x tends to c the limiting value, L, and the function value, fc are equal If any of these assertions fails, then fx fails to be continuous at x c. We can combine theorems 3.1 and 3.4 to obtain the following characterization of continuity. Theorem 3.5 (Sequence definition of continuity ) The function fx on domain D is continuous at the point x c in D if and only if for every sequence a n in D that converges to c, it follows that fa n converges to fc. Theorem 3.5 is most often used to show that a given function is discontinuous at a point. In order to do this, all that is needed is a sequence a n in D that converges to c, for which the sequence of function values, fa n fails to converges to fc. Continuous functions may be combined in various ways to form new functions that are also continuous. It is clear what role theorems 3. has in proving the following result. Theorem 3.6 Suppose fx and gx are two real valued functions on domain D. Suppose also that both f and g are continuous at x c in D. Then afx bgx and fxgx are continuous at x c as is the function fx, provided gc 0. Moreover, if Fx is gx defined and continuous in some neighborhood of the point b fc, then the composed function Ffx is also continuous at x c. The theorems presented so far are all concerned with determining whether a given function is continuous, either at a specified point or at all points of the domain D. Now we begin to consider some of the consequences of continuity. 3
4 Consequences of Continuity There are a number of function properties that follow from continuity. We list the most important of them here. Theorem 3.7 A function that is continuous on a compact domain has a compact range. Theorem 3.7 contains more information than at first meets the eye. The following two results are contained in theorem 3.7 but are often stated separately in order to emphasize their conclusions. Corollary 3.8 (Bounded range theorem) A function that is continuous on a compact domain is bounded Corollary 3.9 (Extreme value theorem) If fx is continuous on a compact domain, D, then there exist points c and d in D such that fc fx fd for all x in D. The corollaries do not contain any information not already asserted by the theorem but they expose the practical implications of the consequence of the theorem. In particular, corollary 3.8 asserts that a function that is continuous on a closed bounded interval is necessarily bounded (above and below). The other corollary asserts in addition that there exist points in the domain where the LUB and GLB of the range of function values are actually assumed. Theorem 3.10 A function that is continuous on a connected domain has a connected range. This is the statement of the result that remains true when discussing the more general situation involving functions from R n into R m. In a one dimensional setting this result can be stated more simply as follows: Theorem 3.10 For any function that is continuous on a domain, D that is an interval, the range is also an interval. The implication of this theorem is often expressed as the statement that the graph of a continuous function of one variable can be drawn without lifting one s pen. More precisely, it can be stated as the following result: Corollary 3.11 (Intermediate value theorem) If fx is continuous on a domain, D, that is an interval, then for any points p q in D and any real number S lying between the values fp and fq, there exists an s in D such that p s q and fs S. Uniform Continuity The previously given definition of continuity asserts that fx is continuous at x x 0 if for every 0 there exists, x 0 0 such that y fx belongs to N fx 0 whenever x belongs to N x 0 D. Note that the which is associated with a given depends not just on but may depend on on the point x 0 as well. In some cases, for each a single choice of serves for all x 0 in D. We say then that the function is uniformly continuous on D. The precise definition is stated as, Definition A function f is said to be uniformly continuous on D if, for every 0 there exists a 0 such that for all x 1, x D, fx 1 fx whenever 4
5 x 1 x ; i.e., fx belongs to N fx 1 whenever x belongs to N x 1 D. Every function that is uniformly continuous on D is necessarily continuous at every point of D but the converse is false unless D is compact (see solved problems 3.3 and 3.4). Theorem 3.1 A function that is continuous at each point of a compact domain, D, is uniformly continuous on D. Theorem 3.13 Suppose fx is uniformly continuous on a domain, D. Then whenevera n is a Cauchy sequence in D it follows that fa n is a Cauchy sequence in the range of f. The converse of theorem 3.13 is also true; i.e., if f maps every Cauchy sequence in D into a Cauchy sequence in rngf then f is uniformly continuous on D. However, it is not true that if f is uniformly continuous on D and fa n is a Cauchy sequence in rngf then a n must be a Cauchy sequence in D. To see this, consider the function fx x which is continuous on the compact domain D 1, 1, and therefore uniformly continuous. For any c 0, 1 let a n denote the sequence a n n c if n is odd n c if n is even where 0 is chosen so that a n D. This is a sequence with two limit points so it is not a Cauchy sequence. On the other hand, fa n c n c n c is convergent and thus Cauchy. Definition A function f is said to be Lipschitz continuous on D if there exists M 0 such that for all x, y D, fx fy M x y. It is evident that any Lipschitz continuous function is necessarily uniformly continuous but the converse is false. The function fx x on 0, 1 is an example of a function that is uniformly continuous but not Lipschitz continuous on 0, 1. Inverse Functions A real valued function fx is said to be one to one or injective if for all x 1, x D, x 1 x implies fx 1 fx. Recall that f is a function on D if the set of ordered pairs x, y : x D, y fx is such that no two pairs have the same first element. If f is injective, then it is also true that no two pairs have the same second element. Recall that f is a well defined function on D if any vertical line through a point of D cuts the graph of f in only one point. Similarly, f is an injective function on D if any horizontal line through a point of fd meets the graph of f in only one point. In this case, the set of reversed pairs y, x : x D, y fx defines a function x gy with the property y fx if and only if x gy 5
6 We say that x gy is the inverse of the function y fx, and we write g f 1. Monotone Functions A real valued function fx whose domain is the interval D is said to be: increasing on D if decreasing on D if x 1 x implies fx 1 fx x 1 x implies fx 1 fx We say that f is monotone on D if fx is either increasing or decreasing on D. If the inequality fx 1 fx is replaced by the strict inequality fx 1 fx, then we say that f is strictly increasing on D. Strictly decreasing is defined in a similar fashion and we say that f is strictly monotone if it is either strictly increasing or strictly decreasing. It is evident that a strictly monotone function must be injective. Conversely we have Theorem 3.14 If fx is injective and continuous on the interval I, then fx is strictly monotone on I. Theorem 3.15 (Continuity of the inverse) If fx is strictly monotone and continuous on the interval I, then g f 1 is strictly monotone and continuous on the interval J fi. Example Injectivity and Monotonicity 3(a) The function fx e x is continuous and strictly increasing for all real x. The range of f is the set of positive real numbers. Then theorem 3.15 implies f 1 is strictly monotone and increasing on the set of positive real numbers. Here f 1 x ln x. 3(b) The continuous function fx x is strictly increasing on the interval 0, b for b 0. The piecewise defined function gx x for 0 x 1 1 for x 1 is increasing but not strictly increasing on 0, b for b 1. Then fx has a continuous inverse given by f 1 x x, but g is not injective and has no inverse. 3(c) The function fx x is continuous on 3, 3 but it is not monotone on this domain. Then theorem 3.14 implies that f is not injective. Of course this is clear since for each x, 0 x 3, fx fx. 3(d) An example of an injective function which is not monotone is hx x for 0 x 1 1 x for x 1 Of course this function is not continuous as is predicted by theorem Solved Problems Continuity Problem 3.1 Show that the function fx 1 x on D 0, is continuous at each x D. Solution: let c D be fixed but arbitrary (note that this means c 0 ). Fix any 0, and 6
