1 Convergence Tests

Total Page:16

File Type:pdf, Size:1020Kb

1 Convergence Tests Lecture 24Section 11.4 Absolute and Conditional Convergence; Alternating Series Jiwen He 1 Convergence Tests Basic Series that Converge or Diverge Basic Series that Converge X Geometric series: xk, if |x| < 1 X 1 p-series: , if p > 1 kp Basic Series that Diverge X Any series ak for which lim ak 6= 0 k→∞ X 1 p-series: , if p ≤ 1 kp Convergence Tests (1) Basic Test for Convergence P Keep in Mind that, if ak 9 0, then the series ak diverges; therefore there is no reason to apply any special convergence test. P k P k k Examples 1. x with |x| ≥ 1 (e.g, (−1) ) diverge since x 9 0. [1ex] k X k X 1 diverges since k → 1 6= 0. [1ex] 1 − diverges since k + 1 k+1 k 1 k −1 ak = 1 − k → e 6= 0. Convergence Tests (2) Comparison Tests Rational terms are most easily handled by basic comparison or limit comparison with p-series P 1/kp Basic Comparison Test 1 X 1 X 1 X k3 converges by comparison with converges 2k3 + 1 k3 k5 + 4k4 + 7 X 1 X 1 X 2 by comparison with converges by comparison with k2 k3 − k2 k3 X 1 X 1 X 1 diverges by comparison with diverges by 3k + 1 3(k + 1) ln(k + 6) X 1 comparison with k + 6 Limit Comparison Test X 1 X 1 X 3k2 + 2k + 1 converges by comparison with . diverges k3 − 1 √ k3 k3 + 1 X 3 X 5 k + 100 by comparison with √ √ converges by comparison with k 2k2 k − 9 k X 5 2k2 Convergence Tests (3) Root Test and Ratio Test The root test is used only if powers are involved. Root Test 2 X k 1/k 2 X 1 1/k converges:(a ) = 1 · k1/k → 1 · 1 converges:(a ) = 2k k 2 2 (ln k)k k 2 k k X 1 1/k 1 → 0 1 − converges:(a ) = 1 + (−1) → e−1 ln k k k k Convergence Tests (4) Root Test and Ratio Test The ratio test is effective with factorials and with combinations of powers and factorials. Ratio Comparison Test 2 2 X k ak+1 1 (k+1) 1 X 1 ak+1 1 converges: = · 2 → converges: = → 0 2k ak 2 k 2 k! ak k+1 k X k a X k a k converges: k+1 = 1 · k+1 → 1 diverges: k+1 = 1 + 1 → e 10k ak 10 k 10 k! ak k k k X 2 ak+1 1−(2/3) 1 X 1 ak+1 converges: = 2 · k → 2 · √ converges: = 3k − 2k ak 3−2(2/3) 3 k! ak q 1 k+1 → 0 2 Absolute Convergence 2.1 Absolute Convergence Absolute Convergence 2 Absolute Convergence P P A series ak is said to converge absolutely if |ak| converges. P P if |ak| converges, then ak converges. i.e., absolutely convergent series are convergent. Alternating p-Series with p > 1 X (−1)k X 1 , p > 1, converge absolutely because converges. ⇒ kp kp ∞ X (−1)k+1 1 1 1 = 1 − + − − · · · converge absolutely. k2 22 32 42 k=1 Geometric Series with −1 < x < 1 X X (−1)j(k)xk, −1 < x < 1, converge absolutely because |x|k converges. 1 1 1 1 1 1 ⇒ 1 − − + − + + − · · · converge absolutely. 2 22 23 24 25 26 Conditional Convergence Conditional Convergence P P P A series ak is said to converge conditionally if ak converges while |ak| diverges. Alternating p-Series with 0 < p ≤ 1 X (−1)k X 1 , 0 < p ≤ 1, converge conditionally because diverges. ⇒ kp kp ∞ X (−1)k+1 1 1 1 = 1 − + − − · · · converge conditionally. k 2 3 4 k=1 3 Alternating Series Alternating Series Alternating Series Let {ak} be a sequence of positive numbers. X k (−1) ak = a0 − a1 + a2 − a3 + a4 − · · · is called an alternating series. Alternating Series Test Let {ak} be a decreasing sequence of positive numbers. X k If ak → 0, then (−1) ak converges. Alternating p-Series with p > 0 X (−1)k 1 p , p > 0, converge since f(x) = is decreasing, i.e., f 0(x) = − > kp xp xp+1 ∞ X (−1)k+1 1 1 1 0 for ∀x > 0, and lim f(x) = 0. ⇒ = 1 − + − − · · · x→∞ k 2 3 4 k=1 converge conditionally. 