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Abel's Identity, 131 Abel's Test, 131–132 Abel's Theorem, 463–464 Absolute Convergence, 113–114 Implication of Condi
INDEX Abel’s identity, 131 Bolzano-Weierstrass theorem, 66–68 Abel’s test, 131–132 for sequences, 99–100 Abel’s theorem, 463–464 boundary points, 50–52 absolute convergence, 113–114 bounded functions, 142 implication of conditional bounded sets, 42–43 convergence, 114 absolute value, 7 Cantor function, 229–230 reverse triangle inequality, 9 Cantor set, 80–81, 133–134, 383 triangle inequality, 9 Casorati-Weierstrass theorem, 498–499 algebraic properties of Rk, 11–13 Cauchy completeness, 106–107 algebraic properties of continuity, 184 Cauchy condensation test, 117–119 algebraic properties of limits, 91–92 Cauchy integral theorem algebraic properties of series, 110–111 triangle lemma, see triangle lemma algebraic properties of the derivative, Cauchy principal value, 365–366 244 Cauchy sequences, 104–105 alternating series, 115 convergence of, 105–106 alternating series test, 115–116 Cauchy’s inequalities, 437 analytic functions, 481–482 Cauchy’s integral formula, 428–433 complex analytic functions, 483 converse of, 436–437 counterexamples, 482–483 extension to higher derivatives, 437 identity principle, 486 for simple closed contours, 432–433 zeros of, see zeros of complex analytic on circles, 429–430 functions on open connected sets, 430–432 antiderivatives, 361 on star-shaped sets, 429 for f :[a, b] Rp, 370 → Cauchy’s integral theorem, 420–422 of complex functions, 408 consequence, see deformation of on star-shaped sets, 411–413 contours path-independence, 408–409, 415 Cauchy-Riemann equations Archimedean property, 5–6 motivation, 297–300 -
Calculus and Differential Equations II
Calculus and Differential Equations II MATH 250 B Sequences and series Sequences and series Calculus and Differential Equations II Sequences A sequence is an infinite list of numbers, s1; s2;:::; sn;::: , indexed by integers. 1n Example 1: Find the first five terms of s = (−1)n , n 3 n ≥ 1. Example 2: Find a formula for sn, n ≥ 1, given that its first five terms are 0; 2; 6; 14; 30. Some sequences are defined recursively. For instance, sn = 2 sn−1 + 3, n > 1, with s1 = 1. If lim sn = L, where L is a number, we say that the sequence n!1 (sn) converges to L. If such a limit does not exist or if L = ±∞, one says that the sequence diverges. Sequences and series Calculus and Differential Equations II Sequences (continued) 2n Example 3: Does the sequence converge? 5n 1 Yes 2 No n 5 Example 4: Does the sequence + converge? 2 n 1 Yes 2 No sin(2n) Example 5: Does the sequence converge? n Remarks: 1 A convergent sequence is bounded, i.e. one can find two numbers M and N such that M < sn < N, for all n's. 2 If a sequence is bounded and monotone, then it converges. Sequences and series Calculus and Differential Equations II Series A series is a pair of sequences, (Sn) and (un) such that n X Sn = uk : k=1 A geometric series is of the form 2 3 n−1 k−1 Sn = a + ax + ax + ax + ··· + ax ; uk = ax 1 − xn One can show that if x 6= 1, S = a . -
Topic 7 Notes 7 Taylor and Laurent Series
Topic 7 Notes Jeremy Orloff 7 Taylor and Laurent series 7.1 Introduction We originally defined an analytic function as one where the derivative, defined as a limit of ratios, existed. We went on to prove Cauchy's theorem and Cauchy's integral formula. These revealed some deep properties of analytic functions, e.g. the existence of derivatives of all orders. Our goal in this topic is to express analytic functions as infinite power series. This will lead us to Taylor series. When a complex function has an isolated singularity at a point we will replace Taylor series by Laurent series. Not surprisingly we will derive these series from Cauchy's integral formula. Although we come to power series representations after exploring other properties of analytic functions, they will be one of our main tools in understanding and computing with analytic functions. 7.2 Geometric series Having a detailed understanding of geometric series will enable us to use Cauchy's integral formula to understand power series representations of analytic functions. We start with the definition: Definition. A finite geometric series has one of the following (all equivalent) forms. 2 3 n Sn = a(1 + r + r + r + ::: + r ) = a + ar + ar2 + ar3 + ::: + arn n X = arj j=0 n X = a rj j=0 The number r is called the ratio of the geometric series because it is the ratio of consecutive terms of the series. Theorem. The sum of a finite geometric series is given by a(1 − rn+1) S = a(1 + r + r2 + r3 + ::: + rn) = : (1) n 1 − r Proof. -