7 note that for any x such that x c/, 3c/, i.e., for x N c/ c, we have 1 x 1 c x xc c x c c c/ x c. c Now let denote the smaller of the two numbers c/, c /. Then c/ and c / which means that 1 x 1 c x c whenever x c. c According to the definition of continuity, this means f is continuous at x c 0. Problem 3. Show that the functions Sinx and Cosx are continuous at each x D,. Solution: We will need to use the following estimates for the sine and cosine functions: Sinx x and Sinx, Cosx 1, for all x R 1 These estimates were proved in problem.3. We have also the following trigonometric identities that hold for all real x and y : Sinx Siny Sin x y x y Cos, Cosx Cosy Sin x y Sin y x, 3 Using (1) in () and (3), leads to the estimates Sinx Siny x y 1 x y 4 Cosx Cosy x y 1 x y 5 It is now evident from 4 and 5 that for all 0, we can choose and have y R, fx N fy whenever x N y in either of the cases fx Sinx or fx Cosx. The sine and cosine functions each satisfy an estimate of the form fx fy M x y for all x, y D, 6 For these functions, the constant M is equal to 1 and D R. Any function that satisfies an estimate of the form (6) is said to be Lipschitz continuous on its domain, D. From examples 3.(a,b), we can see that all linear functions are Lipschitz continuous on D R, and fx x is Lipschitz continuous on any bounded domain, D a, b. Problem 3.3 Use the definition of continuity to show that the function fx 1 for 1 x 0 1 for 0 x 1 on D 1, 1 is discontinuous at x 0 D Solution: Since f0 1, for any 0, to say that fx N f0 is the same as saying fx 1. But for any 0, the neighborhood N 0 contains both positive and negative values for x. For the positive x values we have fx 1, while for the negative x values, we have fx 1. Then for any 0 1, there can be no 0 such that fx N f0 whenever x N 0. This proves that f is not continuous at x 0. 7
8 Equivalent Definitions of Continuity Problem 3.9 (Removable discontinuities) Let fx be defined on D 1, 1 by fx Show that f is not continuous at x 0. x for x 0 1 for x 0 Solution: We can show that lim x0 fx 0. Then the function limit at x 0 exists but, since f0 1, the function limit does not equal the function value at x 0. Then theorem 3.4 asserts that fx is not continuous at x 0. This is an example of a removable discontinuity. This is a discontinuity that can be removed by redefining the function at the point of discontinuity. In this case, we can define f0 to equal 0 in order to make the redefined function continuous. Problem 3.10 (Jump discontinuities) Let fx be defined on D 1, 1 by fx Show that f is not continuous at x 0. 0 for x 1, 0 x 1 for x 0, 1 Solution: We can show that fx tends to no limit as x tends to zero. Then theorem 3.4 asserts that fx is not continuous at x 0. To show that f tends to no limit at x 0, we define a sequence of points a n in D by a n 1n for n N. n 1 Then a n converges to zero as n tends to infinity but a n1 0 while a n 0. Then the sequence of function values, fa n is such that the subsequence fa n converges to 1 while the subsequence fa n1 converges to zero; i.e., the sequence fa n fails to converge by theorem.7.this function, fx, is said to have a finite jump discontinuity at x 0. We will discuss this notion further in the next problem. Problem 3.11 (One sided limits) Let fx be a real valued function with domain D in R and let c be an accumulation point for the set D x D : x c. Then we say that the one sided limit for fx as x approaches c from the right, exists and equals L if for every 0 there exists a 0 such that fx L whenever 0 x c. In this case we write fc L. The one sided limit for fx as x approaches c from the left is defined similarly. If this limit exists and equals L we write fc L. Show that the function of the previous problem has one sided limits from the left and the right but these limits are not equal. Solution: For the fx in the previous problem, we have f0 0 and f0 1. To see this, note that for all x in D 1, 0, we have fx 0. Then for any 0 fx 0 0 for all x D such that 0 x 0, and this holds for any 0. This proves f0 0. To prove f0 1, note that for all x in D 0, 1, we have fx 1 x hence for any 0 fx 1 x for all x D such that 0 x 0. 8
9 Since both the left hand and right hand limits at x 0 exist but are not equal, we say that f has a jump discontinuity at x 0. The difference f0 f0, equals the magnitude of the jump at x 0. Problem 3.1 (An everywhere discontinuous function) Consider the function defined by, fx 0 if x is irrational 1 if x is rational Show that this function, called the Dirichlet function, is discontinuous at every x. Solution:Let c denote any fixed, real number. Then it follows from corollaries 1.5 and 1.6 that there exist sequences a n and b n where the a n s are all rational and the b n s all irrational and both sequences converge to c. Then the definition of f implies that fa n converges to the limit 1 while fb n converges to 0. But theorem 3. 5 asserts that if f is continuous at x c, then for every sequence that converges to c, the corresponding sequence of function values must converge to fc. Then f is not continuous at x c. Since c was arbitrarily chosen, this shows that f is continuous at no points. It is everywhere discontinuous. Problem 3.13 Let the function fx be defined on D 0, as follows: fx 1 x if x 0 a if x 0 a R Show that f is not continuous at x 0 for any choice of the constant, a. Solution: We showed in a previous example that 1/x tends to as x tends to 0 through positive values. This could also be accomplished by let a n 1/n and noting then that fa n n. In any case, it is clear that fx cannot tend to any real number a as x tends to zero through positive values and f is therefore not continuous at x 0. This function has an infinite jump discontinuity or singularity at x 0. Problem 3.14 Let the function fx be defined on D 0, as follows: fx Sin 1 x if x 0 a if x 0 a R. Show that f is not continuous at x 0 for any choice of the constant, a. Solution: We define the sequence a n 1 z n 1 n, n N. Then fa n Sin 1 an Sinz n 1 if n is odd 1 if n is even from which it is evident that the sequence fa n has two limit points and is therefore divergent. Since f tends to no limit at x 0, f is not continuous there. This discontinuity is 9