3 Examples X (−1)k 1 2 , converge since f(x) = is decreasing, i.e., f 0(x) = − > 2k + 1 2x + 1 (2x + 1)2 0 for ∀x > 0, and lim f(x) = 0. x→∞ k 2 X (−1) k x 0 x − 10 2 , converge since f(x) = 2 is decreasing, i.e., f (x) = − 2 2 > k + 10√ x + 10 (x + 10) 0, for ∀x > 10, and lim f(x) = 0. x→∞ An Estimate for Alternating Series An Estimate for Alternating Series Let {ak} be a decreasing sequence of positive numbers that tends to 0 and let ∞ X k L = (−1) ak. Then the sum L lies between consecutive partial sums sn, k=0 sn+1, sn < L < sn+1, if n is odd; sn+1 < L < sn, if n is even. and thus sn approximates L to within an+1 |L − sn| < an+1. Example ∞ X (−1)k+1 1 1 Find s to approximate = 1 − + ··· within 10−2. n k 2 3 k=1 ∞ ∞ X (−1)k+1 X (−1)k Set = . For |L − s | < 10−2, we want k k + 1 n k=1 k=0 1 a = < 10−2 ⇒ n + 2 > 102 ⇒ n > 98. n+1 (n + 1) + 1 Then n = 99 and the 99th partial sum s100 is 1 1 1 1 1 s = 1 − + − + ··· + − ≈ 0.6882. 99 2 3 4 99 100 From the estimate 1 |L − s | < a = ≈ 0.00991. 99 100 101 we conclude that ∞ X (−1)k+1 s ≈ 0.6882 < = ln 2 < 0.6981 ≈ s 99 k 100 k=1 4 Example ∞ X (−1)k+1 1 1 Find s to approximate = 1 − + ··· within 10−2. n (2k + 1)! 3! 5! k=0 −2 For |L − sn| < 10 , we want 1 a = < 10−2 ⇒ n ≥ 1. n+1 (2(n + 1) + 1)! Then n = 1 and the 2nd partial sum s2 is 1 s = 1 − ≈ 0.8333 1 3! From the estimate 1 |L − s | < a = ≈ 0.0083. 1 2 5! we conclude that ∞ X (−1)k+1 s ≈ 0.8333 < = sin 1 < 0.8416 ≈ s 1 (2k + 1)! 2 k=0 4 Rearrangements Why Absolute Convergence Matters: Rearrangements (1) Rearrangement of Absolute Convergence Series ∞ X (−1)k 1 1 1 1 1 2 = 1 − + − + − + ··· = absolutely 2k 2 22 23 24 25 3 k=0 1 1 1 1 1 1 1 1 2 Rearrangement 1 + − + + − + + − ··· ? = = 22 2 24 26 23 28 210 25 3 Theorem 2. All rearrangements of an absolutely convergent series converge absolutely to the same sum. Why Absolute Convergence Matters: Rearrangements (2) Rearrangement of Conditional Convergence Series ∞ X (−1)k+1 1 1 1 1 1 = 1 − + − + − + ··· = ln 2 conditionally k 2 3 4 5 6 k=1 1 1 1 1 1 1 1 1 Rearrangement 1 + − + + − + + − ··· ? = 6= ln 2 3 2 5 7 4 9 11 6 1 Multiply the original series by 2 ∞ 1 X (−1)k+1 1 1 1 1 1 1 = − + − + + ··· = ln 2 2 k 2 4 6 8 10 2 k=1 Adding the two series, we get the rearrangement ∞ ∞ X (−1)k+1 1 X (−1)k+1 1 1 1 1 1 3 + = 1 + − + + − + ··· = ln 2 k 2 k 3 2 5 7 4 2 k=1 k=1 Remark 5 • A series that is only conditionally convergent can be rearranged to converge to any number we please. • It can also be arranged to diverge to +∞ or −∞, or even to oscillate between any two bounds we choose. Outline Contents 1 Convergence Tests 1 2 Absolute Convergence 2 2.1 Absolute Convergence . 2 3 Alternating Series 3 4 Rearrangements 5 6.