11.3-11.4 Integral and Comparison Tests
11.3-11.4 Integral and Comparison Tests The Integral Test: Suppose a function f(x) is continuous, positive, and decreasing on [1; 1). Let an 1 P R 1 be defined by an = f(n). Then, the series an and the improper integral 1 f(x) dx either BOTH n=1 CONVERGE OR BOTH DIVERGE. Notes: • For the integral test, when we say that f must be decreasing, it is actually enough that f is EVENTUALLY ALWAYS DECREASING. In other words, as long as f is always decreasing after a certain point, the \decreasing" requirement is satisfied. • If the improper integral converges to a value A, this does NOT mean the sum of the series is A. Why? The integral of a function will give us all the area under a continuous curve, while the series is a sum of distinct, separate terms. • The index and interval do not always need to start with 1. Examples: Determine whether the following series converge or diverge. 1 n2 • X n2 + 9 n=1 1 2 • X n2 + 9 n=3 1 1 n • X n2 + 1 n=1 1 ln n • X n n=2 Z 1 1 p-series: We saw in Section 8.9 that the integral p dx converges if p > 1 and diverges if p ≤ 1. So, by 1 x 1 1 the Integral Test, the p-series X converges if p > 1 and diverges if p ≤ 1. np n=1 Notes: 1 1 • When p = 1, the series X is called the harmonic series. n n=1 • Any constant multiple of a convergent p-series is also convergent. -
1 Mean Value Theorem 1 1.1 Applications of the Mean Value Theorem
Seunghee Ye Ma 8: Week 5 Oct 28 Week 5 Summary In Section 1, we go over the Mean Value Theorem and its applications. In Section 2, we will recap what we have covered so far this term. Topics Page 1 Mean Value Theorem 1 1.1 Applications of the Mean Value Theorem . .1 2 Midterm Review 5 2.1 Proof Techniques . .5 2.2 Sequences . .6 2.3 Series . .7 2.4 Continuity and Differentiability of Functions . .9 1 Mean Value Theorem The Mean Value Theorem is the following result: Theorem 1.1 (Mean Value Theorem). Let f be a continuous function on [a; b], which is differentiable on (a; b). Then, there exists some value c 2 (a; b) such that f(b) − f(a) f 0(c) = b − a Intuitively, the Mean Value Theorem is quite trivial. Say we want to drive to San Francisco, which is 380 miles from Caltech according to Google Map. If we start driving at 8am and arrive at 12pm, we know that we were driving over the speed limit at least once during the drive. This is exactly what the Mean Value Theorem tells us. Since the distance travelled is a continuous function of time, we know that there is a point in time when our speed was ≥ 380=4 >>> speed limit. As we can see from this example, the Mean Value Theorem is usually not a tough theorem to understand. The tricky thing is realizing when you should try to use it. Roughly speaking, we use the Mean Value Theorem when we want to turn the information about a function into information about its derivative, or vice-versa. -
3.3 Convergence Tests for Infinite Series
3.3 Convergence Tests for Infinite Series 3.3.1 The integral test We may plot the sequence an in the Cartesian plane, with independent variable n and dependent variable a: n X The sum an can then be represented geometrically as the area of a collection of rectangles with n=1 height an and width 1. This geometric viewpoint suggests that we compare this sum to an integral. If an can be represented as a continuous function of n, for real numbers n, not just integers, and if the m X sequence an is decreasing, then an looks a bit like area under the curve a = a(n). n=1 In particular, m m+2 X Z m+1 X an > an dn > an n=1 n=1 n=2 For example, let us examine the first 10 terms of the harmonic series 10 X 1 1 1 1 1 1 1 1 1 1 = 1 + + + + + + + + + : n 2 3 4 5 6 7 8 9 10 1 1 1 If we draw the curve y = x (or a = n ) we see that 10 11 10 X 1 Z 11 dx X 1 X 1 1 > > = − 1 + : n x n n 11 1 1 2 1 (See Figure 1, copied from Wikipedia) Z 11 dx Now = ln(11) − ln(1) = ln(11) so 1 x 10 X 1 1 1 1 1 1 1 1 1 1 = 1 + + + + + + + + + > ln(11) n 2 3 4 5 6 7 8 9 10 1 and 1 1 1 1 1 1 1 1 1 1 1 + + + + + + + + + < ln(11) + (1 − ): 2 3 4 5 6 7 8 9 10 11 Z dx So we may bound our series, above and below, with some version of the integral : x If we allow the sum to turn into an infinite series, we turn the integral into an improper integral. -