10 neither a removable discontinuity nor a jump discontinuity. It is sometimes referred to as an oscillatory discontinuity. Consequences of Continuity Problem 3.15: Prove that a continuous function with a compact domain has a compact range. Solution: Suppose f is continuous on the compact domain D, and that the range of f is denoted by B. We will suppose that B is not compact and show that this leads to a contradiction. If B is not compact, then by the result of problem.19, B must contain a sequence b n that contains no subsequence converging to a point of B. Since b n is in the range of f, it follows that for each n, there is an a n in D such that fa n b n. Since D is compact, a n must contain a subsequence a n such that a n converges to a point c in D. But f is continuous on D so theorem 3.5 implies that fa n converges to fc in B. But this means b n is a subsequence of b n and b n converges to fc in B. This contradiction shows that every sequence in B must contain a subsequence converging to a point of B, which is to say, B is compact. Problem 3.16 Prove the Bounded range theorem: A function that is continuous on a compact domain is bounded. Solution: Suppose f is continuous on the compact domain D, and that the range of f is denoted by B. By the result of the previous problem, B is compact, which is to say, B is closed and bounded. But this means B is contained in a closed bounded interval, a, b, which is just the assertion that a fx b for all x in D; i.e. f is bounded on D. Problem 3.17 Prove the Extreme value theorem: If fx is continuous on a compact domain, D, then there exist points c and d in D such that fc fx fd for all x in D. Solution: Suppose f is continuous on the compact domain D. By the result of the previous problem, the range of f, denoted by B is contained in a closed bounded interval a, b. Then B has a greatest lower bound, a, and a least upper bound, b, and by theorem 1.1, these bounds both belong to B. But since B is the range of f, it then follows that there exist points c and d in D such that fc a and fd b. Since a and b were the greatest lower bound and least upper bound for B, respectively, it follows that fc fx fd for all x in D. This theorem asserts that if f is continuous on a compact domain, then there are points in the domain where f in fact assumes its maximum and minimum values. Problem 3.18 Prove theorem 3.10: A function that is continuous on a connected domain has a connected range. Solution: Suppose f is continuous at each point c of the connected domain D R. Recalling that a connected subset of R must be an interval or a point, we realize that if D is a single point c, then the range of f is the single point fc which is connected, so the result is true in this degenerate case. Now let us suppose that D is an interval of positive length and that the range of f is denoted by B. If B is not connected, then B can be written as the union of sets B 1 and B that are nonempty, disjoint and neither contains an accumulation point of the other. Let D 1 x D : fx B 1 and D x D : fx B ; i.e., fd 1 B 1 and fd B. 10
11 Clearly D 1 and D are disjoint and nonempty, but since D is connected, at least one of them must contain an accumulation point of the other. For example, suppose there is a point c in D 1 that is an accumulation point for D. Then there is a sequence c n in D that converges to c in D 1. But f is continuous at every point of D and hence fc n must converge to fc. But this implies that fc belongs to B 1 and is an accumulation point for B, in contradiction to the assumption that B 1 and B that are nonempty, disjoint and neither contains an accumulation point of the other. Then B must be connected. Theorem can be stated more simply in view of the fact that a connected subset of R must be an interval or a point. We could just say that if f is continuous on an interval I, then the range of f is an interval J such that x I if and only if fx J. This means that for any two points p q in I, as x increases from p to q, fx must assume every value between fp and fq. For if not then we would produce the same contradiction used above. Then for any value S that lies between fp and fq, there must exist a value s lying between p and q such that fs S. This is corollary 3.11, the so called intermediate value theorem. Here is a proof of corollary 3.11 based on the nested interval theorem. We have points p q in D with S lying between fp and fq, say fp S fq. Then we must show there is an s, p s q, such that fs S. We define a sequence, first letting p 1 p, q 1 q and m 1 p 1 q 1 /. Continue recursively, If fm 1 S then let p m 1, q q 1 If fm 1 S then let p p 1, q m 1 Let m p q / If fm S then let p 3 m, q 3 q If fm S then let p 3 p, q 3 m Let m 3 p 3 q 3 / This generates a sequence of p s and q s such that and p p 1 p p n q n q q 1 q q n p n q p n1 0 as n Then the nested interval theorem implies that the intersection of all the intervals p n, q n is a unique point, s, with the monotone sequences p n and q n each converging to s. Since f is continuous on D, it follows that fp n and fq n each converge to fs. But the sequences have been constructed so that, fp n S fq n for every n, and therefore fs S. Problem 3.19 Use the intermediate value theorem to prove that every polynomial of odd degree must have at least one real zero. Solution: Suppose Px denotes a polynomial of odd degree. Then the domain of Px is R and the sign of Px as x tends to must be opposite to the sign of Px as x tends to. Then it is always possible to find numbers p q such that 0 lies between fp and fq. Then the intermediate value theorem implies there exists a real number s between p and q such that Ps 0. 11
12 For example, suppose Px x n a n1 x n1 a 1 x a 0, for n an odd positive integer. Then Pp 0 Pq for p sufficiently large and negative and q sufficiently large positive. Then there must exist some s between p and q where Ps 0. This theorem is the basis of the following algorithm for finding zeroes of a function fx that is continuous on an interval a, b. We select a step length h b M a and let x m a mh for m 1,..., M. Then we evaluate the product, fx m1 fx m for 1 m M, and any m for which this product is negative indicates a zero of the function f between x m1 and x m. Problem 3.0 (persistence of sign) Let fx be defined and continuous on an open interval a, b containing the point c. If fc 0, then show that there is a neighborhood of c throughout which f has the same sign as fc. Solution: Since a, b is an open interval containing the point c, there exists an h 0 such that N h c a, b. Suppose now that fc 0 and let 1 fc 0. Then by the continuity of f, there exists a, 0 h, such that fx N fc whenever x N c. But this is just the statement, 0 1 fc fc fx fc 3 fc for all x such that c x c. Since was chosen such that 0 h, it follows that c, c is contained in a, b. This shows that f remains positive throughout a neighborhood of any point where f is positive. The proof for the negative case is similar. A related result holds when the interval I is allowed to be closed and c is an endpoint. In this case the interval on which f maintains the same sign as fc will lie on one side of c. Problem 3.1 (A fixed point theorem) Let fx be defined and continuous the unit interval, I 0, 1 and suppose that 0 fx 1 for all x in I; i.e., the domain and range of f are both equal to I. Then show that for some c in I, we have fc c (we say that if f maps I continuously into itself, then f has a fixed point). Solution: If fx is continuous on I, then so too is the function gx fx x. In addition, g0 f0 0 and g1 f1 1 0, since f0 and f1 must lie in I 0, 1. If either g0 0 or g1 0 then the fixed point is an endpoint of I. If neither g0 nor g1 is zero, then we have g1 0 g0 and it follows from the intermediate value theorem that there exists a point c in I such that gc 0; ie., such that fc c. Uniform Continuity Problem 3. Give an example of a function fx that is continuous on D 0, and a sequence a n in D such that a n is a Cauchy sequence but fa n is not Cauchy. Solution: In problem 3.1, the function fx 1 x was shown to be continuous at each point in 0,. The sequence a n 1 n is contained in D and a n converges to 0 as n tends to infinity. ( x 0 is an accumulation point of D but does not belong to D. Since the sequence is convergent, it is Cauchy. On the other hand, the sequence of function values, fa n n is not bounded, hence it is not Cauchy. Problem 3.3 Prove theorem 3.13, that for fx uniformly continuous on D, fa n is necessarily a Cauchy sequence whenever a n in D is Cauchy. Solution: Suppose f is uniformly continuous on D and a n is a Cauchy sequence in D. By 1