Recommended publications
  • Riemann Sums Fundamental Theorem of Calculus Indefinite
    Riemann Sums Partition P = {x0, x1, . , xn} of an interval [a, b]. ck ∈ [xk−1, xk] Pn R(f, P, a, b) = k=1 f(ck)∆xk As the widths ∆xk of the subintervals approach 0, the Riemann Sums hopefully approach a limit, the integral of f from a to b, written R b a f(x) dx. Fundamental Theorem of Calculus Theorem 1 (FTC-Part I). If f is continuous on [a, b], then F (x) = R x 0 a f(t) dt is defined on [a, b] and F (x) = f(x). Theorem 2 (FTC-Part II). If f is continuous on [a, b] and F (x) = R R b b f(x) dx on [a, b], then a f(x) dx = F (x) a = F (b) − F (a). Indefinite Integrals Indefinite Integral: R f(x) dx = F (x) if and only if F 0(x) = f(x). In other words, the terms indefinite integral and antiderivative are synonymous. Every differentiation formula yields an integration formula. Substitution Rule For Indefinite Integrals: If u = g(x), then R f(g(x))g0(x) dx = R f(u) du. For Definite Integrals: R b 0 R g(b) If u = g(x), then a f(g(x))g (x) dx = g(a) f( u) du. Steps in Mechanically Applying the Substitution Rule Note: The variables do not have to be called x and u. (1) Choose a substitution u = g(x). du (2) Calculate = g0(x). dx du (3) Treat as if it were a fraction, a quotient of differentials, and dx du solve for dx, obtaining dx = .
    [Show full text]
  • The Radius of Convergence of the Lam\'{E} Function
    The radius of convergence of the Lame´ function Yoon-Seok Choun Department of Physics, Hanyang University, Seoul, 133-791, South Korea Abstract We consider the radius of convergence of a Lame´ function, and we show why Poincare-Perron´ theorem is not applicable to the Lame´ equation. Keywords: Lame´ equation; 3-term recursive relation; Poincare-Perron´ theorem 2000 MSC: 33E05, 33E10, 34A25, 34A30 1. Introduction A sphere is a geometric perfect shape, a collection of points that are all at the same distance from the center in the three-dimensional space. In contrast, the ellipsoid is an imperfect shape, and all of the plane sections are elliptical or circular surfaces. The set of points is no longer the same distance from the center of the ellipsoid. As we all know, the nature is nonlinear and geometrically imperfect. For simplicity, we usually linearize such systems to take a step toward the future with good numerical approximation. Indeed, many geometric spherical objects are not perfectly spherical in nature. The shape of those objects are better interpreted by the ellipsoid because they rotates themselves. For instance, ellipsoidal harmonics are shown in the calculation of gravitational potential [11]. But generally spherical harmonics are preferable to mathematically complex ellipsoid harmonics. Lame´ functions (ellipsoidal harmonic fuctions) [7] are applicable to diverse areas such as boundary value problems in ellipsoidal geometry, Schrodinger¨ equations for various periodic and anharmonic potentials, the theory of Bose-Einstein condensates, group theory to quantum mechanical bound states and band structures of the Lame´ Hamiltonian, etc. As we apply a power series into the Lame´ equation in the algebraic form or Weierstrass’s form, the 3-term recurrence relation starts to arise and currently, there are no general analytic solutions in closed forms for the 3-term recursive relation of a series solution because of their mathematical complexity [1, 5, 13].
    [Show full text]
  • 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 .
    [Show full text]
  • Ch. 15 Power Series, Taylor Series
    Ch. 15 Power Series, Taylor Series 서울대학교 조선해양공학과 서유택 2017.12 ※ 본 강의 자료는 이규열, 장범선, 노명일 교수님께서 만드신 자료를 바탕으로 일부 편집한 것입니다. Seoul National 1 Univ. 15.1 Sequences (수열), Series (급수), Convergence Tests (수렴판정) Sequences: Obtained by assigning to each positive integer n a number zn z . Term: zn z1, z 2, or z 1, z 2 , or briefly zn N . Real sequence (실수열): Sequence whose terms are real Convergence . Convergent sequence (수렴수열): Sequence that has a limit c limznn c or simply z c n . For every ε > 0, we can find N such that Convergent complex sequence |zn c | for all n N → all terms zn with n > N lie in the open disk of radius ε and center c. Divergent sequence (발산수열): Sequence that does not converge. Seoul National 2 Univ. 15.1 Sequences, Series, Convergence Tests Convergence . Convergent sequence: Sequence that has a limit c Ex. 1 Convergent and Divergent Sequences iin 11 Sequence i , , , , is convergent with limit 0. n 2 3 4 limznn c or simply z c n Sequence i n i , 1, i, 1, is divergent. n Sequence {zn} with zn = (1 + i ) is divergent. Seoul National 3 Univ. 15.1 Sequences, Series, Convergence Tests Theorem 1 Sequences of the Real and the Imaginary Parts . A sequence z1, z2, z3, … of complex numbers zn = xn + iyn converges to c = a + ib . if and only if the sequence of the real parts x1, x2, … converges to a . and the sequence of the imaginary parts y1, y2, … converges to b. Ex.
    [Show full text]
  • 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.
    [Show full text]
  • Absolute and Conditional Convergence, and Talk a Little Bit About the Mathematical Constant E
    MATH 8, SECTION 1, WEEK 3 - RECITATION NOTES TA: PADRAIC BARTLETT Abstract. These are the notes from Friday, Oct. 15th's lecture. In this talk, we study absolute and conditional convergence, and talk a little bit about the mathematical constant e. 1. Random Question Question 1.1. Suppose that fnkg is the sequence consisting of all natural numbers that don't have a 9 anywhere in their digits. Does the corresponding series 1 X 1 nk k=1 converge? 2. Absolute and Conditional Convergence: Definitions and Theorems For review's sake, we repeat the definitions of absolute and conditional conver- gence here: P1 P1 Definition 2.1. A series n=1 an converges absolutely iff the series n=1 janj P1 converges; it converges conditionally iff the series n=1 an converges but the P1 series of absolute values n=1 janj diverges. P1 (−1)n+1 Example 2.2. The alternating harmonic series n=1 n converges condition- ally. This follows from our work in earlier classes, where we proved that the series itself converged (by looking at partial sums;) conversely, the absolute values of this series is just the harmonic series, which we know to diverge. As we saw in class last time, most of our theorems aren't set up to deal with series whose terms alternate in sign, or even that have terms that occasionally switch sign. As a result, we developed the following pair of theorems to help us manipulate series with positive and negative terms: 1 Theorem 2.3. (Alternating Series Test / Leibniz's Theorem) If fangn=0 is a se- quence of positive numbers that monotonically decreases to 0, the series 1 X n (−1) an n=1 converges.
    [Show full text]
  • Arxiv:1207.1472V2 [Math.CV]
    SOME SIMPLIFICATIONS IN THE PRESENTATIONS OF COMPLEX POWER SERIES AND UNORDERED SUMS OSWALDO RIO BRANCO DE OLIVEIRA Abstract. This text provides very easy and short proofs of some basic prop- erties of complex power series (addition, subtraction, multiplication, division, rearrangement, composition, differentiation, uniqueness, Taylor’s series, Prin- ciple of Identity, Principle of Isolated Zeros, and Binomial Series). This is done by simplifying the usual presentation of unordered sums of a (countable) family of complex numbers. All the proofs avoid formal power series, double series, iterated series, partial series, asymptotic arguments, complex integra- tion theory, and uniform continuity. The use of function continuity as well as epsilons and deltas is kept to a mininum. Mathematics Subject Classification: 30B10, 40B05, 40C15, 40-01, 97I30, 97I80 Key words and phrases: Power Series, Multiple Sequences, Series, Summability, Complex Analysis, Functions of a Complex Variable. Contents 1. Introduction 1 2. Preliminaries 2 3. Absolutely Convergent Series and Commutativity 3 4. Unordered Countable Sums and Commutativity 5 5. Unordered Countable Sums and Associativity. 9 6. Sum of a Double Sequence and The Cauchy Product 10 7. Power Series - Algebraic Properties 11 8. Power Series - Analytic Properties 14 References 17 arXiv:1207.1472v2 [math.CV] 27 Jul 2012 1. Introduction The objective of this work is to provide a simplification of the theory of un- ordered sums of a family of complex numbers (in particular, for a countable family of complex numbers) as well as very easy proofs of basic operations and properties concerning complex power series, such as addition, scalar multiplication, multipli- cation, division, rearrangement, composition, differentiation (see Apostol [2] and Vyborny [21]), Taylor’s formula, principle of isolated zeros, uniqueness, principle of identity, and binomial series.
    [Show full text]
  • INFINITE SERIES in P-ADIC FIELDS
    INFINITE SERIES IN p-ADIC FIELDS KEITH CONRAD 1. Introduction One of the major topics in a course on real analysis is the representation of functions as power series X n anx ; n≥0 where the coefficients an are real numbers and the variable x belongs to R. In these notes we will develop the theory of power series over complete nonarchimedean fields. Let K be a field that is complete with respect to a nontrivial nonarchimedean absolute value j · j, such as Qp with its absolute value j · jp. We will look at power series over K (that is, with coefficients in K) and a variable x coming from K. Such series share some properties in common with real power series: (1) There is a radius of convergence R (a nonnegative real number, or 1), for which there is convergence at x 2 K when jxj < R and not when jxj > R. (2) A power series is uniformly continuous on each closed disc (of positive radius) where it converges. (3) A power series can be differentiated termwise in its disc of convergence. There are also some contrasts: (1) The convergence of a power series at its radius of convergence R does not exhibit mixed behavior: it is either convergent at all x with jxj = R or not convergent at P n all x with jxj = R, unlike n≥1 x =n on R, where R = 1 and there is convergence at x = −1 but not at x = 1. (2) A power series can be expanded around a new point in its disc of convergence and the new series has exactly the same disc of convergence as the original series.
    [Show full text]
  • 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.
    [Show full text]
  • Formal Power Series - Wikipedia, the Free Encyclopedia
    Formal power series - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Formal_power_series Formal power series From Wikipedia, the free encyclopedia In mathematics, formal power series are a generalization of polynomials as formal objects, where the number of terms is allowed to be infinite; this implies giving up the possibility to substitute arbitrary values for indeterminates. This perspective contrasts with that of power series, whose variables designate numerical values, and which series therefore only have a definite value if convergence can be established. Formal power series are often used merely to represent the whole collection of their coefficients. In combinatorics, they provide representations of numerical sequences and of multisets, and for instance allow giving concise expressions for recursively defined sequences regardless of whether the recursion can be explicitly solved; this is known as the method of generating functions. Contents 1 Introduction 2 The ring of formal power series 2.1 Definition of the formal power series ring 2.1.1 Ring structure 2.1.2 Topological structure 2.1.3 Alternative topologies 2.2 Universal property 3 Operations on formal power series 3.1 Multiplying series 3.2 Power series raised to powers 3.3 Inverting series 3.4 Dividing series 3.5 Extracting coefficients 3.6 Composition of series 3.6.1 Example 3.7 Composition inverse 3.8 Formal differentiation of series 4 Properties 4.1 Algebraic properties of the formal power series ring 4.2 Topological properties of the formal power series
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]