What Is on Today 1 Alternating Series
MA 124 (Calculus II) Lecture 17: March 28, 2019 Section A3 Professor Jennifer Balakrishnan, [email protected] What is on today 1 Alternating series1 1 Alternating series Briggs-Cochran-Gillett x8:6 pp. 649 - 656 We begin by reviewing the Alternating Series Test: P k+1 Theorem 1 (Alternating Series Test). The alternating series (−1) ak converges if 1. the terms of the series are nonincreasing in magnitude (0 < ak+1 ≤ ak, for k greater than some index N) and 2. limk!1 ak = 0. For series of positive terms, limk!1 ak = 0 does NOT imply convergence. For alter- nating series with nonincreasing terms, limk!1 ak = 0 DOES imply convergence. Example 2 (x8.6 Ex 16, 20, 24). Determine whether the following series converge. P1 (−1)k 1. k=0 k2+10 P1 1 k 2. k=0 − 5 P1 (−1)k 3. k=2 k ln2 k 1 MA 124 (Calculus II) Lecture 17: March 28, 2019 Section A3 Recall that if a series converges to a value S, then the remainder is Rn = S − Sn, where Sn is the sum of the first n terms of the series. An upper bound on the magnitude of the remainder (the absolute error) in an alternating series arises form the following observation: when the terms are nonincreasing in magnitude, the value of the series is always trapped between successive terms of the sequence of partial sums. Thus we have jRnj = jS − Snj ≤ jSn+1 − Snj = an+1: This justifies the following theorem: P1 k+1 Theorem 3 (Remainder in Alternating Series). -
On a Series of Goldbach and Euler Llu´Is Bibiloni, Pelegr´I Viader, and Jaume Parad´Is
On a Series of Goldbach and Euler Llu´ıs Bibiloni, Pelegr´ı Viader, and Jaume Parad´ıs 1. INTRODUCTION. Euler’s paper Variae observationes circa series infinitas [6] ought to be considered important for several reasons. It contains the first printed ver- sion of Euler’s product for the Riemann zeta-function; it definitely establishes the use of the symbol π to denote the perimeter of the circle of diameter one; and it introduces a legion of interesting infinite products and series. The first of these is Theorem 1, which Euler says was communicated to him and proved by Goldbach in a letter (now lost): 1 = 1. n − m,n≥2 m 1 (One must avoid repetitions in this sum.) We refer to this result as the “Goldbach-Euler Theorem.” Goldbach and Euler’s proof is a typical example of what some historians consider a misuse of divergent series, for it starts by assigning a “value” to the harmonic series 1/n and proceeds by manipulating it by substraction and replacement of other series until the desired result is reached. This unchecked use of divergent series to obtain valid results was a standard procedure in the late seventeenth and early eighteenth centuries. It has provoked quite a lot of criticism, correction, and, why not, praise of the audacity of the mathematicians of the time. They were led by Euler, the “Master of Us All,” as Laplace christened him. We present the original proof of the Goldbach- Euler theorem in section 2. Euler was obviously familiar with other instances of proofs that used divergent se- ries. -
Series, Cont'd