13 the uniform continuity of f, we know that for any 0 there exists a 0 such that for all x, y D, fx fy whenever x y. Since a n is a Cauchy sequence in D, we know that there exists M N such that a m a n whenever m, n M. Then it follows that for all m, n M, we have fa m fa n, which is to say fa n is a Cauchy sequence. Problem 3.4 Show that fx Sin 1 x is not uniformly continuous on D 0,. Solution: Let a n n, n N. Then a n is a Cauchy sequence in D that is converging to 0. However, the sequence of function values fa n 1 if n is odd 0 if n is even has more than a single limit point and is therefore not a Cauchy sequence. Then theorem 3.13 implies that f is not uniformly continuous on D. Problem 3.5 Show that: a fx 1 x is not uniformly continuous on D 1 0, 1 b fx 1 x is uniformly continuous on D 1, c gx x is uniformly continuous on D 1 0, 1 d gx x is not uniformly continuous on D 1, Solution: a) Consider the sequence of points x n 1 n and y n 1 in D n1 1 0, 1 and note that fx n fy n 1 but x n y n 1 n 1 n 1 1 nn 1 1 n. This means there are points in D 1 which are as close to one another as we like but the corresponding function values are separated by a constant distance of 1. b) For x, y in D, fx fy 1 x 1 y xy 1 x y. Note that if x, y D 1,, then xy 1, hence fx fy x y for all x, y D Then f is Lipschitz continuous on D so it is uniformly continuous there. c) For x, y in D 1, gx gy x y x y x y. When x, y D 1 0, 1, it follows that, x y, hence fx fy x y for all x, y D 1 This shows that g is Lipschitz continuous on D 1 so gx is uniformly continuous there. 13
14 d) Consider the sequence of points x n n 1 and y n n n 1 n gx n gy n n 1 n n 1 n in D and note that n. But for all n, x n y n 1 n, and this means there are points in D which are as close to one another as we like but the corresponding function values are separated by a constant distance of. Does a) imply that fx is not continuous on D 1? Not at all. Note that in order for x and y to get closer together, they both must move closer to x 0. If we fix one of the points, say we fix y c, we can make fx close to fy by moving x close to y c. This is the continuity of fx on D. which we showed in problem However, as the fixed point, y c, is fixed closer to x 0 the moveable point, x, has to be moved even closer to y c in order to obtain closeness of fx to fy. This shows the lack of uniform continuity of fx on D 1. Problem 3.6 Prove that a function which is continuous on a compact domain is uniformly continuous. Solution: Suppose fx is continuous on the closed bounded interval I a, b. If f is not uniformly continuous on I, then there exists an 0 and sequences a n, b n in I such that for each n, a n b n 1 n and fa n fb n. 1 Since I is compact, a n must contain a subsequence a n that converges to a in I. Then b n a b n a n a n a 1 n a n a and it follows that the subsequence b n of b n must also converge to a. Since f is continuous on I, both of the sequences fa n and fb n must converge to the limit fa which contradicts (1). Then if f is continuous on a compact domain I, it follows that f must in fact be uniformly continuous on I. Note that this result implies fx x is uniformly continuous on 0, 1. On the other hand, f is not Lipschitz continuous on 0, 1. To see this, consider x, y such that 0 x 1 and y 0. Then fx fy x M x y Mx is equivalent to x 1. Since this is obviously not true for all x in 0, 1, it follows that f is M not Lipschitz continuous on 0, 1. Inverse Functions Problem 3.7 Prove that if f is continuous and one to one on I a, b, then f must be strictly monotone on I. Solution; Note first that since f is one to one on I, fa fb. Suppose then that fb fa. We will show then that f is strictly increasing on I; i.e., if we choose an x, a x b, then we will show that fa fx fb. To see this, suppose we had fx fa fb. Then applying the intermediate value theorem to the interval x, b leads to the existence of a point c, x c b, such that fc fa. But this contradicts the assumption that f is one to one and we conclude that we cannot have fx fa fb with a x b. Similarly, if we had fa fb fx with 14
15 a x b, we would obtain the same contradiction and it follows that fa fx fb for every x such that a x b. Now choose y, a x y b, and proceed as above to show first that fa fy fb. Then one final application of this same argument leads to the result, fx fy whenever x y. This establishes that f is strictly increasing. The same sort of argument can be used to show f is strictly decreasing in the case that fb fa. Problem 3.8 Prove that if f is continuous and strictly monotone on I a, b, then g f 1 must be continuous on J c, d. Solution; Suppose that f is strictly increasing on I. The proof in the case where f is strictly decreasing is similar. Note first that if f is continuous and strictly increasing on I a, b, then g f 1 is defined and has as its domain, J c, d where c fa and d fb. In order to prove gy is continuous, we shall suppose that gy is discontinuous at some point y 0 in J and show that this leads to a contradiction. If gy is discontinuous at some point y 0 in J, it means that there exists a sequence y n in J such that y n converges to y 0 but gy n does not converge to gy 0. Let gy 0 be denoted by X 0. Then fx 0 y 0. Let x n gy n and note that since x n I a, b for every n, this bounded sequence must contain a subsequence, x n converging to a limit, x 0 in I. Since gy n does not converge to gy 0, it must be that x 0 gy 0 X 0. Now the continuity of f on I implies that fx n is convergent to fx 0. But fx n y n and since this is a subsequence of y n, it must be that y n converges to y 0. Therefore, fx 0 y 0. On the other hand, we have fx 0 y 0 and x 0 X 0. This is in contradiction to the assumption that f is strictly monotone (hence one to one) so our assumption that gy can be discontinuous at some point in J leads to a contradiction, proving the result. Exercises 1. Use the definition of function limit to find the following limits and prove they exist. a. lim x 1 1 x b. lim x x x x x 8 c. lim x1 x 1 x d. lim x4 x x 4. Find the following function limits a. lim x x 1 x b. lim x cosx c. lim x cosx 1 x 15