Jim Lambers MAT 169 Fall Semester 2009-10 Lecture 5 Notes These notes correspond to Section 8.2 in the text. Series, cont'd In the previous lecture, we defined the concept of an infinite series, and what it means for a series to converge to a finite sum, or to diverge. We also worked with one particular type of series, a geometric series, for which it is particularly easy to determine whether it converges, and to compute its limit when it does exist. Now, we consider other types of series and investigate their behavior. Telescoping Series Consider the series 1 X 1 1 − : n n + 1 n=1 If we write out the first few terms, we obtain 1 X 1 1 1 1 1 1 1 1 1 − = 1 − + − + − + − + ··· n n + 1 2 2 3 3 4 4 5 n=1 1 1 1 1 1 1 = 1 + − + − + − + ··· 2 2 3 3 4 4 = 1: We see that nearly all of the fractions cancel one another, revealing the limit. This is an example of a telescoping series. It turns out that many series have this property, even though it is not immediately obvious. Example The series 1 X 1 n(n + 2) n=1 is also a telescoping series. To see this, we compute the partial fraction decomposition of each term. This decomposition has the form 1 A B = + : n(n + 2) n n + 2 1 To compute A and B, we multipy both sides by the common denominator n(n + 2) and obtain 1 = A(n + 2) + Bn: Substituting n = 0 yields A = 1=2, and substituting n = −2 yields B = −1=2. -
Sequences and Series
From patterns to generalizations: sequences and series Concepts ■ Patterns You do not have to look far and wide to fi nd 1 ■ Generalization visual patterns—they are everywhere! Microconcepts ■ Arithmetic and geometric sequences ■ Arithmetic and geometric series ■ Common diff erence ■ Sigma notation ■ Common ratio ■ Sum of sequences ■ Binomial theorem ■ Proof ■ Sum to infi nity Can these patterns be explained mathematically? Can patterns be useful in real-life situations? What information would you require in order to choose the best loan off er? What other Draftscenarios could this be applied to? If you take out a loan to buy a car how can you determine the actual amount it will cost? 2 The diagrams shown here are the first four iterations of a fractal called the Koch snowflake. What do you notice about: • how each pattern is created from the previous one? • the perimeter as you move from the first iteration through the fourth iteration? How is it changing? • the area enclosed as you move from the first iteration to the fourth iteration? How is it changing? What changes would you expect in the fifth iteration? How would you measure the perimeter at the fifth iteration if the original triangle had sides of 1 m in length? If this process continues forever, how can an infinite perimeter enclose a finite area? Developing inquiry skills Does mathematics always reflect reality? Are fractals such as the Koch snowflake invented or discovered? Think about the questions in this opening problem and answer any you can. As you work through the chapter, you will gain mathematical knowledge and skills that will help you to answer them all. -
Beyond Mere Convergence
Beyond Mere Convergence James A. Sellers Department of Mathematics The Pennsylvania State University 107 Whitmore Laboratory University Park, PA 16802 [email protected] February 5, 2002 – REVISED Abstract In this article, I suggest that calculus instruction should include a wider variety of examples of convergent and divergent series than is usually demonstrated. In particular, a number of convergent series, P k3 such as 2k , are considered, and their exact values are found in a k≥1 straightforward manner. We explore and utilize a number of math- ematical topics, including manipulation of certain power series and recurrences. During my most recent spring break, I read William Dunham’s book Euler: The Master of Us All [3]. I was thoroughly intrigued by the material presented and am certainly glad I selected it as part of the week’s reading. Of special interest were Dunham’s comments on series manipulations and the power series identities developed by Euler and his contemporaries, for I had just completed teaching convergence and divergence of infinite series in my calculus class. In particular, Dunham [3, p. 47-48] presents Euler’s proof of the Basel Problem, a challenge from Jakob Bernoulli to determine the 1 P 1 exact value of the sum k2 . Euler was the first to solve this problem by k≥1 π2 proving that the sum equals 6 . I was reminded of my students’ interest in this result when I shared it with them just weeks before. I had already mentioned to them that exact values for relatively few families of convergent series could be determined. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1.