16 d. lim x0 x 4 e. lim x0 cos 1 x 3. For each of the following functions, find the points where they are discontinuous or give a reason they are continuous for all x : a. x 3 1 x 1 b. sin x cosx c. cosx 1 x d. x x 4. The functions fx x3 3x and gx x 4x 5 are each undefined at x 1 x 3 x x two points. Are these singularities removable? 5. If Fx fx gx hx, Gx gx hx and Hx gx hxare all continuous on, then is it the case that f, g, and h are also all continuous on? 6. Give an example of functions f and g that are both discontinuous at x c but i f g is continuous at x c ii fg is continuous at x c. 7. Suppose f is continuous for all x and that fx 0 for every rational x. Show that fx 0 for all x. 8. If f and g are continuous on and f p q g p q for all non-zero integers, p, q, then is it true that fx gx for all x?. 9. Suppose fx x if x rational x 3 if x irrational. Then find all the points where f is continuous. 10. Suppose f is defined on 0, 1 and that fx 1 for all x 0, 1. If lim x0 fx does not exist then show there must be sequences a n, b n converging to 0 for which the sequences fa n and fb n converge but to different limits. 11. Suppose f is continuous at all x and let P x : fx 0. If c P then show that there is an 0 such that N c P. 1. Suppose f and g are continuous for all x and let S x : fx gx. If c is an accumulation point for S, show that c S. 13. If f and g are continuous on 0, 1 and fx gx for 0 x 1 then does there exist a p 0 such that fx gx p for 0 x If f and g are continuous on 0, 1 and fx gx for 0 x 1 then there does exist a p 0 such that fx gx p for 0 x Let fx x 1 if 0 x 1, x 1 0 if x 1 gx 1 x if 0 x 1 0 if x 0 16
17 Explain how you would prove the continuity or lack of continuity for these two functions; i.e., in each case, cite a theorem which supports your answer. 16. Let fx x and let x 1 A f1 x 8 rngf M f 1. y. 4 domf. In answering the following questions, give reasons i.e., cite theorems or give examples for your answers a. Is A closed? Is it bounded? b. Find the sup and the inf for A. Do these belong to the set? c. Find the set of all y that belong to A d. Find the set of all x that belong to M e. Either prove that M is closed or show that it is not closed Suppose A is an infinite subset of the reals and that p is the LUB for A but p does not belong to A. Tell whether the following statements are true or false and give coherent reasons for your answer. a. p is an accumulation point for A b. there is a largest value x in A such that x p. 18. State the compact range (extreme value, intermediate value) theorem. Give an example where one of the hypotheses is not satisfied and the conclusion then fails to hold. 19. State the persistence of sign theorem Explain the use of this theorem to prove the following result:: If f and g are continuous on and fx gx for each rational x, then fx gx for all real x a. State a condition on fx, and D that implies that f is uniformly continuous on D b. State a condition on fx, that implies that f is uniformly continuous on a, b c. Is fx 1 uniformly continuous on 0,? 1 x 0. Use the intermediate value theorem to tell how many real zeroes exist for the function fx sin x cosx as well as to determine the approximate location of these zeroes. 1. Suppose f and g are continuous on, and that fx gx at every rational value for x. Use the persistence of sign result to show that f and g must be equal at every real value.. Given that the function fx sin 1 x is continuous on 0, 1, is f uniformly continuous on 0, 1? Given that the function gx x is continuous on 0, 1, is g uniformly continuous on 0, 1? 3. Let A denote the set of all the rational numbers between 0 and 1. 17
18 a. Is this a closed set? b. Is it an open set? c. What are the accumulation points (boundary points, interior points) for A? 4. For each of the following statements, first, tell whether the following are true or false, then state a theorem which shows a statement is true or give a counter example that shows it is false. a. If fx is continuous and injective on a, b and fa fb, then for all x, y a, b, x y if and only if fx fy. b. There exist sequences a n which are bounded but which contain no convergent subsequences. c. For every real value, x, there is a sequence of rational numbers that converges to x. d. If a n in D converges to c D, and fa n converges to fc, then f must be continuous at x c. e. If F is continuous on D where Fx fxgx, then f and g are continuous on D. 5. Use the intermediate value theorem to tell how many real zeroes exist for the function fx sin x cosx as well as to determine the approximate location of these zeroes. 6. Use the intermediate value theorem to tell how many positive zeroes exist for the function fx sin x 1 x as well as to determine the approximate location of these zeroes. 7. Tell whether the following statements are true or false and cite a theorem to justify your answer: a. If fx is continuous and monotone on a, b then x, y a, b, x y implies fx fy. b. If a n is a monotone sequence that does not converge then 1 an must tend to zero as n tends to infinity.. c. If fx is continuous on, and fx 0 if x is rational, then fx 0 at every real x. d. If A is an infinite subset of the reals and p sup A but p is not in A then there is no largest x in A such that x p. e. If F is continuous on D where Fx 5fx 4gx, then f and g are continuous on D. 8. Let fx sin x. Can f0 be defined in such a way that f is uniformly continuous x on 0, 1? Is f uniformly continuous on 0,? 9. Suppose fx is continuous on. Show that S x fx 0 is an open set. 30. A continuous function on a compact domain has a compact range. Give examples to show that if either (a) the domain is not closed or (b) the domain is not 18
19 bounded, then the range is not compact. 31. Can you use the compact range theorem to show that fx 1/x on domain 0, 1 is not continuous? 3. Suppose fx is continuous at x a and fa 0. Show 0 such that fx 0 x N a. 33. Suppose: fx is continuous on domf and x n domf is such that x n and fx n 0 for each n. Show that: f 0. Hint: Suppose f 0 and show this leads to a contradiction. 34. Suppose f is continuous on 0, and f0 f. Show there exist points x and y in 0, such that x y 1 and fx fy. Hint: Consider the function gx fx 1 fx for x 0, These functions are each undefined at two points. Are these singularities removable? fx x3 4x 5 x 3 x x gx x3 3x x If Fx fx gx hx Gx gx hx Hx gx hx are all three continuous on the whole real line, then is it the case that f, g, h are also continuous on the real line? 37. If f and g are continuous on and f m n g m n for all non-zero integers m, n, then is it true that fx gx x? 38. If f and g are continuous on 0, 1 and fx gx for 0 x 1, then does there exist a p 0 such that fx gx p for 0 x 1? 39. Suppose f is continuous on 0, 1 and that fx 0 for all x 0, 1. Then show that fx 0 for all x 0, Let S x : x 4. Show that if c is an accumulation point for S, then c belongs to S. 41. Let T x : sin x 0. Show that every point of T is an interior point. What are the boundary points of T? 4. Show that gx f x is continuous at every point where fx is continuous. 43. Prove that fx x is continuous at all values of x. Does x 0 require special attention? 44. Let fx sin x. Can f0be defined in such a way that f is continuous for all x? x 19
FIRST YEAR CALCULUS. Chapter 7 CONTINUITY. It is a parabola, and we can draw this parabola without lifting our pencil from the paper.
FIRST YEAR CALCULUS WWLCHENW L c WWWL W L Chen, 1982, 2008. 2006. This chapter originates from material used by the author at Imperial College, University of London, between 1981 and 1990. It It is is
MA4001 Engineering Mathematics 1 Lecture 10 Limits and Continuity
MA4001 Engineering Mathematics 1 Lecture 10 Limits and Dr. Sarah Mitchell Autumn 2014 Infinite limits If f(x) grows arbitrarily large as x a we say that f(x) has an infinite limit. Example: f(x) = 1 x
Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics
Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights
x a x 2 (1 + x 2 ) n.
Limits and continuity Suppose that we have a function f : R R. Let a R. We say that f(x) tends to the limit l as x tends to a; lim f(x) = l ; x a if, given any real number ɛ > 0, there exists a real number
LIMITS AND CONTINUITY
LIMITS AND CONTINUITY 1 The concept of it Eample 11 Let f() = 2 4 Eamine the behavior of f() as approaches 2 2 Solution Let us compute some values of f() for close to 2, as in the tables below We see from
Metric Spaces Joseph Muscat 2003 (Last revised May 2009)
1 Distance J Muscat 1 Metric Spaces Joseph Muscat 2003 (Last revised May 2009) (A revised and expanded version of these notes are now published by Springer.) 1 Distance A metric space can be thought of
Continuity. DEFINITION 1: A function f is continuous at a number a if. lim
Continuity DEFINITION : A function f is continuous at a number a if f(x) = f(a) REMARK: It follows from the definition that f is continuous at a if and only if. f(a) is defined. 2. f(x) and +f(x) exist.
Practice with Proofs
Practice with Proofs October 6, 2014 Recall the following Definition 0.1. A function f is increasing if for every x, y in the domain of f, x < y = f(x) < f(y) 1. Prove that h(x) = x 3 is increasing, using
1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: [email protected] Proof: For any element x of the empty set, x is also an element of every set since
SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties
SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces
36 CHAPTER 1. LIMITS AND CONTINUITY. Figure 1.17: At which points is f not continuous?
36 CHAPTER 1. LIMITS AND CONTINUITY 1.3 Continuity Before Calculus became clearly de ned, continuity meant that one could draw the graph of a function without having to lift the pen and pencil. While this
INTRODUCTORY SET THEORY
M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,
Notes on metric spaces
Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.
TOPIC 4: DERIVATIVES
TOPIC 4: DERIVATIVES 1. The derivative of a function. Differentiation rules 1.1. The slope of a curve. The slope of a curve at a point P is a measure of the steepness of the curve. If Q is a point on the
Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011
Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011 A. Miller 1. Introduction. The definitions of metric space and topological space were developed in the early 1900 s, largely
Real Roots of Univariate Polynomials with Real Coefficients
Real Roots of Univariate Polynomials with Real Coefficients mostly written by Christina Hewitt March 22, 2012 1 Introduction Polynomial equations are used throughout mathematics. When solving polynomials
Metric Spaces. Chapter 1
Chapter 1 Metric Spaces Many of the arguments you have seen in several variable calculus are almost identical to the corresponding arguments in one variable calculus, especially arguments concerning convergence
Chapter 7. Continuity
Chapter 7 Continuity There are many processes and eects that depends on certain set of variables in such a way that a small change in these variables acts as small change in the process. Changes of this
Homework # 3 Solutions
Homework # 3 Solutions February, 200 Solution (2.3.5). Noting that and ( + 3 x) x 8 = + 3 x) by Equation (2.3.) x 8 x 8 = + 3 8 by Equations (2.3.7) and (2.3.0) =3 x 8 6x2 + x 3 ) = 2 + 6x 2 + x 3 x 8
Rolle s Theorem. q( x) = 1
Lecture 1 :The Mean Value Theorem We know that constant functions have derivative zero. Is it possible for a more complicated function to have derivative zero? In this section we will answer this question
Metric Spaces. Chapter 7. 7.1. Metrics
Chapter 7 Metric Spaces A metric space is a set X that has a notion of the distance d(x, y) between every pair of points x, y X. The purpose of this chapter is to introduce metric spaces and give some
Section 3.7. Rolle s Theorem and the Mean Value Theorem. Difference Equations to Differential Equations
Difference Equations to Differential Equations Section.7 Rolle s Theorem and the Mean Value Theorem The two theorems which are at the heart of this section draw connections between the instantaneous rate
No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics
No: 10 04 Bilkent University Monotonic Extension Farhad Husseinov Discussion Papers Department of Economics The Discussion Papers of the Department of Economics are intended to make the initial results
The Mean Value Theorem
The Mean Value Theorem THEOREM (The Extreme Value Theorem): If f is continuous on a closed interval [a, b], then f attains an absolute maximum value f(c) and an absolute minimum value f(d) at some numbers
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete
I. GROUPS: BASIC DEFINITIONS AND EXAMPLES
I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called
CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.
CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,
Continued Fractions and the Euclidean Algorithm
Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction
Limits and Continuity
Math 20C Multivariable Calculus Lecture Limits and Continuity Slide Review of Limit. Side limits and squeeze theorem. Continuous functions of 2,3 variables. Review: Limits Slide 2 Definition Given a function
THE BANACH CONTRACTION PRINCIPLE. Contents
THE BANACH CONTRACTION PRINCIPLE ALEX PONIECKI Abstract. This paper will study contractions of metric spaces. To do this, we will mainly use tools from topology. We will give some examples of contractions,
Mathematical Methods of Engineering Analysis
Mathematical Methods of Engineering Analysis Erhan Çinlar Robert J. Vanderbei February 2, 2000 Contents Sets and Functions 1 1 Sets................................... 1 Subsets.............................
BANACH AND HILBERT SPACE REVIEW
BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but
Solutions to Homework 10
Solutions to Homework 1 Section 7., exercise # 1 (b,d): (b) Compute the value of R f dv, where f(x, y) = y/x and R = [1, 3] [, 4]. Solution: Since f is continuous over R, f is integrable over R. Let x
Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm.
Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. We begin by defining the ring of polynomials with coefficients in a ring R. After some preliminary results, we specialize
5.3 Improper Integrals Involving Rational and Exponential Functions
Section 5.3 Improper Integrals Involving Rational and Exponential Functions 99.. 3. 4. dθ +a cos θ =, < a
MA651 Topology. Lecture 6. Separation Axioms.
MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples
Cartesian Products and Relations
Cartesian Products and Relations Definition (Cartesian product) If A and B are sets, the Cartesian product of A and B is the set A B = {(a, b) :(a A) and (b B)}. The following points are worth special
PUTNAM TRAINING POLYNOMIALS. Exercises 1. Find a polynomial with integral coefficients whose zeros include 2 + 5.
PUTNAM TRAINING POLYNOMIALS (Last updated: November 17, 2015) Remark. This is a list of exercises on polynomials. Miguel A. Lerma Exercises 1. Find a polynomial with integral coefficients whose zeros include
9 More on differentiation
Tel Aviv University, 2013 Measure and category 75 9 More on differentiation 9a Finite Taylor expansion............... 75 9b Continuous and nowhere differentiable..... 78 9c Differentiable and nowhere monotone......
2.2 Separable Equations
2.2 Separable Equations 73 2.2 Separable Equations An equation y = f(x, y) is called separable provided algebraic operations, usually multiplication, division and factorization, allow it to be written
it is easy to see that α = a
21. Polynomial rings Let us now turn out attention to determining the prime elements of a polynomial ring, where the coefficient ring is a field. We already know that such a polynomial ring is a UF. Therefore
Introduction to Topology
Introduction to Topology Tomoo Matsumura November 30, 2010 Contents 1 Topological spaces 3 1.1 Basis of a Topology......................................... 3 1.2 Comparing Topologies.......................................
Lecture Notes on Analysis II MA131. Xue-Mei Li
Lecture Notes on Analysis II MA131 Xue-Mei Li March 8, 2013 The lecture notes are based on this and previous years lectures by myself and by the previous lecturers. I would like to thank David Mond, who
Mathematics Review for MS Finance Students
Mathematics Review for MS Finance Students Anthony M. Marino Department of Finance and Business Economics Marshall School of Business Lecture 1: Introductory Material Sets The Real Number System Functions,
God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)
Chapter 2 Numbers God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) God created the integers and the rest is the work
Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products
Chapter 3 Cartesian Products and Relations The material in this chapter is the first real encounter with abstraction. Relations are very general thing they are a special type of subset. After introducing
1. First-order Ordinary Differential Equations
Advanced Engineering Mathematics 1. First-order ODEs 1 1. First-order Ordinary Differential Equations 1.1 Basic concept and ideas 1.2 Geometrical meaning of direction fields 1.3 Separable differential
CHAPTER SIX IRREDUCIBILITY AND FACTORIZATION 1. BASIC DIVISIBILITY THEORY
January 10, 2010 CHAPTER SIX IRREDUCIBILITY AND FACTORIZATION 1. BASIC DIVISIBILITY THEORY The set of polynomials over a field F is a ring, whose structure shares with the ring of integers many characteristics.
Math 4310 Handout - Quotient Vector Spaces
Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable
I. Pointwise convergence
MATH 40 - NOTES Sequences of functions Pointwise and Uniform Convergence Fall 2005 Previously, we have studied sequences of real numbers. Now we discuss the topic of sequences of real valued functions.
4.5 Linear Dependence and Linear Independence
4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then
Quotient Rings and Field Extensions
Chapter 5 Quotient Rings and Field Extensions In this chapter we describe a method for producing field extension of a given field. If F is a field, then a field extension is a field K that contains F.
INCIDENCE-BETWEENNESS GEOMETRY
INCIDENCE-BETWEENNESS GEOMETRY MATH 410, CSUSM. SPRING 2008. PROFESSOR AITKEN This document covers the geometry that can be developed with just the axioms related to incidence and betweenness. The full
4.3 Lagrange Approximation
206 CHAP. 4 INTERPOLATION AND POLYNOMIAL APPROXIMATION Lagrange Polynomial Approximation 4.3 Lagrange Approximation Interpolation means to estimate a missing function value by taking a weighted average
Understanding Basic Calculus
Understanding Basic Calculus S.K. Chung Dedicated to all the people who have helped me in my life. i Preface This book is a revised and expanded version of the lecture notes for Basic Calculus and other
So let us begin our quest to find the holy grail of real analysis.
1 Section 5.2 The Complete Ordered Field: Purpose of Section We present an axiomatic description of the real numbers as a complete ordered field. The axioms which describe the arithmetic of the real numbers
Factoring Polynomials
Factoring Polynomials Sue Geller June 19, 2006 Factoring polynomials over the rational numbers, real numbers, and complex numbers has long been a standard topic of high school algebra. With the advent
Exercises and Problems in Calculus. John M. Erdman Portland State University. Version August 1, 2013
Exercises and Problems in Calculus John M. Erdman Portland State University Version August 1, 2013 c 2010 John M. Erdman E-mail address: [email protected] Contents Preface ix Part 1. PRELIMINARY MATERIAL
Fixed Point Theorems
Fixed Point Theorems Definition: Let X be a set and let T : X X be a function that maps X into itself. (Such a function is often called an operator, a transformation, or a transform on X, and the notation
MOP 2007 Black Group Integer Polynomials Yufei Zhao. Integer Polynomials. June 29, 2007 Yufei Zhao [email protected]
Integer Polynomials June 9, 007 Yufei Zhao [email protected] We will use Z[x] to denote the ring of polynomials with integer coefficients. We begin by summarizing some of the common approaches used in dealing
Convex analysis and profit/cost/support functions
CALIFORNIA INSTITUTE OF TECHNOLOGY Division of the Humanities and Social Sciences Convex analysis and profit/cost/support functions KC Border October 2004 Revised January 2009 Let A be a subset of R m
Mathematics for Econometrics, Fourth Edition
Mathematics for Econometrics, Fourth Edition Phoebus J. Dhrymes 1 July 2012 1 c Phoebus J. Dhrymes, 2012. Preliminary material; not to be cited or disseminated without the author s permission. 2 Contents
3.3 Real Zeros of Polynomials
3.3 Real Zeros of Polynomials 69 3.3 Real Zeros of Polynomials In Section 3., we found that we can use synthetic division to determine if a given real number is a zero of a polynomial function. This section
To define function and introduce operations on the set of functions. To investigate which of the field properties hold in the set of functions
Chapter 7 Functions This unit defines and investigates functions as algebraic objects. First, we define functions and discuss various means of representing them. Then we introduce operations on functions
How To Understand The Theory Of Hyperreals
Ultraproducts and Applications I Brent Cody Virginia Commonwealth University September 2, 2013 Outline Background of the Hyperreals Filters and Ultrafilters Construction of the Hyperreals The Transfer
The Division Algorithm for Polynomials Handout Monday March 5, 2012
The Division Algorithm for Polynomials Handout Monday March 5, 0 Let F be a field (such as R, Q, C, or F p for some prime p. This will allow us to divide by any nonzero scalar. (For some of the following,
Objective: Use calculator to comprehend transformations.
math111 (Bradford) Worksheet #1 Due Date: Objective: Use calculator to comprehend transformations. Here is a warm up for exploring manipulations of functions. specific formula for a function, say, Given
Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson
Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement
6.4 Logarithmic Equations and Inequalities
6.4 Logarithmic Equations and Inequalities 459 6.4 Logarithmic Equations and Inequalities In Section 6.3 we solved equations and inequalities involving exponential functions using one of two basic strategies.
Separation Properties for Locally Convex Cones
Journal of Convex Analysis Volume 9 (2002), No. 1, 301 307 Separation Properties for Locally Convex Cones Walter Roth Department of Mathematics, Universiti Brunei Darussalam, Gadong BE1410, Brunei Darussalam
NOV - 30211/II. 1. Let f(z) = sin z, z C. Then f(z) : 3. Let the sequence {a n } be given. (A) is bounded in the complex plane
Mathematical Sciences Paper II Time Allowed : 75 Minutes] [Maximum Marks : 100 Note : This Paper contains Fifty (50) multiple choice questions. Each question carries Two () marks. Attempt All questions.
Geometric Transformations
Geometric Transformations Definitions Def: f is a mapping (function) of a set A into a set B if for every element a of A there exists a unique element b of B that is paired with a; this pairing is denoted
An important theme in this book is to give constructive definitions of mathematical objects. Thus, for instance, if you needed to evaluate.
Chapter 10 Series and Approximations An important theme in this book is to give constructive definitions of mathematical objects. Thus, for instance, if you needed to evaluate 1 0 e x2 dx, you could set
3.6 The Real Zeros of a Polynomial Function
SECTION 3.6 The Real Zeros of a Polynomial Function 219 3.6 The Real Zeros of a Polynomial Function PREPARING FOR THIS SECTION Before getting started, review the following: Classification of Numbers (Appendix,
GRE Prep: Precalculus
GRE Prep: Precalculus Franklin H.J. Kenter 1 Introduction These are the notes for the Precalculus section for the GRE Prep session held at UCSD in August 2011. These notes are in no way intended to teach
The last three chapters introduced three major proof techniques: direct,
CHAPTER 7 Proving Non-Conditional Statements The last three chapters introduced three major proof techniques: direct, contrapositive and contradiction. These three techniques are used to prove statements
n k=1 k=0 1/k! = e. Example 6.4. The series 1/k 2 converges in R. Indeed, if s n = n then k=1 1/k, then s 2n s n = 1 n + 1 +...
6 Series We call a normed space (X, ) a Banach space provided that every Cauchy sequence (x n ) in X converges. For example, R with the norm = is an example of Banach space. Now let (x n ) be a sequence
Inner Product Spaces
Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and
CS 103X: Discrete Structures Homework Assignment 3 Solutions
CS 103X: Discrete Structures Homework Assignment 3 s Exercise 1 (20 points). On well-ordering and induction: (a) Prove the induction principle from the well-ordering principle. (b) Prove the well-ordering
correct-choice plot f(x) and draw an approximate tangent line at x = a and use geometry to estimate its slope comment The choices were:
Topic 1 2.1 mode MultipleSelection text How can we approximate the slope of the tangent line to f(x) at a point x = a? This is a Multiple selection question, so you need to check all of the answers that
Calculus. Contents. Paul Sutcliffe. Office: CM212a.
Calculus Paul Sutcliffe Office: CM212a. www.maths.dur.ac.uk/~dma0pms/calc/calc.html Books One and several variables calculus, Salas, Hille & Etgen. Calculus, Spivak. Mathematical methods in the physical
t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d).
1. Line Search Methods Let f : R n R be given and suppose that x c is our current best estimate of a solution to P min x R nf(x). A standard method for improving the estimate x c is to choose a direction
1 Local Brouwer degree
1 Local Brouwer degree Let D R n be an open set and f : S R n be continuous, D S and c R n. Suppose that the set f 1 (c) D is compact. (1) Then the local Brouwer degree of f at c in the set D is defined.
Mathematical Induction
Mathematical Induction (Handout March 8, 01) The Principle of Mathematical Induction provides a means to prove infinitely many statements all at once The principle is logical rather than strictly mathematical,
H/wk 13, Solutions to selected problems
H/wk 13, Solutions to selected problems Ch. 4.1, Problem 5 (a) Find the number of roots of x x in Z 4, Z Z, any integral domain, Z 6. (b) Find a commutative ring in which x x has infinitely many roots.
WORKBOOK. MATH 30. PRE-CALCULUS MATHEMATICS.
WORKBOOK. MATH 30. PRE-CALCULUS MATHEMATICS. DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE Contributor: U.N.Iyer Department of Mathematics and Computer Science, CP 315, Bronx Community College, University
LINEAR INEQUALITIES. Mathematics is the art of saying many things in many different ways. MAXWELL
Chapter 6 LINEAR INEQUALITIES 6.1 Introduction Mathematics is the art of saying many things in many different ways. MAXWELL In earlier classes, we have studied equations in one variable and two variables
Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2
CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2 Proofs Intuitively, the concept of proof should already be familiar We all like to assert things, and few of us
3. INNER PRODUCT SPACES
. INNER PRODUCT SPACES.. Definition So far we have studied abstract vector spaces. These are a generalisation of the geometric spaces R and R. But these have more structure than just that of a vector space.
Differential Operators and their Adjoint Operators
Differential Operators and their Adjoint Operators Differential Operators inear functions from E n to E m may be described, once bases have been selected in both spaces ordinarily one uses the standard
8 Divisibility and prime numbers
8 Divisibility and prime numbers 8.1 Divisibility In this short section we extend the concept of a multiple from the natural numbers to the integers. We also summarize several other terms that express
Polynomial Invariants
Polynomial Invariants Dylan Wilson October 9, 2014 (1) Today we will be interested in the following Question 1.1. What are all the possible polynomials in two variables f(x, y) such that f(x, y) = f(y,
ON LIMIT LAWS FOR CENTRAL ORDER STATISTICS UNDER POWER NORMALIZATION. E. I. Pancheva, A. Gacovska-Barandovska
Pliska Stud. Math. Bulgar. 22 (2015), STUDIA MATHEMATICA BULGARICA ON LIMIT LAWS FOR CENTRAL ORDER STATISTICS UNDER POWER NORMALIZATION E. I. Pancheva, A. Gacovska-Barandovska Smirnov (1949) derived four
Taylor and Maclaurin Series
Taylor and Maclaurin Series In the preceding section we were able to find power series representations for a certain restricted class of functions. Here we investigate more general problems: Which functions
Critical points of once continuously differentiable functions are important because they are the only points that can be local maxima or minima.
Lecture 0: Convexity and Optimization We say that if f is a once continuously differentiable function on an interval I, and x is a point in the interior of I that x is a critical point of f if f (x) =
4. Expanding dynamical systems
4.1. Metric definition. 4. Expanding dynamical systems Definition 4.1. Let X be a compact metric space. A map f : X X is said to be expanding if there exist ɛ > 0 and L > 1 such that d(f(x), f(y)) Ld(x,
SOLUTIONS TO ASSIGNMENT 1 MATH 576
SOLUTIONS TO ASSIGNMENT 1 MATH 576 SOLUTIONS BY OLIVIER MARTIN 13 #5. Let T be the topology generated by A on X. We want to show T = J B J where B is the set of all topologies J on X with A J. This amounts
Duality of linear conic problems
Duality of linear conic problems Alexander Shapiro and Arkadi Nemirovski Abstract It is well known that the optimal values of a linear programming problem and its dual are equal to each other if at least
1. Let P be the space of all polynomials (of one real variable and with real coefficients) with the norm
Uppsala Universitet Matematiska Institutionen Andreas Strömbergsson Prov i matematik Funktionalanalys Kurs: F3B, F4Sy, NVP 005-06-15 Skrivtid: 9 14 Tillåtna hjälpmedel: Manuella skrivdon, Kreyszigs bok
Vector and Matrix Norms
Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty
