Schaum's Mathematical Handbook of Formulas and Tables

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SCHAUM'S outlines

Mathematical Handbook of Formulas and Tables

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SCHAUM'S outlines

Mathematical Handbook of Formulas and Tables Third Edition

Murray R. Spiegel, PhD Former Professor and Chairman Mathematics Department Rensselaer Polytechnic Institute Hartford Graduate Center

Seymour Lipschutz, PhD Mathematics Department Temple University

John Liu, PhD Mathematics Department University of Maryland

Schaum’s Outline Series

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Copyright © 2009, 1999, 1968 by The McGraw-Hill Companies, Inc. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-154856-4 The material in this eBook also appears in the print version of this title: 0-07-154855-6. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. For more information, please contact George Hoare, Special Sales, at [email protected] or (212) 904-4069. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licensors reserve all rights in and to the work. Use of this work is subject to these terms. Except as permitted under the Copyright Act of 1976 and the right to store and retrieve one copy of the work, you may not decompile, disassemble, reverse engineer, reproduce, modify, create derivative works based upon, transmit, distribute, disseminate, sell, publish or sublicense the work or any part of it without McGraw-Hill’s prior consent. You may use the work for your own noncommercial and personal use; any other use of the work is strictly prohibited. Your right to use the work may be terminated if you fail to comply with these terms. THE WORK IS PROVIDED “AS IS.” McGRAW-HILL AND ITS LICENSORS MAKE NO GUARANTEES OR WARRANTIES AS TO THE ACCURACY, ADEQUACY OR COMPLETENESS OF OR RESULTS TO BE OBTAINED FROM USING THE WORK, INCLUDING ANY INFORMATION THAT CAN BE ACCESSED THROUGH THE WORK VIA HYPERLINK OR OTHERWISE, AND EXPRESSLY DISCLAIM ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. McGraw-Hill and its licensors do not warrant or guarantee that the functions contained in the work will meet your requirements or that its operation will be uninterrupted or error free. Neither McGraw-Hill nor its licensors shall be liable to you or anyone else for any inaccuracy, error or omission, regardless of cause, in the work or for any damages resulting therefrom. McGraw-Hill has no responsibility for the content of any information accessed through the work. Under no circumstances shall McGraw-Hill and/or its licensors be liable for any indirect, incidental, special, punitive, consequential or similar damages that result from the use of or inability to use the work, even if any of them has been advised of the possibility of such damages. This limitation of liability shall apply to any claim or cause whatsoever whether such claim or cause arises in contract, tort or otherwise. DOI: 10.1036/0071548556

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Preface This handbook supplies a collection of mathematical formulas and tables which will be valuable to students and research workers in the fields of mathematics, physics, engineering, and other sciences. Care has been taken to include only those formulas and tables which are most likely to be needed in practice, rather than highly specialized results which are rarely used. It is a “user-friendly” handbook with material mostly rooted in university mathematics and scientific courses. In fact, the first edition can already be found in many libraries and offices, and it most likely has moved with the owners from office to office since their college times. Thus, this handbook has survived the test of time (while most other college texts have been thrown away). This new edition maintains the same spirit as the second edition, with the following changes. First of all, we have deleted some out-of-date tables which can now be easily obtained from a simple calculator, and we have deleted some rarely used formulas. The main change is that sections on Probability and Random Variables have been expanded with new material. These sections appear in both the physical and social sciences, including education. Topics covered range from elementary to advanced. Elementary topics include those from algebra, geometry, trigonometry, analytic geometry, probability and statistics, and calculus. Advanced topics include those from differential equations, numerical analysis, and vector analysis, such as Fourier series, gamma and beta functions, Bessel and Legendre functions, Fourier and Laplace transforms, and elliptic and other special functions of importance. This wide coverage of topics has been adopted to provide, within a single volume, most of the important mathematical results needed by student and research workers, regardless of their particular field of interest or level of attainment. The book is divided into two main parts. Part A presents mathematical formulas together with other material, such as definitions, theorems, graphs, diagrams, etc., essential for proper understanding and application of the formulas. Part B presents the numerical tables. These tables include basic statistical distributions (normal, Student’s t, chi-square, etc.), advanced functions (Bessel, Legendre, elliptic, etc.), and financial functions (compound and present value of an amount, and annuity). McGraw-Hill wishes to thank the various authors and publishers—for example, the Literary Executor of the late Sir Ronald A. Fisher, F.R.S., Dr. Frank Yates, F.R.S., and Oliver and Boyd Ltd., Edinburgh, for Table III of their book Statistical Tables for Biological, Agricultural and Medical Research—who gave their permission to adapt data from their books for use in several tables in this handbook. Appropriate references to such sources are given below the corresponding tables. Finally, I wish to thank the staff of the McGraw-Hill Schaum’s Outline Series, especially Charles Wall, for their unfailing cooperation.

SEYMOUR LIPSCHUTZ Temple University

v Copyright © 2009, 1999, 1968 by The McGraw-Hill Companies, Inc. Click here for terms of use.


For more information about this title, click here

Contents Part A

FORMULAS

1

Section I

Elementary Constants, Products, Formulas

3

1. 2. 3. 4. 5. 6.

Section II

Geometric Formulas Formulas from Plane Analytic Geometry Special Plane Curves Formulas from Solid Analytic Geometry Special Moments of Inertia

Derivatives Indefinite Integrals Tables of Special Indefinite Integrals Definite Integrals

43 43 53 56

62 62 67 71 108

Differential Equations and Vector Analysis 19. Basic Differential Equations and Solutions 20. Formulas from Vector Analysis

Section VI

16 22 28 34 41

Calculus 15. 16. 17. 18.

Section V

16

Elementary Transcendental Functions 12. Trigonometric Functions 13. Exponential and Logarithmic Functions 14. Hyperbolic Functions

Section IV

3 5 7 10 13 15

Geometry 7. 8. 9. 10. 11.

Section III

Greek Alphabet and Special Constants Special Products and Factors The Binomial Formula and Binomial Coefficients Complex Numbers Solutions of Algebraic Equations Conversion Factors

116 116 119

Series 21. 22. 23. 24.

Series of Constants Taylor Series Bernoulli and Euler Numbers Fourier Series

134 134 138 142 144

vii

CONTENTS

viii

Section VII

Special Functions and Polynomials 25. 26. 27. 28. 29. 30. 31. 32.

Section VIII

The Gamma Function The Beta Function Bessel Functions Legendre and Associated Legendre Functions Hermite Polynomials Laguerre and Associated Laguerre Polynomials Chebyshev Polynomials Hypergeometric Functions

198 198 203

205 205 207

Probability and Statistics 39. Descriptive Statistics 40. Probability 41. Random Variables

Section XII

180 193

Inequalities and Infinite Products 37. Inequalities 38. Infinite Products

Section XI

180

Elliptic and Miscellaneous Special Functions 35. Elliptic Functions 36. Miscellaneous and Riemann Zeta Functions

Section X

149 152 153 164 169 171 175 178

Laplace and Fourier Transforms 33. Laplace Transforms 34. Fourier Transforms

Section IX

149

208 208 217 223

Numerical Methods 42. 43. 44. 45. 46. 47.

Interpolation Quadrature Solution of Nonlinear Equations Numerical Methods for Ordinary Differential Equations Numerical Methods for Partial Differential Equations Iteration Methods for Linear Systems

Part B

TABLES

Section I

Logarithmic,Trigonometric, Exponential Functions 1. 2. 3. 4.

Four Place Common Logarithms log1 0 N o r log N Sin x (x in degrees and minutes) Cos x (x in degrees and minutes) Tan x (x in degrees and minutes)

227 227 231 233 235 237 240

243 245 245 247 248 249

CONTENTS

ix

5. Conversion of Radians to Degrees, Minutes, and Seconds or Fractions of Degrees 6. Conversion of Degrees, Minutes, and Seconds to Radians 7. Natural or Napierian Logarithms loge x or ln x 8. Exponential Functions ex 9. Exponential Functions eⴚx 10. Exponential, Sine, and Cosine Integrals

Section II

Factorial and Gamma Function, Binomial Coefficients 11. Factorial n 12. Gamma Function 13. Binomial coefficients

Section III

Bessel Functions J0(x) Bessel Functions J1(x) Bessel Functions Y0(x) Bessel Functions Y1(x) Bessel Functions I0(x) Bessel Functions I1(x) Bessel Functions K0(x) Bessel Functions K1(x) Bessel Functions Ber(x) Bessel Functions Bei(x) Bessel Functions Ker(x) Bessel Functions Kei(x) Values for Approximate Zeros of Bessel Functions

261 261 261 262 262 263 263 264 264 265 265 266 266 267

268 268 269

Elliptic Integrals 29. Complete Elliptic Integrals of First and Second Kinds 30. Incomplete Elliptic Integral of the First Kind 31. Incomplete Elliptic Integral of the Second Kind

Section VI

257 258 259

Legendre Polynomials 27. Legendre Polynomials Pn(x) 28. Legendre Polynomials Pn(cos ␪)

Section V

257

Bessel Functions 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26.

Section IV

250 251 252 254 255 256

270 270 271 271

Financial Tables 32. Compound amount: (1 + r)n 33. Present Value of an Amount: (1 + r)n (1+ r )n – 1 34. Amount of an Annuity: r –n 1 – (1+ r ) 35. Present Value of an Annuity: r

272 272 273 274 275

CONTENTS

x

Section VII

Probability and Statistics 36. 37. 38. 39. 40. 41. 42.

Areas Under the Standard Normal Curve Ordinates of the Standard Normal curve Percentile Values (tp) for Student's t Distribution Percentile Values (2p) for 2 (Chi-Square) Distribution 95th Percentile Values for the F distribution 99th Percentile Values for the F distribution Random Numbers

276 276 277 278 279 280 281 282

Index of Special Symbols and Notations

283

Index

285

PART A

FORMULAS

Copyright © 2009, 1999, 1968 by The McGraw-Hill Companies, Inc. Click here for terms of use.


Section I: Elementary Constants, Products, Formulas

1

GREEK ALPHABET and SPECIAL CONSTANTS

Greek Alphabet

Greek name

Greek letter Lower case

Capital

Alpha

a

A

Beta

b

Gamma

Greek name

Greek letter Lower case

Capital

Nu

n

N

B

Xi

j

g

Omicron

o

O

Delta

d

Pi

p

Epsilon

E

Rho

r

P

Zeta

z

Z

Sigma

s

Eta

h

H

Tau

t

T

Theta

u

Upsilon

y

Iota

i

I

Phi

f

Kappa

k

K

Chi

x

X

Lambda

l

Psi

c

Mu

m

M

Omega

v

Special Constants 1.1. p = 3.14159 26535 89793 … 1 1.2. e = 2.71828 18284 59045 … = lim ⎛1 + ⎞ n⎠ n→∞ ⎝

n

= natural base of logarithms 1.3. γ = 0.57721 56649 01532 86060 6512 … = Euler’s constant ⎛ 1 1 1 ⎞ = lim ⎜1 + + + + − ln n⎟ n→∞ ⎝ 2 3 n ⎠

1.4. eγ = 1.78107 24179 90197 9852 … [see 1.3]

3 Copyright © 2009, 1999, 1968 by The McGraw-Hill Companies, Inc. Click here for terms of use.

GREEK ALPHABET AND SPECIAL CONSTANTS

4

1.5.

e = 1.64872 12707 00128 1468 …

1.6.

π = Γ( 12 ) = 1.77245 38509 05516 02729 8167 … where Γ is the gamma function [see 25.1].

1.7.

Γ( 13 ) = 2.67893 85347 07748 …

1.8.

Γ( 14 ) = 3.62560 99082 21908 …

1.9.

1 radian = 180°/p = 57.29577 95130 8232 …°

1.10.

1° = p/180 radians = 0.01745 32925 19943 29576 92 … radians

2

SPECIAL PRODUCTS and FACTORS

2.1.

( x + y)2 = x 2 + 2 xy + y 2

2.2.

( x − y)2 = x 2 − 2 xy + y 2

2.3.

( x + y)3 = x 3 + 3x 2 y + 3xy 2 + y 3

2.4.

( x − y)3 = x 3 − 3x 2 y + 3xy 2 − y 3

2.5.

( x + y)4 = x 4 + 4 x 3 y + 6 x 2 y 2 + 4 xy 3 + y 4

2.6.

( x − y)4 = x 4 − 4 x 3 y + 6 x 2 y 2 − 4 xy 3 + y 4

2.7.

( x + y)5 = x 5 + 5x 4 y + 10 x 3 y 2 + 10 x 2 y3 + 5xy 4 + y5

2.8.

( x − y)5 = x 5 − 5x 4 y + 10 x 3 y 2 − 10 x 2 y 3 + 5xy 4 − y 5

2.9.

( x + y)6 = x 6 + 6 x 5 y + 15x 4 y 2 + 20 x 3 y 3 + 15x 2 y 4 + 6 xy 5 + y 6

2.10.

( x − y)6 = x 6 − 6 x 5 y + 15x 4 y 2 − 20 x 3 y3 + 15x 2 y 4 − 6 xy5 + y 6

The results 2.1 to 2.10 above are special cases of the binomial formula [see 3.3]. 2.11.

x 2 − y 2 = ( x − y)( x + y)

2.12.

x 3 − y 3 = ( x − y)( x 2 + xy + y 2 )

2.13.

x 3 + y 3 = ( x + y)( x 2 − xy + y 2 )

2.14.

x 4 − y 4 = ( x − y)( x + y)( x 2 + y 2 )

2.15.

x 5 − y 5 = ( x − y)( x 4 + x 3 y + x 2 y 2 + xy 3 + y 4 )

2.16.

x 5 + y 5 = ( x + y)( x 4 − x 3 y + x 2 y 2 − xy 3 + y 4 )

2.17.

x 6 − y 6 = ( x − y)( x + y)( x 2 + xy + y 2 )( x 2 − xy + y 2 )

5

SPECIAL PRODUCTS AND FACTORS

6

2.18.

x 4 + x 2 y 2 + y 4 = ( x 2 + xy + y 2 )( x 2 − xy + y 2 )

2.19.

x 4 + 4 y 4 = ( x 2 + 2 xy + 2 y 2 )( x 2 − 2 xy + 2 y 2 )

Some generalizations of the above are given by the following results where n is a positive integer. 2.20.

x 2 n+1 − y 2 n+1 = ( x − y)( x 2 n + x 2 n −1 y + x 2 n − 2 y 2 + + y 2 n ) ⎛ ⎞⎛ ⎞ 2π 4π = ( x − y) ⎜ x 2 − 2 xy cos + y 2⎟ ⎜ x 2 − 2 xy cos + y 2⎟ 2n + 1 2n + 1 ⎝ ⎠⎝ ⎠ ⎛ ⎞ 2nπ ⎜ x 2 − 2 xy cos + y 2⎟ 2n + 1 ⎝ ⎠

2.21.

x 2 n+1 + y 2 n+1 = ( x + y)( x 2 n − x 2 n −1 y + x 2 n − 2 y 2 − + y 2 n ) ⎛ ⎞⎛ ⎞ 2π 4π = ( x + y) ⎜ x 2 + 2 xy cos + y 2⎟ ⎜ x 2 + 2 xy cos + y 2⎟ 2n + 1 2n + 1 ⎝ ⎠⎝ ⎠ ⎛ ⎞ 2nπ ⎜ x 2 + 2 xy cos + y 2⎟ n + 2 1 ⎝ ⎠

2.22.

x 2 n − y 2 n = ( x − y)( x + y)( x n −1 + x n − 2 y + x n −3 y 2 + )( x n −1 − x n − 2 y + x n −3 y 2 − ) ⎛ ⎞⎛ ⎞ 2π π = ( x − y)( x + y) ⎜ x 2 − 2 xy cos + y 2⎟ ⎜ x 2 − 2 xy cos + y 2⎟ n n ⎝ ⎠⎝ ⎠ ⎛ ⎞ (n − 1)π + y 2⎟ ⎜ x 2 − 2 xy cos n ⎝ ⎠

2.23.

⎛ ⎞⎛ ⎞ 3π π x 2 n + y 2 n = ⎜ x 2 + 2 xy cos + y 2⎟ ⎜ x 2 + 2 xy cos + y 2⎟ 2n 2n ⎝ ⎠⎝ ⎠ ⎛ ⎞ (2n − 1)π + y 2⎟ ⎜ x 2 + 2 xy cos 2n ⎝ ⎠

3

THE BINOMIAL FORMULA and BINOMIAL COEFFICIENTS

Factorial n For n = 1, 2, 3, …, factorial n or n factorial is denoted and defined by 3.1.

n! = n(n − 1) ⋅ ⋅ 3 ⋅ 2 ⋅1

Zero factorial is defined by 3.2.

0! = 1

Alternately, n factorial can be defined recursively by 0! = 1 EXAMPLE:

and

n! = n ⋅ (n – 1)!

4! = 4 ⋅ 3 ⋅ 2 ⋅ 1 = 24, 5! = 5 ⋅ 4 ⋅ 3 ⋅ 2 ⋅ 1 = 5 ⋅ 4! = 5(24) = 120, 6! = 6 ⋅ 5! = 6(120) = 720

Binomial Formula for Positive Integral n For n = 1, 2, 3, …, 3.3.

( x + y)n = x n + nx n−1 y +

n(n − 1) n− 2 2 n(n − 1)(n − 2) n−3 3 x y + x y + + yn 2! 3!

This is called the binomial formula. It can be extended to other values of n, and also to an infinite series [see 22.4]. EXAMPLE:

(a)

(a − 2b)4 = a 4 + 4 a 3 (−2b) + 6a 2 (−2b)2 + 4 a(−2b)3 + (−2b)4 = a 4 − 8a 3 b + 24 a 2 b 2 − 32ab 3 + 16b 4 Here x = a and y = −2b.

(b)

See Fig. 3-1a.

Binomial Coefficients Formula 3.3 can be rewritten in the form 3.4.

⎛ n⎞ ⎛ n⎞ ⎛ n⎞ ⎛ n⎞ ( x + y)n = x n + ⎜ ⎟ x n−1 y + ⎜ ⎟ x n− 2 y 2 + ⎜ ⎟ x n−3 y 3 + + ⎜ ⎟ y n ⎝1⎠ ⎝ n⎠ ⎝ 2⎠ ⎝ 3⎠

7

THE BINOMIAL FORMULA AND BINOMIAL COEFFICIENTS

8

where the coefficients, called binomial coefficients, are given by

3.5.

n! ⎛ n⎞ n(n − 1)(n − 2) (n − k + 1) ⎛ n ⎞ = = ⎜⎝ k⎟⎠ = k! k !(n − k )! ⎜⎝ n − k⎟⎠

EXAMPLE:

⎛ 9⎞ 9 ⋅ 8 ⋅ 7 ⋅ 6 ⎜⎝ 4⎟⎠ = 1 ⋅ 2 ⋅ 3 ⋅ 4 = 126,

⎛12⎞ 12 ⋅ 11 ⋅ 10 ⋅ 9 ⋅ 8 ⎜⎝ 5 ⎟⎠ = 1 ⋅ 2 ⋅ 3 ⋅ 4 ⋅ 5 = 792,

⎛10⎞ ⎛10⎞ 10 ⋅ 9 ⋅ 8 ⎜⎝ 7 ⎟⎠ = ⎜⎝ 3 ⎟⎠ = 1 ⋅ 2 ⋅ 3 = 120

n Note that ⎛⎜ ⎞⎟ has exactly r factors in both the numerator and the denominator. ⎝ r⎠

The binomial coefficients may be arranged in a triangular array of numbers, called Pascal’s triangle, as shown in Fig. 3.1b. The triangle has the following two properties: (1) The first and last number in each row is 1. (2) Every other number in the array can be obtained by adding the two numbers appearing directly above it. For example 10 = 4 + 6,

15 = 5 + 10,

20 = 10 + 10

Property (2) may be stated as follows: 3.6.

⎛ n⎞ ⎛ n ⎞ ⎛ n + 1⎞ ⎜⎝ k⎟⎠ + ⎜⎝ k + 1⎟⎠ = ⎜⎝ k + 1⎟⎠

Fig. 3-1

Properties of Binomial Coefficients The following lists additional properties of the binomial coefficients: 3.7.

⎛ n⎞ ⎛ n⎞ ⎛ n⎞ ⎛ n⎞ n ⎜⎝ 0⎟⎠ + ⎜⎝1⎟⎠ + ⎜⎝ 2⎟⎠ + + ⎜⎝ n⎟⎠ = 2

3.8.

n ⎛ n⎞ ⎛ n⎞ ⎛ n⎞ n ⎛ ⎞ ⎜⎝ 0⎟⎠ − ⎜⎝1⎟⎠ + ⎜⎝ 2⎟⎠ − (−1) ⎜⎝ n⎟⎠ = 0

3.9.

⎛ n⎞ ⎛ n + 1⎞ ⎛ n + 2⎞ ⎛n + m⎞ ⎛ n + m + 1⎞ ⎜⎝ n⎟⎠ + ⎜⎝ n ⎟⎠ + ⎜⎝ n ⎟⎠ + + ⎜⎝ n ⎟⎠ = ⎜⎝ n + 1 ⎟⎠

THE BINOMIAL FORMULA AND BINOMIAL COEFFICIENTS

3.10.

⎛ n⎞ ⎛ n⎞ ⎛ n⎞ n −1 ⎜⎝ 0⎟⎠ + ⎜⎝ 2⎟⎠ + ⎜⎝ 4⎟⎠ + = 2

3.11.

⎛ n⎞ ⎛ n⎞ ⎛ n⎞ n −1 ⎜⎝1⎟⎠ + ⎜⎝ 3⎟⎠ + ⎜⎝ 5⎟⎠ + = 2

3.12.

⎛ n⎞ ⎛ n⎞ ⎛ n⎞ ⎛ n⎞ ⎛ 2n⎞ ⎜⎝ 0⎟⎠ + ⎜⎝1⎟⎠ + ⎜⎝ 2⎟⎠ + + ⎜⎝ n⎟⎠ = ⎜⎝ n ⎟⎠

3.13.

⎛ m⎞ ⎛ n⎞ ⎛ m⎞ ⎛ n ⎞ ⎛ m⎞ ⎛ n⎞ ⎛ m + n⎞ ⎜⎝ 0 ⎟⎠ ⎜⎝ p⎟⎠ + ⎜⎝ 1 ⎟⎠ ⎜⎝ p − 1⎟⎠ + + ⎜⎝ p⎟⎠ ⎜⎝ 0⎟⎠ = ⎜⎝ p ⎟⎠

3.14.

⎛n ⎛n ⎛n ⎛n (1) ⎜ ⎞⎟ + (2) ⎜ ⎞⎟ + (3) ⎜ ⎞⎟ + + (n) ⎜ ⎞⎟ = n 2n−1 ⎝1⎠ ⎝ 2⎠ ⎝ 3⎠ ⎝ n⎠

3.15.

⎛ n⎞ ⎛n ⎛n ⎛n (1) ⎜ ⎟ − (2) ⎜ ⎞⎟ + (3) ⎜ ⎞⎟ − (−1)n+1 (n) ⎜ ⎞⎟ = 0 ⎝1⎠ ⎝ n⎠ ⎝ 2⎠ ⎝ 3⎠

2

2

2

9

2

Multinomial Formula Let n1, n2, …, nr be nonnegative integers such that n1 + n2 + + nr = n. Then the following expression, called a multinomial coefficient, is defined as follows: 3.16.

⎛ n! n ⎞ ⎜⎝ n , n , … , n ⎟⎠ = n ! n ! n ! 1 2 r 1 2 r

EXAMPLE:

7! ⎛ 7 ⎞ ⎜⎝2, 3, 2⎟⎠ = 2! 3! 2! = 210,

8 8! ⎛ ⎞ ⎜⎝4, 2, 2, 0⎟⎠ = 4! 2! 2! 0! = 420

The name multinomial coefficient comes from the following formula: 3.17.

⎛ n ⎞ n n ( x1 + x 2 + + x p )n = ∑ ⎜ x x xrn ⎝ n1, n2 , … , nr ⎟⎠ 1 2 1

2

r

where the sum, denoted by Σ, is taken over all possible multinomial coefficients.

4

COMPLEX NUMBERS

Definitions Involving Complex Numbers A complex number z is generally written in the form z = a + bi where a and b are real numbers and i, called the imaginary unit, has the property that i2 = −1. The real numbers a and b are called the real and imaginary parts of z = a + bi, respectively. The complex conjugate of z is denoted by z; it is defined by a + bi = a − bi

Thus, a + bi and a – bi are conjugates of each other.

Equality of Complex Numbers 4.1.

a + bi = c + di

if and only if

a = c and b = d

Arithmetic of Complex Numbers Formulas for the addition, subtraction, multiplication, and division of complex numbers follow: 4.2.

(a + bi) + (c + di) = (a + c) + (b + d )i

4.3.

(a + bi) − (c + di) = (a − c) + (b − d )i

4.4.

(a + bi)(c + di) = (ac − bd ) + (ad + bc)i

4.5.

a + bi a + bi c − di ac + bd ⎛ bc − ad ⎞ = i = + i c + di c + di c − di c 2 + d 2 ⎝ c 2 + d 2 ⎠

Note that the above operations are obtained by using the ordinary rules of algebra and replacing i2 by −1 wherever it occurs. EXAMPLE:

Suppose z = 2 + 3i and w = 5 − 2i. Then z + w = (2 + 3i) + (5 − 2i) = 2 + 5 + 3i − 2i = 7 + i zw = (2 + 3i)(5 − 2i) = 10 + 15i − 4i − 6i 2 = 16 + 11i z = 2 + 3i = 2 − 3i and w = 5 − 2i = 5 + 2i w 5 − 2i (5 − 2i)(2 − 3i) 4 − 19i 4 19 = = = = − i z 2 + 3i (2 + 3i)(2 − 3i) 13 13 13

10

COMPLEX NUMBERS

11

Complex Plane Real numbers can be represented by the points on a line, called the real line, and, similarly, complex numbers can be represented by points in the plane, called the Argand diagram or Gaussian plane or, simply, the complex plane. Specifically, we let the point (a, b) in the plane represent the complex number z = a + bi. For example, the point P in Fig. 4-1 represents the complex number z = −3 + 4i. The complex number can also be interpreted as a vector from the origin O to the point P. The absolute value of a complex number z = a + bi, written | z |, is defined as follows: 4.6.

| z | = a 2 + b 2 = zz

We note | z | is the distance from the origin O to the point z in the complex plane.

Fig. 4-1

Fig. 4-2

Polar Form of Complex Numbers The point P in Fig. 4-2 with coordinates (x, y) represents the complex number z = x + iy. The point P can also be represented by polar coordinates (r, q ). Since x = r cos q and y = r sin q , we have 4.7.

z = x + iy = r (cos θ + i sin θ )

called the polar form of the complex number. We often call r = | z | = x 2 + y 2 the modulus and q the amplitude of z = x + iy.

Multiplication and Division of Complex Numbers in Polar Form 4.8.

[r1 (cos θ1 + i sin θ1 )][r2 (cos θ 2 + i sin θ 2 )] = r1r2 [cos(θ1 + θ 2 ) + i sin(θ1 + θ 2 )]

4.9.

r1 (cos θ1 + i sin θ1 ) r1 = [cos (θ1 − θ 2 ) + i sin (θ1 − θ 2 )] r2 (cos θ 2 + i sin θ 2 ) r2

De Moivre’s Theorem For any real number p, De Moivre’s theorem states that 4.10.

[r (cos θ + i sin θ )] p = r p (cos pθ + i sin pθ )

COMPLEX NUMBERS

12

Roots of Complex Numbers Let p = 1/n where n is any positive integer. Then 4.10 can be written 4.11.

θ + 2kπ θ + 2kπ ⎞ ⎛ [r (cos θ + i sin θ )]1/n = r 1/n ⎜ cos + i sin n n ⎟⎠ ⎝

where k is any integer. From this formula, all the nth roots of a complex number can be obtained by putting k = 0, 1, 2, …, n – 1.

5

SOLUTIONS of ALGEBRAIC EQUATIONS

Quadratic Equation: ax 2 + bx + c = 0 5.1.

x=

Solutions:

− b ± b 2 − 4 ac 2a

If a, b, c are real and if D = b2 − 4ac is the discriminant, then the roots are (i) real and unequal if D > 0 (ii) real and equal if D = 0 (iii) complex conjugate if D < 0 5.2.

If x1, x2 are the roots, then x1 + x2 = −b/a and x1x2 = c/a.

Cubic Equation: x 3 + a1 x 2 + a2 x + a3 = 0 Let

Q=

9a a − 27a3 − 2a13 3a2 − a12 , R= 1 2 , 9 54

S = 3 R + Q3 + R2 ,

T = 3 R − Q3 + R2

where ST = – Q.

5.3. Solutions:

⎧x1 = S + T − 13 a1 ⎪ ⎨x 2 = − 12 (S + T ) − 13 a1 + 12 i 3 (S − T ) ⎪x = − 1 (S + T ) − 1 a − 1 i 3 (S − T ) 2 3 1 2 ⎩ 3

If a1, a2, a3, are real and if D = Q3 + R2 is the discriminant, then (i) one root is real and two are complex conjugate if D > 0 (ii) all roots are real and at least two are equal if D = 0 (iii) all roots are real and unequal if D < 0. If D < 0, computation is simplified by use of trigonometry. 5.4. Solutions:

if D < 0 :

⎧x = 2 −Q cos( 13 θ ) − 13 a 1 ⎪1 ⎨x 2 = 2 −Q cos( 13 θ + 120° ) − 13 a1 ⎪x = 2 −Q cos( 1 θ + 240° ) − 1 a 3 3 ⎩ 3

where cos θ = R/ −Q 3

13

SOLUTIONS OF ALGEBRAIC EQUATIONS

14

5.5.

x1 + x 2 + x3 = − a1 , x1 x 2 + x 2 x3 + x3 x1 = a2 , x1 x 2 x3 = − a3

where x1, x2, x3 are the three roots.

Quartic Equation: x 4 + a1 x 3 + a2 x 2 + a3 x + a4 = 0 Let y1 be a real root of the following cubic equation: 5.6.

y 3 − a2 y 2 + (a1a3 − 4 a4 ) y + (4 a2 a4 − a32 − a12 a4 ) = 0

The four roots of the quartic equation are the four roots of the following equation: 5.7.

(

)

(

)

z 2 + 12 a1 ± a12 − 4 a2 + 4 y1 z + 12 y1 ∓ y12 − 4 a4 = 0

Suppose that all roots of 5.6 are real; then computation is simplified by using the particular real root that produces all real coefficients in the quadratic equation 5.7.

5.8.

⎧x1 + x 2 + x3 + x 4 = − a1 ⎪⎪x1 x 2 + x 2 x3 + x3 x 4 + x 4 x1 + x1 x3 + x 2 x 4 = a2 ⎨x x x + x x x + x x x + x x x = − a 2 3 4 1 2 4 1 3 4 3 ⎪1 2 3 ⎪⎩x1 x 2 x3 x 4 = x 4

where x1, x2, x3, x4 are the four roots.

6

CONVERSION FACTORS = 1000 meters (m) = 100 centimeters (cm) = 10−2 m = 10−3 m = 10−6 m = 10−9 m = 10−10 m

Length

1 kilometer (km) 1 meter (m) 1 centimeter (cm) 1 millimeter (mm) 1 micron (m) 1 millimicron (mm) 1 angstrom (Å)

Area

1 square meter (m2) = 10.76 ft2 1 square foot (ft2) = 929 cm2

1 inch (in) 1 foot (ft) 1 mile (mi) 1 millimeter 1 centimeter 1 meter 1 kilometer

= 2.540 cm = 30.48 cm = 1.609 km = 10−3 in = 0.3937 in = 39.37 in = 0.6214 mi

1 square mile (mi2) = 640 acres 1 acre = 43,560 ft2

Volume 1 liter (l) = 1000 cm3 = 1.057 quart (qt) = 61.02 in3 = 0.03532 ft3 1 cubic meter (m3) = 1000 l = 35.32 ft3 1 cubic foot (ft3) = 7.481 U.S. gal = 0.02832 m3 = 28.32 l 1 U.S. gallon (gal) = 231 in3 = 3.785 l; 1 British gallon = 1.201 U.S. gallon = 277.4 in3 Mass

1 kilogram (kg) = 2.2046 pounds (lb) = 0.06852 slug; 1 lb = 453.6 gm = 0.03108 slug 1 slug = 32.174 lb = 14.59 kg

Speed

1 km/hr = 0.2778 m/sec = 0.6214 mi/hr = 0.9113 ft/sec 1 mi/hr = 1.467 ft/sec = 1.609 km/hr = 0.4470 m/sec

Density 1 gm/cm3 = 103 kg/m3 = 62.43 lb/ft3 = 1.940 slug/ft3 1 lb/ft3 = 0.01602 gm/cm3; 1 slug/ft3 = 0.5154 gm/cm3 Force

1 newton (nt) = 105 dynes = 0.1020 kgwt = 0.2248 lbwt 1 pound weight (lbwt) = 4.448 nt = 0.4536 kgwt = 32.17 poundals 1 kilogram weight (kgwt) = 2.205 lbwt = 9.807 nt 1 U.S. short ton = 2000 lbwt; 1 long ton = 2240 lbwt; 1 metric ton = 2205 lbwt

Energy

1 joule = 1 nt m = 107 ergs = 0.7376 ft lbwt = 0.2389 cal = 9.481 × 10−4 Btu 1 ft lbwt = 1.356 joules = 0.3239 cal = 1.285 × 10–3 Btu 1 calorie (cal) = 4.186 joules = 3.087 ft lbwt = 3.968 × 10–3 Btu 1 Btu (British thermal unit) = 778 ft lbwt = 1055 joules = 0.293 watt hr 1 kilowatt hour (kw hr) = 3.60 × 106 joules = 860.0 kcal = 3413 Btu 1 electron volt (ev) = 1.602 × 10−19 joule

Power

1 watt = 1 joule/sec = 107 ergs/sec = 0.2389 cal/sec 1 horsepower (hp) = 550 ft lbwt/sec = 33,000 ft lbwt/min = 745.7 watts 1 kilowatt (kw) = 1.341 hp = 737.6 ft lbwt/sec = 0.9483 Btu/sec

Pressure 1 nt/m2 = 10 dynes/cm2 = 9.869 × 10−6 atmosphere = 2.089 × 10−2 lbwt/ft2 1 lbwt/in2 = 6895 nt/m2 = 5.171 cm mercury = 27.68 in water 1 atm = 1.013 × 105 nt/m2 = 1.013 × 106 dynes/cm2 = 14.70 lbwt/in2 = 76 cm mercury = 406.8 in water

15

Section II: Geometry

7

GEOMETRIC FORMULAS

Rectangle of Length b and Width a 7.1. Area = ab 7.2. Perimeter = 2a + 2b

Fig. 7-1

Parallelogram of Altitude h and Base b 7.3. Area = bh = ab sin u 7.4. Perimeter = 2a + 2b

Fig. 7-2

Triangle of Altitude h and Base b 7.5. Area = 12 bh = 12 ab sin θ = s(s − a)(s − b)(s − c)

where s = 12 (a + b + c) = semiperimeter 7.6. Perimeter = a + b + c

Fig. 7-3

Trapezoid of Altitude h and Parallel Sides a and b 7.7. Area = 12 h (a + b) ⎛ 1 1 ⎞ 7.8. Perimeter = a + b + h ⎜ + ⎝ sin θ sin φ ⎟⎠ = a + b + h (csc θ + csc φ ) Fig. 7-4


GEOMETRIC FORMULAS

17

Regular Polygon of n Sides Each of Length b 7.9. Area = 14 nb 2 cot

π cos(π / n) = 14 nb 2 n sin (π / n)

7.10. Perimeter = nb

Fig. 7-5

Circle of Radius r 7.11. Area = pr2

7.12. Perimeter = 2pr Fig. 7-6

Sector of Circle of Radius r 7.13. Area = 12 r 2θ [q in radians] 7.14. Arc length s = rq

Fig. 7-7

Radius of Circle Inscribed in a Triangle of Sides a, b, c 7.15.

r=

s(s − a)(s − b)(s − c) s

where s = 12 (a + b + c) = semiperimeter.

Fig. 7-8

Radius of Circle Circumscribing a Triangle of Sides a, b, c 7.16.

R=

abc 4 s(s − a)(s − b)(s − c)

where s = 12 (a + b + c) = semiperimeter.

Fig. 7-9

GEOMETRIC FORMULAS

18

Regular Polygon of n Sides Inscribed in Circle of Radius r 7.17. Area = 12 nr 2 sin

2π 1 2 360° = 2 nr sin n n

7.18. Perimeter = 2nr sin

π 180° = 2nr sin n n

Fig. 7-10

Regular Polygon of n Sides Circumscribing a Circle of Radius r 7.19. Area = nr 2 tan

π 180° = nr 2 tan n n

7.20. Perimeter = 2nr tan

π 180° = 2nr tan n n Fig. 7-11

Segment of Circle of Radius r 7.21. Area of shaded part = 12 r 2 (θ − sin θ )

Fig. 7-12

Ellipse of Semi-major Axis a and Semi-minor Axis b 7.22. Area = pab 7.23. Perimeter = 4 a ∫ = 2π

π/ 2

1 − k 2 sin 2 θ dθ

0 1 2

(a 2 + b 2 ) [approximately]

where k = a 2 − b 2 /a. See Table 29 for numerical values.

Fig. 7-13

Segment of a Parabola 7.24. Area = 23 ab 7.25. Arc length ABC =

1 2

b 2 + 16a 2 +

⎛ 4 a + b 2 + 16a 2 ⎞ b2 ln ⎜ ⎟ b 8a ⎝ ⎠ Fig. 7-14

GEOMETRIC FORMULAS

19

Rectangular Parallelepiped of Length a, Height b, Width c 7.26. Volume = abc 7.27. Surface area = 2(ab + ac + bc) Fig. 7-15

Parallelepiped of Cross-sectional Area A and Height h 7.28. Volume = Ah = abc sinq

Fig. 7-16

Sphere of Radius r 7.29. Volume =

4 3 πr 3

7.30. Surface area = 4πr2

Fig. 7-17

Right Circular Cylinder of Radius r and Height h 7.31. Volume = pr2h 7.32. Lateral surface area = 2prh

Fig. 7-18

Circular Cylinder of Radius r and Slant Height l 7.33. Volume = pr2h = pr2l sin u 7.34. Lateral surface area = 2π rl =

2π rh = 2π rh csc θ sin θ

Fig. 7-19

GEOMETRIC FORMULAS

20

Cylinder of Cross-sectional Area A and Slant Height l 7.35. Volume = Ah = Al sinq 7.36. Lateral surface area = ph = pl sinq Note that formulas 7.31 to 7.34 are special cases of formulas 7.35 and 7.36.

Fig. 7-20

Right Circular Cone of Radius r and Height h 7.37. Volume = 13 πr 2 h 7.38. Lateral surface area = π r r 2 + h 2 = π rl

Fig. 7-21

Pyramid of Base Area A and Height h 7.39. Volume = 13 Ah

Fig. 7-22

Spherical Cap of Radius r and Height h 7.40. Volume (shaded in figure) = 13 π h 2 (3r − h) 7.41. Surface area = 2p rh

Fig. 7-23

Frustum of Right Circular Cone of Radii a, b and Height h 7.42. Volume = 13 π h(a 2 + ab + b 2 ) 7.43. Lateral surface area = π (a + b) h 2 + (b − a)2 = p (a + b)l Fig. 7-24

GEOMETRIC FORMULAS

21

Spherical Triangle of Angles A, B, C on Sphere of Radius r 7.44. Area of triangle ABC = (A + B + C − p)r 2

Fig. 7-25

Torus of Inner Radius a and Outer Radius b 7.45. Volume = 14 π 2 (a + b)(b − a)2 7.46. Surface area = p 2(b2 − a2)

Fig. 7-26

Ellipsoid of Semi-axes a, b, c 7.47. Volume = 43 π abc

Fig. 7-27

Paraboloid of Revolution 7.48. Volume = 12 π b 2 a

Fig. 7-28

FORMULAS from PLANE ANALYTIC GEOMETRY

8

Distance d Between Two Points P1(x1, y1) and P2(x2, y2) 8.1. d = ( x 2 − x1 )2 + ( y2 − y1 )2

Fig. 8-1

Slope m of Line Joining Two Points P1(x1, y1) and P2(x2, y2) 8.2.

m=

y2 − y1 = tan θ x 2 − x1

Equation of Line Joining Two Points P1(x1, y1) and P2(x2, y2) 8.3.

y − y1 y2 − y1 = = m or x − x1 x 2 − x1

y − y1 = m( x − x1 )

8.4. y = mx + b where b = y1 − mx1 =

x 2 y1 − x1 y2 is the intercept on the y axis, i.e., the y intercept. x 2 − x1

Equation of Line in Terms of x Intercept a ≠ 0 and y Intercept b ≠ 0 8.5.

x y + =1 a b

Fig. 8-2

22

FORMULAS FROM PLANE ANALYTIC GEOMETRY

23

Normal Form for Equation of Line 8.6. x cos a + y sin a = p where p = perpendicular distance from origin O to line and a = angle of inclination of perpendicular with positive x axis.

Fig. 8-3

General Equation of Line 8.7. Ax + By + C = 0

Distance from Point (x1, y1) to Line Ax + By + C = 0 8.8.

Ax1 + By1 + C ± A2 + B 2

where the sign is chosen so that the distance is nonnegative.

Angle x Between Two Lines Having Slopes m1 and m2 8.9.

tan ψ =

m2 − m1 1 + m1m2

Lines are parallel or coincident if and only if m1 = m2. Lines are perpendicular if and only if m2 = −1/m1.

Fig. 8-4

Area of Triangle with Vertices at (x1, y1), (x2, y2), (x3, y3) 8.10. Area = ±

x 1 1 x2 2 x3

y1 1 y2 1 y3 1

1 = ± ( x1 y2 + y1 x3 + y3 x 2 − y2 x3 − y1 x 2 − x1 y3 ) 2

where the sign is chosen so that the area is nonnegative. If the area is zero, the points all lie on a line.

Fig. 8-5


24

Transformation of Coordinates Involving Pure Translation 8.11.

⎧x = x ′ + x 0 ⎨y = y ′ + y 0 ⎩

or

⎧x ′ = x − x 0 ⎨y ′ = y − y 0 ⎩

where (x, y) are old coordinates (i.e., coordinates relative to xy system), (x′, y′) are new coordinates (relative to x′, y′ system), and (x0, y0) are the coordinates of the new origin O′ relative to the old xy coordinate system. Fig. 8-6

Transformation of Coordinates Involving Pure Rotation 8.12.

{

x = x ′ cos α − y ′ sin α y = x ′ sin α + y ′ cos α

or

{

x ′ = x cos α + y sin α y ′ = y cos α − x sin α

where the origins of the old [xy] and new [x′y′] coordinate systems are the same but the x′ axis makes an angle a with the positive x axis. Fig. 8-7

Transformation of Coordinates Involving Translation and Rotation ⎧x = x ′ cos α − y ′ sin α + x 0 ⎨ y = x ′ sin α + y ′ cos α + y 0 ⎩

8.13. or

⎧x ′ = ( x − x 0 ) cos α + ( y − y0 ) sin α ⎨ y ′ = ( y − y ) cos α − ( x − x ) sin α 0 0 ⎩

where the new origin O′ of x′y′ coordinate system has coordinates (x0, y0) relative to the old xy coordinate system and the x′ axis makes an angle a with the positive x axis.

Fig. 8-8

Polar Coordinates (r, p ) A point P can be located by rectangular coordinates (x, y) or polar coordinates (r, u). The transformation between these coordinates is as follows:

8.14.

{

x = r cos θ y = r sin θ

or

⎧ r = x 2 + y2 ⎨ −1 ⎩ θ = tan ( y / x) Fig. 8-9


25

Equation of Circle of Radius R, Center at (x0, y0) 8.15.

(x − x0)2 + (y − y0)2 = R2

Fig. 8-10

Equation of Circle of Radius R Passing Through Origin 8.16. r = 2R cos(u − a) where (r, u) are polar coordinates of any point on the circle and (R, a) are polar coordinates of the center of the circle.

Fig. 8-11

Conics (Ellipse, Parabola, or Hyperbola) If a point P moves so that its distance from a fixed point (called the focus) divided by its distance from a fixed line (called the directrix) is a constant ⑀ (called the eccentricity), then the curve described by P is called a conic (so-called because such curves can be obtained by intersecting a plane and a cone at different angles). If the focus is chosen at origin O, the equation of a conic in polar coordinates (r, u) is, if OQ = p and LM = D (see Fig. 8-12), 8.17.

r =

D p = 1 − cos θ 1 − cos θ

The conic is (i) an ellipse if ⑀ < 1 (ii) a parabola if ⑀ = 1 (iii) a hyperbola if ⑀ > 1

Fig. 8-12


26

Ellipse with Center C(x0, y0) and Major Axis Parallel to x Axis 8.18. Length of major axis A′A = 2a 8.19. Length of minor axis B′B = 2b 8.20. Distance from center C to focus F or F′ is c = a2 − b2

8.21. Eccentricity = =

c a2 − b2 = a a

Fig. 8-13

8.22. Equation in rectangular coordinates: ( x − x 0 )2 ( y − y0 )2 + =1 a2 b2

8.23. Equation in polar coordinates if C is at O: r 2 =

a2b2 a sin θ + b 2 cos 2 θ 2

2

8-24. Equation in polar coordinates if C is on x axis and F′ is at O: r =

a (1 − 2 ) 1 − cos θ

8.25. If P is any point on the ellipse, PF + PF′ = 2a If the major axis is parallel to the y axis, interchange x and y in the above or replace u by 12 π − θ (or 90° − u).

Parabola with Axis Parallel to x Axis If vertex is at A (x0, y0) and the distance from A to focus F is a > 0, the equation of the parabola is 8.26.

(y − y0)2 = 4a(x − x0)

if parabola opens to right (Fig. 8-14)

8.27.

(y − y0)2 = −4a(x − x0)

if parabola opens to left (Fig. 8-15)

If focus is at the origin (Fig. 8-16), the equation in polar coordinates is 8.28.

r =

2a 1 − cos θ

Fig. 8-14

Fig. 8-15

In case the axis is parallel to the y axis, interchange x and y or replace u by

Fig. 8-16 1 2

π − θ (or 90° − u).


27

Hyperbola with Center C(x0, y0) and Major Axis Parallel to x Axis

Fig. 8-17

8.29. Length of major axis A′A = 2a 8.30. Length of minor axis B′B = 2b 8.31. Distance from center C to focus F or F ′ = c = a 2 + b 2 8.32. Eccentricity =

c = a

a2 + b2 a

8.33. Equation in rectangular coordinates:

( x − x 0 )2 ( y − y0 )2 − =1 a2 b2

8.34. Slopes of asymptotes G′H and GH ′ = ±

b a

8.35. Equation in polar coordinates if C is at O: r 2 =

a2b2 b cos θ − a 2 sin 2 θ 2

2

8.36. Equation in polar coordinates if C is on x axis and F′ is at O: r =

a( 2 − 1) 1 − cos θ

8.37. If P is any point on the hyperbola, PF − PF′ = ±2a (depending on branch) If the major axis is parallel to the y axis, interchange x and y in the above or replace u by (or 90° − u).

1 2

π −θ

9

SPECIAL PLANE CURVES

Lemniscate 9.1. Equation in polar coordinates: r2 = a2 cos 2u 9.2. Equation in rectangular coordinates: (x2 + y2)2 = a2(x2 − y2) 9.3. Angle between AB′ or A′B and x axis = 45° Fig. 9-1

9.4. Area of one loop = a

2

Cycloid 9.5. Equations in parametric form:

{

x = a(φ − sin φ ) y = a(1 − cos φ )

9.6. Area of one arch = 3πa2 9.7. Arc length of one arch = 8a This is a curve described by a point P on a circle of radius a rolling along x axis.

Fig. 9-2

Hypocycloid with Four Cusps 9.8. Equation in rectangular coordinates: x2/3 + y2/3 = a2/3 9.9. Equations in parametric form: ⎧x = a cos3 θ ⎨ y = a sin 3 θ ⎩

9.10. Area bounded by curve = 83 π a 2 9.11. Arc length of entire curve = 6a This is a curve described by a point P on a circle of radius a/4 as it rolls on the inside of a circle of radius a.

28

Fig. 9-3


29

Cardioid 9.12. Equation: r = 2a(1 + cos u) 9.13. Area bounded by curve = 6pa2 9.14. Arc length of curve = 16a This is the curve described by a point P of a circle of radius a as it rolls on the outside of a fixed circle of radius a. The curve is also a special case of the limacon of Pascal (see 9.32). Fig. 9-4

Catenary 9.15. Equation: y =

a x /a − x /a x (e + e ) = a cos h 2 a

This is the curve in which a heavy uniform chain would hang if suspended vertically from fixed points A and B. Fig. 9-5

Three-Leaved Rose 9.16. Equation: r = a cos 3u The equation r = a sin 3u is a similar curve obtained by rotating the curve of Fig. 9-6 counterclockwise through 30° or p/6 radians. In general, r = a cos nu or r = a sin nu has n leaves if n is odd.

Fig. 9-6

Four-Leaved Rose 9.17. Equation: r = a cos 2u The equation r = a sin 2u is a similar curve obtained by rotating the curve of Fig. 9-7 counterclockwise through 45° or p/4 radians. In general, r = a cos nu or r = a sin nu has 2n leaves if n is even.

Fig. 9-7


30

Epicycloid 9.18. Parametric equations: ⎧ ⎛ a + b⎞ ⎪⎪x = (a + b) cosθ − b cos ⎜⎝ b ⎟⎠ θ ⎨ ⎪y = (a + b)sin θ − b sin ⎛⎜ a + b⎞⎟ θ ⎝ b ⎠ ⎪⎩

This is the curve described by a point P on a circle of radius b as it rolls on the outside of a circle of radius a. The cardioid (Fig. 9-4) is a special case of an epicycloid. Fig. 9-8

General Hypocycloid 9.19. Parametric equations: ⎧ ⎛ a − b⎞ φ ⎪x = (a − b) cos φ + b cos ⎜ ⎝ b ⎟⎠ ⎪ ⎨ ⎛ a − b⎞ ⎪ ⎪y = (a − b) sin φ − b sin ⎜⎝ b ⎟⎠ φ ⎩

This is the curve described by a point P on a circle of radius b as it rolls on the inside of a circle of radius a. If b = a/4, the curve is that of Fig. 9-3.

Fig. 9-9

Trochoid 9.20. Parametric equations:

{

x = aφ − b sin φ y = a − b cos φ

This is the curve described by a point P at distance b from the center of a circle of radius a as the circle rolls on the x axis. If b < a, the curve is as shown in Fig. 9-10 and is called a curtate cycloid. If b > a, the curve is as shown in Fig. 9-11 and is called a prolate cycloid. If b = a, the curve is the cycloid of Fig. 9-2.

Fig. 9-10

Fig. 9-11


31

Tractrix ⎧x = a(ln cot 12 φ − cos φ ) 9.21. Parametric equations: ⎨ ⎩y = a sin φ

This is the curve described by endpoint P of a taut string PQ of length a as the other end Q is moved along the x axis. Fig. 9-12

Witch of Agnesi 9.22. Equation in rectangular coordinates: y =

8a 3 x 2 + 4a 2

⎧x = 2a cot θ 9.23. Parametric equations: ⎨ ⎩y = a(1 − cos 2θ )

In Fig. 9-13 the variable line QA intersects y = 2a and the circle of radius a with center (0, a) at A and B, respectively. Any point P on the “witch” is located by constructing lines parallel to the x and y axes through B and A, respectively, and determining the point P of intersection.

Fig. 9-13

Folium of Descartes 9.24. Equation in rectangular coordinates: x3 + y3 = 3axy 9.25. Parametric equations: ⎧ 3at ⎪⎪x = 1 + t 3 ⎨ 2 ⎪y = 3at 1 + t3 ⎪⎩

9.26. Area of loop =

3 2 a 2

Fig. 9-14

9.27. Equation of asymptote: x + y + a = 0

Involute of a Circle 9.28. Parametric equations: ⎧x = a(cos φ + φ sin φ ) ⎨ ⎩y = a(sin φ − φ cos φ )

This is the curve described by the endpoint P of a string as it unwinds from a circle of radius a while held taut.

Fig. 9-15


32

Evolute of an Ellipse 9.29. Equation in rectangular coordinates: (ax)2/3 + (by)2/3 = (a2 − b2)2/3 9.30. Parametric equations: ⎧ax = (a 2 − b 2 ) cos3 θ ⎨ 2 2 3 ⎩by = (a − b ) sin θ

This curve is the envelope of the normals to the ellipse x2/a2 + y2/b2 = 1 shown dashed in Fig. 9-16.

Fig. 9-16

Ovals of Cassini 9.31. Polar equation: r4 + a4 − 2a2r2 cos 2u = b4 This is the curve described by a point P such that the product of its distance from two fixed points (distance 2a apart) is a constant b2. The curve is as in Fig. 9-17 or Fig. 9-18 according as b < a or b > a, respectively. If b = a, the curve is a lemniscate (Fig. 9-1).

Fig. 9-17

Fig. 9-18

Limacon of Pascal 9.32. Polar equation: r = b + a cos u Let OQ be a line joining origin O to any point Q on a circle of diameter a passing through O. Then the curve is the locus of all points P such that PQ = b. The curve is as in Fig. 9-19 or Fig. 9-20 according as 2a > b > a or b < a, respectively. If b = a, the curve is a cardioid (Fig. 9-4). If b 2a, the curve is convex.

Fig. 9-19

Fig. 9-20


33

Cissoid of Diocles 9.33. Equation in rectangular coordinates: y2 =

x2 2a − x

9.34. Parametric equations: ⎧x = 2a sin 2 θ ⎪ 2a sin 3 θ ⎨ y = ⎪⎩ cos θ

This is the curve described by a point P such that the distance OP = distance RS. It is used in the problem of duplication of a cube, i.e., finding the side of a cube which has twice the volume of a given cube.

Fig. 9-21

Spiral of Archimedes 9.35. Polar equation: r = au

Fig. 9-22

FORMULAS from SOLID ANALYTIC GEOMETRY

10

Distance d Between Two Points P1(x1, y1, z1) and P2(x2, y2, z2) 10.1.

d = ( x 2 − x1 )2 + ( y2 − y1 )2 + (z 2 − z1 )2

z d

γ

P2 (x2, y2, z2)

β

P1 (x1, y1, z1) α

y

O

x

Fig. 10-1

Direction Cosines of Line Joining Points P1(x1, y1, z1) and P2(x2, y2, z2) 10.2.

l = cos α =

x 2 − x1 y − y1 z − z1 , m = cos β = 2 , n = cos γ = 2 d d d

where a, b, g are the angles that line P1P2 makes with the positive x, y, z axes, respectively, and d is given by 10.1 (see Fig. 10-1).

Relationship Between Direction Cosines 10.3.

cos 2 α + cos 2 β + cos 2 γ = 1 or l 2 + m 2 + n 2 = 1

Direction Numbers Numbers L, M, N, which are proportional to the direction cosines l, m, n, are called direction numbers. The relationship between them is given by 10.4.

l=

L L +M +N 2

2

2

, m=

M L +M +N 2

2

2

, n=

N L + M2 + N2 2

Equations of Line Joining P1(x1, y1, z1) and P2(x2, y2, z2) in Standard Form 10.5.

x − x1 y − y1 z − z1 = = x 2 − x1 y2 − y1 z 2 − z1

or

x − x1 y − y1 z − z1 = = l m n

These are also valid if l, m, n are replaced by L, M, N, respectively.

34

FORMULAS FROM SOLID ANALYTIC GEOMETRY

35

Equations of Line Joining P1(x1, y1, z1) and P2(x2, y2, z2) in Parametric Form x = x1 + lt , y = y1 + mt , z = z1 + nt

10.6.

These are also valid if l, m, n are replaced by L, M, N, respectively.

Angle e Between Two Lines with Direction Cosines l1, m1, n1 and l2, m2, n2 cos φ = l1l2 + m1m2 + n1n2

10.7.

General Equation of a Plane Ax + By + Cz + D = 0

10.8.

(A, B, C, D are constants)

Equation of Plane Passing Through Points (x1, y1, z1), (x2, y2, z2), (x3, y3, z3) x − x1 y − y1 z − z1 x 2 − x1 y2 − y1 z2 − z1 = 0 x3 − x1 y3 − y1 z3 − z1

10.9.

or 10.10.

y2 − y1 y3 − y1

z 2 − z1 z −z ( x − x1 ) + 2 1 z3 − z1 z3 − z1

x 2 − x1 x − x1 ( y − y1 ) + 2 x3 − x1 x3 − x1

y2 − y1 (z − z1 ) = 0 y3 − y1

Equation of Plane in Intercept Form 10.11.

x y z + + =1 a b c

z

where a, b, c are the intercepts on the x, y, z axes, respectively. c b a

O

x

Fig. 10-2

Equations of Line Through (x0, y0, z0) and Perpendicular to Plane Ax + By + Cz + D = 0 10.12.

x − x0 y − y0 z − z0 = = A B C

or

x = x 0 + At , y = y0 + Bt , z = z0 + Ct

Note that the direction numbers for a line perpendicular to the plane Ax + By + Cz + D = 0 are A, B, C.

y


36

Distance from Point (x0, y0, z0) to Plane Ax + By + Cz + D = 0 10.13.

Ax 0 + By0 + Cz 0 + D ± A2 + B 2 + C 2

where the sign is chosen so that the distance is nonnegative.

Normal Form for Equation of Plane 10.14.

x cos α + y cos β + z cos γ = p

z

where p = perpendicular distance from O to plane at P and a, b, g are angles between OP and positive x, y, z axes. γ α

P p β y

O

x

Fig. 10-3

Transformation of Coordinates Involving Pure Translation 10.15.

⎧x = x ′ + x 0 ⎪ ⎨y = y ′ + y0 ⎪z = z′ + z 0 ⎩

or

⎧x ′ = x − x 0 ⎪ ⎨ y ′ = y − y0 ⎪z′ = z − z 0 ⎩

z

z'

(x0, y0, z0) y' O'

where (x, y, z) are old coordinates (i.e., coordinates relative to xyz system), (x′, y′, z′) are new coordinates (relative to x′y′z′ system) and (x0, y0, z0) are the coordinates of the new origin O′ relative to the old xyz coordinate system.

x'

y

O x

Fig. 10-4

Transformation of Coordinates Involving Pure Rotation 10.16.

⎧x = l1x ′ + l2 y′ + l3 z ′ ⎪ ⎨y = m1x ′ + m2 y′ + m3 z ′ ⎪⎩z = n1x ′ + n2 y ′ + n3 z ′

z z' y'

⎧x ′ = l1x + m1 y + n1z ⎪ or ⎨y′ = l2 x + m2 y + n2 z ⎪⎩z ′ = l3 x + m3 y + n3 z

where the origins of the xyz and x′y′z′ systems are the same and l1, m1, n1; l2, m2, n2; l3, m3, n3 are the direction cosines of the x′,y′,z′ axes relative to the x, y, z axes, respectively.

y

O

x

x'

Fig. 10-5


37

Transformation of Coordinates Involving Translation and Rotation 10.17.

⎧x = l1x ′ + l2 y′ + l3 z ′ + x 0 ⎪ ⎨y = m1x ′ + m2 y′ + m3 z ′ + y0 ⎪⎩z = n1x ′ + n2 y′ + n3 z ′ + z 0

z

⎧x ′ = l1 ( x − x 0 ) + m1 ( y − y0 ) + n1 (z − z0 ) ⎪ or ⎨y′ = l2 ( x − x 0 ) + m2 ( y − y0 ) + n2 (z − z 0 ) ⎪⎩z ′ = l3 ( x − x 0 ) + m3 ( y − y0 ) + n3 (z − z 0 )

y'

O' (x0 , y0 , z0) y O x'

where the origin O′ of the x′y′z′ system has coordinates (x0, y0, z0) relative to the xyz system and l1 , m1 , n1; l2 , m2 , n2 ; l3 , m3 , n3

z'

x

Fig. 10-6

are the direction cosines of the x′, y′, z′ axes relative to the x, y, z axes, respectively.

Cylindrical Coordinates (r, p, z) A point P can be located by cylindrical coordinates (r, u, z) (see Fig. 10-7) as well as rectangular coordinates (x, y, z). The transformation between these coordinates is 10.18.

⎧r = x 2 + y 2 ⎧x = r cos θ ⎪ ⎪ −1 ⎨y = r sin θ or ⎨θ = tan ( y / x ) ⎪⎩z = z ⎪z = z ⎩

Fig. 10-7

Spherical Coordinates (r, p, e ) A point P can be located by spherical coordinates (r, u, f) (see Fig. 10-8) as well as rectangular coordinates (x, y, z). The transformation between those coordinates is 10.19.

⎧x = r sin θ cos φ ⎪ ⎨y = r sin θ sin φ ⎪⎩z = r cos θ ⎧r = x 2 + y 2 + z 2 ⎪ or ⎨φ = tan −1 ( y / x ) ⎪θ = cos −1 (z / x 2 + y 2 + z 2 ) ⎩ Fig. 10-8


38

Equation of Sphere in Rectangular Coordinates 10.20.

( x − x 0 )2 + ( y − y0 )2 + (z − z0 )2 = R 2

where the sphere has center (x0, y0, z0) and radius R.

Fig. 10-9

Equation of Sphere in Cylindrical Coordinates 10.21.

r 2 − 2r0 r cos(θ − θ 0 ) + r02 + (z − z 0 )2 = R 2

where the sphere has center (r0, u0, z0) in cylindrical coordinates and radius R. If the center is at the origin the equation is 10.22.

r 2 + z 2 = R2

Equation of Sphere in Spherical Coordinates 10.23.

r 2 + r02 − 2r0 r sin θ sin θ 0 cos(φ − φ0 ) = R 2

where the sphere has center (r0, u0, f0) in spherical coordinates and radius R. If the center is at the origin the equation is 10.24. r = R

Equation of Ellipsoid with Center (x0, y0, z0) and Semi-axes a, b, c 10.25.

( x − x 0 )2 ( y − y0 )2 (z − z 0 )2 + + =1 a2 b2 c2

Fig. 10-10


39

Elliptic Cylinder with Axis as z Axis 10.26.

x 2 y2 + =1 a2 b2

where a, b are semi-axes of elliptic cross-section. If b = a it becomes a circular cylinder of radius a.

Fig. 10-11

Elliptic Cone with Axis as z Axis 10.27.

x 2 y2 z 2 + = a2 b2 c2

Fig. 10-12

Hyperboloid of One Sheet 10.28.

x 2 y2 z 2 + − =1 a2 b2 c2

Fig. 10-13

Hyperboloid of Two Sheets 10.29.

x 2 y2 z 2 − − =1 a2 b2 c2

Note orientation of axes in Fig. 10-14.

Fig. 10-14


40

Elliptic Paraboloid 10.30.

x 2 y2 z + = a2 b2 c

Fig. 10-15

Hyperbolic Paraboloid 10.31.

x 2 y2 z − = a2 b2 c

Note orientation of axes in Fig. 10-16.

Fig. 10-16

11

SPECIAL MOMENTS of INERTIA

The table below shows the moments of inertia of various rigid bodies of mass M. In all cases it is assumed the body has uniform (i.e., constant) density. TYPE OF RIGID BODY

MOMENT OF INERTIA

11.1. Thin rod of length a (a)

about axis perpendicular to the rod through the center of mass

1 12

Ma 2

(b)

about axis perpendicular to the rod through one end

1 3

Ma 2

11.2.

Rectangular parallelepiped with sides a, b, c

(a)

about axis parallel to c and through center of face ab

(b)

about axis through center of face bc and parallel to c

1 12

M (a 2 + b 2 ) M (4 a 2 + b 2 )

1 12

11.3. Thin rectangular plate with sides a, b M (a 2 + b 2 )

(a)

about axis perpendicular to the plate through center

(b)

about axis parallel to side b through center

11.4.

Circular cylinder of radius a and height h

(a)

about axis of cylinder

(b)

about axis through center of mass and perpendicular to cylindrical axis

(c)

about axis coinciding with diameter at one end

11.5.

Hollow circular cylinder of outer radius a, inner radius b and height h

(a)

about axis of cylinder

(b)

about axis through center of mass and perpendicular to cylindrical axis

(c)

about axis coinciding with diameter at one end

11.6.

Circular plate of radius a

(a)

about axis perpendicular to plate through center

1 2

Ma 2

(b)

about axis coinciding with a diameter

1 4

Ma 2

1 12

1 12

1 2

Ma 2

M (4 h 2 + 3a 2 )

1 2 1 12

Ma 2

M (h 2 + 3a 2 )

1 12 1 12

1 12

M (a 2 + b 2 )

M (3a 2 + 3b 2 + h 2 ) M (3a 2 + 3b 2 + 4 h 2 )

41

SPECIAL MOMENTS OF INERTIA

42

11.7.

Hollow circular plate or ring with outer radius a and inner radius b

(a)

about axis perpendicular to plane of plate through center

1 2

M (a 2 + b 2 )

(b)


1 4

M (a 2 + b 2 )

11.8.

Thin circular ring of radius a

(a)

about axis perpendicular to plane of ring through center

(b)

about axis coinciding with diameter

11.9.

Sphere of radius a

(a)

Ma2 1 2

Ma 2


2 5

Ma 2

(b)

about axis tangent to the surface

7 5

Ma 2

11.10.

Hollow sphere of outer radius a and inner radius b

(a)


(b)


11.11.

Hollow spherical shell of radius a

(a)


2 3

Ma 2

(b)


5 3

Ma 2

11.12.

Ellipsoid with semi-axes a, b, c

(a)

about axis coinciding with semi-axis c

(b)

about axis tangent to surface, parallel to semi-axis c and at distance a from center

M (a 5 − b 5 )/(a 3 − b 3 ) 5 5 3 3 2 2 5 M (a − b )/(a − b ) + Ma 2 5

1 5 1 5

M (a 2 + b 2 ) M (6a 2 + b 2 )

11.13. Circular cone of radius a and height h 3 10

Ma 2

(a)

about axis of cone

(b)

about axis through vertex and perpendicular to axis

3 20

M (a 2 + 4 h 2 )

(c)

about axis through center of mass and perpendicular to axis

3 80

M (4 a 2 + h 2 )

11.14. Torus with outer radius a and inner radius b (a)

about axis through center of mass and perpendicular to the plane of torus

(b)

about axis through center of mass and in the plane of torus

1 4

1 4

M (7a 2 − 6ab + 3b 2 ) M (9a 2 − 10 ab + 5b 2 )

Section III: Elementary Transcendental Functions

12

TRIGONOMETRIC FUNCTIONS

Definition of Trigonometric Functions for a Right Triangle Triangle ABC has a right angle (90°) at C and sides of length a, b, c. The trigonometric functions of angle A are defined as follows: 12.1. sine of A = sin A =

a opposite = c hypotenuse

12.2. cosine of A = cos A = b = adjacent c hypotenuse 12.3. tangent of A = tan A = a = opposite b adjacent 12.4. cotangent of A = cot A = 12.5. secant of A = sec A =

b adjacent = a opposite

c hypotenuse = b adjacent

12.6. cosecant of A = csc A =

Fig. 12-1

c hypotenuse = a opposite

Extensions to Angles Which May be Greater Than 90° Consider an xy coordinate system (see Figs. 12-2 and 12-3). A point P in the xy plane has coordinates (x, y) where x is considered as positive along OX and negative along OX′ while y is positive along OY and negative along OY′. The distance from origin O to point P is positive and denoted by r = x 2 + y 2 . The angle A described counterclockwise from OX is considered positive. If it is described clockwise from OX it is considered negative. We call X′OX and Y′OY the x and y axis, respectively. The various quadrants are denoted by I, II, III, and IV called the first, second, third, and fourth quadrants, respectively. In Fig. 12-2, for example, angle A is in the second quadrant while in Fig. 12-3 angle A is in the third quadrant.

Fig. 12-2

Fig. 12-3



44

For an angle A in any quadrant, the trigonometric functions of A are defined as follows. 12.7. sin A = y/r 12.8. cos A = x/r 12.9. tan A = y/x 12.10. cot A = x/y 12.11. sec A = r/x 12.12. csc A = r/y

Relationship Between Degrees and Radians A radian is that angle q subtended at center O of a circle by an arc MN equal to the radius r. Since 2p radians = 360° we have 12.13.

1 radian = 180°/p = 57.29577 95130 8232 …°

12.14.

1° = p/180 radians = 0.01745 32925 19943 29576 92 … radians Fig. 12-4

Relationships Among Trigonometric Functions 12.15.

tan A =

sin A cos A

12.19.

sin 2 A + cos 2 A = 1

12.16.

cot A =

1 cos A = tan A sin A

12.20.

sec 2 A − tan 2 A = 1

12.17.

sec A =

1 cos A

12.21.

csc 2 A − cot 2 A = 1

12.18.

csc A =

1 sin A

Signs and Variations of Trigonometric Functions Quadrant I II III IV

sin A + 0 to 1 + 1 to 0 – 0 to –1 – –1 to 0

cos A + 1 to 0 – 0 to –1 – –1 to 0 + 0 to 1

tan A + 0 to ∞ – –∞ to 0 + 0 to ∞ – –∞ to 0

cot A + ∞ to 0 – 0 to –∞ + ∞ to 0 – 0 to –∞

sec A + 1 to ∞ – –∞ to –1 – –1 to –∞ + ∞ to 1

csc A + ∞ to 1 + 1 to ∞ – –∞ to –1 – –1 to –∞


45

Exact Values for Trigonometric Functions of Various Angles Angle A in degrees

Angle A in radians

sin A

cos A

tan A

cot A

sec A

csc A

0°

0

0

1

0

∞

1

∞

15°

p/12

2− 3

2+ 3

6− 2

6+ 2

30°

p/6

45°

p/4

1 2

2

60°

p/3

1 2

3

75°

5p/12

90°

p/2

105°

7p/12

120°

2p/3

1 2

3

− 12

− 3

− 13 3

–2

135°

3p/4

1 2

2

− 12 2

–1

–1

− 2

2

150°

5p/6

− 12 3

− 13 3

− 3

− 23 3

2

165°

11p/12

180°

p

195°

13p/12

210°

7p/6

− 12

− 12 3

225°

5p/4

− 12 2

− 12 2

1

240°

4p/3

− 12 3

− 12

3

255°

17p/12

270°

3p/2

–1

285°

19p/12

− 14 ( 6 + 2 )

300°

5p/3

− 12 3

315°

7p/4

− 12 2

1 2

330°

11p/6

− 12

1 2

345°

23p/12

− 14 ( 6 − 2 )

360°

2p

0

1 4

( 6 − 2)

1 4

1 2

1 4

( 6 + 2)

( 6 + 2) 1 2

3

1 2

2

1 4

( 6 − 2)

3

2 3

1

3

0

1 3

3

3

− 14 ( 6 + 2 ) − 14 ( 6 − 2 )

For other angles see Tables 2, 3, and 4.

0 1 4

2− 3

6+ 2

6− 2

±∞

0

±∞

1

0

− +∞

2− 3

2+ 3

1 3

3

2 3

3

6+ 2

±∞

− ( 6 − 2) − ( 6 + 2) − 23 3

–2

1

− 2

− 2

–2

− 23 3

1 3

3

2+ 3

2− 3

±∞

0

− ( 6 + 2) − ( 6 − 2)

− +∞

–1

6+ 2

− ( 6 − 2)

− 3

− 13 3

2

− 23 3

2

–1

–1

2

− 2

3

− 13 3

− 3

( 6 + 2 ) − (2 − 3 ) − (2 + 3 )

1

–1

6− 2

3

( 6 − 2 ) − (2 + 3 ) − (2 − 3 ) 1 2

1 4

3

2+ 3

–1

− 14 ( 6 − 2 ) − 14 ( 6 + 2 )

2 2 3

2

( 6 − 2 ) − 14 ( 6 + 2 ) − (2 − 3 ) − (2 + 3 ) − ( 6 − 2 )

0

2

2

( 6 + 2 ) − 14 ( 6 − 2 ) − (2 + 3 ) − (2 − 3 ) − ( 6 + 2 )

1 2 1 4

3

1

1 2

1 1 4

1 3

0

− +∞

2 3

3

–2

6− 2

− ( 6 + 2)

1

− +∞


46

Graphs of Trigonometric Functions In each graph x is in radians. 12.22. y = sin x

12.23.

y = cos x

Fig. 12-5

Fig. 12-6

12.24. y = tan x

12.25.

y = cot x

Fig. 12-7

Fig. 12-8

12.26. y = sec x

12.27. y = csc x

Fig. 12-9

Fig. 12-10

Functions of Negative Angles 12.28. sin(–A) = – sin A

12.29.

cos(–A) = cos A

12.30.

tan(–A) = – tan A

12.31. csc(–A) = – csc A

12.32.

sec(–A) = sec A

12.33.

cot(–A) = – cot A


47

Addition Formulas 12.34. sin (A ± B) = sin A cos B ± cos A sin B 12.35. cos (A ± B) = cos A cos B − + sin A sin B 12.36.

tan ( A ± B) =

12.37.

cot ( A ± B) =

tan A ± tan B − tan A tan B 1+ cot A cot B − +1 cot B ± cot A

Functions of Angles in All Quadrants in Terms of Those in Quadrant I

–A sin

– sin A

cos

cos A

tan

– tan A

csc

– csc A

sec

sec A

cot

– cot A

90° ± A π ±A 2

180° ± A p±A

cos A

sin A

− + sin A − + cot A

– cos A ± tan A − + csc A

sec A − + csc A − + tan A

– sec A ± cot A

270° ± A 3π ±A 2

k(360°) ± A 2kp ± A k = integer

– cos A − + sin A

± sin A

− + cot A

± tan A

– sec A

± csc A

± csc A − + tan A

sec A

cos A

± cot A

Relationships Among Functions of Angles in Quadrant I sin A = u sin A

u

cos A = u

tan A = u

cot A = u

sec A = u

csc A = u

1 − u2

u/ 1 + u 2

1/ 1 + u 2

u 2 − 1/u

1/u

u

1/ 1 + u 2

u/ 1 + u 2

1/u

u2 − 1

1/ u 2 − 1

1/ u 2 − 1

u2 − 1

cos A

1 − u2

tan A

u/ 1 − u 2

1 − u 2 /u

u

1/u

cot A

1 − u 2 /u

u/ 1 − u 2

1/u

u

sec A

1/ 1 − u 2

1/u

1+ u2

1 + u 2 /u

csc A

1/u

1/ 1 − u 2

1 + u 2 /u

1+ u2

u

u/ u 2 − 1

For extensions to other quadrants use appropriate signs as given in the preceding table.

u 2 − 1/u

u/ u 2 − 1 u


48

Double Angle Formulas 12.38. sin 2A = 2 sin A cos A 12.39. cos 2A = cos2 A – sin2 A = 1 – 2 sin2 A = 2 cos2 A – 1 12.40.

tan 2 A =

2 tan A 1 − tan 2 A

Half Angle Formulas 12.41.

sin

A 1 − cos A =± 2 2

⎡+ if A / 2 is in quadrant I or II ⎤ ⎢ ⎥ ⎢⎣− if A / 2 is in quadrant III or IV⎥⎦

12.42.

cos

A 1 + cos A =± 2 2

⎡+ if A / 2 is in quadrant I or IV ⎤ ⎢ ⎥ ⎢⎣− if A / 2 is in quadrant II or III⎥⎦

12.43.

tan

A 1 − cos A ⎡+ if A / 2 is in quadrant I orr III ⎤ =± 2 1 + cos A ⎢⎢− if A / 2 is in quadrant II or IV⎥⎥ ⎣ ⎦ =

sin A 1 − cos A = = csc A − cot A 1 + cos A sin A

Multiple Angle Formulas 12.44. sin 3A = 3 sin A – 4 sin3 A 12.45. cos 3A = 4 cos3 A –3 cos A 12.46.

tan 3A =

3 tan A − tan 3 A 1 − 3 tan 2 A

12.47. sin 4A = 4 sin A cos A – 8 sin3 A cos A 12.48. cos 4A = 8 cos4 A – 8 cos2 A + 1 12.49.

tan 4 A =

4 tan A − 4 tan 3 A 1 − 6 tan 2 A + tan 4 A

12.50. sin 5A = 5 sin A – 20 sin3 A + 16 sin5 A 12.51. cos 5A = 16 cos5 A – 20 cos3 A + 5 cos A 12.52.

tan 5 A =

tan 5 A − 10 tan 3 A + 5 tan A 1 − 10 tan 2 A + 5 tan 4 A

See also formulas 12.68 and 12.69.

Powers of Trignometric Functions 12.53.

sin 2 A = 12 − 12 cos 2 A

12.57.

sin 4 A = 83 − 12 cos 2 A + 18 cos 4 A

12.54.

cos 2 A = 12 + 12 cos 2 A

12.58.

cos 4 A = 83 + 12 cos 2 A + 18 cos 4 A

12.55.

sin 3 A =

12.59.

sin 5 A = 58 sin A − 165 sin 3A + 161 sin 5 A

12.56.

cos 3 A = 43 cos A + 14 cos 3A

12.60.

cos 5 A = 58 cos A + 165 cos 3A + 161 cos 5 A

3 4

sin A − 14 sin 3A

See also formulas 12.70 through 12.73.


49

Sum, Difference, and Product of Trignometric Functions 12.61.

sin A + sin B = 2 sin 12 ( A + B) cos 12 ( A − B)

12.62.

sin A − sin B = 2 cos 12 ( A + B)sin 12 ( A − B)

12.63.

cos A + cos B = 2 cos 12 ( A + B) cos 12 ( A − B)

12.64.

cos A − cos B = 2 sin 12 ( A + B)sin 12 ( B − A)

12.65.

sin A sin B = 12 {cos( A − B) − cos ( A − B)}

12.66.

cos A cos B = 12 {cos( A − B) + cos ( A + B)}

12.67.

sin A cos B = 12 {sin( A − B) + sin( A + B)}

General Formulas 12.68.

⎫ ⎧ ⎛ n − 3⎞ ⎛ n − 2⎞ n−3 n−5 sin nA = sin A ⎨(2 cos A)n−1 − ⎜ ⎟⎠ (2 cos A) + ⎜⎝ 2 ⎟⎠ (2 cos A) − ⋅⋅⋅⎬ 1 ⎝ ⎭ ⎩

12.69.

cos nA =

1⎧ n n ⎛ n − 3⎞ (2 cos A)n − (2 cos A)n− 2 + ⎜ (2 cos A)n− 4 2 ⎨⎩ 1 2 ⎝ 1 ⎟⎠ n ⎛ n − 4⎞ ⎫ (2 cos A)n−6 + ⋅⋅⋅⎬ − ⎜ 3 ⎝ 2 ⎟⎠ ⎭

12.70.

sin 2 n−1 A =

⎫ (−1)n−1 ⎧ ⎛ 2n − 1⎞ ⎛ 2n − 1⎞ sin (2n − 1) A − ⎜ sin (2n − 3) A + ⋅⋅⋅ (−1)n−1 ⎜ sin A⎬ 22 n− 2 ⎨⎩ ⎝ 1 ⎟⎠ ⎝ n − 1 ⎟⎠ ⎭

12.71.

cos 2 n−1 A =

1 ⎧ ⎫ ⎛ 2n − 1⎞ ⎛ 2n − 1⎞ cos (2n − 1) A + ⎜ cos (2n − 3) A + ⋅⋅⋅ + ⎜ cos A⎬ 22 n− 2 ⎨⎩ ⎝ 1 ⎟⎠ ⎝ n − 1 ⎟⎠ ⎭

12.72.

sin 2 n A =

1 ⎛ 2n⎞ (−1)n + 2 n−1 ⎟ 2n ⎜ 2 2 ⎝ n⎠

12.73.

cos 2 n A =

⎫ 1 ⎧ 1 ⎛ 2n⎞ ⎛ 2n ⎞ ⎛ 2n⎞ cos 2 A⎬ + 2 n−1 ⎨cos 2nA + ⎜ ⎟ cos (2n − 2) A + ⋅⋅⋅ + ⎜ ⎟ ⎟ 2n ⎜ 2 ⎝ n⎠ 2 ⎝ n − 1⎠ ⎝ 1⎠ ⎭ ⎩

⎫ ⎧ ⎛ 2n⎞ n −1 ⎛ 2n ⎞ ⎨cos 2nA − ⎜ 1 ⎟ cos (2n − 2) A + ⋅⋅⋅ (−1) ⎜ n − 1⎟ cos 2 A⎬ ⎝ ⎝ ⎠ ⎠ ⎭ ⎩

Inverse Trigonometric Functions If x = sin y, then y = sin–1x, i.e. the angle whose sine is x or inverse sine of x is a many-valued function of x which is a collection of single-valued functions called branches. Similarly, the other inverse trigonometric functions are multiple-valued. For many purposes a particular branch is required. This is called the principal branch and the values for this branch are called principal values.


50

Principal Values for Inverse Trigonometric Functions Principal values for x 0

Principal values for x < 0

0 sin–1 x p/2

–p/2 sin–1 x < 0

0 cos–1 x p/2

p/2 < cos–1 x p

0 tan–1 x < p/2

–p/2 < tan–1 x < 0

0 < cot–1 x p/2

p/2 < cot–1 x < p

0 sec–1 x < p/2

p/2 < sec–1 x p

0 < csc–1 x p/2

–p/2 csc–1 x < 0

Relations Between Inverse Trigonometric Functions In all cases it is assumed that principal values are used. 12.74.

sin −1 x + cos−1 x = π /2

12.80.

sin −1 (− x ) = − sin −1 x

12.75.

tan −1 x + cot −1 x = π / 2

12.81.

cos−1 (− x ) = π − cos−1 x

12.76.

sec−1 x + csc−1 x = π /2

12.82.

tan −1 (− x ) = − tan −1 x

12.77.

csc−1 x = sin −1 (1/x )

12.83.

cot −1 (− x ) = π − cot −1 x

12.78.

sec−1 x = cos−1 (1/x )

12.84.

sec−1 (− x ) = π − sec −1 x

12.79.

cot −1 x = tan −1 (1/x )

12.85.

csc −1 (− x ) = − csc −1 x

Graphs of Inverse Trigonometric Functions In each graph y is in radians. Solid portions of curves correspond to principal values. 12.86.

y = sin −1 x

Fig. 12-11

12.87.

y = cos−1 x

Fig. 12-12

12.88.

y = tan −1 x

Fig. 12-13


12.89.

y = cot −1 x

Fig. 12-14

12.90.

51

y = sec−1 x

12.91.

y = csc −1 x

Fig. 12-15

Fig. 12-16

Relationships Between Sides and Angles of a Plane Triangle The following results hold for any plane triangle ABC with sides a, b, c and angles A, B, C. 12.92. Law of Sines: a b c = = sin A sin B sin C

12.93. Law of Cosines: c 2 = a 2 + b 2 − 2 ab cos C

with similar relations involving the other sides and angles. 12.94. Law of Tangents: a + b tan 12 ( A + B) = a − b tan 12 ( A − B) with similar relations involving the other sides and angles.

Fig. 12-17

2 s(s − a )(s − b )(s − c) bc where s = 12 (a + b + c) is the semiperimeter of the triangle. Similar relations involving angles B and C can be obtained.

12.95.

sin A =

See also formula 7.5.

Relationships Between Sides and Angles of a Spherical Triangle Spherical triangle ABC is on the surface of a sphere as shown in Fig. 12-18. Sides a, b, c (which are arcs of great circles) are measured by their angles subtended at center O of the sphere. A, B, C are the angles opposite sides a, b, c, respectively. Then the following results hold. 12.96. Law of Sines: sin a sin b sin c = = sin A sin B sin C

12.97. Law of Cosines: cos a = cos b cos c + sin b sin c cos A cos A = –cos B cos C + sin B sin C cos a with similar results involving other sides and angles.

Fig. 12-18


52

12.98. Law of Tangents: tan 12 ( A + B) tan 12 (a + b ) = tan 12 ( A − B) tan 12 (a − b )

with similar results involving other sides and angles. 12.99.

cos

A = 2

sin s sin (s − c) sin b sin c

where s = 12 (a + b + c). Similar results hold for other sides and angles. 12.100.

cos

a cos(S − B) cos(S − C ) = 2 sin B sin C

where S = 12 ( A + B + C ). Similar results hold for other sides and angles. See also formula 7.44.

Napier’s Rules for Right Angled Spherical Triangles Except for right angle C, there are five parts of spherical triangle ABC which, if arranged in the order as given in Fig. 12-19, would be a, b, A, c, B.

Fig. 12-19

Fig. 12-20

Suppose these quantities are arranged in a circle as in Fig. 12-20 where we attach the prefix “co” (indicating complement) to hypotenuse c and angles A and B. Any one of the parts of this circle is called a middle part, the two neighboring parts are called adjacent parts, and the two remaining parts are called opposite parts. Then Napier’s rules are 12.101. The sine of any middle part equals the product of the tangents of the adjacent parts. 12.102. The sine of any middle part equals the product of the cosines of the opposite parts. EXAMPLE: Since co-A = 90° – A, co-B = 90° – B, we have

sin a = tan b (co-B) sin (co-A) = cos a cos (co-B)

or or

sin a = tan b cot B cos A = cos a sin B

These can of course be obtained also from the results of 12.97.

13

EXPONENTIAL and LOGARITHMIC FUNCTIONS

Laws of Exponents In the following p, q are real numbers, a, b are positive numbers, and m, n are positive integers. 13.1.

a p ⋅ a q = a p+ q

13.2.

a p /a q = a p−q

13.3.

(a p )q = a pq

13.4.

a 0 = 1, a ≠ 0

13.5.

a − p = 1/a p

13.6.

(ab ) p = a p b p

13.7.

n

13.8.

n

13.9.

n

a = a1/n

a m = a m/n

a/b = n a / n b

In ap, p is called the exponent, a is the base, and ap is called the pth power of a. The function y = ax is called an exponential function.

Logarithms and Antilogarithms If ap = N where a ≠ 0 or 1, then p = loga N is called the logarithm of N to the base a. The number N = ap is called the antilogarithm of p to the base a, written antiloga p. Example: Since 32 = 9 we have log3 9 = 2. antilog3 2 = 9. The function y = loga x is called a logarithmic function.

Laws of Logarithms 13.10. loga MN = loga M + loga N 13.11.

log a

M = log a M − log a N N

13.12. loga Mp = p loga M

Common Logarithms and Antilogarithms Common logarithms and antilogarithms (also called Briggsian) are those in which the base a = 10. The common logarithm of N is denoted by log10 N or briefly log N. For numerical values of common logarithms, see Table 1.

Natural Logarithms and Antilogarithms Natural logarithms and antilogarithms (also called Napierian) are those in which the base a = e = 2.71828 18 … [see page 3]. The natural logarithm of N is denoted by loge N or In N. For numerical values of natural logarithms see Table 7. For values of natural antilogarithms (i.e., a table giving ex for values of x) see Table 8.

53

EXPONENTIAL AND LOGARITHMIC FUNCTIONS

54

Change of Base of Logarithms The relationship between logarithms of a number N to different bases a and b is given by 13.13.

log a N =

log b N log b a

In particular, 13.14. loge N = ln N = 2.30258 50929 94 … log10 N 13.15. log10 N = log N = 0.43429 44819 03 … loge N

Relationship Between Exponential and Trigonometric Functions 13.16. eiθ = cos θ + i sin θ,

e–iθ = cos θ – i sin θ

These are called Euler’s identities. Here i is the imaginary unit [see page 10]. 13.17.

sin θ =

eiθ − e−iθ 2i

13.18.

cos θ =

eiθ + e−iθ 2

13.19.

tan θ =

⎛ eiθ − e−iθ ⎞ eiθ − e−iθ = −i ⎜ iθ −iθ ⎟ iθ − iθ i (e + e ) ⎝e + e ⎠

13.20.

⎛ eiθ + e−iθ ⎞ cot θ = i ⎜ iθ −iθ ⎟ ⎝e − e ⎠

13.21.

sec θ =

2 e + e−iθ

13.22.

csc θ =

2i eiθ − e−iθ

iθ

Periodicity of Exponential Functions 13.23. ei(θ + 2kp) = eiθ

k = integer

From this it is seen that ex has period 2pi.

Polar Form of Complex Numbers Expressed as an Exponential The polar form (see 4.7) of a complex number z = x + iy can be written in terms of exponentials as follows: 13.24.

z = x + iy = r (cos θ + i sin θ ) = reiθ

EXPONENTIAL AND LOGARITHMIC FUNCTIONS

Operations with Complex Numbers in Polar Form Formulas 4.8 to 4.11 are equivalent to the following: 13.25.

(r1eiθ )(r2 eiθ ) = r1r2 ei (θ +θ )

13.26.

r1eiθ r = 1 ei (θ −θ ) r2 r2 eiθ

1

2

1

2

1

1

2

2

13.27.

(reiθ ) p = r p eipθ

13.28.

(reiθ )1/n = [ rei (θ +2 kπ ) ]1/n = r1/n ei (θ +2 kπ )/n

(De Moivre’s theorem)

Logarithm of a Complex Number 13.29.

ln (re iθ ) = ln r + iθ + 2kπ i

k = integer

55

14

HYPERBOLIC FUNCTIONS

Definition of Hyperbolic Functions 14.1. Hyperbolic sine of x

= sinh x =

e x − e− x 2

14.2. Hyperbolic cosine of x

= cosh x =

e x + e− x 2

14.3. Hyperbolic tangent of x

= tanh x =

e x − e− x e x + e− x

14.4. Hyperbolic cotangent of x = coth x =

e x + e− x e x − e− x

14.5. Hyperbolic secant of x

= sech x =

2 e x + e− x

14.6. Hyperbolic cosecant of x

= csch x =

2 e x − e− x

Relationships Among Hyperbolic Functions 14.7.

tanh x =

sinh x cosh x

14.8.

coth x =

1 cosh x = tanh x sinh x

14.9.

sech x =

1 cosh x

14.10.

csch x =

1 sinh x

14.11.

cosh 2 x − sinh 2x = 1

14.12.

sech 2 x + tanh 2 x = 1

14.13.

coth 2 x − csc h 2 x = 1

Functions of Negative Arguments 14.14. sinh (–x) = – sinh x

14.15. cosh (–x) = cosh x

14.16. tanh (–x) = – tanh x

14.17. csch (–x) = – csch x

14.18. sech (–x) = sech x

14.19. coth (–x) = – coth x

56


Addition Formulas 14.20.

sinh ( x ± y) = sinh x cosh y ± cosh x sinh y

14.21.

cosh ( x ± y) = cosh x cosh y ± sinh x sinh y

14.22.

tanh ( x ± y) =

tanh x ± tanh y 1 ± tanh x tanh y

14.23.

coth ( x ± y) =

coth x coth y ± 1 coth y ± coth x

Double Angle Formulas 14.24.

sinh 2 x = 2 sinh x cosh x

14.25.

cosh 2 x = cos h 2 x + sin h 2 x = 2 cos h 2 x − 1 = 1 + 2 sin h 2 x

14.26.

tanh 2 x =

2 tan h x 1 + tanh 2 x

Half Angle Formulas cosh x − 1 [+ if x > 0, − if x < 0 ] 2

14.27.

sinh

x =± 2

14.28.

cosh

x cosh x + 1 = 2 2

14.29.

tanh

x =± 2 =

cosh x − 1 [+ if x > 0, − if x < 0 ] cosh x + 1

cosh x − 1 sinh x = cosh x + 1 sinh x

Multiple Angle Formulas 14.30.

sinh 3x = 3 sinh x + 4 sinh 3 x

14.31.

cosh 3x = 4 cosh 3 x − 3 cosh x

14.32.

tanh 3x =

14.33.

sinh 4 x = 8 sinh 3 x cosh x + 4 sinh x cosh x

14.34.

cosh 4 x = 8 cosh 4 x − 8 cosh 2 x + 1

14.35.

tanh 4 x =

3 tanh x + tanh 3 x 1 + 3 tanh 2 x

4 tanh x + 4 tanh 3 x 1 + 6 tanh 2 x + tanh 4 x

57


58

Powers of Hyperbolic Functions 14.36.

sinh 2 x = 12 cosh 2 x − 12

14.37.

cosh 2 x = 12 cosh 2 x + 12

14.38.

sinh 3 x = 14 sinh 3x − 43 sinh x

14.39.

cosh 3 x = 14 cosh 3x + 43 cosh x

14.40.

sinh 4 x = 83 − 12 cosh 2 x + 18 cosh 4 x

14.41.

cosh 4 x = 83 + 12 cosh 2 x + 18 cosh 4 x

Sum, Difference, and Product of Hyperbolic Functions 14.42.

sinh x + sinh y = 2 sinh 12 ( x + y) cosh 12 ( x − y )

14.43.

sinh x − sinh y = 2 cosh 12 ( x + y)sinh 12 ( x − y)

14.44.

cosh x + cosh y = 2 cosh 12 ( x + y) cosh 12 ( x − y)

14.45.

cosh x − cosh y = 2 sinh 12 ( x + y)sinh 12 ( x − y )

14.46.

sinh x sinh y = 12 {cosh ( x + y) − cosh ( x − y)}

14.47.

cosh x cosh y = 12 {cosh ( x + y) + cosh ( x − y)}

14.48.

sinh x cosh y = 12 {sinh ( x + y) + sinh ( x − y)}

Expression of Hyperbolic Functions in Terms of Others In the following we assume x > 0. If x < 0, use the appropriate sign as indicated by formulas 14.14 to 14.19. sinh x = u sinh x

u

cosh x

1+ u2

cosh x = u

tanh x = u

coth x = u

sech x = u

csch x = u

u/ 1 − u 2

1/ u 2 − 1

1 − u 2 /u

1/u

u

1/ 1 − u 2

u/ u 2 − 1

1/u

1 + u 2 /u

u

1/u

1 − u2

1/ 1 + u 2 1+ u2

u2 − 1

tanh x

u/ 1 + u 2

u 2 − 1/u

coth x

u 2 + 1/u

u/ u 2 − 1

1/u

u

1/ 1 − u 2

sech x

1/ 1 + u 2

1/u

1 − u2

u 2 − 1/u

u

u/ 1 + u 2

csch x

1/u

1/ u 2 − 1

1 − u 2 /u

u2 − 1

u/ 1 − u 2

u


59

Graphs of Hyperbolic Functions 14.49. y = sinh x

14.50.

y = cosh x

Fig. 14-1

14.51. y = tanh x

Fig. 14-2

14.52. y = coth x

14.53.

y = sech x

Fig. 14-4

Fig. 14-3

14.54. y = csch x

Fig. 14-5

Fig. 14-6

Inverse Hyperbolic Functions If x = sinh y, then y = sinh–1 x is called the inverse hyperbolic sine of x. Similarly we define the other inverse hyperbolic functions. The inverse hyperbolic functions are multiple-valued and as in the case of inverse trigonometric functions [see page 49] we restrict ourselves to principal values for which they can be considered as single-valued. The following list shows the principal values (unless otherwise indicated) of the inverse hyperbolic functions expressed in terms of logarithmic functions which are taken as real valued. 14.55.

sinh −1 x = ln ( x + x 2 + 1)

− < x <

14.56.

cosh −1 x = ln ( x + x 2 − 1)

x 1

14.57.

tanh −1 x =

1 ⎛1 + x ⎞ ln ⎜ ⎟ 2 ⎝1 − x ⎠

−1 < x < 1

14.58.

coth −1 x =

1 ⎛ x + 1⎞ ln 2 ⎝ x − 1⎠

x > 1 or x < −1

14.59.

⎛1 sech −1 x = ln ⎜ + ⎝x

⎞ 1 2 − 1⎟ x ⎠

0 < x 1

14.60.

⎛1 csch −1 x = ln ⎜ + ⎝x

⎞ 1 + 1⎟ x2 ⎠

x≠0

(cosh −1 x > 0 is prinncipal value)

(sech −1 x > 0 is principal value)


60

Relations Between Inverse Hyperbolic Functions 14.61.

csch −1 x = sinh −1 (1/x )

14.62.

sech −1 x = cosh −1 (1/x )

14.63.

coth −1 x = tanh −1 (1/x )

14.64.

sinh −1 (− x ) = − sinh −1 x

14.65.

tanh −1 (− x ) = − tanh −1 x

14.66.

coth −1 (− x ) = − coth −1 x

14.67.

csch −1 (− x ) = −csch −1 x

Graphs of Inverse Hyperbolic Functions 14.68.

y = sinh −1 x

14.69.

Fig. 14-7

14.71.

y = coth −1 x

Fig. 14-10

y = cosh −1 x

14.70.

Fig. 14-8

14.72.

y = sech −1 x

Fig. 14-11

y = tanh −1 x

Fig. 14-9

14.73.

y = csch −1 x

Fig. 14-12


61

Relationship Between Hyperbolic and Trigonometric Functions 14.74.

sin (ix ) = i sinh x

14.75.

cos (ix ) = cosh x

14.76.

tan (ix ) = i tanh x

14.77.

csc (ix ) = −i csch x

14.78.

sec (ix ) = sech x

14.79.

cot (ix ) = −i coth x

14.80.

sinh (ix ) = i sin x

14.81.

cosh (ix ) = cos x

14.82.

tanh (ix ) = i tan x

14.83.

csch (ix ) = −i csc x

14.84.

sech (ix ) = sec x

14.85.

coth (ix ) = −i cot x

Periodicity of Hyperbolic Functions In the following k is any integer. 14.86.

sinh ( x + 2 k π i ) = sinh x

14.87.

cosh ( x + 2kπ i) = cosh x

14.88.

tanh ( x + kπ i) = tanh x

14.89.

csch ( x + 2 k π i ) = csch x

14.90.

sech ( x + 2 k π i ) = sech x

14.91.

coth ( x + k π i ) = coth x

Relationship Between Inverse Hyperbolic and Inverse Trigonometric Functions 14.92.

sin −1 (ix ) = i sin −1 x

14.93.

sinh −1 (ix ) = i sin −1 x

14.94.

cos−1 x = ± i cosh −1 x

14.95.

cosh −1 x = ± i cos −1 x

14.96.

tan −1 (ix ) = i tanh −1 x

14.97.

tanh −1 (ix ) = i tan −1 x

14.98.

cot −1 (ix ) = i coth −1 x

14.99.

coth −1 (ix ) = −i cot −1 x

14.100.

sec−1 x = ± i sech −1 x

14.101.

sech −1 x = ± i sec −1 x

14.102.

csc −1 (ix ) = −i csch −1 x

14.103.

csch −1 (ix ) = −i csc −1 x

Section IV: Calculus

15

DERIVATIVES

Definition of a Derivative Suppose y = f(x). The derivative of y or f(x) is defined as 15.1.

dy f ( x + h) − f ( x ) f ( x + Δx ) − f ( x ) = lim = lim Δx dx h→0 h Δx → 0

where h = Δx. The derivative is also denoted by y′, df/dx or f ′(x). The process of taking a derivative is called differentiation.

General Rules of Differentiation In the following, u, , w are functions of x; a, b, c, n are constants (restricted if indicated); e = 2.71828 … is the natural base of logarithms; ln u is the natural logarithm of u (i.e., the logarithm to the base e) where it is assumed that u > 0 and all angles are in radians. 15.2.

d (c) = 0 dx

15.3.

d (cx ) = c dx

15.4.

d (cx n ) = ncx n−1 dx

15.5.

d du d dw (u ± ± w ± ) = ± ± ± dx dx dx dx

15.6.

d du (cu) = c dx dx

15.7.

d d du + (u ) = u dx dx dx

15.8.

d dw d du + uw + w (u w) = u dx dx dx dx

15.9.

d ⎛ u ⎞ (du /dx ) − u(d /dx ) = 2 dx ⎜⎝ ⎟⎠

15.10.

d n du (u ) = nu n−1 dx dx

15.11.

dy dy du = dx du dx

(Chain rule)


DERIVATIVES

15.12.

du 1 = dx dx /du

15.13.

dy dy/du = dx dx/du

63

Derivatives of Trigonometric and Inverse Trigonometric Functions 15.14.

d du sin u = cos u dx dx

15.15.

d du cos u = − sin u dx dx

15.16.

d du tan u = sec 2 u dx dx

15.17.

d du cot u = − csc 2 u dx dx

15.18.

d du sec u = sec u tan u dx dx

15.19.

d du csc u = − csc u cot u dx dx

15.20.

d 1 du sin −1 u = 2 dx dx 1− u

π⎤ ⎡ π −1 ⎢⎣− 2 < sin u < 2 ⎥⎦

15.21.

d −1 du cos −1 u = dx 1 − u 2 dx

[0 < cos −1 u < π ]

15.22.

d 1 du tan −1 u = dx 1 + u 2 dx

π⎤ ⎡ π −1 ⎢⎣− 2 < tan u < 2 ⎥⎦

15.23.

d −1 du cot −1 u = dx 1 + u 2 dx

[0 < cot −1 u < π ]

15.24.

d 1 ±1 du du = sec −1 u = 2 2 dx dx u u − 1 dx | u | u −1

⎡+ if 0 < sec −1 u < π / 2⎤ ⎢⎣− if π / 2 < sec −1 u < π ⎥⎦

15.25.

d du ∓1 −1 du = csc −1 u = 2 2 dx dx | u | u −1 u u − 1 dx

⎡− if 0 < csc −1 u < π / 2 ⎤ ⎢+ if − π / 2 < csc −1 u < 0⎥ ⎣ ⎦

Derivatives of Exponential and Logarithmic Functions 15.26.

loga e du d loga u = dx u dx

15.27.

d d 1 du ln u = loge u = dx dx u dx

15.28.

d u du a = a u ln a dx dx

a ≠ 0, 1

DERIVATIVES

64

15.29.

d u du e = eu dx dx

15.30.

d d ln u d du d u = e = e ln u [ ln u] = u −1 + u ln u dx dx dx dx dx

Derivatives of Hyperbolic and Inverse Hyperbolic Functions 15.31.

d du sinh u = cosh u dx dx

15.32.

d du cosh u = sinh u dx dx

15.33.

d du tanh u = sech 2 u dx dx

15.34.

d du coth u = −csch 2 u dx dx

15.35.

d du sech u = −sech u tanh u dx dx

15.36.

d du csch u = − csch u coth u dx dx

15.37.

d sinh −1 u = dx

du u + 1 dx

15.38.

d cosh −1 u = dx

±1 du u 2 − 1 dx

15.39.

d 1 du tanh −1 u = dx 1 − u 2 dx

[–1 < u < 1]

15.40.

d 1 du coth −1 u = dx 1 − u 2 dx

[u > 1 or u < –1]

15.41.

d ∓1 du sech −1u = 2 dx dx u 1− u

⎡− if sech −1 u > 0, 0 < u < 1⎤ ⎢+ if sech −1 u < 0, 0 < u < 1⎥ ⎣ ⎦

15.42.

d du ∓1 −1 du = csch −1u = 2 dx 2 dx dx | u | 1+ u u 1+ u

[– if u > 0, + if u < 0]

1

2

Higher Derivatives The second, third, and higher derivatives are defined as follows. 15.43.

Second derivative =

d ⎛ dy⎞ d 2 y = = f ′′( x ) = y ′′ dx ⎜⎝ dx ⎟⎠ dx 2

2 3 15.44. Third derivative = d ⎛ d y⎞ = d y = f ′′′( x ) = y ′′′ ⎜ 2⎟ dx ⎝ dx ⎠ dx 3

15.45.

n −1 n nth derivative = d ⎛ d y⎞ = d y = f ( n ) ( x ) = y ( n ) ⎜ n −1 ⎟ dx ⎝ dx ⎠ dx n

⎡+ if cosh −1 u > 0, u > 1⎤ ⎢⎣− if cosh −1 u < 0, u > 1⎥⎦

DERIVATIVES

65

Leibniz’s Rule for Higher Derivatives of Products Let Dp stand for the operator 15.46.

dp d pu p so that D u = = the pth derivative of u. Then dx p dx p

⎛ n⎞ ⎛ n⎞ D n (u ) = uD n + ⎜ ⎟ ( Du)( D n −1 ) + ⎜ ⎟ ( D 2 u)( D n − 2 ) + + D n u ⎝1⎠ ⎝ 2⎠

⎛ n⎞ ⎛ n⎞ where ⎜ ⎟ , ⎜ ⎟ ,… are the binomial coefficients (see 3.5). ⎝1⎠ ⎝ 2⎠ As special cases we have 15.47.

d2 d 2 du d d 2u ( ) = + 2 + u u dx 2 dx 2 dx dx dx 2

15.48.

d3 d 3u d 3 du d 2 d 2 u d ( ) = + 3 + 3 + u u dx 3 dx 3 dx 3 dx dx 2 dx 2 dx

Differentials Let y = f(x) and Δy = f ( x + Δx ) − f ( x ). Then 15.49.

Δy f ( x + Δx ) − f ( x ) dy = = f ′( x ) + = + Δx Δx dx

where → 0 as Δx → 0. Thus, 15.50.

Δy = f ′( x )Δx + Δx

If we call Δx = dx the differential of x, then we define the differential of y to be 15.51.

dy = f ′( x ) dx

Rules for Differentials The rules for differentials are exactly analogous to those for derivatives. As examples we observe that 15.52.

d (u ± ± w ± ) = du ± d ± dw ±

15.53.

d (u ) = u d + du

15.54.

⎛ u ⎞ du − u d d⎜ ⎟ = 2 ⎝⎠

15.55.

d (u n ) = nu n−1du

15.56.

d (sin u) = cos u du

15.57.

d (cos u) = − sin u du

DERIVATIVES 66

DERIVATIVES

Partial Derivatives Let z = f (x, y) be a function of the two variables x and y. Then we define the partial derivative of z or f(x, y) with respect to x, keeping y constant, to be 15.58.

∂f f ( x + Δx , y) − f ( x , y) = lim Δx ∂x Δx →0

This partial derivative is also denoted by ∂z/∂x , f x , or z x . Similarly the partial derivative of z = f (x, y) with respect to y, keeping x constant, is defined to be 15.59.

∂f f ( x , y + Δy) − f ( x , y) = lim Δy ∂y Δy→0

This partial derivative is also denoted by ∂z/∂y, f y , or z y . Partial derivatives of higher order can be defined as follows: 15.60.

∂2 f ∂ ⎛ ∂f ⎞ = , ∂x 2 ∂x ⎜⎝ ∂x ⎟⎠

15.61.

∂2 f ∂ ⎛ ∂f ⎞ = , ∂x ∂y ∂x ⎜⎝ ∂y⎟⎠

∂ ⎛ ∂f ⎞ ∂2 f = ∂y 2 ∂y ⎜⎝ ∂y⎟⎠ ∂ ⎛ ∂f ⎞ ∂2 f = ∂y ∂x ∂y ⎜⎝ ∂x ⎟⎠

The results in 15.61 will be equal if the function and its partial derivatives are continuous; that is, in such cases, the order of differentiation makes no difference. Extensions to functions of more than two variables are exactly analogous.

Multivariable Differentials The differential of z = f(x, y) is defined as 15.62.

dz = df =

∂f ∂f dx + dy ∂x ∂y

where dx = Δx and dy = Δy. Note that dz is a function of four variables, namely x, y, dx, dy, and is linear in the variables dx and dy. Extensions to functions of more than two variables are exactly analogous. EXAMPLE:

Let z = x2 + 5xy + 2y3. Then

zx = 2x + 5y

and

zy = 5x + 6y2

and hence

dz = (2x + 5y) dx + (5x + 6y2) dy Suppose we want to find dz for dx = 2, dy = 3 and at the point P (4, 1), i.e., when x = 4 and y = 1. Substitution yields

dz = (8 + 5)2 + (20 + 6)3 = 26 + 78 = 104

16

INDEFINITE INTEGRALS

Definition of an Indefinite Integral dy = f ( x ), then y is the function whose derivative is f(x) and is called the anti-derivative of f(x) or the indefidx dy nite integral of f(x), denoted by ∫ f ( x ) dx. Similarly if y = ∫ f (u) du, then = f (u). Since the derivative of a du constant is zero, all indefinite integrals differ by an arbitrary constant. For the definition of a definite integral, see 18.1. The process of finding an integral is called integration.

If

General Rules of Integration In the following, u, , w are functions of x; a, b, p, q, n any constants, restricted if indicated; e = 2.71828 … is the natural base of logarithms; ln u denotes the natural logarithm of u where it is assumed that u > 0 (in general, to extend formulas to cases where u < 0 as well, replace ln u by ln |u|); all angles are in radians; all constants of integration are omitted but implied. 16.1.

∫ a dx = ax

16.2.

∫ af ( x ) dx = a ∫ f ( x ) dx

16.3.

∫ (u ± ± w ± ) dx = ∫ u dx ± ∫ dx ± ∫ w dx ±

16.4.

∫ u d = u − ∫ du

(Integration by parts)

For generalized integration by parts, see 16.48.

1

16.5.

∫ f (ax ) dx = a ∫ f (u) du

16.6.

∫ F{ f ( x )} dx = ∫ F(u) du du = ∫

16.7.

∫ u n du =

16.8.

∫

dx

F (u) du where u = f ( x ) f ′( x )

u n+1 , n ≠ −1 (For n = −1, see 16.8) n +1

du = ln u if u > 0 or ln(−u) if u < 0 u = ln | u |

16.9. 16.10.

∫ e du = e u

∫ au du =

u

∫ eu ln a du =

e u ln a au = , a > 0, a ≠ 1 ln a ln a

67


68

16.11.

∫ sin u du = − cos u

16.12.

∫ cos u du = sin u

16.13.

∫ tan u du = ln sec u = − ln cos u

16.14.

∫ cot u du = ln sin u

16.15.

∫ sec u du = ln (sec u + tan u) = ln tan ⎜⎝ 2 + 4 ⎟⎠

16.16.

∫ csc u du = ln(csc u − cot u) = ln tan 2

16.17.

∫ sec

16.18.

∫ csc

16.19.

∫ tan

16.20.

∫ cot

16.21.

∫ sin

16.22.

∫ cos

16.23.

∫ sec u tan u du = sec u

16.24.

∫ csc u cot u du = − csc u

16.25.

∫ sinh u du = cosh u

16.26.

∫ cosh u du = sinh u

16.27.

∫ tanh u du = ln cosh u

16.28.

∫ coth u du = ln sinh u

16.29.

∫ sech u du = sin

16.30.

∫ csch u du = ln tanh 2

16.31.

∫ sech u du = tanh u

⎛u

π⎞

u

2

u du = tan u

2

u du = − cot u

2

u du = tan u − u

2

u du = − cot u − u

2

u du =

u sin 2u 1 − = (u − sin u cos u) 2 4 2

u du =

u sin 2u 1 + = (u + sin u cos u) 2 4 2

2

−1

(tanh u) or 2 tan −1 eu u

2

or − coth −1 e u


16.32.

∫ csch u du = − coth u

16.33.

∫ tanh u du = u − tanh u

16.34.

∫ coth u du = u − coth u

16.35.

∫ sinh u du =

sinh 2u u 1 − = (sinh u cosh u − u) 4 2 2

16.36.

∫ cosh u du =

sinh 2u u 1 + = (sinh u cosh u + u) 4 2 2

16.37.

∫ sech u tanh u du = −sech u

16.38.

∫ csch u coth u du = − csch u

16.39.

∫u

2

16.40.

∫u

2

16.41.

∫a

2

16.42.

∫

du u = sin −1 2 a a −u

16.43.

∫

du u = ln(u + u 2 + a 2 ) or sinh −1 2 a u +a

16.44.

∫

du = ln (u + u 2 − a 2 ) u2 − a2

16.45.

∫u

16.46.

du 1 ⎛ a + u2 + a2 ⎞ = − ⎟ ∫ u u 2 + a 2 a ln ⎜⎝ u ⎠

16.47.

∫u

16.48.

∫f

69

2

2

2

2

2

u du 1 = tan −1 a + a2 a u du 1 ⎛ u − a⎞ 1 = ln = − coth −1 u 2 > a 2 a a − a 2 2a ⎜⎝ u + a⎟⎠ u du 1 ⎛ a + u⎞ 1 = ln = tanh −1 u 2 < a 2 a − u 2 2a ⎜⎝ a − u⎟⎠ a 2

2

du u2 − a2

=

u 1 sec −1 a a

du 1 ⎛ a + a2 − u2 ⎞ = − ln ⎜ ⎟ 2 2 a ⎝ u a −u ⎠ (n)

g dx = f ( n −1) g − f ( n − 2) g ′ + f ( n −3) g ′′ − (−1)n

This is called generalized integration by parts.

∫ fg

(n)

dx


70

Important Transformations Often in practice an integral can be simplified by using an appropriate transformation or substitution together with Formula 16.6. The following list gives some transformations and their effects.

1

16.49.

∫ F(ax + b)dx = a ∫ F(u) du

16.50.

∫ F(

16.51.

∫ F(

16.52.

∫ F(

a 2 − x 2 ) dx = a

∫ F(a cos u) cos u du

where x = a sin u

16.53.

∫ F(

x 2 + a 2 ) dx = a

∫ F(a sec u) sec

where x = a tan u

16.54.

∫ F(

x 2 − a 2 ) dx = a

∫ F(a tan u) sec u tan u du

16.55.

∫ F (e

16.56.

∫ F(ln x) dx = ∫ F(u) e du

16.57.

∫ F ⎜⎝sin

n

where u = ax + b

ax + b ) dx =

2 a

∫ u F(u) du

where u = ax + b

ax + b ) dx =

n a

∫u

where u = n ax + b

ax

) dx =

1 a

∫

n −1

F(u) du

2

u du

F (u) du u u

⎛

−1

x⎞ dx = a a ⎟⎠

∫ F(u) cos u du

where x = a sec u where u = eax where u = ln x where u = sin −1

Similar results apply for other inverse trigonometric functions. 16.58.

∫ F(sin x , cos x) dx = 2

⎛ 2u 1 − u 2 ⎞ du F ∫ ⎜⎝1 + u 2 , 1 + u 2 ⎟⎠ 1 + u 2

where u = tan

x 2

x a

17

TABLES of SPECIAL INDEFINITE INTEGRALS

Here we provide tables of special indefinite integrals. As stated in the remarks on page 67, here a, b, p, q, n are constants, restricted if indicated; e = 2.71828 . . . is the natural base of logarithms; ln u denotes the natural logarithm of u, where it is assumed that u > 0 (in general, to extend formulas to cases where u < 0 as well, replace ln u by ln |u|); all angles are in radians; and all constants of integration are omitted but implied. It is assumed in all cases that division by zero is excluded. Our integrals are divided into types which involve the following algebraic expressions and functions: (1)

ax + b

ax + b

(2) (3)

ax + b and px + q

(5)

(25)

eax

(14) x3 + a3

(26)

ln x

(27)

sinh ax

(16)

x 4 ± a4 x n ± an

(28)

cosh ax

(17)

sin ax

(29)

sinh ax and cosh ax

(15)

ax + b and px + q ax + b and px + q

(4)

ax 2 + bx + c

(13)

(6)

x2 + a2

(18) cos ax

(30)

tanh ax

(7)

x2 – a2, with x2 > a2

(19)

sin ax and cos ax

(31)

coth ax

(8)

a2 – x2, with x2 < a2

(20)

tan ax

(32)

sech ax csch ax

(9)

x 2 + a2

(21) cot ax

(33)

(10)

x 2 − a2

(22) sec ax

(34) inverse hyperbolic functions

a2 − x 2 (12) ax2 + bx + c

(11)

(23) csc ax (24)

inverse trigonometric functions

Some integrals contain the Bernouilli numbers Bn and the Euler numbers En defined in Chapter 23.

(1)

Integrals Involving ax ⴙ b dx

1

x dx

x

17.1.1.

∫ ax + b = a ln (ax + b)

17.1.2.

∫ ax + b = a − a

17.1.3. 17.1.4.

2

ln (ax + b)

x 2dx (ax + b)2 2b(ax + b) b 2 = − + 3 ln (ax + b) ∫ ax + b a3 a 2a 3 dx 1 ⎛ x ⎞ ∫ x (ax + b) = b ln ⎜⎝ ax + b⎟⎠ a ⎛ ax + b⎞ dx 1 =− + ln bx b 2 ⎜⎝ x ⎟⎠ (ax + b)

17.1.5.

∫x

17.1.6.

∫ (ax + b)

17.1.7.

b

2

−1 a(ax + b) x dx b 1 ∫ (ax + b)2 = a 2 (ax + b) + a 2 ln (ax + b) dx

2

=

2

=

x 2 dx

17.1.8.

∫ (ax + b)

17.1.9.

∫ x (ax + b)

dx

2

ax + b b2 2b − 3 − ln (ax + b) 3 a a (ax + b) a 3

=

1 1 ⎛ x ⎞ + ln b(ax + b) b 2 ⎜⎝ ax + b⎟⎠

71

TABLES OF SPECIAL INDEFINITE INTEGRALS

72

dx −a 1 2a ⎛ ax + b⎞ − 2 + 3 ln ⎜ 2 = 2 (ax + b) b (ax + b) b x b ⎝ x ⎟⎠

17.1.10.

∫x

17.1.11.

∫ (ax + b)

17.1.12.

∫ (ax + b)

17.1.13.

∫ (ax + b)

17.1.14.

∫ (ax + b)

17.1.15.

∫ x (ax + b) dx =

2

3

=

−1 2(ax + b)2

3

=

−1 b + a 2 (ax + b) 2a 2 (ax + b)2

3

=

2b b2 1 − 3 + ln (ax + b) a (ax + b) 2a (ax + b)2 a 3

dx

x dx

x 2 dx

n

3

(ax + b)n+1 . If n = −1, see 17.1.1. (n + 1)a

dx = n

(ax + b)n+ 2 b(ax + b)n+1 , n ≠ −1, − 2 − (n + 2)a 2 (n + 1))a 2

If n = –1, –2, see 17.1.2 and 17.1.7.

∫x

17.1.16.

2

(ax + b)n dx =

(ax + b)n+3 2b(ax + b)n+ 2 b 2 (ax + b)n+1 − + (n + 3)a 3 (n + 2)a 3 (n + 1)a 3

If n = –1, –2, –3, see 17.1.3, 17.1.8, and 17.1.13.

nb ⎧ x m +1 (ax + b)n n −1 m ⎪ m + n + 1 + m + n + 1 ∫ x (ax + b) dx ⎪⎪ x m (ax + b)n+1 mb m −1 m n n x ( ax + b ) dx = ⎨ (m + n + 1)a − (m + n + 1)a ∫ x (ax + b) dx ∫ ⎪ m +1 n +1 ⎪− x (ax + b) + m + n + 2 x m (ax + b)n+1 dx (n + 1)b (n + 1)b ∫ ⎪⎩

17.1.17.

(2)

Integrals Involving

ax ⴙ b

17.2.1.

∫

dx 2 ax + b = a ax + b

17.2.2.

∫

x dx 2(ax − 2b) = ax + b 3a 2 ax + b

17.2.3.

∫

x 2 dx 2(3a 2 x 2 − 4 abx + 8b 2 ) = ax + b 15a 3 ax + b

17.2.4.

∫

⎧ ⎪ dx ⎪ =⎨ x ax + b ⎪ ⎪ ⎩

17.2.5.

∫x

17.2.6.

∫

17.2.7.

∫x

dx 2

ax + b

=−

ax + b dx =

⎛ ax + b − b ⎞ ln ⎜ ⎟ b ⎝ ax + b + b ⎠

1

2 −b

tan −1

ax + b −b

ax + b a − 2b bx

∫x

dx ax + b

2 (ax + b)3 3a

ax + b dx =

2(3ax − 2b) (ax + b)3 15a 2

(see 17.2.12.)


2(15a 2 x 2 − 12abx + 8b 2 ) (ax + b)3 105a 3

17.2.8.

∫x

17.2.9.

∫

ax + b dx = 2 ax + b + b x

17.2.10.

∫

ax + b ax + b a dx = − + 2 2 x x

17.2.11.

∫

xm 2 x m ax + b 2mb dx = − 2 m 1 a 2 m ( ) ( + + 1)a ax + b

17.2.12.

∫x

17.2.13.

∫x

17.2.14.

∫

ax + b ax + b a dx = − + xm (m − 1) x m −1 2(m − 1)

∫x

17.2.15.

∫

ax + b −(ax + b)3/ 2 (2m − 5)a dx = − m x (m − 1)bx m −1 (2m − 2)b

∫

17.2.16.

∫ (ax + b)m / 2 dx =

17.2.17.

∫ x (ax + b)m / 2 dx =

17.2.18.

∫x

17.2.19.

(ax + b)m / 2 2(ax + b)m / 2 = +b dx ∫ x m

17.2.20.

(ax + b)m / 2 (ax + b)( m + 2)/ 2 ma dx = − + 2 ∫ x 2b bx

17.2.21.

∫ x (ax + b)

(3)

ax + b dx =

2

m

m

2

∫x ∫x

dx

(See 17.2.12.)

ax + b dx

ax + b dx =

=

x m −1 dx ax + b

∫

∫x

m −1

dx ax + b

2x m 2mb (ax + b)3/ 2 − (2m + 3)a (2m + 3)a

m −1

∫x

m −1

ax + b dx

dx ax + b

ax + b dx x m −1

2(ax + b)( m + 2)/ 2 a 2 (m + 2) 2(ax + b)( m + 4 )/ 2 2b(ax + b)( m + 2)/ 2 − a 2 (m + 2) a 2 (m + 4)

(ax + b)m / 2 dx =

m/2

(See 17.2.12.)

ax + b

dx ax + b (2m − 3)a =− m −1 − ( 2m − 2)b (m − 1)bx ax + b

dx

73

2(ax + b)( m +6)/ 2 4 b(ax + b)( m + 4 )/ 2 2b 2 (ax + b)( m + 2)/ 2 − + a 3 (m + 4) a 3 (m + 2) a 3 (m + 6) (ax + b)( m − 2)/ 2 dx ∫ x (ax + b)m / 2 ∫ x dx

2 1 + (m − 2)b(ax + b)( m − 2)/ 2 b

dx

∫ x(ax + b)

( m − 2 )/ 2

Integrals Involving ax ⴙ b and px ⴙ q dx

1

x dx

1

⎛ px + q⎞

17.3.1.

∫ (ax + b)( px + q) = bp − aq ln ⎜⎝ ax + b ⎟⎠

17.3.2.

∫ (ax + b)( px + q) = bp − aq ⎨⎩a ln (ax + b) − p ln ( px + q)⎬⎭

⎧b

q

⎫


74

dx

1

⎧ 1

1

⎧

⎛ px + q⎞ ⎫

p

17.3.3.

∫ (ax + b) ( px + q) = bp − aq ⎨⎩ax + b + bp − aq ln ⎜⎝ ax + b ⎟⎠ ⎬⎭

17.3.4.

∫ (ax + b) ( px + q) = bp − aq ⎨⎩bp − aq ln ⎜⎝ px + q⎟⎠ − a(ax + b) ⎬⎭

17.3.5.

∫ (ax + b) ( px + q) = (bp − aq)a (ax + b) + (bp − aq)

17.3.6.

∫ (ax + b)

2

x dx

⎛ ax + b ⎞

q

⎫

b

2

x 2 dx

b2

2

1

2

{

2

⎧q2 ⎫ b(bp − 2aq) ln (ax + b)⎬ ⎨ ln ( px + q) + 2 p a ⎩ ⎭

dx 1 −1 = ( px + q)n (n − 1)(bp − aq) (ax + b)m −1 ( px + q)n−1

m

+ a(m + n − 2) ax + b

∫ (ax + b)

dx ( px + q)n−1

m

ax bp − aq ln( px + q) + p p2

17.3.7.

∫ px + q dx =

17.3.8.

⎧ ⎧(ax + b)m +1 ⎫ (ax + b)m −1 2 + ( n − m − ) a dx⎬ ⎨ ⎪ ∫ n −1 n −1 ( px + q) ⎭ ⎪(n − 1)(bp − aq) ⎩ ( px + q) m m m −1 ⎪ ⎧ (ax + b) ⎫ (ax + b) (ax + b) −1 ∫ ( px + q)n dx = ⎨(n − m − 1) p ⎨⎩( px + q)n−1 + m(bp − aq) ∫ ( px + q)n dx⎬⎭ ⎪ ⎪ −1 ⎧ (ax + b)m (ax + b)m −1 ⎫ a m dx⎬ − ⎨ ⎪ ∫ n −1 ( px + q)n −1 ⎭ ⎩(n − 1) p ⎩( px + q)

(4)

}

ax ⴙ b and px ⴙ q

Integrals Involving

px + q 2(apx + 3aq − 2bp) dx = 3a 2 ax + b

17.4.1.

∫

17.4.2.

⎧ ⎛ p(ax + b) − bp − aq ⎞ 1 ⎪ ln ⎜ ⎟ ⎪ bp − aq p ⎝ p(ax + b) + bp − aq ⎠ dx = ∫ ( px + q) ax + b ⎨ 2 p(ax + b) ⎪ tan −1 ⎪ aq − bp ⎩ aq − bp p

ax + b

⎧ 2 ax + b bp − aq ⎛ p(ax + b) − bp − aq ⎞ ⎪ + ln ⎜ ⎟ p ⎪ p p ax + b ⎝ p(ax + b) + bp − aq ⎠ dx = ⎨ px + q p(ax + b) ⎪ 2 ax + b 2 aq − bp tan −1 − ⎪ p aq − bp p p ⎩

17.4.3.

∫

17.4.4.

∫ ( px + q)n ax + b dx =

17.4.5.

∫ ( px + q)

17.4.6.

( px + q)n 2( px + q)n ax + b 2n(aq − bp) dx = + ∫ ax + b (2n + 1)a (2n + 1)a

17.4.7.

∫ ( px + q)

dx n

ax + b n

ax + b

dx =

=

2( px + q)n+1 ax + b bp − aq + (2n + 3) p (2n + 3) p

( px + q)n ∫ ax + b

ax + b (2n − 3)a n −1 + 2 ( n − 1)(aq − bp) (n − 1)(aq − bp)( px + q)

− ax + b a + (n − 1) p( px + q)n−1 2(n − 1) p

( px + q)n−1 dx ∫ ax + b

∫ ( px + q)

dx n −1

ax + b

∫ ( px + q)

dx n −1

ax + b


(5)

ax ⴙ b and

Integrals Involving

∫

17.5.1.

⎧ ⎪ dx ⎪ =⎨ (ax + b)( px + q) ⎪ ⎪ ⎩

2

ln

ap 2 − ap

(

75

px + q

a( px + q ) + p(ax + b)

tan −1

)

− p(ax + b) a( px + q)

17.5.2.

∫

x dx = (ax + b)( px + q)

(ax + b)( px + q) bp + aq − ap 2ap

17.5.3.

∫

(ax + b)( px + q) dx =

2apx + bp + aq (bp − aq)2 (ax + b)( px + q) − 8ap 4ap

17.5.4.

∫

px + q dx = ax + b

17.5.5.

∫ ( px + q)

(6)

(ax + b)( px + q) aq − bp + a 2a

dx 2 ax + b = (ax + b)( px + q) (aq − bp) px + q

Integrals Involving x2 ⴙ a2 x dx 1 = tan −1 a + a2 a

17.6.1.

∫x

17.6.2.

∫x

17.6.3.

∫x

x 2 dx −1 x 2 2 = x − a tan a +a

17.6.4.

∫x

x 3 dx x 2 a2 − ln ( x 2 + a 2 ) 2 2 = 2 2 +a

17.6.5.

∫ x(x

17.6.6.

∫x

17.6.7.

∫ x (x

17.6.8.

∫ (x

2

17.6.9.

∫ (x

2

17.6.10.

2

x dx 1 = ln ( x 2 + a 2 ) 2 +a 2

2

2

⎛ x2 ⎞ dx 1 = ln ⎜ ⎟ 2 + a 2 ) 2a 2 ⎝ x 2 + a 2 ⎠

dx 1 1 x = − 2 − 3 tan −1 a (x 2 + a2 ) a x a ⎛ x2 ⎞ dx 1 1 = − − ln ⎜ ⎟ 2 + a2 ) 2a 2 x 2 2a 4 ⎝ x 2 + a 2 ⎠

3

dx x x 1 tan −1 = + a + a 2 ) 2 2a 2 ( x 2 + a 2 ) 2a 3

x dx −1 2 2 = 2 +a ) 2( x + a 2 )

∫ (x

x x 2 dx 1 −x = + tan −1 a + a 2 )2 2( x 2 + a 2 ) 2a

2

∫

∫

dx (ax + b)( px + q)


∫



76

x 3dx a2 1 = ∫ ( x 2 + a 2 )2 2( x 2 + a 2 ) + 2 ln ( x 2 + a 2 )

17.6.11.

dx 1 1 ⎛ x2 ⎞ = + ln x ( x 2 + a 2 )2 2a 2 ( x 2 + a 2 ) 2a 4 ⎜⎝ x 2 + a 2 ⎟⎠

17.6.12.

∫

17.6.13.

∫x

2

dx 1 x 3 x =− 4 − 4 2 − tan −1 a ( x 2 + a 2 )2 a x 2a ( x + a 2 ) 2a 5

dx 1 1 1 ⎛ x2 ⎞ 2 2 = − 4 2 − 4 2 2 − 6 ln ⎜ 2 x (x + a ) 2a x 2a ( x + a ) a ⎝ x + a 2 ⎟⎠

17.6.14.

∫

17.6.15.

∫ (x

2

17.6.16.

∫ (x

2

17.6.17.

∫ x(x

17.6.18.

∫ (x

17.6.19.

∫x

(7)

3

2

dx x 2n − 3 = + + a 2 )n 2(n − 1)a 2 ( x 2 + a 2 )n−1 (2n − 2)a 2

2

dx 1 1 = + + a 2 )n 2(n − 1)a 2 ( x 2 + a 2 )n−1 a 2

x m dx = + a 2 )n

∫ (x

x m − 2 dx − a2 + a 2 )n−1

2

∫x

2

17.7.2.

∫x

2

dx 1 = ( x 2 + a 2 )n a 2

∫x

m

dx 1 ⎛ x − a⎞ = ln − a 2 2a ⎜⎝ x + a⎟⎠

dx 1 − ( x 2 + a 2 )n−1 a 2

or

−

∫

17.7.4.

∫x

x 1 coth −1 a a

x dx 1 = ln ( x 2 − a 2 ) − a2 2

x 3dx x 2 a2 = + ln ( x 2 − a 2 ) 2 2 −a 2 2

dx 1 ⎛ x 2 − a2 ⎞ = ln x ( x 2 − a 2 ) 2a 2 ⎜⎝ x 2 ⎟⎠

17.7.5.

∫

17.7.6.

∫ x (x

dx 1 1 ⎛ x − a⎞ = + ln 2 − a 2 ) a 2 x 2a 3 ⎜⎝ x + a⎟⎠

2

dx 1 1 ⎛ x2 ⎞ = − ln x 3( x 2 − a 2 ) 2a 2 x 2 2a 4 ⎜⎝ x 2 − a 2 ⎟⎠

17.7.7.

∫

17.7.8.

∫ (x

2

∫ x(x

2

dx + a 2 )n−1

x m − 2 dx 2 + a 2 )n

x 2 dx a ⎛ x − a⎞ ln 2 2 = x + 2 ⎜⎝ x + a⎟⎠ x −a

17.7.3.

dx + a 2 )n−1

∫ (x

Integrals Involving x2 ⴚ a2, x2 > a2

17.7.1.

2

x dx −1 = + a 2 )n 2(n − 1)( x 2 + a 2 )n−1

2

m

∫ (x

dx −x 1 ⎛ x − a⎞ ln = − − a 2 )2 2a 2 ( x 2 − a 2 ) 4 a 3 ⎜⎝ x + a⎟⎠

∫x

m−2

dx ( x 2 + a 2 )n


∫ (x

17.7.9.

x dx −1 = − a 2 )2 2( x 2 − a 2 )

2

17.7.10.

x 2 dx −x 1 ⎛ x − a⎞ ∫ ( x − a 2 )2 = 2( x 2 − a 2 ) + 4a ln ⎜⎝ x + a⎟⎠

17.7.11.

∫ (x

2

x 3dx −a2 1 = + ln ( x 2 − a 2 ) 2 2 −a ) 2( x 2 − a 2 ) 2

2

dx −1 1 ⎛ x2 ⎞ = + ln x ( x 2 − a 2 )2 2a 2 ( x 2 − a 2 ) 2a 4 ⎜⎝ x 2 − a 2 ⎟⎠

17.7.12.

∫

17.7.13.

∫ x (x 2

dx 1 x 3 ⎛ x − a⎞ =− 4 − 4 2 − ln − a 2 )2 a x 2a ( x − a 2 ) 4 a 5 ⎜⎝ x + a⎟⎠

2

dx 1 1 1 ⎛ x2 ⎞ 2 2 = − 4 2 − 4 2 2 + 6 ln ⎜ 2 x (x − a ) 2a x 2a ( x − a ) a ⎝ x − a 2 ⎟⎠

17.7.14.

∫

17.7.15.

∫ (x

2

17.7.16.

∫ (x

2

17.7.17.

∫ x(x

17.7.18.

x m dx ∫ ( x − a 2 )n =

17.7.19.

∫x

(8)

77

3

2

dx −x 2n − 3 = − − a 2 )n 2(n − 1)a 2 ( x 2 − a 2 )n−1 (2n − 2)a 2

2

dx −1 1 = − − a 2 )n 2(n − 1)a 2 ( x 2 − a 2 )n−1 a 2 x m − 2 dx ∫ ( x − a 2 )n−1 + a 2 2

dx 1 2 n = a2 (x − a ) 2

∫x

m−2

dx 1 ⎛ a + x⎞ ln 2 = 2 a ⎜⎝ a − x ⎟⎠ −x

17.8.1.

∫a

2

17.8.2.

∫a

2

17.8.3.

∫a

x 2 dx a ⎛ a + x⎞ = − x + ln ⎜ 2 2 ⎝ a − x ⎟⎠ − x2

17.8.4.

∫a

dx 1 2 2 n − 2 a (x − a )

or

1 x tanh −1 a a

x dx 1 = − ln (a 2 − x 2 ) 2 − x2

x 3dx x 2 a2 = − − ln (a 2 − x 2 ) 2 2 −x 2 2

dx 1 ⎛ x2 ⎞ 2 = 2 ln ⎜ 2 x (a − x ) 2a ⎝ a − x 2 ⎟⎠

∫

17.8.6.

∫x

2

2

dx − a 2 )n−1

∫ x(x

2

dx − a 2 )n−1

x m − 2 dx ∫ ( x 2 − a 2 )n

Integrals Involving x2 ⴚ a2, x2 < a2

17.8.5.

2

x dx −1 = − a 2 )n 2(n − 1)( x 2 − a 2 )n−1

2

m

∫ (x

dx 1 1 ⎛ a + x⎞ = − 2 + 3 ln ⎜ (a 2 − x 2 ) a x 2a ⎝ a − x ⎟⎠

∫x

m

dx ( x − a 2 )n−1 2


78

dx 1 1 ⎛ x2 ⎞ 2 2 = − 2 2 + 4 ln ⎜ 2 x (a − x ) 2a x 2a ⎝ a − x 2 ⎟⎠

17.8.7.

∫

17.8.8.

∫ (a

2

17.8.9.

∫ (a

2

3

dx x 1 ⎛ a + x⎞ = + ln − x 2 )2 2a 2 (a 2 − x 2 ) 4a 3 ⎜⎝ a − x ⎟⎠ x dx 1 = − x 2 )2 2(a 2 − x 2 )

17.8.10.

x 2 dx x 1 ⎛ a + x⎞ ∫ (a 2 − x 2 )2 = 2(a 2 − x 2 ) − 4a ln ⎜⎝ a − x ⎟⎠

17.8.11.

x 3dx a2 1 = ∫ (a 2 − x 2 )2 2(a 2 − x 2 ) + 2 ln (a 2 − x 2 ) dx 1 1 ⎛ x2 ⎞ = + ln x (a 2 − x 2 )2 2a 2 (a 2 − x 2 ) 2a 4 ⎜⎝ a 2 − x 2 ⎟⎠

17.8.12.

∫

17.8.13.

∫ x (a

2

17.8.14.

∫ x (a

2

17.8.15.

∫ (a

2

17.8.16.

∫ (a

2

17.8.17.

∫ x (a

17.8.18.

∫ (a

2

17.8.19.

∫x

dx 1 = (a 2 − x 2 ) n a 2

(9)

2

3

dx −1 x 3 ⎛ a + x⎞ + 4 2 2 2 = 4 2 + 5 ln ⎜ −x ) a x 2a (a − x ) 4 a ⎝ a − x ⎟⎠

dx −1 1 1 ⎛ x2 ⎞ 2 2 = 4 2 + 4 2 2 + 6 ln ⎜ 2 −x ) 2a x 2a (a − x ) a ⎝ a − x 2 ⎟⎠

dx x 2n − 3 2 n = 2 2 2 n −1 + −x ) 2(n − 1)a (a − x ) (2n − 2)a 2

2

dx 1 1 = + − x 2 )n 2(n − 1)a 2 (a 2 − x 2 )n−1 a 2

dx

x m − 2 dx − 2 − x 2 )n

∫ (a

Integrals Involving

∫x

m

∫ (a

∫ x (a

dx − x 2 )n−1

dx − x 2 )n−1

x m − 2 dx − x 2 )n−1

dx 1 + (a 2 − x 2 )n−1 a 2

∫x

x 2 + a2 x a

∫

x +a

17.9.2.

∫

x dx = x 2 + a2 x 2 + a2

17.9.3.

∫

x 2 dx x x 2 + a2 a2 = − ln( x + x 2 + a 2 ) 2 2 x 2 + a2

17.9.4.

∫

x 3 dx ( x 2 + a 2 )3 / 2 = − a2 x 2 + a2 3 x 2 + a2

2

2

2

17.9.1.

2

2

x dx 1 = − x 2 )n 2(n − 1)(a 2 − x 2 )n−1

x m dx = a2 − x 2 )n

m

∫ (a

= ln ( x + x 2 + a 2 ) or sinh −1

m−2

dx (a 2 − x 2 ) n


17.9.5.

∫x

17.9.6.

∫x

17.9.7.

∫x

17.9.8. 17.9.9.

dx 1 ⎛ a + x 2 + a2 ⎞ = − ln ⎟ a ⎜⎝ x x 2 + a2 ⎠ dx x 2 + a2 =− 2 2 a2 x x +a

2

⎛ a + x 2 + a2 ⎞ dx x 2 + a2 1 =− + 3 ln ⎜ 2 2 ⎟ 2 2 x 2a x 2a x +a ⎝ ⎠ 2 2 2 x x a a + ∫ x 2 + a 2 dx = 2 + 2 ln ( x + x 2 + a 2 ) ( x 2 + a 2 )3 / 2 ∫ x x 2 + a 2 dx = 3 3

17.9.10.

∫ x 2 x 2 + a 2 dx =

x ( x 2 + a 2 )3 / 2 a 2 x x 2 + a 2 a 4 − − ln ( x + x 2 + a 2 ) 4 8 8

17.9.11.

∫x

( x 2 + a 2 )5 / 2 a 2 ( x 2 + a 2 )3 / 2 − 5 3

x 2 + a 2 dx =

3

∫

⎛ a + x 2 + a2 ⎞ x 2 + a2 dx = x 2 + a 2 − a ln ⎜ ⎟ x x ⎝ ⎠

∫

x 2 + a2 x 2 + a2 dx = − + ln ( x + x 2 + a 2 ) x2 x

17.9.14.

∫

x 2 + a2 x 2 + a2 1 ⎛ a + x 2 + a2 ⎞ dx = − ln − 3 2 ⎟ x 2a ⎜⎝ x 2x ⎠

17.9.15.

∫ (x

2

17.9.16.

∫ (x

2

17.9.17.

x 2 dx ∫ ( x 2 + a 2 )3 / 2 =

17.9.18.

∫ (x

17.9.12.

17.9.13.

2

dx x = + a 2 )3 / 2 a 2 x 2 + a 2 x dx = + a 2 )3 / 2

−1 x 2 + a2 −x + ln ( x + x 2 + a 2 ) x + a2 2

x 3 dx = x 2 + a2 + + a 2 )3 / 2

a2 x + a2 2

∫

dx 1 1 ⎛ a + x 2 + a2 ⎞ − 2 3/ 2 = 3 ln ⎜ ⎟ x x(x + a ) a2 x 2 + a2 a ⎝ ⎠

∫

dx x 2 + a2 x = − − 4 2 2 2 2 3/ 2 4 x (x + a ) a x a x + a2

17.9.21.

∫

⎛ a + x 2 + a2 ⎞ dx 3 3 −1 l = − + n ⎟ x x 3( x 2 + a 2 )3/ 2 2a 2 x 2 x 2 + a 2 2a 4 x 2 + a 2 2a 5 ⎜⎝ ⎠

17.9.22.

∫ (x

17.9.23.

∫ x ( x 2 + a 2 )3/ 2 dx =

17.9.19.

17.9.20.

2

2

+ a 2 )3/ 2 dx =

x ( x 2 + a 2 )3/ 2 3a 2 x x 2 + a 2 3 4 + + a ln ( x + x 2 + a 2 ) 4 8 8 ( x 2 + a 2 )5 / 2 5

79


80

2

+ a 2 )3/ 2 dx =

x ( x 2 + a 2 )5 / 2 a 2 x ( x 2 + a 2 )3 / 2 a 4 x x 2 + a 2 a 6 − − − ln ( x + x 2 + a 2 ) 6 24 16 16

2

+ a 2 )3/ 2 dx =

( x 2 + a 2 ) 7 / 2 a 2 ( x 2 + a 2 )5 / 2 − 7 5

17.9.24.

∫ x (x

17.9.25.

∫ x (x

17.9.26.

∫

⎛ a + x 2 + a2 ⎞ ( x 2 + a 2 )3 / 2 ( x 2 + a 2 )3 / 2 + a 2 x 2 + a 2 − a 3 ln ⎜ dx = ⎟ 3 x x ⎝ ⎠

17.9.27.

∫

( x 2 + a 2 )3 / 2 ( x 2 + a 2 )3 / 2 3 x x 2 + a 2 3 2 + + a ln ( x + x 2 + a 2 ) dx = − 2 x 2 2 x

17.9.28.

∫

⎛ a + x 2 + a2 ⎞ ( x 2 + a 2 )3 / 2 ( x 2 + a 2 )3 / 2 3 2 3 dx = − x + a 2 − a ln ⎜ + 3 2 ⎟ x 2 2 x 2x ⎝ ⎠

(10)

2

3

Integrals Involving dx

x 2 − a2

∫

x −a

17.10.2.

∫

x 2 dx x x 2 − a2 a2 = + ln ( x + x 2 − a 2 ) 2 2 2 2 x −a

17.10.3.

∫

x 3 dx ( x 2 − a 2 )3 / 2 = + a2 x 2 − a2 3 x 2 − a2

17.10.4.

∫x

2

17.10.5.

∫x

17.10.6.

∫x

17.10.7.

∫

17.10.8.

∫x

17.10.9.

∫x

2

= ln ( x + x 2 − a 2 ),

dx x 2 − a2 2

2

= x 2 − a2

x 1 sec −1 a a x 2 − a2 a2 x

dx

1 x 2 − a2 x + 3 sec −1 2a 2 x 2 2a a

=

x 2 − a 2 dx =

x x 2 − a2 a2 − ln ( x + x 2 − a 2 ) 2 2

x 2 − a 2 dx =

( x 2 − a 2 )3 / 2 3

x 2 − a 2 dx =

2

x −a 2

dx = x 2 − a2 x 2 − a2

3

=

∫

x dx

17.10.1.

x ( x 2 − a 2 )3 / 2 a 2 x x 2 − a 2 a 4 + − ln ( x + x 2 − a 2 ) 4 8 8 ( x 2 − a 2 )5 / 2 a 2 ( x 2 − a 2 )3 / 2 + 5 3

17.10.10.

∫x

17.10.11.

∫

x 2 − a2 x dx = x 2 − a 2 − a sec −1 x a

17.10.12.

∫

x 2 − a2 x 2 − a2 dx = − + ln ( x + x 2 − a 2 ) 2 x x

3

x 2 − a 2 dx =


1 x 2 − a2 x 2 − a2 x + dx = − sec −1 3 2x 2 2a x a

17.10.13.

∫

17.10.14.

∫ (x

2

17.10.15.

∫ (x

2

17.10.16.

x 2dx x ∫ ( x 2 − a 2 )3/ 2 = − x 2 − a 2 + ln ( x + x 2 − a 2 )

17.10.17.

∫ (x

17.10.18.

∫ x(x

17.10.19.

∫x

17.10.20.

∫x

17.10.21.

∫ (x

17.10.22.

∫ x ( x 2 − a 2 )3/ 2 dx =

17.10.23.

∫ x 2 ( x 2 − a 2 )3/ 2 dx =

x ( x 2 − a 2 )5 / 2 a 2 x ( x 2 − a 2 )3 / 2 a 4 x x 2 − a 2 a 6 + − + ln ( x + x 2 − a 2 ) 6 24 16 16

17.10.24.

∫x

( x 2 − a 2 ) 7 / 2 a 2 ( x 2 − a 2 )5 / 2 + 7 5

17.10.25.

∫

( x 2 − a 2 )3 / 2 ( x 2 − a 2 )3 / 2 x dx = − a 2 x 2 − a 2 + a 3 sec −1 x 3 a

17.10.26.

∫

( x 2 − a 2 )3 / 2 ( x 2 − a 2 )3 / 2 3 x x 2 − a 2 3 2 dx = − + − a ln ( x + x 2 − a 2 ) 2 x 2 2 x

17.10.27.

( x 2 − a 2 )3 / 2 ( x 2 − a 2 )3 / 2 3 x 2 − a 2 3 x = − + − a sec −1 dx ∫ x3 2 2x 2 2 a

(11)

2

2

3

x dx = − a 2 )3 / 2

−1 x 2 − a2

x 3 dx = x 2 − a2 − − a 2 )3 / 2

2

a2 x 2 − a2

dx 1 x −1 = − 3 sec −1 2 3/ 2 2 2 2 −a ) a a a x −a

dx x 2 − a2 x − 4 2 2 3/ 2 = − a4 x (x − a ) a x − a2 2

dx 1 3 3 x = − − 5 sec −1 2 3/ 2 2 2 2 2 4 2 2 (x − a ) 2a a 2a x x − a 2a x − a

2

3

dx x =− 2 2 − a 2 )3 / 2 a x − a2

2

− a 2 )3/ 2 dx =

x ( x 2 − a 2 )3/ 2 3a 2 x x 2 − a 2 3 4 − + a ln ( x + x 2 − a 2 ) 4 8 8 ( x 2 − a 2 )5 / 2 5

( x 2 − a 2 )3/ 2 dx =

Integrals Involving

17.11.1.

∫

dx x = sin −1 2 a a −x

17.11.2.

∫

x dx = − a2 − x 2 a2 − x 2

2

a2 − x 2

81


82

17.11.3.

∫

x 2 dx x a2 − x 2 a2 x = − + sin −1 2 2 a 2 2 a −x

17.11.4.

∫

x 3 dx (a 2 − x 2 )3 / 2 = − a2 a2 − x 2 2 2 3 a −x

17.11.5.

∫

dx 1 ⎛ a + a2 − x 2 ⎞ = − ln ⎟ a ⎜⎝ x x a2 − x 2 ⎠ dx a2 − x 2 = − a2 x a2 − x 2

17.11.6.

∫x

17.11.7.

∫x

17.11.8.

∫

17.11.9.

∫x

17.11.10.

∫x

17.11.11.

∫x

17.11.12.

∫

⎛ a + a2 − x 2 ⎞ a2 − x 2 dx = a 2 − x 2 − a ln ⎜ ⎟ x x ⎝ ⎠

∫

a2 − x 2 a2 − x 2 x dx = − − sin −1 2 x a x

17.11.14.

∫

a2 − x 2 a2 − x 2 1 ⎛ a + a2 − x 2 ⎞ dx = − + ln 3 2 ⎟ x 2a ⎜⎝ x 2x ⎠

17.11.15.

∫ (a

2

17.11.16.

∫ (a

2

17.11.17.

∫ (a

2

17.11.18.

x 3 dx a2 2 2 a x = − + / 2 3 2 ∫ (a − x ) a2 − x 2

17.11.19.

∫ x (a

17.11.13.

2

⎛ a + a2 − x 2 ⎞ dx a2 − x 2 1 = − − ln 2 2 3 ⎜ ⎟ x 2a x 2a a2 − x 2 ⎝ ⎠

3

a 2 − x 2 dx =

x a2 − x 2 a2 x + sin −1 2 2 a

a 2 − x 2 dx = −

(a 2 − x 2 ) 3 / 2 3

2

a 2 − x 2 dx = −

3

a 2 − x 2 dx =

x x (a 2 − x 2 ) 3 / 2 a 2 x a 2 − x 2 a 4 + + sin −1 4 8 8 a

(a 2 − x 2 ) 5 / 2 a 2 (a 2 − x 2 ) 3 / 2 − 5 3

dx x 2 3/ 2 = 2 −x ) a a2 − x 2 x dx = − x 2 )3 / 2

1 a − x2

x 2 dx = − x 2 )3 / 2

x x − sin −1 2 a a −x

2

2

2

2

dx 1 1 ⎛ a + a2 − x 2 ⎞ − 3 ln ⎜ 2 3/ 2 = ⎟ 2 2 2 x −x ) a a a −x ⎝ ⎠

dx a2 − x 2 x = − + 4 2 2 2 2 3/ 2 4 x (a − x ) a x a a − x2

17.11.20.

∫

17.11.21.

∫x

3

⎛ a + a2 − x 2 ⎞ dx 3 3 −1 + − l n 2 3/ 2 = 5 ⎜ ⎟ x (a − x ) 2a 2 x 2 a 2 − x 2 2a 4 a 2 − x 2 2a ⎝ ⎠ 2


83

x (a 2 − x 2 )3/ 2 3a 2 x a 2 − x 2 3 4 −1 x + + a sin a 4 8 8

17.11.22.

∫ (a

17.11.23.

∫ x (a

17.11.24.

∫ x 2 (a 2 − x 2 )3/ 2 dx = −

17.11.25.

∫x

17.11.26.

⎛ a + a2 − x 2 ⎞ (a 2 − x 2 )3 / 2 (a 2 − x 2 )3 / 2 2 2 2 3 dx a a x a ln = + − − ⎟ ⎜ ∫ x x 3 ⎠ ⎝

17.11.27.

∫

(a 2 − x 2 )3 / 2 (a 2 − x 2 )3/ 2 3x a 2 − x 2 3 2 −1 x dx = − − − a sin 2 x x 2 2 a

17.11.28.

∫

⎛ a + a2 − x 2 ⎞ (a 2 − x 2 )3 / 2 (a 2 − x 2 )3 / 2 3 a 2 − x 2 3 dx = − − + a ln ⎜ 3 2 ⎟ x 2 2 x 2x ⎝ ⎠

(12)

17.12.1.

− x 2 )3/ 2 dx =

2

3

2

− x 2 )3/ 2 dx = −

(a 2 − x 2 )3/ 2 dx =

(a 2 − x 2 )5 / 2 5 x x (a 2 − x 2 ) 5 / 2 a 2 x (a 2 − x 2 ) 3 / 2 a 4 x a 2 − x 2 a 6 + + + sin −1 6 24 16 16 a

(a 2 − x 2 ) 7 / 2 a 2 (a 2 − x 2 ) 5 / 2 − 7 5

Integrals Involving ax2 ⴙ bx ⴙ c ⎧ 2 2ax + b tan −1 ⎪ 2 4 ac − b 2 dx ⎪ 4 ac − b = ∫ ax 2 + bx + c ⎨ 1 ⎛ 2ax + b − b 2 − 4 ac ⎞ ⎪ ln ⎜ ⎪ b 2 − 4 ac ⎝ 2ax + b + b 2 − 4 ac ⎟⎠ ⎩

If b 2 = 4 ac, ax 2 + bx + c = a( x + b / 2a)2 and the results 17.1.6 to 17.1.10 and 17.1.14 to 17.1.17 can be used. If b = 0 use results on page 75. If a or c = 0 use results on pages 71–72.

x dx 1 b = ln (ax 2 + bx + c) − 2a + bx + c 2a

∫ ax

17.12.3.

x 2 dx x b b 2 − 2ac 2 ax bx c = − + + + ln ( ) ∫ ax 2 + bx + c a 2a 2 2a 2

17.12.4.

x m dx x m −1 c = ∫ ax + bx + c (m − 1)a − a

17.12.5.

∫ x (ax

17.12.6.

∫x

2

17.12.7.

∫x

n

17.12.8.

∫ (ax

2

17.12.9.

∫ (ax

2

2

2

2

∫ ax

dx + bx + c

17.12.2.

2

x m − 2 dx b ∫ ax + bx + c − a

∫ ax

2

dx + bx + c

dx b ⎛ ax 2 + bx + c⎞ 1 b 2 − 2ac = 2 ln ⎜ ⎟⎠ − cx + 2c 2 x2 (ax + bx + c) 2c ⎝ dx 1 b =− n −1 − c (ax + bx + c) (n − 1)cx

∫x

n −1

dx + bx + c

2

2

2

2

x m−1dx ∫ ax + bx + c

2

x2 dx 1 ⎛ ⎞ b ln ⎜ 2 = − + bx + c) 2c ⎝ ax + bx + c⎟⎠ 2c

∫ ax

∫ ax

dx a − (ax + bx + c) c 2

dx 2ax + b 2a + 2 = 2 2 + bx + c) (4 ac − b )(ax + bx + c) 4 ac − b 2 x dx bx + 2c b =− − + bx + c)2 (4 ac − b 2 )(ax 2 + bx + c) 4 ac − b 2

∫ ax

2

2

dx + bx + c

∫x

n− 2

dx (ax + bx + c)

dx + bx + c

∫ ax

2

dx + bx + c

2


84

17.12.10.

∫ (ax

2

17.12.11.

∫ (ax

2

x 2 dx (b 2 − 2ac) x + bc 2c + 2 = + bx + c) a(4 ac − b 2 )(ax 2 + bx + c) 4 ac − b 2

2

dx + bx + c

x m dx x m −1 (m − 1)c + n = − + bx + c) (2n − m − 1)a(ax 2 + bx + c)n−1 (2n − m − 1)a −

17.12.12.

x 2 n−1dx 1 ∫ (ax 2 + bx + c)n = a

17.12.13.

∫ x (ax

17.12.14.

∫x

2

17.12.15.

∫x

m

2

(n − m )b (2n − m − 1)a

∫ (ax

x m −1dx ∫ (ax + bx + c)n

dx 1 b = − + bx + c)2 2c(ax 2 + bx + c) 2c

x 2 n−3 dx b ∫ (ax 2 + bx + c)n − a

∫ (ax

dx 1 3a =− − (ax 2 + bx + c)2 cx (ax 2 + bx + c) c

2

dx 1 + + bx + c)2 c

∫ (ax

2

x 2 n− 2 dx ∫ (ax 2 + bx + c)n

∫ x (ax

dx 2b − c + bx + c)2

dx 1 (m + 2n − 3)a n = − m −1 2 n −1 − (m − 1)c (ax + bx + c) (m − 1)cx (ax + bx + c) 2

Integrals Involving

(m + n − 2)b (m − 1)c

x m − 2 dx + bx + c)n

2

2

x 2 n−3 dx c ∫ (ax 2 + bx + c)n−1 − a

−

(13)

∫ ax

∫x

m −1

dx + bx + c)

2

∫ x (ax ∫x

m−2

2

dx + bx + c)2

dx (ax + bx + c)n 2

dx (ax + bx + c)n 2

ax 2 ⴙ bx ⴙ c

In the following results if b 2 = 4 ac, ax 2 + bx + c = a ( x + b / 2a) and the results 17.1 can be used. If b = 0 use the results 17.9. If a = 0 or c = 0 use the results 17.2 and 17.5.

17.13.1.

17.13.2.

17.13.3.

∫

⎧ 1 2 ⎪⎪ a ln (2 a ax + bx + c + 2ax + b) dx =⎨ ax 2 + bx + c ⎪− 1 sin −1 ⎛ 2ax + b ⎞ or 1 sinh −1 ⎛ 2ax + b ⎞ ⎜⎝ b 2 − 4 ac ⎟⎠ ⎜⎝ 4 ac − b 2 ⎟⎠ a ⎪⎩ − a

∫

x dx = 2 ax + bx + c

∫

x 2 dx 2ax − 3b 3b 2 − 4 ac 2 = ax + bx + c + 4a 2 8a 2 ax 2 + bx + c

ax 2 + bx + c b − 2a a

∫

dx ax + bx + c 2

dx ax + bx + c

∫

2

⎧ 1 ⎛ 2 c ax 2 + bx + c + bx + 2c⎞ ln ⎜ ⎪− ⎟ x dx ⎪ c ⎝ ⎠ = ⎨ 2 x ax + bx + c ⎪ 1 ⎛ ⎛ 1 bx + 2c ⎞ bx + 2c ⎞ −1 −1 ⎪ −c sin ⎜⎝ | x | b 2 − 4 ac ⎟⎠ or − c sinh ⎜⎝ | x | 4 ac − b 2 ⎟⎠ ⎩

17.13.4.

∫

17.13.5.

∫x

17.13.6.

∫

2

dx ax 2 + bx + c b =− − 2c cx ax + bx + c 2

ax 2 + bx + c dx =

∫x

dx ax 2 + bx + c

(2ax + b) ax 2 + bx + c 4 ac − b 2 + 4a 8a

∫

dx ax 2 + bx + c


17.13.7.

∫x

ax 2 + bx + c dx =

(ax 2 + bx + c)3/ 2 b(2ax + b) − ax 2 + bx + c 3a 8a 2 −

17.13.8.

∫ x 2 ax 2 + bx + c dx =

b(4 ac − b 2 ) 16a 2

∫

∫

ax 2 + bx + c b dx = ax 2 + bx + c + x 2

17.13.10.

∫

ax 2 + bx + c ax 2 + bx + c dx = − +a 2 x x

17.13.11.

∫ (ax

2

17.13.12.

∫ (ax

2

17.13.13.

∫ (ax

2

17.13.14.

∫ x (ax ∫

dx

∫

ax + bx + c 2

dx

∫

+

ax + bx + c 2

∫x b 2

ax 2 + bx + c dx dx ax + bx + c 2

∫x

dx ax + bx + c 2

x dx 2(bx + 2c) = + bx + c)3/ 2 (b 2 − 4 ac) ax 2 + bx + c x 2 dx (2b 2 − 4 ac) x + 2bc 1 + 3/ 2 = + bx + c) a(4 ac − b 2 ) ax 2 + bx + c a

2

dx 1 1 = + + bx + c)3/ 2 c ax 2 + bx + c c

∫x

∫

dx b − 2 c ax + bx + c

2

∫ (ax

17.13.17.

∫ x (ax 2 + bx + c)n+1/ 2 dx =

17.13.18.

∫ (ax

2

2

2

dx + bx + c)3/ 2

dx + bx + c)3/ 2

dx ax 2 + bx + c

b (ax 2 + bx + c)n+3/ 2 − 2a a(2n + 3)

∫ (ax

2

+ bx + c)n+1/ 2 dx

dx 2(2ax + b) = + bx + c)n+1/ 2 (2n − 1)(4 ac − b 2 )(ax 2 + bx + c)n−1/ 2 +

∫ x (ax

∫x

∫ (ax

∫ (ax

(2ax + b)(ax 2 + bx + c)n+1/ 2 (2n + 1)(4 ac − b 2 ) + ∫ (ax 2 + bx + c)n−1/ 2 dx 4 a(n + 1) 8a(n + 1)

17.13.16.

+ bx + c)n+1/ 2 dx =

3b 2c 2

dx ax 2 + bx + c

2

dx ax 2 + 2bx + c b 2 − 2ac + 3/ 2 = − 2 x (ax + bx + c) 2c 2 c x ax 2 + bx + c 2

2

+c

∫

dx 2(2ax + b) = + bx + c)3/ 2 (4 ac − b 2 ) ax 2 + bx + c

−

17.13.19.

dx ax 2 + bx + c

6ax − 5b 5b 2 − 4 ac 2 3/ 2 ( + + ) + ax bx c 24 a 2 16a 2

17.13.9.

17.13.15.

85

2

8a(n − 1) (2n − 1)(4 ac − b 2 )

∫ (ax

2

dx + bx + c)n−1/ 2

dx 1 = + bx + c)n+1/ 2 (2n − 1)c(ax 2 + bx + c)n−1/ 2 +

1 c

∫ x (ax

2

dx b − + bx + c)n−1/ 2 2c

∫ (ax

2

dx + bx + c)n+1/22


86

(14)

Integrals Involving x3 ⴙ a3

Note that for formulas involving x3 – a3 replace a with –a.

∫

⎛ ( x + a) 2 ⎞ dx 1 1 2x − a = ln tan −1 + ⎜⎝ 2 3 3 2 2⎟ ⎠ 2 x +a 6a x − ax + a a 3 a 3

∫

x dx 1 ⎛ x 2 − ax + a 2 ⎞ 1 2x − a = ln ⎜ tan −1 + ⎟ 3 3 2 ⎝ ⎠ ( x + a) x +a 6a a 3 a 3

17.14.3.

∫

x 2 dx 1 = ln ( x 3 + a 3 ) x 3 + a3 3

17.14.4.

∫ x(x

17.14.1.

17.14.2.

dx 1 ⎛ x3 ⎞ 3 = 3 ln ⎜ 3 + a ) 3a ⎝ x + a 3 ⎟⎠

3

⎛ x 2 − ax + a 2 ⎞ dx 1 1 1 2x − a − tan −1 = − − ln ⎟ ⎜ 2 3 3 3 4 2 4 ⎝ ( x + a) ⎠ a 3 x (x + a ) a x 6a a 3

17.14.5.

∫

17.14.6.

∫ (x

17.14.7.

∫ (x

17.14.8.

x 2 dx 1 ∫ ( x 3 + a3 )2 = − 3( x 3 + a3 )

17.14.9.

∫ x(x

3

⎛ ( x + a) 2 ⎞ 1 dx x 2 2x − a tan −1 = 3 3 + 5 ln ⎜ 2 + 3 2 3 ⎝ x − ax + a 2 ⎟⎠ 3a 5 3 +a ) 3a ( x + a ) 9a a 3

⎛ x 2 − ax + a 2 ⎞ 1 x dx x2 1 2x − a = 3 3 + tan −1 ln ⎜ + 3 2 3 4 ⎝ ( x + a)2 ⎟⎠ 3a 4 3 +a ) 3a ( x + a ) 18a a 3

3

3

dx 1 1 ⎛ x3 ⎞ 3 2 = 3 3 3 + 6 ln ⎜ 3 +a ) 3a ( x + a ) 3a ⎝ x + a 3 ⎟⎠

∫

dx 1 x2 4 = − − − 6 2 3 3 2 6 6 3 3 x (x + a ) a x 3a ( x + a ) 3a

17.14.11.

∫

x m dx x m−2 x m −3dx 3 a = − ∫ x 3 + a3 x 3 + a3 m − 2

17.14.12.

∫x

17.14.10.

(15)

dx −1 1 = − ( x 3 + a 3 ) a 3 (n − 1) x n−1 a 3

∫x

n−3

x dx + a3

3

(See 17.14.2.)

dx ( x 3 + a3 )

Integrals Involving x4 ⴞ a4

17.15.1.

∫x

4

17.15.2.

∫x

4

17.15.3.

n

∫x

∫

⎛ x 2 + ax 2 + a 2 ⎞ ⎛ x 2⎞⎤ dx 1 1 ⎡ −1 ⎛ x 2 ⎞ − 3 ln ⎜ 2 − tan −1 ⎜1 + tan ⎜1 − ⎢ 4 = ⎟ ⎟ 3 2 a ⎠ a ⎟⎠ ⎥⎥ +a 4 a 2 ⎝ x − ax 2 + a ⎠ 2a 2 ⎣⎢ ⎝ ⎝ ⎦

2 x dx 1 −1 x 4 = 2 tan 2 +a 2a a

⎛ x 2 − ax 2 + a 2 ⎞ ⎛ x 2⎞⎤ x 2 dx 1 1 ⎡ −1 ⎛ x 2 ⎞ − ln ⎜ 2 ⎢tan ⎜1 − a ⎟ − tan −1 ⎜1 + a ⎟ ⎥ 4 4 = 2⎟ x +a 4 a 2 ⎝ x + ax 2 + a ⎠ 2a 2 ⎢⎣ ⎝ ⎠ ⎝ ⎠ ⎥⎦


x 3 dx 1 = ln ( x 4 + a 4 ) 4 + a4 4

17.15.4.

∫x

17.15.5.

∫ x(x ∫

17.15.6.

dx 1 ⎛ x4 ⎞ 4 = 4 ln ⎜ 4 + a ) 4a ⎝ x + a 4 ⎟⎠

4

⎛ x 2 − ax 2 + a 2 ⎞ dx 1 1 − 5 ln ⎜ 2 4 4 = − 4 x (x + a ) a x 4 a 2 ⎝ x + ax 2 + a 2 ⎟⎠ 2

+

17.15.7.

∫x

3

17.15.8.

∫x

4

17.15.9.

∫x

4

∫

17.15.11.

∫x ∫

17.15.13.

∫x

(16)

2 dx 1 1 −1 x 4 4 = − 4 2 − 6 tan 2 (x + a ) 2a x 2a a

dx 1 1 x ⎛ x − a⎞ = ln tan −1 − a − a 4 4 a 3 ⎜⎝ x + a⎟⎠ 2a 3

x dx 1 ⎛ x 2 − a2 ⎞ 4 = 2 ln ⎜ 2 −a 4a ⎝ x + a 2 ⎟⎠

x 3dx 1 = ln ( x 4 − a 4 ) 4 − a4 4 4

3

dx 1 1 1 x ⎛ x − a⎞ = + ln tan −1 + a ( x 4 − a 4 ) a 4 x 4 a 5 ⎜⎝ x + a⎟⎠ 2a 5

dx 1 1 ⎛ x 2 − a2 ⎞ 4 4 = 4 2 + 6 ln ⎜ 2 x ( x − a ) 2a x 4a ⎝ x + a 2 ⎟⎠ 3

Integrals Involving xn ⴞ an

∫

⎛ xn ⎞ dx 1 = ln ⎜ ⎟ x ( x n + a n ) na n ⎝ x n + a n ⎠

17.16.2.

∫

x n−1dx 1 = ln ( x n + a n ) x n + an n

17.16.3.

x m dx ∫ ( x + a n )r =

17.16.4.

∫x

17.16.5.

∫x

17.16.1.

17.16.6.

2a

⎡ −1 ⎛ x 2 ⎞ ⎛ x 2⎞⎤ ⎢tan ⎜1 − a ⎟ − tan −1 ⎜1 + a ⎟ ⎥ 2 ⎢⎣ ⎝ ⎠ ⎝ ⎠ ⎥⎦

dx 1 ⎛ x 4 − a4 ⎞ 4 = 4 ln ⎜ x ( x − a ) 4a ⎝ x 4 ⎟⎠

17.15.12.

∫

1 5

x x 2 dx 1 ⎛ x − a⎞ 1 ln tan −1 = + a x 4 − a 4 4 a ⎜⎝ x + a⎟⎠ 2a

17.15.10.

17.15.14.

87

n

∫

m

x m − n dx ∫ ( x + a n )r −1 − a n n

dx 1 = ( x n + a n )r a n

∫x

m

x m − n dx ∫ ( x n + a n )r

dx 1 − ( x n + a n )r −1 a n

⎛ x n + an − an ⎞ dx 1 ln = x n + a n n a n ⎜⎝ x n + a n + a n ⎟⎠

dx 1 ⎛ x n − an ⎞ n = n ln ⎜ x ( x − a ) na ⎝ x n ⎟⎠ n

∫x

m−n

dx ( x n + a n )r


88

x n −1dx 1 = ln ( x n − a n ) n − an n

17.16.7.

∫x

17.16.8.

x m dx x m − n dx x m − n dx n a = + n r n n r n ∫ (x − a ) ∫ ( x − a ) ∫ ( x − a n )r −1

17.16.9.

∫x

17.16.10.

∫x

17.16.11.

∫x

n

m

dx 1 = n n r (x − a ) a n

dx x n − an

2

=

n an

∫x

m−n

cos −1

dx 1 − n n n r (x − a ) a

∫x

m

dx ( x − a n )r − n

an xn

m ⎛ x + a cos[(2k − 1)π /2m]⎞ x p−1dx 1 (2k − 1) pπ sin tan −1 ⎜ = 2m 2m− p ∑ ma 2 m +a ⎝ a sin[(2k − 1)π /2m] ⎟⎠ k =1

2m

−

m ⎞ 1 (2k − 1) pπ ⎛ 2 (2k − 1)π cos ln ⎜ x + 2ax cos + a 2⎟ ∑ 2ma 2 m − p k =1 2m 2 m ⎝ ⎠

where 0 < p 2m. 17.16.12.

∫x

m −1 ⎞ x p−1dx 1 kpπ ⎛ 2 kπ cos ln ⎜ x − 2ax cos + a 2⎟ = 2m 2m− p ∑ 2ma m m −a ⎝ ⎠ k =1

2m

−

⎛ x − a cos (kπ /m)⎞ 1 m −1 kpπ sin tan −1 ⎜ ma 2 m − p ∑ m ⎝ a sin (kπ /m) ⎟⎠ k =1 1 {ln ( x − a) + (−1) p ln ( x + a)} 2ma 2 m − p

+ where 0 < p 2m. 17.16.13.

∫

m ⎛ x + a cos[2kπ /(2m + 1)]⎞ x p−1dx 2(−1) p−1 2kpπ sin tan −1 ⎜ = 2 m +1 2 m +1 2 m − p+1 ∑ x (2m + 1)a +a 2m + 1 ⎝ a sin[2kπ /(2m + 1)] ⎟⎠ k =1

−

m ⎞ pπ ⎛ 2 (−1) p−1 2kp 2kπ ln ⎜ x + 2ax cos cos + a 2⎟ 2 m − p+1 ∑ (2m + 1)a 2m + 1 ⎝ 2m + 1 ⎠ k =1

+

(−1) p−1 ln( x + a) (2m + 1)a 2 m − p+1

where 0 < p 2m + 1. 17.16.14.

∫

m ⎛ x − a cos[2kπ /(2m + 1)]⎞ x p−1dx 2kpπ −2 sin tan −1 ⎜ = ∑ 2 m +1 2 m +1 2 m − p+1 x (2m + 1)a 2m + 1 −a ⎝ a sin[2kπ /(2m + 1)] ⎟⎠ k =1

+

m ⎞ 1 2kpπ ⎛ 2 2kπ cos ln ⎜ x − 2ax cos + a 2⎟ ∑ 2 m − p+1 (2m + 1)a 2m + 1 ⎝ 2m + 1 ⎠ k =1

+ where 0 < p 2m + 1.

ln ( x − a) (2m + 1)a 2 m − p+1


(17)

89

Integrals Involving sin ax

17.17.1.

∫ sin ax dx = −

cos ax a

17.17.2.

∫ x sin ax dx =

sin ax x cos ax − a a2

17.17.3.

∫x

17.17.4.

∫x

17.17.5.

sin ax (ax )3 (ax )5 dx = ax − + − ⋅⋅⋅ ∫ x 3 ⋅ 3! 5 ⋅ 5!

17.17.6.

∫

17.17.7.

∫ sin ax = α ln(csc ax − cot ax ) = α ln tan

17.17.8.

∫ sin ax = a

17.17.9.

2x ⎛ 2 x2 ⎞ cos ax 2 sin ax + ⎜ 3 − a ⎟⎠ a ⎝a

2

sin ax dx =

3

⎛ 3x 2 6 ⎞ ⎛ 6x x 3 ⎞ sin ax dx = ⎜ 2 − 4 ⎟ sin ax + ⎜ 3 − ⎟ cos ax a⎠ a ⎠ ⎝a ⎝a

sin ax sin ax cos ax + a∫ dx = − dx (See 17.18.5.) x2 x x dx

1

1

ax 2

⎫⎪ 2(22 n −1 − 1) Bn (ax )2 n+1 (ax )3 7(ax )5 1 ⎧⎪ + ⎬ + ++ ax + 2 ⎨ (2n + 1)! 18 1800 ⎪⎭ ⎩⎪ x sin 2ax ∫ sin 2 ax dx = 2 − 4a x dx

x 2 x sin 2ax cos 2ax − − 4 4a 8a 2

17.17.10.

∫ x sin

17.17.11.

∫ sin

17.17.12.

∫ sin ax dx =

17.17.13.

∫ sin

17.17.14.

∫ sin

17.17.15.

∫ sin px sin qx dx =

17.17.16.

∫ 1 − sin ax = a tan ⎜⎝ 4 +

17.17.17.

∫ 1 − sin ax = a tan ⎜⎝ 4 +

17.17.18.

∫ 1 + sin ax = − α tan ⎜⎝ 4 −

17.17.19.

∫ 1 + sin ax = − a tan ⎜⎝ 4 −

3

2

ax dx =

ax dx = −

4

cos ax cos3 ax + 3a a

3x sin 2ax sin 4 ax − + 8 4a 32a

dx 1 = − cot ax 2 a ax

cos ax ax dx 1 ln tan =− + 3 2 ax 2a sin 2 ax 2a sin ( p − q) x sin ( p + q) x − 2( p − q) 2( p + q)

(If p = ± q, see 17.17.9.)

dx

1

⎛π

ax ⎞ 2 ⎟⎠

x dx

x

⎛π

⎛ π ax ⎞ ax ⎞ 2 + ln sin ⎜ − ⎟ 2 ⎟⎠ a 2 ⎝4 2 ⎠

dx

1

⎛π

ax ⎞ 2 ⎟⎠

x dx

x

⎛π

ax ⎞ 2 ⎛ π ax ⎞ + ln sin ⎜ + ⎟ 2 ⎟⎠ a 2 ⎝4 2 ⎠


90

dx

17.17.20.

∫ (1 − sin ax)

17.17.21.

∫ (1 + sin ax )

17.17.22.

=

2

=−

dx

∫

1 ⎛ π ax ⎞ 1 ⎛ π ax ⎞ tan ⎜ + ⎟ + tan 3 ⎜ + ⎟ a 2a 4 2 6 ⎝ ⎝4 2 ⎠ ⎠

2

⎛ π ax ⎞ 1 ⎛ π ax ⎞ 1 tan ⎜ − ⎟ − tan 3 ⎜ − ⎟ 2a ⎝ 4 2 ⎠ 6a ⎝4 2 ⎠

1 2 ⎧ −1 p tan 2 ax + q tan ⎪a p2 − q 2 p2 − q 2 ⎪ dx = p + q sin ax ⎨ ⎛ p tan 12 ax + q − q 2 − p2 ⎞ ⎪ 1 ln ⎜ ⎟ ⎪ 2 2 ⎝ p tan 12 ax + q + q 2 − p2 ⎠ ⎩a q − p

(If p = ± q, see 17.17.16 and 17.17.18.) 17.17.23.

dx

∫ ( p + q sin ax)

2

=

q cos ax p + a( p2 − q 2 )( p + q sin ax ) p2 − q 2

dx

∫ p + q sin ax

(If p = ± q, see 17.17.20 and 17.17.21.) 17.17.24.

∫p

⎧ p2 − q 2 tan ax 1 −1 tan ⎪ 2 2 p dx ⎪ ap p − q =⎨ 2 2 2 p − q sin ax ⎛ q 2 − p2 tan ax + p⎞ 1 ⎪ ln ⎜ 2 ⎟ 2 2 ⎪ ⎝ q − p2 tan ax − p⎠ ⎩2ap q − p

17.17.25.

∫

17.17.26.

∫x

17.17.27.

∫

17.17.28.

∫ sin

17.17.29.

∫ sin

17.17.30.

∫ sin

(18)

p2 + q 2 tan ax p

dx 1 = tan −1 + q 2 sin 2 ax ap p2 + q 2

2

sin ax dx = −

m

x m cos ax mx m −1 sin ax m(m − 1) + − a a2 a2

sin ax sin ax a + dx = − xn (n − 1) x n −1 n − 1 n

ax dx = −

∫

m−2

sin ax dx

cos ax dx (See 17.18.30.) x n −1

sin n−1 ax cos ax n − 1 + an n

dx n−2 − cos ax = + n n −1 ax a(n − 1) sin ax n − 1

∫x

∫ sin

∫ sin

n− 2

ax dx

dx n− 2

ax

x dx − x cos ax 1 n−2 + = − n ax a(n − 1) sin n−1 ax a 2 (n − 1)(n − 2) sin n− 2 ax n − 1

Integrals Involving cos ax sin ax a

17.18.1.

∫ cos ax dx =

17.18.2.

∫ x cos ax dx =

17.18.3.

∫ x 2 cos ax dx =

cos ax x sin ax + a a2 2x ⎛ x2 2 ⎞ cos + ax ⎜⎝ a − a 3 ⎟⎠ sin ax a2

x dx n− 2 ax

∫ sin


91

17.18.4.

⎛ 3x 2 6 ⎞ ⎛ x 3 6x⎞ 3 cos = − cos x ax dx ax + ⎜ ⎟ ⎜⎝ a − a 3 ⎟⎠ sin ax 2 4 ∫ a ⎠ ⎝a

17.18.5.

cos ax (ax )2 (ax )4 (ax )6 dx = ln x − + − + ∫ x 2 ⋅ 2! 4 ⋅ 4 ! 6 ⋅ 6!

17.18.6.

∫

17.18.7.

∫ cos ax = a ln (sec ax + tan ax ) = a ln tan ⎜⎝ 4 +

17.18.8.

En (ax )2 n+ 2 x dx 1 ⎧(ax )2 (ax )4 5(ax )6 ⎫ = + + + + + ⎬ ⎨ ∫ cos ax a 2 ⎩ 2 (2n + 2)(2n)! 8 144 ⎭

17.18.9.

∫ cos

cos ax cos ax −a dx = − x2 x dx

2

∫

sin ax dx (See 17.17.5.) x

1

1

ax dx =

x 2 x sin 2ax cos 2ax + + 4 4a 8a 2

∫ x cos2 ax dx =

17.18.11.

∫ cos3 ax dx =

sin ax sin 3 ax − 3a a

17.18.12.

∫ cos

3x sin 2ax sin 4 ax + + 8 4a 32a

17.18.13.

∫ cos

17.18.14.

∫ cos

17.18.15.

∫ cos ax cos px dx =

17.18.16.

∫ 1 − cos ax = − a cot

17.18.17.

∫ 1 − cos ax = − a cot

17.18.18.

∫ 1 + cos ax = a tan

17.18.19.

∫ 1 + cos ax = a tan

17.18.20.

∫ (1 − cos ax)

17.18.21.

∫ (1 + cos ax)

17.18.22.

ax dx =

dx 2

ax

=

ax ⎞ 2 ⎟⎠

x sin 2ax + 2 4a

17.18.10.

4

⎛π

tan ax a

sin ax dx 1 ⎛ π ax ⎞ ln tan ⎜ + ⎟ = + 3 ax 2a cos 2 ax 2a ⎝4 2 ⎠

dx

1

ax 2

x dx

x

ax 2 ax + ln sin 2 a2 2

dx

1

ax 2

x dx

x

ax 2 ax + ln cos 2 a2 2

dx

2

=−

2

=

dx

∫

sin(a − p) x sin(a + p) x + 2(a − p) 2(a + p)

(If a = ± p, see 17.18.9.)

1 ax 1 ax cot cot 3 − 2a 2 6a 2

1 ax 1 ax tan tan 3 + 2a 2 6a 2

⎧ 2 1 tan −1 ( p − q) / ( p + q) tan ax ⎪ 2 2 2 dx ⎪ a p −q =⎨ ⎛ tan 1 ax + (q + p) / (q − p) ⎞ p + q cos ax ⎪ 1 2 ln ⎜ ⎪ a q 2 − p2 ⎝ tan 1 ax − (q + p) / (q − p) ⎟⎠ 2 ⎩

(If p = ± q, see 17.18.16 and 17.18.18.)


92

dx

17.18.23.

∫ ( p + q cos ax )

17.18.24.

∫p

2

q sin ax p − a(q 2 − p2 )( p + q cos ax ) q 2 − p2

(If p = ± q see 17.18.19 and 17.18.20.)

dx

∫ p + q cos ax

dx 1 p tan ax = tan −1 + q 2 cos 2 ax ap p2 + q 2 p2 + q 2

2

1 ⎧ −1 p tan ax ⎪ap p2 − q 2 tan p2 − q 2 dx ⎪ = ⎛ p tan ax − q 2 − p2 ⎞ p2 − q 2 cos 2 ax ⎨ 1 ⎪ ln ⎪2ap q 2 − p2 ⎜⎝ p tan ax + q 2 − p2 ⎟⎠ ⎩

17.18.25.

∫

17.18.26.

∫ x m cos ax dx =

17.18.27.

∫

17.18.28.

∫ cosn ax dx =

17.18.29.

∫ cos

17.18.30.

∫ cos

(19)

=

x m sin ax mx m −1 m(m − 1) + cos ax − a a2 a2

cos ax cos ax a − dx = − n n −1 x (n − 1) x n −1

dx n

ax

=

∫

∫x

m−2

cos ax dx

sin ax dx (Seee 17.17.27.) x n −1

sin ax cos n−1 ax n − 1 + an n

∫ cos

n−2 sin ax + a(n − 1) cos n−1 ax b − 1

∫ cos

n− 2

ax dx

dx n− 2

ax

x dx x sin ax 1 n−2 = − + n ax a(n − 1) cos n−1 ax a 2 (n − 1)(n − 2) cos n− 2 ax n − 1

Integrals Involving sin ax and cos ax

17.19.1.

∫ sin ax cos ax dx =

sin 2 ax 2a

17.19.2.

sin px cos qx dx = −

cos( p − q) x cos( p + q) x − 2( p − q) 2( p + q)

17.19.3.

∫ sin n ax cos ax dx =

sin n+1 ax (n + 1)a

17.19.4.

∫ cos ax sin ax dx = −

cos n+1 ax (n + 1)a

17.19.5.

∫ sin

x sin 4 ax − 8 32a

17.19.6.

∫ sin ax cos ax = a ln tan ax

17.19.7.

∫ sin

17.19.8.

∫ sin ax cos

17.19.9.

∫ sin

n

2

ax cos 2 ax dx = dx

2

(If n = −1, see 17.20.1.)

1

dx 1 1 ⎛ π ax ⎞ = ln tan ⎜ + ⎟ − ax cos ax a ⎝ 4 2 ⎠ a sin ax dx

2

(If n = −1, see 17.21.1.)

2

ax

=

ax 1 1 ln tan + a 2 a cos ax

dx 2 cot 2ax =− a ax cos 2 ax

x dx

∫ cos

n− 2

ax


93

17.19.10.

sin 2 ax sin ax 1 ⎛ ax π ⎞ ∫ cos ax dx = − a + a ln tan ⎜⎝ 2 + 4 ⎟⎠

17.19.11.

∫

17.19.12.

∫ cos ax (1 ± sin ax ) = ∓ 2a(1 ± sin ax ) + 2a ln tan ⎜⎝ 2

17.19.13.

∫ sin ax (1 ± cos ax ) = ± 2a(1 ± cos ax ) + 2a ln tan

17.19.14.

∫ sin ax ± cos ax = a

17.19.15.

∫ sin ax ± cos ax = 2 ∓ 2a ln (sin ax ± cos ax )

17.19.16.

∫ sin ax ± cos ax = ± 2 + 2a ln (sin ax ± cos ax )

17.19.17.

∫ p + q cos ax = − aq ln ( p + q cos ax)

17.19.18.

∫ p + q sin ax = aq ln ( p + q sin ax)

17.19.19.

∫ ( p + q cos ax )

17.19.20.

∫ ( p + q sin ax)

17.19.21.

17.19.22.

cos 2 ax cos ax 1 ax dx = + ln tan sin ax 2 a a dx

1

1

⎛ ax

dx

1

1

ax 2

dx

sin ax dx

1

x

sin ax dx

⎛ ax π ⎞ ln tan ⎜ ± ⎟ ⎝ 2 8⎠ 2 1

x

cos ax dx

π⎞ + ⎟ 4⎠

1

1

cos ax dx

1

sin ax dx

n

cos ax dx

n

=

1 aq(n − 1)( p + q cos ax )n−1

=

−1 aq(n − 1)( p + q sin ax )n−1

∫

⎛ ax + tan −1 (q / p) ⎞ dx 1 ln tan ⎜ = ⎟⎠ p sin ax + q cos ax a p2 + q 2 2 ⎝

∫

⎧ ⎛ p + (r − q) tan(ax / 2 ) ⎞ 2 ⎪ tan −1 ⎜ ⎟ ⎪ a r 2 − p2 − q 2 r 2 − p2 − q 2 ⎝ ⎠ dx =⎨ p sin ax + q cos ax + r ⎪ ⎛ p − p2 + q 2 − r 2 + (r − q) tan (ax / 2 ) ⎞ 1 ln ⎜ ⎟ ⎪ 2 2 2 ⎝ p + p2 + q 2 − r 2 + (r − q) tan (ax / 2 ) ⎠ ⎩a p + q − r

(If r = q see 17.19.23. If r2 = p2 + q2 see 17.19.24.) 17.19.23.

dx

1

⎛

∫ p sin ax + q(1 + cos ax ) = ap ln ⎜⎝q + p tan

ax ⎞ 2 ⎟⎠

dx −1 ⎛ π ax + tan −1 (q / p)⎞ = tan ⎜⎝ 4 ∓ ⎟⎠ 2 p sin ax + q cos ax ± p2 + q 2 a p2 + q 2

17.19.24.

∫

17.19.25.

∫p

2

17.19.26.

∫p

2

dx 1 ⎛ p tan ax ⎞ = tan −1 ⎜ sin 2 ax + q 2 cos 2 ax apq ⎝ q ⎟⎠ dx 1 ⎛ p tan ax − q⎞ ln ⎜ = 2 2 apq 2 sin ax − q cos ax ⎝ p tan ax + q⎟⎠ 2


94

17.19.27.

⎧ sin m −1 ax cos n+1 ax m − 1 sin m − 2 ax cos n ax dx + ⎪− a(m + n) m+n ∫ m n sin ax cos ax dx = ⎨ m +1 n −1 ∫ ⎪ sin ax cos ax + n − 1 sin m ax cos n− 2 ax dx m+n ∫ a (m + n ) ⎩

17.19.28.

sin m −1 ax m − 1 sin m − 2 ax ⎧ − − 1 n ⎪ a(n − 1) cos ax n − 1 ∫ cos n− 2 ax dx ⎪ m sin m +1 ax sin ax m−n+2 sin m ax ⎪ dx = − ⎨ ∫ cosn−2 ax dx ∫ cosn ax n −1 a(n − 1) cos n−1 ax ⎪ m − 1 sin m − 2 ax − sin m −1 ax ⎪ dx + n −1 ⎪⎩ a(m − n) co os ax m − n ∫ cos n ax

17.19.29.

− cos m −1 ax m − 1 cos m − 2 ax ⎧ ⎪ a(n − 1) sin n−1 ax − n − 1 ∫ sin n− 2 ax dx ⎪ − cos m +1 ax cos m ax cos m ax m−n+2 ⎪ dx = − ⎨ n n − 1 ∫ sin n−2 ax dx ∫ sin ax n −1 a(n − 1) sin ax ⎪ cos m −1 ax m − 1 cos m − 2 ax ⎪ + n −1 ∫ sin n ax dx ⎩⎪ a(m − n) sin ax m − n

17.19.30.

1 m+n−2 ⎧ ⎪ a(n − 1) sin m −1 ax cos n−1 ax + n − 1 dx ∫ sin m ax cosn ax = ⎨ −1 m+n−2 ⎪ + m −1 ⎪⎩a(m − 1) sin m −1 ax cos n−1 ax

(20)

dx ax cos n− 2 ax dx ∫ sin m−2 ax cosn ax

∫ sin

m

Integrals Involving tan ax 1

1

17.20.1.

∫ tan ax dx = − a ln cos ax = a ln sec ax

17.20.2.

∫ tan

17.20.3.

∫ tan3 ax dx =

17.20.4.

∫ tan

17.20.5.

∫

17.20.6.

∫ tan ax = a ln sin ax

17.20.7.

∫ x tan ax dx =

2

n

ax dx =

tan ax −x a tan 2 ax 1 + ln cos ax 2a a

ax sec 2 ax dx =

tan n+1 ax (n + 1)a

sec 2 ax 1 dx = ln tan ax tan ax a dx

1

22 n (22 n − 1) Bn (ax )2 n+1 1 ⎧(ax )3 (ax )5 2(ax )7 ⎫ + ⎬ + + + + ⎨ 2 ( 2 n + 1 )! 3 15 105 a ⎩ ⎭

22 n (22 n − 1) Bn (ax )2 n−1 tan ax (ax )3 2(ax )5 + ++ + dx = ax + 9 75 (2n − 1)(2n)! x

17.20.8.

∫

17.20.9.

∫ x tan

2

ax dx =

x tan ax 1 x2 + 2 ln cos ax − 2 a a


dx

17.20.10.

∫ p + q tan ax =

17.20.11.

∫ tan

(21)

n

ax dx =

95

px q + ln (q sin ax + p cos ax ) p 2 + q 2 a( p 2 + q 2 )

tan n −1 ax − tan n − 2 ax dx (n − 1)a ∫

Integrals Involving cot ax 1

17.21.1.

∫ cot ax dx = a ln sin ax

17.21.2.

∫ cot

17.21.3.

∫ cot ax dx = −

17.21.4.

∫ cot n ax csc2 ax dx = −

17.21.5.

∫

17.21.6.

∫ cot ax = − a ln cos ax

17.21.7.

∫ x cot ax dx =

17.21.8.

∫

17.21.9.

∫ x cot

2

ax dx = −

3

cot ax −x a cot 2 ax 1 − ln sin ax 2a a cot n+1 ax (n + 1)a

csc 2 ax 1 dx = − ln cot ax cot ax a dx

1

1 a2

22 n Bn (ax )2 n+1 (ax )3 (ax )5 ⎪⎧ ⎪⎫ − − − − ⎬ ⎨ax − (2n + 1)! 9 225 ⎪⎩ ⎪⎭

22 n Bn (ax )2 n −1 cot ax 1 ax (ax )3 − − − − − dx = − x ax 3 135 (2n − 1)(2n)! 2

ax dx = −

17.21.10.

∫ p + q cot ax =

px q − ln (q sin ax + q cos ax ) p 2 + q 2 a( p 2 + q 2 )

17.21.11.

∫ cot n ax dx = −

cot n −1 ax − cot n − 2 ax dx (n − 1)a ∫

(22)

dx

x cot ax 1 x2 + 2 ln sin ax − 2 a a

Integrals Involving sec ax 1

1

⎛ ax

π⎞ + ⎟ 4⎠

17.22.1.

∫ sec ax dx = a ln (sec ax + tan ax) = a ln tan ⎜⎝ 2

17.22.2.

∫ sec ax dx =

tan ax a

17.22.3.

∫ sec ax dx =

sec ax tan ax 1 + ln (sec ax + tan ax ) 2a 2a

2

3


96

sec n ax na

17.22.4.

∫ secn ax tan ax dx =

17.22.5.

∫ sec ax =

17.22.6.

∫ x sec ax dx =

17.22.7.

En (ax )2 n sec ax (ax )2 5(ax )4 61(ax )6 + dx = ln x + + + + + ∫ x 4 96 4320 2n(2n)!

17.22.8.

∫ x sec ax dx = a tan ax + a

17.22.9.

∫ q + p sec ax = q − q ∫ p + q cos ax

dx

sin ax a

1

x

2

dx

x

2

ln cos ax

p

dx

sec n − 2 ax tan ax n − 2 + a(n − 1) n −1

∫ sec

n−2

17.23.1.

∫ csc ax dx = a ln (csc ax − cot ax) = a ln tan

ax 2

17.23.2.

∫ csc ax dx = −

cot ax a

17.23.3.

∫ csc ax dx = −

csc ax cot ax 1 ax + ln tan 2a 2a 2

17.23.4.

∫ cscn ax cot ax dx = −

17.23.5.

∫ csc ax = −

17.23.6.

∫ x csc ax dx =

17.23.7.

∫

17.23.8.

∫ x csc ax dx = −

17.23.9.

∫ q + p csc ax = q − q ∫ p + q sin ax

17.22.10.

(23)

∫ sec

E (ax )2 n+ 2 ⎪⎧(ax )2 (ax )4 5(ax )6 ⎪⎫ + ⎬ + + ++ n ⎨ (2n + 2)(2n)! 8 144 ⎪⎩ 2 ⎪⎭

1 a2

n

ax dx =

ax dx

Integrals Involving csc ax

17.23.10.

1

1

2

3

dx

csc n ax na

cos ax a 2(22 n −1 − 1) Bn (ax )2 n+1 (ax )3 7(ax )5 ⎪⎧ ⎪⎫ + ⎬ + ++ ⎨ax + (2n + 1)! 18 1800 ⎪⎩ ⎪⎭

1 a2

2(22 n −1 − 1) Bn (ax )2 n −1 csc ax 1 ax 7(ax )3 + dx = − + + ++ (2n − 1)(2n)! x ax 6 1080 2

dx

∫ cscn ax dx = −

x cot ax 1 + 2 ln sin ax a a

x

p

dx

csc n − 2 ax cot ax n − 2 + a(n − 1) n −1

(See 17.17.22.)

∫ csc

n−2

ax dx


(24)

Integrals Involving Inverse Trigonometric Functions x x dx = x sin −1 + a 2 − x 2 a a

17.24.1.

∫ sin

17.24.2.

∫ x sin −1

x x x a2 − x 2 ⎛ x 2 a2 ⎞ dx = ⎜ − ⎟ sin −1 + 4⎠ 4 a a ⎝2

17.24.3.

∫ x 2 sin −1

x x3 x ( x 2 + 2a 2 ) a 2 − x 2 sin −1 + dx = 3 9 a a

17.24.4.

∫

sin −1 ( x /a) x ( x/a)3 1 i 3( x/a)5 1 i 3 i 5( x /a)7 + + + dx = + x a 2i 3i 3 2i 4 i 5i 5 2i 4 i 6i 7i 7

17.24.5.

∫

sin −1 ( x /a) sin −1 ( x/a) 1 ⎛ a + a 2 − x 2 ⎞ dx = − − ln ⎜ 2 ⎟ x a ⎝ x x ⎠

17.24.6.

⎛ ∫ ⎜⎝sin −1

x x⎞ x⎞ ⎛ dx = x ⎜sin −1 ⎟ − 2 x + 2 a 2 − x 2 sin −1 a a⎟⎠ a ⎝ ⎠

17.24.7.

∫ cos

x x dx = x cos −1 − a 2 − x 2 a a

17.24.8.

∫ x cos−1

x x x a2 − x 2 ⎛ x 2 a2 ⎞ dx = ⎜ − ⎟ cos −1 − a a 4⎠ 4 ⎝2

17.24.9.

∫x

x x3 x ( x 2 + 2a 2 ) a 2 − x 2 dx = cos −1 − a a 3 9

2

−1

−1

2

cos −1

2

17.24.10.

cos −1 ( x/a) π sin −1 ( x/a) dx x ln = − ∫ x ∫ x dx (See 17.224.4.) 2

17.24.11.

∫

17.24.12.

⎛ ∫ ⎜⎝cos−1

17.24.13.

∫ tan

17.24.14.

∫ x tan

17.24.15.

∫x

17.24.16.

∫

tan −1 ( x/a) x ( x/a)3 ( x/a)5 ( x/a)7 + − dx = − + x a 32 52 72

17.24.17.

∫

tan −1 ( x/a) 1 x 1 ⎛ x 2 + a2 ⎞ dx = − tan −1 − ln 2 x x a 2a ⎜⎝ x 2 ⎠⎟

cos −1 ( x /a) cos −1 ( x/a) 1 ⎛ a + a 2 − x 2 ⎞ dx = − + ln ⎜ 2 ⎟ x a ⎝ x x ⎠

2

−1

2

2

x x⎞ x⎞ ⎛ dx = x ⎜ cos −1 ⎟ − 2 x − 2 a 2 − x 2 cos −1 a a⎟⎠ a ⎝ ⎠

x x a dx = x tan −1 − ln ( x 2 + a 2 ) a a 2 −1

tan −1

1 x x ax dx = ( x 2 + a 2 ) tan −1 − 2 a a 2 x x3 x ax 2 a 3 dx = + ln ( x 2 + a 2 ) tan −1 − a a 3 6 6

97


98

x x a + ln (x 2 + a 2 ) dx = x cot −1 a a 2

17.24.18.

∫ cot

17.24.19.

∫ x cot

17.24.20.

∫ x 2 cot −1

17.24.21.

cot −1 ( x/a) π tan −1 ( x /a) = ln − dx x ∫ x ∫ x dx 2

17.24.22.

cot −1 ( x/a) cot −1 ( x /a) 1 ⎛ x 2 + a 2 ⎞ = + dx ln ∫ x2 2a ⎜⎝ x 2 ⎟⎠ x

17.24.23.

⎧ −1 ⎪x sec x −1 sec = dx ⎨ ∫ a ⎪x sec −1 ⎩

17.24.24.

⎧ x2 a x 2 − a2 −1 x − sec ⎪ x ⎪ 2 a ∫ x sec −1 a dx = ⎨ x22 2 x a x − a2 ⎪ sec −1 + ⎪⎩ 2 2 a

17.24.25.

⎧ x3 ax x 2 − a 2 a 3 −1 x − − ln(x + x 2 − a 2 ) sec ⎪ ⎪3 6 6 a 2 −1 x ∫ x sec a dx = ⎨ x 3 2 2 3 − x ax x a a ⎪ sec −1 + + ln(x + x 2 − a 2 ) ⎪⎩ 3 6 6 a

17.24.26.

sec −1 ( x/a) π a (a/x )3 1i3(a/x )5 1 i 3 i 5(a/x )7 = ln + + + dx x + + ⋅⋅⋅ ∫ x 2 x 2 i 3 i 3 2 i 4 i 5i 5 2 i 4 i 6 i 7i 7

17.24.27.

⎧ sec −1 ( x /a) + ⎪− x ⎪ −1 sec ( x/a) ⎪ ∫ x 2 dx = ⎨ sec −1 ( x/a) ⎪− − x ⎪ ⎩⎪

17.24.28.

⎧ x x csc −1 + a ln ( x + x 2 − a 2 ) ⎪ x ∫ csc −1 a dx = ⎨ −1 xa ⎪x csc − a ln ( x + x 2 − a 2 ) a ⎩

17.24.29.

⎧ x2 ⎪⎪ csc −1 −1 x ∫ x csc a dx = ⎨ x22 ⎪ csc −1 ⎪⎩ 2

17.24.30.

⎧x3 x ax x 2 − a 2 a 3 + ln ( x + x 2 − a 2 ) ⎪ csc −1 + 3 6 6 a x ⎪ ∫ x 2 csc −1 a dx = ⎨ 3 ⎪x ax x 2 − a 2 a 3 −1 x − − ln ( x + x 2 − a 2 ) ⎪ csc 6 6 a ⎩3

−1

−1

1 x x ax + dx = ( x 2 + a 2 ) cot −1 2 2 a a x x3 x ax 2 a3 + − ln ( x 2 + a 2 ) cot −1 dx = 3 6 6 a a (See 17.24.16.)

x π < a 2 π x < sec −1 < π 2 a

x − a ln ( x + x 2 − a 2 ) a x + a ln (x + x 2 − a 2 ) a

0 < sec −1

x π < a 2 π x < sec −1 < π 2 a 0 < sec −1

x π < a 2 π x < sec −1 < π 2 a 0 < sec −1

x π < a 2

x 2 − a2 ax

0 < sec −1

x 2 − a2 ax

π x < sec −1 < π 2 a x π < a 2 π x − < csc −1 < 0 2 a 0 < csc −1

x a x 2 − a2 x π + < 0 < csc −1 a a 2 2 x a x 2 − a2 π x − − < csc −1 a ⎨ ∫ x 2 2i2i2 2 i 4 i 4 i 4 2i 4 i 6 i 6 i 6 ⎪ ⎪ 4 6 2 2 ⎪− ln (−2 x /a) + (a /x ) − 1 i 3(a /x ) + 1 i3 i5(a /x ) − ⋅⋅⋅ x < −a 2 2i2i2 ⎪⎩ 2 i 4 i 4 i 4 2i 4 i 6 i 6 i 6

17.34.4.

⎧x cosh −1 ( x /a) − x 2 − a 2 , cosh −1 ( x /a) > 0 x ⎪ ∫ cosh a dx = ⎨ −1 2 2 −1 ⎩⎪x cosh ( x /a) + x − a , cosh ( x /a) < 0

17.34.5.

1 ⎧1 (2 x 2 − a 2 ) cosh −1 ( x /a) − x x 2 − a 2 , cosh −1 ( x /a) > 0 ⎪ 4 4 x ⎪ ∫ x cosh −1 a dx = ⎨1 ⎪ (2 x 2 − a 2 ) cosh −1 ( x /a) + 1 x x 2 − a 2 , cosh −1 ( x /a) < 0 ⎪⎩4 4

17.34.6.

−1

−1

x x x 2 + a2 ⎛ x 2 a2 ⎞ dx = ⎜ + ⎟ sinh −1 − x a a 4⎠ 4 ⎝2

−1

cosh −1 ( x /a) (a /x )2 1 i 3(a /x )4 1 i 3 i 5(a /x )6 ⎡1 2 ⎤ ln ( ) = ± + + 2 + + ⋅⋅⋅⎥ dx x / a ∫ ⎢⎣ 2 x 2i2i2 2i4i4i4 2i4i6i6i6 ⎦ + if cosh −1 ( x /a) > 0, − if cosh −1 ( x /a) < 0

17.34.7.

∫ tanh

17.34.8.

∫ x tanh

17.34.9.

∫

x x a dx = x tanh −1 + ln (a 2 − x 2 ) a a 2

−1

−1

x ax 1 2 x + ( x − a 2 ) tanh −1 dx = 2 2 a a

tanh −1 ( x /a) x ( x /a)3 ( x /a)5 + + ⋅⋅⋅ dx = + x a 32 52 x a dx = x coth −1 x + ln ( x 2 − a 2 ) a 2

17.34.10.

∫ coth

17.34.11.

∫ x coth

17.34.12.

coth −1 ( x /a) ⎛ a (a /x )3 (a /x )5 ⎞ = − dx ⎜⎝ x + 32 + 52 + ⋅⋅⋅⎟⎠ ∫ x

17.34.13.

−1 −1 −1 x ⎪⎧x sech ( x /a) + a sin ( x /a), sech ( x /a) > 0 ∫ sech a dx = ⎨⎪x sech −1 ( x /a) − a sin −1 ( x /a), sech −1 ( x /a) < 0 ⎩

17.34.14.

∫ csch

−1

−1

x ax 1 2 x dx = + ( x − a 2 ) coth −1 a a 2 2

−1

−1

x x x dx = x csch −1 ± a sinh −1 a a a

(+ if x > 0, − if x < 0)


1 x x m +1 x sinh −1 − dx = a m +1 a m +1

17.34.15.

∫ x m sinh −1

17.34.16.

⎧ x m +1 −1 ⎪ m + 1 cosh x ⎪ ∫ x m cosh −1 a dx = ⎨ x m+1 ⎪ −1 ⎪ m + 1 cosh ⎩

107

x m +1

∫

dx

x 2 + a2

1 x − a m +1

∫

x 1 + a m +1

∫

x m +1 x 2 − a2 x m +1 x −a 2

2

17.34.17.

∫ x m tanh −1

x x m +1 x a dx = tanh −1 − a m +1 a m +1

x m +1 ∫ a 2 − x 2 dx

17.34.18.

∫x

1 x x m +1 x coth −1 − dx = a m +1 a m +1

∫a

17.34.19.

⎧ x m +1 −1 ⎪ m + 1 sech x ⎪ ∫ x m sech −1 a dx = ⎨ x m+1 ⎪ −1 ⎪ m + 1 sech ⎩

17.34.20.

m

coth −1

∫ x m csch −1

cosh −1 ( x /a) > 0

dx

cosh −1 ( x /a) < 0

x m +1 dx − x2

2

x m dx

x a + a m +1

∫

x 1 − a m +1

∫

a2 − x 2

∫

x m dx x 2 + a2

x x m +1 x a csch −1 ± dx = a m +1 a m +1

dx

a −x 2

2

x m dx

sech −1 ( x /a) > 0 sech −1 ( x /a) < 0

(+ if x > 0, − if x < 0)

18

DEFINITE INTEGRALS

Definition of a Definite Integral Let f(x) be defined in an interval a x b. Divide the interval into n equal parts of length x = (b − a)/n. Then the definite integral of f(x) between x = a and x = b is defined as 18.1.

∫

b

f ( x ) dx = lim{ f (a) Δx + f (a + Δx ) Δx + f (a + 2Δx ) Δx + + f (a + (n − 1) Δx ) Δx} n→∞

a

The limit will certainly exist if f(x) is piecewise continuous. d If f ( x ) = g( x ), then by the fundamental theorem of the integral calculus the above definite integral can dx be evaluated by using the result 18.2.

∫

b

f ( x ) dx = ∫

a

b d g( x ) dx = g( x ) = g(b) − g(a) a dx

b a

If the interval is infinite or if f(x) has a singularity at some point in the interval, the definite integral is called an improper integral and can be defined by using appropriate limiting procedures. For example, ∞

b

18.3.

∫

a

18.4.

∫

−∞

∫

a

18.5. 18.6.

∫

f ( x ) dx = lim ∫ f ( x ) dx b→∞ a

∞

b

b a

b

f ( x ) dx = lim ∫ f ( x ) dx a→−∞ a

b→∞

f ( x ) dx = lim ∫

b −∈

f ( x ) dx = lim ∫

b

α

∈→ 0

∈→ 0

a +∈

f ( x ) dx

if b is a singular point..

f ( x ) dx if a is a singulaar point.

General Formulas Involving Definite Integrals b

18.7.

∫

a

18.8.

∫

a

18.9.

∫

18.10.

∫

18.11.

∫

108

b

a a b a b a

{ f ( x ) ± g( x ) ± h( x ) ± } dx =

∫

b

b a

f ( x ) dx ±

∫

b a

cf ( x ) dx = c ∫ f ( x ) dx where c is any constant. a

f ( x ) dx = 0 a

f ( x ) dx = − ∫ f ( x ) dx b

c

b

a

c

f ( x ) dx = ∫ f ( x ) dx + ∫ f ( x ) dx

b

g( x ) dx ± ∫ h( x ) dx ± a

DEFINITE INTEGRALS

18.12.

∫

b

109

f ( x ) dx = (b − a) f (c)

a

where c is between a and b.

This is called the mean value theorem for definite integrals and is valid if f(x) is continuous in a x b. 18.13.

∫

b

b

f ( x ) g( x ) dx = f (c) ∫ g( x ) dx

a

a

where c is between a and b

This is a generalization of 18.12 and is valid if f(x) and g(x) are continuous in a x b and g(x) 0.

Leibnitz’s Rules for Differentiation of Integrals 18.14.

d dα

∫

φ 2 (α )

φ 1 (α )

F ( x , α ) dx = ∫

φ 2 (α )

φ 1(α )

dφ dφ ∂F dx + F (φ2 , α ) 2 − F (φ1 , α ) 1 dα dα dα

Approximate Formulas for Definite Integrals In the following the interval from x = a to x = b is subdivided into n equal parts by the points a = x0, x1, x2, …, xn–1, xn = b and we let y0 = f(x0), y1 = f(x1), y2 = f(x2), …, yn = f(xn), h = (b – a)/n. Rectangular formula: 18.15.

∫

b a

f ( x ) dx ≈ h( y0 + y1 + y2 + + yn−1 )

Trapezoidal formula: 18.16.

∫

b a

f ( x ) dx ≈

h ( y + 2 y1 + 2 y2 + + 2 yn−1 + yn ) 2 0

Simpson’s formula (or parabolic formula) for n even: 18.17.

∫

b a

f ( x ) dx ≈

h ( y + 4 y1 + 2 y2 + 4 y3 + + 2 yn− 2 + 4 yn−1 + yn ) 3 0

Definite Integrals Involving Rational or Irrational Expressions 18.18.

∫

18.19.

∫

∞ 0 ∞ 0 ∞

dx π = x 2 + a 2 2a x p−1dx π = , 1+ x sin pπ

0 < p 0 sin px ⎪⎪ dx = ⎨ 0 p = 0 x ⎪ ⎪⎩−π /2 p < 0 ⎧ 0 ⎪⎪ sin px cos qx dx = ⎨π /2 x ⎪ ⎩⎪π /4 ⎧⎪π p/2 sin px sin qx dx = ⎨ 2 x ⎪⎩π q/2 sin 2 px πp dx = 2 x2 1 − cos px πp dx = 2 x2

p>q>0 0< p0 0 < pq pq > 0

m = 1, 2,… m = 1, 2,…

DEFINITE INTEGRALS

∞

18.38.

∫

0

18.39.

∫

0

18.40.

∫

0

18.41.

∫

0

18.42.

∫

0

18.43.

∫

18.44.

∫

18.45.

∫

18.46.

∫

18.47.

∫

18.48.

∫

∞

∞

∞

∞

x sin mx π − ma e 2 2 dx = 2 x +a sin mx π dx = 2 (1 − e − ma ) x(x 2 + a2 ) 2a

0

π /2 0 2π 0 2π

0

∫

0

∫

0

18.52.

∫

0

18.53.

∫

0

18.54.

∫

18.55.

∫

2π a2 − b2

dx = a + b cos x

2π a2 − b2

cos −1 (b / a) dx = a + b cos x a2 − b2 dx = (a + b sin x )2

2π 0

dx 2π a = (a + b cos x )2 (a 2 − b 2 )3/2

dx 2π = , 1 − 2a cos x + a 2 1 − a 2

∞

∫

sin ax 2 dx =

0 < a 1

1 π Γ (1/n) cos , 2n na1/n

cos ax n dx = sin x dx = x

∞ 0

a 2 < 1,

1 π Γ (1/n) sin , 2n na1/n

∞

0

∫

cos mx dx π am , 2 = 1 − 2a cos x + a 1 − a2

π

18.50.

dx = a + b sin x

⎧⎪(π /a) ln (1 + a), x sin x dx = 1 − 2a cos x + a 2 ⎨π ln (1 + 1/a), ⎪⎩

π

0

∫

cos mx π − ma e 2 2 dx = a 2 x +a

2π

∫

18.56.

cos px − cos qx π (q − p) dx = 2 2 x

0

18.49.

18.51.

cos px − cos qx q dx = ln x p

2π

0

111

n >1

cos x π dx = 2 x

sin x π dx = , 2Γ ( p) sin ( pπ /2) xp cos x π dx = , 2Γ ( p) cos ( pπ /2) xp

sin ax 2 cos 2bx dx =

1 2

π 2a

0 < p 0

π /4

π /2

a 0

115

ln (1 + tan x ) dx =

π ln 2 8

⎛ 1 + b cos x ⎞ 1 sec x ln ⎜ dx = {(cos −1 a)2 − (cos −1 b)2} ⎟ 2 ⎝ 1 + a cos x ⎠

x⎞ ⎛ ⎛ sin a sin 2a sin 3a ⎞ ln ⎜ 2 sin ⎟ dx = − ⎜ 2 + + 2 + ⎟ 2⎠ 22 3 ⎝ ⎝ 1 ⎠

See also 18.102.

Definite Integrals Involving Hyperbolic Functions 18.112.

∫

18.113.

∫

18.114.

∫

18.115.

∫

∞ 0 ∞ 0 ∞ 0 ∞ 0

sin ax π aπ dx = tanh 2b 2b sinh bx cos ax π aπ sech dx = 2b 2b cosh bx x dx π2 = 2 sinh ax 4 a

{

}

x n dx 2n+1 − 1 1 1 1 = n n+1 Γ (n + 1) n+1 + n+1 + n+1 + sinh ax 2 a 1 2 3

If n is an odd positive integer, the series can be summed. 18.116.

∫

18.117.

∫

∞ 0 ∞ 0

1 sinh ax π aπ dx = csc − b 2a 2b e bx + 1 aπ sinh ax π 1 dx = cot − b 2a 2b e bx − 1

Miscellaneous Definite Integrals 18.118.

∫

f (ax ) − f (bx ) b dx = { f (0) − f (∞)}ln x a

∞ 0

This is called Frullani’s integral. It holds if f ′(x) is continuous and 18.119.

∫

18.120.

∫

1 0

dx 1 1 1 x = 1 + 2 + 3 + x 1 2 3

a −a

(a + x )m −1 (a − x )n−1 dx = (2a)m + n−1

Γ (m ) Γ (n) Γ (m + n)

∫

∞ 0

f ( x ) − f (∞) dx converges. x

Section V: Differential Equations and Vector Analysis

19

BASIC DIFFERENTIAL EQUATIONS and SOLUTIONS

DIFFERENTIAL EQUATION

SOLUTION

19.1. Separation of variables f1 ( x ) g ( y) dx + ∫ 2 dy = c f2 ( x ) g1 ( y)

∫

f1(x) g1(y) dx + f2(x) g2(y) dy = 0

19.2. Linear first order equation ye ∫

dy + p( x ) y = Q( x ) dx

Pdx

= ∫ Qe ∫

Pdx

dx + c

19.3. Bernoulli’s equation dy + P( x ) y = Q( x ) y n dx

e

∫

(1− n ) Pdx

= (1 − n) ∫ Qe

∫

(1− n ) Pdx

dx + c

where y = y1−n. If n = 1, the solution is ln y = ∫ (Q − P) dx + c

19.4. Exact equation M(x, y)dx + N(x, y)dy = 0 where ∂M/∂y = ∂N/∂x.

⎛

∂

⎞

∫ M ∂x + ∫ ⎜⎝ N − ∂y ∫ M ∂x⎟⎠ dy = c where ∂x indicates that the integration is to be performed with respect to x keeping y constant.

19.5 Homogeneous equation

⎛ y⎞ dy = F⎜ ⎟ dx ⎝ x⎠

ln x =

d

∫ F ( ) − + c

where y = y/x. If F(y) = y, the solution is y = cx.


BASIC DIFFERENTIAL EQUATIONS AND SOLUTIONS

117

19.6.

y F(xy) dx + x G(xy) dy = 0

ln x =

G ( ) d

∫ {G( ) − F ( )} + c

where y = xy. If G(y) = F(y), the solution is xy = c.

19.7. Linear, homogeneous second order equation

d2y dy +a + by = 0 2 dx dx

Let m1, m2 be the roots of m2 + am + b = 0. Then there are 3 cases. Case 1. m1, m2 real and distinct: y = c1e m x + c2 e m x 1

Case 2.

2

m1, m2 real and equal: y = c1 e m x + c2 x e m x 1

a, b are real constants. Case 3.

m1 = p + qi,

1

m2 = p − qi:

y = e px (c1 cos qx + c2 sin qx )

where p = −a/2, q = b − a 2 /4 . 19.8. Linear, nonhomogeneous second order equation

There are 3 cases corresponding to those of entry 19.7 above. Case 1. y = c1e m x + c2 e m x

2

d y dy +a + by = R( x ) 2 dx dx

1

+

a, b are real constants.

2

em x e − m x R( x ) dx m1 − m2 ∫ 1

1

+

em x e − m x R( x ) dx m2 − m1 ∫ 2

2

Case 2. y = c1 e m x + c2 x e m x 1

1

+ xe m x ∫ e − m x R( x ) dx 1

1

− e m x ∫ xe − m x R( x ) dx 1

1

Case 3. y = e px (c1 cos qx + c2 sin qx ) +

e px sin qx − px ∫ e R( x ) cos qx dx q −

e px cos qx − px ∫ e R( x ) sin qx dx q

BASIC DIFFERENTIAL EQUATIONS AND SOLUTIONS

118

19.9. Euler or Cauchy equation x2

Putting x = et, the equation becomes d2y dy + (a − 1) + by = S (e t ) dt 2 dt

d2y dy + ax + by = S ( x ) 2 dx dx

and can then be solved as in entries 19.7 and 19.8 above. 19.10. Bessel’s equation x2

d2y dy +x + ( 2 x 2 − n 2 ) y = 0 2 dx dx

y = c1 J n ( x ) + c2 Yn (x )

See 27.1 to 27.15.

19.11. Transformed Bessel’s equation

x2

d2y dy + (2 p + 1) x + (a 2 x 2 r + β 2 ) y = 0 dx 2 dx

⎛α ⎞ ⎛ α ⎞ ⎪⎫ ⎪⎧ y = x − p ⎨c1 J q/r ⎜ x r ⎟ + c2 Yq/r ⎜ x r ⎟ ⎬ ⎝r ⎠ ⎝ r ⎠ ⎪⎭ ⎪⎩

where q =

p2 − β 2 .

19.12. Legendre’s equation (1 − x 2 )

d2y dy − 2x + n(n + 1) y = 0 dx 2 dx

y = c1 Pn ( x ) + c2 Qn ( x )

See 28.1 to 28.48.

20

FORMULAS from VECTOR ANALYSIS

Vectors and Scalars Various quantities in physics such as temperature, volume, and speed can be specified by a real number. Such quantities are called scalars. Other quantities such as force, velocity, and momentum require for their specification a direction as well as magnitude. Such quantities are called vectors. A vector is represented by an arrow or directed line segment indicating direction. The magnitude of the vector is determined by the length of the arrow, using an appropriate unit.

Notation for Vectors A vector is denoted by a bold faced letter such as A (Fig. 20.1). The magnitude is denoted by |A| or A. The tail end of the arrow is called the initial point, while the head is called the terminal point.

Fundamental Definitions 1.

Equality of vectors. Two vectors are equal if they have the same magnitude and direction. Thus, A = B in (Fig. 20-1).

2.

Multiplication of a vector by a scalar. If m is any real number (scalar), then mA is a vector whose magnitude is |m| times the magnitude of A and whose direction is the same as or opposite to A according as m > 0 or m < 0. If m = 0, then mA = 0 is called the zero or null vector.

Fig. 20-1

3. Sums of vectors. The sum or resultant of A and B is a vector C = A + B formed by placing the initial point B on the terminal point A and joining the initial point of A to the terminal point of B as in Fig. 20-2b. This definition is equivalent to the parallelogram law for vector addition as indicated in Fig. 20-2c. The vector A − B is defined as A + (−B).

Fig. 20-2

Extension to sums of more than two vectors are immediate. Thus, Fig. 20-3 shows how to obtain the sum E of the vectors A, B, C, and D.

119

FORMULAS FROM VECTOR ANALYSIS

120

Fig. 20-3

4.

Unit vectors. A unit vector is a vector with unit magnitude. If A is a vector, then a unit vector in the direction of A is a = A/A where A > 0.

Laws of Vector Algebra If A, B, C are vectors and m, n are scalars, then: 20.1. A + B = B + A

Commutative law for addition

20.2. A + (B + C) = (A + B) + C

Associative law for addition

20.3. m(nA) = (mn)A = n(mA)

Associative law for scalar multiplication

20.4. (m + n)A = mA + nA

Distributive law

20.5. m(A + B) = mA + mB

Distributive law

Components of a Vector A vector A can be represented with initial point at the origin of a rectangular coordinate system. If i, j, k are unit vectors in the directions of the positive x, y, z axes, then

z A

k

20.6. A = A1i + A2j + A3k

i

where A1i, A2j, A3k are called component vectors of A in the i, j, k directions and A1, A2, A3 are called the components of A.

Dot or Scalar Product 20.7. A • B = AB cos q

0qp

where q is the angle between A and B. Fundamental results follow:

j

A3k A2j

x

Fig. 20-4

y A2i


20.8. A • B = B • A

Commutative law

20.9. A • (B + C) = A • B + A • C

Distributive law

121

20.10. A • B = A1B1 + A2B2 + A3B3 where A = A1i + A2j + A3k, B = B1i + B2j + B3k.

Cross or Vector Product 20.11. A × B = AB sin q u

0qp

where q is the angle between A and B and u is a unit vector perpendicular to the plane of A and B such that A, B, u form a right-handed system (i.e., a right-threaded screw rotated through an angle less than 180° from A to B will advance in the direction of u as in Fig. 20-5). Fundamental results follow: i A × = A B 20.12. 1 B1

j A2 B2

k A3 B3

u

B

A

= ( A2 B3 − A3 B2 )i + ( A3 B1 − A1 B3 ) j + ( A1 B2 − A2 B1 )k

Fig. 20-5

20.13. A × B = − (B × A) 20.14. A × (B + C) = A × B + A × C 20.15. | A × B | = area of parallelogram having sides A and B

Miscellaneous Formulas Involving Dot and Cross Products 20.16.

A1 A2 A i (B × C) = B1 B2 C1 C2

A3 B3 = A1 B2C3 + A2 B3C1 + A3 B1C2 − A3 B2C1 − A2 B1C3 − A1 B3C2 C3

20.17. | A • (B × C) | = volume of parallelepiped with sides A, B, C 20.18. A × (B × C) = B(A • C) − C(A • B) 20.19. (A × B) × C = B(A • C) − A(B • C) 20.20.

(A × B) • (C × D) = (A • C)(B • D) − (A • D)(B • C)

20.21. (A × B) × (C × D) = C{A • (B × D)}− D{A • (B × C)} = B{A • (C × D)} − A{B • (C × D)}


122

Derivatives of Vectors The derivative of a vector function A(u) = A1(u)i + A2(u)j + A3(u)k of the scalar variable u is given by 20.22.

dA dA dA A(u + Δu) − A(u) dA1 = lim = i+ 2 j+ 3 k Δ u → 0 du du Δu du du

Partial derivatives of a vector function A(x, y, z) are similarly defined. We assume that all derivatives exist unless otherwise specified.

Formulas Involving Derivatives 20.23.

d dB dA (A i B) = A i + iB du du du

20.24.

d dB dA (A × B) = A × + ×B du du du

20.25.

⎛ dB ⎛ ⎞ d dA dC ⎞ {A i (B × C )} = i (B × C ) + A i ⎜ × C⎟ + A i ⎜ B × du du du ⎟⎠ ⎝ du ⎝ ⎠

20.26.

Ai

dA dA =A du du

20.27.

Ai

dA =0 du

if | A | is a constant

The Del Operator The operator del is defined by

20.28.

∇=i

∂ ∂ ∂ + j +k ∂x ∂y ∂z

In the following results we assume that U = U(x, y, z), V = V(x, y, z), A = A(x, y, z) and B = B(x, y, z) have partial derivatives.

The Gradient ⎛ ∂ ∂ ∂⎞ ∂U ∂U ∂U 20.29. Gradient of U = grad U = ∇U = ⎜ i + j + k ⎟ U = i+ j+ k ∂y ∂z ⎠ ∂x ∂y ∂z ⎝ ∂x

The Divergence ⎛ ∂ ∂ ∂⎞ 20.30. Divergence of A = div A = ∇ • A = ⎜ i + j + k ⎟ i ( A1 i + A2 j + A3 k) ∂y ∂z ⎠ ⎝ ∂x =

∂A1 ∂A2 ∂A3 + + ∂y ∂z ∂x


The Curl 20.31. Curl of A = curl A = ∇ × A ⎛ ∂ ∂ ∂⎞ = ⎜ i + j + k ⎟ × ( A1 i + A2 j + A3 k) ∂ ∂ ∂ x y z⎠ ⎝ i ∂ = ∂x A1

j ∂ ∂y A2

k ∂ ∂z A3

⎛ ∂A ∂A ⎞ ⎛ ∂A ∂A ⎞ ⎛ ∂A ∂A ⎞ = ⎜ 3 − 2 ⎟ i + ⎜ 1 − 3⎟ j + ⎜ 2 − 1⎟ k ∂x ⎠ ∂y ⎠ ∂z ⎠ ⎝ ∂x ⎝ ∂z ⎝ ∂y

The Laplacian 20.32. Laplacian of U = ∇ 2U = ∇ i (∇U ) = 20.33.

Laplacian of A = ∇ 2 A =

∂ 2U ∂ 2U ∂ 2U + + 2 ∂x 2 ∂y 2 ∂z

∂2 A ∂2 A ∂2 A + 2 + 2 ∂x 2 ∂y ∂z

The Biharmonic Operator 20.34. Biharmonic operator on U = ∇ 4U = ∇ 2 (∇ 2U ) =

∂ 4U ∂ 4U ∂ 4U ∂ 4U ∂ 4U ∂ 4U 2 + 2 2 + + + + ∂y 2 ∂z 2 ∂x 4 ∂y 4 ∂z 4 ∂x 2 ∂y 2 ∂x 2 ∂z 2

Miscellaneous Formulas Involving ∇ 20.35.

∇(U + V ) = ∇U + ∇V

20.36.

∇ i (A + B) = ∇ i A + ∇ i B

20.37.

∇ × (A + B) = ∇ × A + ∇ × B

20.38.

∇ i (UA) = (∇U ) i A + U (∇ i A)

20.39.

∇ × (UA) = (∇U ) × A + U (∇ × A)

20.40.

∇ i (A × B) = B i (∇ × A) − A i (∇ × B)

20.41.

∇ × (A × B) = (B i ∇) A − B(∇ i A) − (A i ∇)B + A(∇ i B)

20.42.

∇(A i B) = (B i ∇) A + ( A i ∇)B + B × (∇ × A) + A × (∇ × B)

20.43.

∇ × (∇U ) = 0,

20.44.

∇ i (∇ × A) = 0, that is, the divergence of the curl of A is zero.

20.45.

∇ × (∇ × A) = ∇(∇ i A) − ∇ 2 A

that is, the curl of the gradient of U is zero.

123


124

Integrals Involving Vectors d B(u). then the indefinite integral of A(u) is as follows: du

If A(u) = 20.46.

∫ A(u)du = B(u) + c,

c = constant vector

The definite integral of A(u) from u = a to u = b in this case is given by 20.47.

∫

b

A(u) du = B(b) − B(a)

a

The definite integral can be defined as in 18.1.

Line Integrals z

Consider a space curve C joining two points P1(a1, a2, a3) and P2(b1, b2, b3) as in Fig. 20-6. Divide the curve into n parts by points of subdivision (x1, y1, z1), . . . , (xn−1, yn−1, zn−1). Then the line integral of a vector A(x, y, z) along C is defined as

P2 C (xp , yp , zp)

P1

20.48.

∫

c

A i dr =

∫

P2

n→∞

P1

y

n

A i dr = lim ∑ A( x p , y p , z p ) i Δrp p =1

x

Fig. 20-6

where Δrp = Δx p i + Δy p j + Δz pk, Δx p = x p+1 − x p , Δy p = y p+1 − y p , Δz p = z p+1 − z p and where it is assumed that as n → ∞ the largest of the magnitudes |rp | approaches zero. The result 20.48 is a generalization of the ordinary definite integral (see 18.1). The line integral 20.48 can also be written as 20.49.

∫

C

A i dr =

∫

( A1dx + A2 dy + A3 dz )

C

using A = A1i + A2j + A3k

dr = dxi + dyj + dzk.

and

Properties of Line Integrals 20.50. 20.51.

p2

∫

p1

∫

P1

P1

A i dr = − ∫ A i dr P2

P2

A i dr =

∫

P3 P1

P2

A i dr + ∫ A i dr P3

Independence of the Path In general, a line integral has a value that depends on the particular path C joining points P1 and P2 in a region . However, in the case of A = ∇f or ∇ × A = 0 where f and its partial derivatives are continuous in , the line integral ∫ A i dr is independent of the path. In such a case, C

20.52.

∫

C

A i dr =

∫

P2 P1

A i dr = φ (P2 ) − φ (P1 )

where f(P1) and f(P2) denote the values of f at P1 and P2, respectively. In particular if C is a closed curve,


20.53.

∫

C

125

A i dr = ∫ A i dr = 0 C

where the circle on the integral sign is used to emphasize that C is closed.

Multiple Integrals Let F(x, y) be a function defined in a region of the xy plane as in Fig. 20-7. Subdivide the region into n parts by lines parallel to the x and y axes as indicated. Let Ap = xp yp denote an area of one of these parts. Then the integral of F(x, y) over is defined as 20.54.

∫

n

F( x , y) dA = lim ∑ F( x p , y p )ΔAp n→∞

p =1

provided this limit exists. In such a case, the integral can also be written as 20.55.

b

∫ ∫ x =a

f2 (x) y = f1 (x)

Fig. 20-7

F( x , y) dy dx

{∫

⎫ F( x , y) dy⎬ dx ⎭ where y = f1(x) and y = f2(x) are the equations of curves PHQ and PGQ, respectively, and a and b are the x coordinates of points P and Q. The result can also be written as =

20.56.

∫

b

x =a

d

g2( y )

y =c

x = g1( y )

∫ ∫

f2( x )

y = f1( x )

F ( x , y) dx dy =

∫

d y =c

{∫

g2( y ) x = g1( y )

}

F ( x , y) dx dy

where x = g1(y), x = g2(y) are the equations of curves HPG and HQG, respectively, and c and d are the y coordinates of H and G. These are called double integrals or area integrals. The ideas can be similarly extended to triple or volume integrals or to higher multiple integrals.

Surface Integrals z

Subdivide the surface S (see Fig. 20-8) into n elements of area ΔS p , p = 1, 2, … , n, Let A( x p , y p , z p ) = A p where ( x p , y p , z p ) is a point P in Sp. Let Np be a unit normal to Sp at P. Then the surface integral of the normal component of A over S is defined as 20.57.

∫

Np

γ

ΔSp S

n

S

A i N dS = lim ∑ A p i N p ΔS p n→∞

y

p =1

Δ xp Δyp x

Fig. 20-8


126

Relation Between Surface and Double Integrals If is the projection of S on the xy plane, then (see Fig. 20-8) 20.58.

∫

S

A i N dS = ∫

∫ AiN

dx dy Nik

The Divergence Theorem Let S be a closed surface bounding a region of volume V; and suppose N is the positive (outward drawn) normal and dS = N dS. Then (see Fig. 20-9) 20.59.

∫

V

∇ i A dV =

∫

S

A i dS

The result is also called Gauss’ theorem or Green’s theorem.

z

z N

N S

dS S

dS

C

y

y

x

x

Fig. 20-10

Fig. 20-9

Stokes’ Theorem Let S be an open two-sided surface bounded by a closed non-intersecting curve C(simple closed curve) as in Fig. 20-10. Then 20.60.

∫

C

A i dr =

∫

S

(∇ × A) i dS

where the circle on the integral is used to emphasize that C is closed.

Green’s Theorem in the Plane 20.61.

∫

C

(P dx + Q dy) =

∫

⎛ ∂Q ∂P ⎞ − dx dy R⎜ ⎝ ∂x ∂y ⎟⎠

where R is the area bounded by the closed curve C. This result is a special case of the divergence theorem or Stokes’ theorem.


127

Green’s First Identity 20.62.

∫

V

{(φ ∇ 2ψ + ( ∇φ ) i (∇ψ )}dV = ∫ (φ ∇ψ ) i dS

where f and y are scalar functions.

Green’s Second Identity 20.63.

∫

V

(φ ∇ 2ψ − ψ ∇ 2φ ) dV =

∫

S

(φ ∇ψ − ψ ∇φ ) i dS

Miscellaneous Integral Theorems 20.64.

∫

∇ × A dV =

∫

φ dr =

V

20.65.

C

∫

S

∫

S

dS × A

dS × ∇φ

Curvilinear Coordinates A point P in space (see Fig. 20-11) can be located by rectangular coordinates (x, y, z,) or curvilinear coordinates (u1, u2, u3) where the transformation equations from one set of coordinates to the other are given by 20.66.

z u3 curve e3

x = x (u1, u2 , u3)

c2 u2 =

y = y(u1, u2 , u3) z = z (u1, u2 , u3)

u1 curve

e1

P

u3 = c3

u1 = c1

e2 u2 curve y

If u2 and u3 are constant, then as u1 varies, the position vector r = xi + yj + zk of P describes a curve called the u1 coordinate curve. Similarly, we define the u2 and u3 coordinate curves through P. The vectors ∂r/∂u1, ∂r/∂u2 , ∂r/∂u3 represent tangent vectors to the u1, u2, u3 coordinate curves. Letting e1, e2, e3 be unit tangent vectors to these curves, we have 20.67.

∂r = h1e1 , ∂u1

∂r = h2 e 2 , ∂u2

x

Fig. 20-11

∂r = h3 e3 ∂u3

where 20.68.

h1 =

∂r , ∂u1

h2 =

∂r , ∂u2

h3 =

∂r ∂u3

are called scale factors. If e1, e2, e3 are mutually perpendicular, the curvilinear coordinate system is called orthogonal.


128

Formulas Involving Orthogonal Curvilinear Coordinates ∂r ∂r ∂r du + du + du = h1 du1e1 + h2 du2 e 2 + h3 du3 e 3 ∂u1 1 ∂u2 2 ∂u3 3

20.69.

dr =

20.70.

ds 2 = dr i dr = h12 du12 + h22 du22 + h32 du32

where ds is the element of are length. If dV is the element of volume, then 20.71.

dV = | (h1e1du1 ) i (h2 e 2 du2 ) × (h3 e 3 du3 ) | = h1h2 h3 du1du2 du3 =

∂r ∂r ∂r ∂( x , y, z ) du1 du2 du3 = du du du × i ∂(u1, u2 , u3 ) 1 2 3 ∂u1 ∂u2 ∂u3

where 20.72.

∂x /∂u1 ∂( x , y, z ) = ∂y/∂u1 ∂(u1, u2 , u3 ) ∂z/∂u1

∂x /∂u2 ∂y/∂u2 ∂z/∂u2

∂x /∂u3 ∂y/∂u3 ∂z/du3

sometimes written J(x, y, z; u1, u2, u3), is called the Jacobian of the transformation.

Transformation of Multiple Integrals Result 20.72 can be used to transform multiple integrals from rectangular to curvilinear coordinates. For example, we have 20.73.

∫ ∫ ∫ F ( x , y, z) dx dy dz = ∫ ∫ ∫ G(u , u , u ) 1

2

3

′

∂( x , y, z ) du du du ∂(u1, u2 , u3) 1 2 3

where ′ is the region into which is mapped by the transformation and G(u1, u2, u3) is the value of F(x, y, z) corresponding to the transformation.

Gradient, Divergence, Curl, and Laplacian In the following, Φ is a scalar function and A = A1e1 + A2e2 + A3e3 is a vector function of orthogonal curvilinear coordinates u1, u2, u3. 20.74. Gradient of Φ = grad Φ = ∇Φ =

e1 ∂ Φ e 2 ∂ Φ e3 ∂ Φ + + h1 ∂u1 h2 du2 h3 ∂u3 1 h1h2 h3

20.75.

Divergence of A = div A = ∇ i A =

20.76.

h1e1 1 ∂ Curl of A = curl A = ∇ × A = h1h2 h3 ∂u1 h1 A1 =

1 h2 h3

⎤ ⎡ ∂ ∂ ∂ (h1h2 A3 )⎥ (h2 h3 A1 ) + (h3 h1 A2 ) + ⎢ ∂u3 ∂u2 ⎦ ⎣ ∂u1 h2 e 2 ∂ ∂u2 h2 A2

h3 e 3 ∂ ∂u3 h3 A3

⎤ ⎤ ⎡ ∂ 1 ⎡ ∂ ∂ ∂ (h3 A3 ) − (h2 A2 )⎥ e1 + (h1 A1 ) − (h3 A3 )⎥ e 2 ⎢ ⎢ ∂ ∂ h h u u u u ∂ ∂ 3 1 3 ⎣ 3 1 ⎦ ⎦ ⎣ 2 +

1 h1h2

⎤ ⎡ ∂ ∂ (h2 A2 ) − (h1 A1 )⎥ e 3 ⎢ ∂u2 ⎦ ⎣ ∂u1


20.77. Laplacian of Φ = ∇ 2 Φ =

1 h1h2 h3

129

⎡ ∂ ⎛ h2 h3 ∂ Φ⎞ ∂ ⎛ h1h2 ∂ Φ⎞ ⎤ ∂ ⎛ h3 h1 ∂ Φ⎞ + + ⎢ ⎟⎥ ⎟ ⎜ ⎟ ⎜ ⎜ ⎢⎣ ∂u1 ⎝ h1 ∂u1 ⎠ ∂u2 ⎝ h2 ∂u2 ⎠ ∂u3 ⎝ h3 ∂u3 ⎠ ⎥⎦

Note that the biharmonic operator ∇ 4 Φ = ∇ 2 (∇ 2 Φ) can be obtained from 20.77.

Special Orthogonal Coordinate Systems Cylindrical Coordinates (r, q, z) (See Fig. 20-12) 20.78. x = r cos q, 20.79.

h12 = 1,

20.80.

∇2Φ =

y = r sin q,

h22 = r 2 ,

z=z

h32 = 1

∂2 Φ 1 ∂ Φ 1 ∂2 Φ ∂2 Φ + + + 2 ∂ r 2 r ∂r r 2 ∂θ 2 ∂z ez

z

z

er

eq

ef

(r, q, z)

P

P(r, q, f,)

er

z

z

x

x q y

y

r

f y

eq

x

x

Fig. 20-12.

Fig. 20-13.

Cylindrical coordinates.

Spherical coordinates.

Spherical Coordinates (r, q, f) (See Fig. 20-13)

20.81. x = r sin q cos f,

y = r sin q sin f,

20.82.

h12 = 1,

h32 = r 2 sin 2 θ

20.83.

∇2Φ =

h22 = r 2 ,

z = r cos q

1 ∂2 Φ 1 ∂ ⎛ 2 ∂Φ ⎞ 1 ∂ ⎛ ∂Φ ⎞ + 2 2 + 2 r sin θ ⎜ ⎟ ⎜ ⎟ 2 r ∂r ⎝ dr ⎠ r sin θ ∂θ ⎝ ∂θ ⎠ r sin θ ∂φ 2

Parabolic Cylindrical Coordinates (u, y, z) 20.84.

x = 12 (u 2 − 2 ),

20.85.

h12 = h22 = u 2 + 2 ,

20.86.

∇2Φ =

y = u ,

z=z

h32 = 1

1 ⎛ ∂2 Φ ∂2 Φ ⎞ ∂2 Φ + + u 2 + 2 ⎜⎝ ∂u 2 ∂ 2 ⎟⎠ ∂z 2

The traces of the coordinate surfaces on the xy plane are shown in Fig. 20-14. They are confocal parabolas with a common axis.

Fig. 20-14

y


130

Paraboloidal Coordinates (u, y, f) 20.87.

x = u cos φ ,

where

y = u sin φ ,

u 0,

20.88.

h12 = h22 = u 2 + 2 ,

20.89.

∇2Φ =

0,

z = 12 (u 2 − 2 )

0 φ < 2π

h32 = u 2 2

∂ ⎛ ∂Φ ⎞ 1 1 1 ∂2 Φ ∂ ⎛ ∂Φ ⎞ u + + u(u 2 + 2 ) ∂u ⎜⎝ ∂u ⎟⎠ (u 2 + 2 ) ∂ ⎜⎝ ∂ ⎟⎠ u 2 2 ∂φ 2

Two sets of coordinate surfaces are obtained by revolving the parabolas of Fig. 20-14 about the x axis which is then relabeled the z axis. Elliptic Cylindrical Coordinates (u, y, z) 20.90.

x = a cosh u cos ,

where

u 0,

y = a sinh u sin , 0 < 2π ,

20.91.

h12 = h22 = a 2 (sinh 2 u + sin 2 ),

20.92.

∇2Φ =

z=z

− ∞< z < ∞ h32 = 1

⎛ ∂2 Φ ∂2 Φ ⎞ ∂2 Φ 1 + + 2 a (sinh u + sin ) ⎜⎝ ∂u 2 ∂ 2 ⎟⎠ ∂z 2 2

2

The traces of the coordinate surfaces on the xy plane are shown in Fig. 20-15. They are confocal ellipses and hyperbolas.

Fig. 20-15.

Elliptic cylindrical coordinates.


131

Prolate Spheroidal Coordinates (x, h, f) 20.93.

x = a sinh ξ sin η cos φ ,

y = a sinh ξ sin η sin φ ,

ξ 0,

where

20.94.

h12 = h22 = a 2 (sinh 2 ξ sin 2 η),

20.95.

∇2Φ =

z = a cosh ξ cos η

0 η π,

0 φ < 2π

h32 = a 2 sinh 2 ξ sin 2 η

∂ ⎛ ∂Φ ⎞ 1 sinh ξ ∂ξ ⎟⎠ a 2 (sinh 2 ξ + sin 2 η) sinh ξ ∂ξ ⎜⎝ +

∂ ⎛ ∂Φ ⎞ ∂2 Φ 1 1 sin η + ⎜ ⎟ a 2 (sinh 2 ξ + sin 2 η) sin η ∂η ⎝ ∂η ⎠ a 2 sinh 2 ξ sin 2 η ∂φ 2

Two sets of coordinate surfaces are obtained by revolving the curves of Fig. 20-15 about the x axis which is relabeled the z axis. The third set of coordinate surfaces consists of planes passing through this axis. Oblate Spheroidal Coordinates (x, h, f) 20.96.

x = a cosh ξ cos η cos φ ,

y = a cosh ξ cos η sin φ ,

where

ξ 0,

20.97.

h12 = h22 = a 2 (sinh 2 ξ + sin 2 η),

20.98.

∇2Φ =

z = a sinh ξ sin η

− π /2 η π /2,

0 φ < 2π

h32 = a 2 cosh 2 ξ cos 2 η

∂ ⎛ ∂Φ ⎞ 1 cosh ξ ⎜ 2 ∂ξ ⎠⎟ a (sinh ξ + sin η) cosh ξ ∂ξ ⎝ 2

2

+

∂ ⎛ ∂Φ ⎞ ∂2 Φ 1 1 cos η + 2 ⎜ ⎟ 2 2 2 a (sinh ξ + sin η) cos η ∂η ⎝ ∂η ⎠ a cosh ξ cos η ∂φ 2 2

2

Two sets of coordinate surfaces are obtained by revolving the curves of Fig. 20-15 about the y axis which is relabeled the z axis. The third set of coordinate surfaces are planes passing through this axis. Bipolar Coordinates (u, y, z)

20.99.

x=

a sinh , cosh − cos u

where

y=

a sin u , cosh − cos u 0 u < 2π ,

z=z −∞< < ∞,

−∞< z < ∞

or 20.100.

x 2 + (y − a cot u)2 = a 2 csc 2 u,

( x − a coth )2 + y 2 = a 2 csch 2 ,

z=z


132

20.101.

h12 = h22 =

20.102.

∇2Φ =

a2 , (cosh − cos u)2

(cosh − cos u)2 a2

h32 = 1

⎛ ∂2 Φ ∂2 Φ ⎞ ∂2 Φ ⎜⎝ ∂u 2 + ∂ 2 ⎟⎠ + ∂z 2

The traces of the coordinate surfaces on the xy plane are shown in Fig. 20-16.

Fig. 20-16.

Bipolar coordinates.

Toroidal Coordinates (u, y, f) a sinh cos φ , cosh − cos u

20.103.

x=

20.104.

h12 = h22 =

20.105.

∇ 2Φ =

y=

a sinh sin φ , cosh − cos u

a2 , (cosh − cos u)2

h32 =

z=

a sin u cosh − cos u

a 2 sinh 2 (cosh − cos u)2

(cosh − cos u)3 ∂ ⎛ 1 ∂Φ⎞ 2 ⎜ a ∂u ⎝ cosh − cos u ∂u ⎟⎠ +

(cosh − cos u)3 ∂ ⎛ sinh ∂Φ⎞ (cosh − cos u)2 ∂ 2Φ + a 2 sinh ∂ ⎜⎝ cosh − cos u ∂ ⎟⎠ a 2 sinh 2 ∂f 2

The coordinate surfaces are obtained by revolving the curves of Fig. 20.16 about the y axis which is relabeled the z axis. Conical Coordinates (l, m, v)

μ v , ab

20.106.

x=

20.107.

h12 = 1,

y= h22 =

(μ 2 − a 2 )(v 2 − a 2 ) , a a2 − b2

2 (μ 2 − v 2 ) , (μ 2 − a 2 )(b 2 − μ 2 )

h32 =

z=

(μ 2 − b 2 )(v 2 − b 2 ) b b2 − a2

2 (μ 2 − v 2 ) (v 2 − a 2 )(v 2 − b 2 )


133

Confocal Ellipsoidal Coordinates (l, m, y) ⎧ x2 y2 z2 ⎪ a 2 − + b 2 − + c 2 − = 1, ⎪ z2 y2 ⎪ x2 + 2 + 2 = 1, ⎨ 2 ⎪a − μ b − μ c − μ ⎪ x2 y2 z2 + 2 + 2 = 1, ⎪ 2 ⎩a − v b − v c − v

20.108.

< c2 < b2 < a2 c2 < μ < b2 < a2 c2 < b2 < v < a2

or

20.109.

⎧ 2 (a 2 − )(a 2 − μ )(a 2 − v) ⎪x = (a 2 − b 2 )(a 2 − c 2 ) ⎪ ⎪ 2 (b 2 − )(b 2 − μ )(b 2 − v) ⎨y = (b 2 − a 2 )(a 2 − c 2 ) ⎪ ⎪ (c 2 − )(c 2 − μ )(c 2 − v) ⎪z 2 = (c 2 − a 2 )(c 2 − b 2 ) ⎩

20.110.

⎧ 2 ⎪h1 = 4(a 2 ⎪ ⎪ 2 ⎨h2 = 4(a 2 ⎪ ⎪ ⎪h32 = 4(a 2 ⎩

(μ − )(v − ) − )(b 2 − )(c 2 − ) (v − μ )( − μ ) − μ )(b 2 − μ )(c 2 − μ ) ( − v)(μ − v) − v)(b 2 − v)(c 2 − v)

Confocal Paraboloidal Coordinates (l, m, v)

20.111.

20.112.

.

20.113.

⎧ x2 h2 ⎪ a 2 − + b 2 − = z − , ⎪ y2 ⎪ x2 + 2 = z − μ, ⎨ 2 ⎪a − μ b − μ ⎪ x2 y2 + 2 = z − v, ⎪ 2 ⎩a − v b − v or

− ∞< < b 2 b2 < μ < a2 a2 < v < ∞

⎧ 2 (a 2 − )(a 2 − μ )(a 2 − v) ⎪x = b2 − a2 ⎪⎪ ⎨ 2 (b 2 − )(b 2 − μ )(b 2 − v) ⎪y = a2 − b2 ⎪ ⎪⎩z = + μ + v − a 2 − b 2 ⎧ 2 (μ − )(v − ) ⎪h1 = 4(a 2 − )(b 2 − ) ⎪ ⎪ 2 (v − μ )( − μ ) ⎨h2 = a 2 − μ )(b 2 − μ ) 4 ( ⎪ ⎪ ( − v)(μ − v) ⎪h32 = 16(a 2 − v)(b 2 − v) ⎩

Section VI: Series

21

SERIES of CONSTANTS

Arithmetic Series 21.1.

a + (a + d ) + (a + 2 d ) + ⋅⋅⋅ + {a + (n − 1)d } = 12 n{2 a + (n − 1)d } = 12 n(a + l )

where l a (n 1)d is the last term. Some special cases are 21.2.

1 + 2 + 3 + ⋅⋅⋅ + n = 12 n(n + 1)

21.3.

1 + 3 + 5 + ⋅⋅⋅ + (2 n − 1) = n 2

Geometric Series 21.4.

a + ar + ar 2 + ar 3 + ⋅⋅⋅ + ar n−1 =

a(1 − r n ) a − rl = 1− r 1− r

where l ar n1 is the last term and r ≠ 1. If 1 < r < 1, then 21.5.

a + ar + ar 2 + ar 3 + ⋅⋅⋅ =

a 1− r

Arithmetic-Geometric Series 21.6.

a + (a + d )r + (a + 2 d )r 2 + ⋅⋅⋅ + {a + (n − 1)d }r n−1 =

a(1 − r n ) rd {1 − nr n−1 + (n − 1)r n } + 1− r (1 − r )2

where r ≠ 1. If 1 < r < 1, then 21.7.

a + (a + d )r + (d + 2 d )r 2 + ⋅⋅⋅ =

a rd + 1 − r (1 − r )2

Sums of Powers of Positive Integers 21.8.

1p + 2 p + 3 p + ⋅⋅⋅ + n p =

n p+1 1 p B1 pn p−1 B2 p( p − 1)( p − 2 )n p− 3 + ⋅⋅⋅ + n + − 4! p +1 2 2!

where the series terminates at n2 or n according as p is odd or even, and Bk are the Bernoulli numbers (see page 142).


SERIES OF CONSTANTS

135

Some special cases are 21.9.

1 + 2 + 3 + ⋅⋅⋅ + n =

n(n + 1) 2

21.10.

12 + 2 2 + 32 + ⋅⋅⋅ + n 2 =

n(n + 1)(2 n + 1) 6

21.11.

13 + 2 3 + 33 + ⋅⋅⋅ + n 3 =

n 2 (n + 1)2 = (1 + 2 + 3 + ⋅⋅⋅ + n )2 4

21.12.

14 + 2 4 + 34 + ⋅⋅⋅ + n 4 =

n(n + 1)(2 n + 1)(3n 2 + 3n − 1) 30

If Sk = 1k + 2 k + 3k + ⋅⋅⋅ + n k where k and n are positive integers, then 21.13.

⎛ k + 1⎞ ⎛ k + 1⎞ ⎛ k + 1⎞ k +1 ⎜⎝ 1 ⎟⎠ S1 + ⎜⎝ 2 ⎟⎠ S2 + ⋅⋅⋅ + ⎜⎝ k ⎟⎠ Sk = (n + 1) − (n + 1)

Series Involving Reciprocals of Powers of Positive Integers 21.14.

1−

1 1 1 1 + − + − ⋅⋅⋅ = ln 2 2 3 4 5

21.15.

1−

π 1 1 1 1 + − + − ⋅⋅⋅ = 3 5 7 9 4

21.16.

1−

π 3 1 1 1 1 1 + − + − ⋅⋅⋅ = + ln 2 4 7 10 13 9 3

21.17.

1−

π 2 1 1 1 1 + − + − ⋅⋅⋅ = + 5 9 13 17 8

21.18.

1 1 1 1 1 π 3 1 − + − + − ⋅⋅⋅ = + ln 2 2 5 8 11 14 9 3

21.19.

1 1 1 1 π2 2 + 2 + 2 + 2 + ⋅⋅⋅ = 6 1 2 3 4

21.20.

1 1 1 1 π4 4 + 4 + 4 + 4 + ⋅⋅⋅ = 90 1 2 3 4

21.21.

1 1 1 1 π6 6 + 6 + 6 + 6 + ⋅⋅⋅ = 945 1 2 3 4

21.22.

1 1 1 1 π2 2 − 2 + 2 − 2 + ⋅⋅⋅ = 12 1 2 3 4

21.23.

1 1 1 1 7π 4 4 − 4 + 4 − 4 + ⋅⋅⋅ = 720 1 2 3 4

21.24.

1 1 1 1 31π 6 6 − 6 + 6 − 6 + ⋅⋅⋅ = 30, 240 1 2 3 4

21.25.

1 1 1 1 π2 2 + 2 + 2 + 2 + ⋅⋅⋅ = 8 1 3 5 7

21.26.

1 1 1 1 π4 4 + 4 + 4 + 4 + ⋅⋅⋅ = 96 1 3 5 7

2 ln (1 + 2 ) 4

SERIES OF CONSTANTS

136

21.27.

1 1 1 1 π6 6 + 6 + 6 + 6 + ⋅⋅⋅ = 960 1 3 5 7

21.28.

1 1 1 1 π3 3 − 3 + 3 − 3 + ⋅⋅⋅ = 32 1 3 5 7

21.29.

1 1 1 1 3π 3 2 3 + 3 − 3 − 3 + ⋅⋅⋅ = 128 1 3 5 7

21.30.

1 1 1 1 1 + + + + ⋅⋅⋅ = 1i 3 3 i5 5 i 7 7 i9 2

21.31.

1 1 1 1 3 + + + + ⋅⋅⋅ = 1i 3 2 i 4 3 i5 4 i6 4

21.32.

1 1 1 1 π2 −8 2 + 2 2 + 2 2 + 2 2 + ⋅⋅⋅ = 16 1 i3 3 i5 5 i7 7 i9

21.33.

1 1 1 4π 2 − 39 2 2 + 2 2 2 + 2 2 2 + ⋅⋅⋅ = 16 1 i 2 i3 2 i3 i 4 3 i4 i5

21.34.

1 u a −1du 1 1 1 1 − + − + ⋅⋅⋅ = ∫ 0 1 + ud a a + d a + 2 d a + 3d

2

2

21.35.

2 2 p−1π 2 p Bp 1 1 1 1 2p + 2p + 2p + 2 p + ⋅⋅⋅ = (2 p)! 1 2 3 4

21.36.

(2 2 p − 1) π 2 p Bp 1 1 1 1 + + + + ⋅⋅⋅ = 2(2 p)! 12 p 32 p 5 2 p 7 2 p

21.37.

(2 2 p−1 − 1) π 2 p Bp 1 1 1 1 2p − 2p + 2p − 2 p + ⋅⋅⋅ = (2 p)! 1 2 3 4

21.38.

1 12 p+1

−

1 32 p+1

+

1 5 2 p+1

−

1 7 2 p+1

+ ⋅⋅⋅ =

π 2 p+1E p 2 2 p+ 2 (2 p)!

Miscellaneous Series 21.39.

1 sin(n + 1/2)α + cos α + cos 2α + ⋅⋅⋅ + cos nα = 2 2 sin(α /2)

21.40.

sin α + sin 2α + sin 3α + ⋅⋅⋅ + sin nα =

21.41.

1 + r cos α + r 2 cos 2α + r 3 cos 3α + ⋅⋅⋅ =

21.42.

r sin α + r 2 sin 2α + r 3 sin 3α + ⋅⋅⋅ =

21.43.

1 + r cos α + r 2 cos 2α + ⋅⋅⋅ + r n cos nα =

21.44.

r sin α + r 2 sin 2α + ⋅⋅⋅ + r n sin nα =

sin[1/2(n + 1)]α sin 1/2nα sin (α /2) 1 − r cos α , |r |< 1 1 − 2r cos α + r 2

r sin α , |r |< 1 1 − 2r cos α + r 2 r n+ 2 cos nα − r n+1 cos(n + 1)α − r cos α + 1 1 − 2 r cos α + r 2

r sin α − r n+1 sin(n + 1))α + r n+ 2 sin nα 1 − 2 r cos α + r 2

SERIES OF CONSTANTS

137

The Euler-Maclaurin Summation Formula n −1

21.45.

∑ F (k ) = ∫ k =1

n 0

1 F (k )dk − {F (0) + F (n)} 2

+

1 1 {F ′(n) − F (0)} − {F ′′′(n) − F ′′′(0)} 720 12

+

1 1 {F ( vii ) (n) − F ( vii ) (0)} {F ( v)) (n) − F ( v ) (0)} − 30, 240 1, 209, 600

+ ⋅⋅⋅ (−1) p−1

Bp {F ( 2 p−1) (n) − F ( 2 p−1) (0)} + ⋅⋅⋅ (2 p)!

The Poisson Summation Formula ∞

21.46.

∞

{

∑ F (k ) = ∑ ∫

k =−∞

m =−∞

∞

}

e 2π imx F ( x )dx

−∞

22

TAYLOR SERIES

Taylor Series for Functions of One Variable 22.1.

f ( x ) = f (a ) + f ′(a )( x − a ) +

f ′′(a )( x − a )2 f ( n−1) (a )( x − a ) + ⋅⋅⋅ + (n − 1)! 2!

n −1

+ Rn

where Rn, the remainder after n terms, is given by either of the following forms: 22.2. Lagrange’s form: Rn = 22.3. Cauchy’s form: Rn =

f ( n ) (ξ )( x − a )n n!

f ( n ) (ξ )( x − ξ )n−1 ( x − a ) (n − 1)!

The value x, which may be different in the two forms, lies between a and x. The result holds if f(x) has continuous derivatives of order n at least. If lim Rn = 0, the infinite series obtained is called the Taylor series for f(x) about x a. If a 0, the series n→∞ is often called a Maclaurin series. These series, often called power series, generally converge for all values of x in some interval called the interval of convergence and diverge for all x outside this interval. Some series contain the Bernoulli numbers Bn and the Euler numbers En defined in Chapter 23, pages 142143.

Binomial Series 22.4.

(a + x )n = a n + na n−1 x +

n(n − 1) n− 2 2 n(n − 1)(n − 2 ) n− 3 3 a x + ⋅⋅⋅ a x + 2! 3!

⎛ n⎞ ⎛ n⎞ ⎛ n⎞ = a n + ⎜ ⎟ a n−1 x + ⎜ ⎟ a n− 2 x 2 + ⎜ ⎟ a n− 3 x 3 + ⋅⋅⋅ ⎝ 3⎠ ⎝1⎠ ⎝ 2⎠

Special cases are 22.5.

(a + x )2 = a 2 + 2 ax + x 2

22.6.

(a + x )3 = a 3 + 3a 2 x + 3ax 2 + x 3

22.7.

(a + x )4 = a 4 + 4 a 3 x + 6 a 2 x 2 + 4 ax 3 + x 4

22.8.

(1 + x )−1 = 1 − x + x 2 − x 3 + x 4 − ⋅⋅⋅

1 < x < 1

22.9.

(1 + x )−2 = 1 − 2 x + 3x 2 − 4 x 3 + 5 x 4 − ⋅⋅⋅

1 < x < 1

(1 + x )−3 = 1 − 3x + 6 x 2 − 10 x 3 + 15 x 4 − ⋅⋅⋅

1 < x < 1

22.10.

138

TAYLOR SERIES

139

22.11.

(1 + x )−1/ 2 = 1 −

1 1i 3 2 1i 3i 5 3 x+ x − x + ⋅⋅⋅ 2 2i4 2i4i6

1 < x 1

22.12.

(1 + x )1/ 2 = 1 +

1 1 2 1i 3 3 x− x + x − ⋅⋅⋅ 2 2i4 2i4i6

1 < x 1

22.13.

(1 + x )−1/ 3 = 1 −

1 1i 4 2 1i 4 i 7 3 x+ x − x + ⋅⋅⋅ 3 3i6 3i6i9

1 < x 1

22.14.

(1 + x )1/ 3 = 1 +

1 2 2 2i5 3 x− x + x − ⋅⋅⋅ 3 3i6 3i6i9

1 < x 1

Series for Exponential and Logarithmic Functions x2 x3 + + ⋅⋅⋅ 2 ! 3!

∞ < x < ∞

22.15.

ex = 1 + x +

22.16.

a x = e x ln a = 1 + x ln a +

22.17.

ln (1 + x ) = x −

22.18.

1 ⎛1 + x⎞ x3 x5 x7 ln ⎜ =x+ + + + ⋅⋅⋅ ⎟ 2 ⎝1 − x⎠ 3 5 7

1 < x < 1

22.19.

3 5 1 ⎛ x − 1⎞ ⎪⎧⎛ x − 1⎞ 1 ⎛ x − 1⎞ ⎪⎫ ln x = 2 ⎨⎜ + ⋅⋅⋅⎬ + ⎜ + ⎜ ⎟ ⎟ ⎟ x + x x + 1 3 1 5 + 1 ⎝ ⎝ ⎠ ⎠ ⎝ ⎠ ⎪⎩ ⎪⎭

x>0

22.20.

⎛ x − 1⎞ 1 ⎛ x − 1⎞ 1 ⎛ x − 1⎞ + + + ⋅ ⋅⋅ ln x = ⎜ ⎝ x ⎟⎠ 2 ⎜⎝ x ⎟⎠ 3 ⎜⎝ x ⎟⎠

( x ln a )2 ( x ln a )3 + + ⋅⋅⋅ 2! 3!

x2 x3 x4 + − + ⋅⋅⋅ 2 3 4

2

∞ < x < ∞ 1 < x 1

3

x

1 2

Series for Trigonometric Functions 22.21.

sin x = x −

x3 x5 x7 + − + 3! 5! 7!

− ∞< x < ∞

22.22.

cos x = 1 −

x2 x4 x6 + − + 2! 4 ! 6 !

− ∞< x < ∞

22.23.

tan x = x +

2 2 n (2 2 n − 1)Bn x 2 n−1 x 3 2 x 5 17 x 7 + + + ++ 3 15 315 (2 n )!

| x |
1, q > 1

The equality holds if and only if | a1 | p−1/| b1 | = | a2 | p−1/| b2 | = = | an | p−1/| bn | . For p = q = 2 it reduces to 37.3.


INEQUALITIES

206

Chebyshev’s Inequality If a1 a2 an and b1 b2 bn , then 37.10.

⎛ a1 + a2 + + an ⎞ ⎛ b1 + b2 + + bn ⎞ a1 b1 + a2 b2 + + an bn ⎟ ⎟⎜ ⎜ n n n ⎠ ⎠⎝ ⎝

or 37.11.

(a1 + a2 + + an )(b1 + b2 + + bn ) n(a1 b1 + a2 b2 + + an bn )

Minkowski’s Inequality If a1, a2,…an, b1, b2,… bn are all positive and p > 1, then 37.12.

{(a1 + b1) p + (a2 + b2 ) p + + (an + bn ) p}1/ p (a1p + a2p + + anp )1/ p + (b1p + b2p + + bnp )1/ p

The equality holds if and only if a1 /b1 = a2 /b2 = = an /bn .

Cauchy-Schwarz Inequality for Integrals 2

{

}{

}

b ⎡ b f ( x )g( x ) dx⎤ b [ f ( x )]2 dx ∫a ∫a [g( x)]2 dx ⎢⎣∫a ⎥⎦ The equality holds if and only if f (x)/g(x) is a constant.

37.13.

Holder’s Inequality for Integrals 37.14.

∫

b

a

| f ( x )g( x )|dx

{∫ | f (x)| dx} {∫ |g(x)| dx} b

1/ p

1/q

b

p

q

a

a

where 1/p + 1/q = 1, p > 1, q >1. If p = q = 2, this reduces to 37.13. The equality holds if and only if | f ( x )| p−1/| g( x )| is a constant.

Minkowski’s Inequality for Integrals If p > 1, 37.15.

{∫ | f (x) + g(x)| dx} {∫ | f (x)| dx} + {∫ |g(x)| dx} b

a

1/ p

p

b

a

1/ p

p

b

a

The equality holds if and only if f (x)/g(x) is a constant.

1/ p

p

38

INFINITE PRODUCTS

38.1.

⎛ x2 ⎞ ⎛ x2 ⎞ ⎛ x2 ⎞ sin x = x ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎝ x ⎠ ⎝ 4π ⎠ ⎝ 9π ⎠

38.2.

⎛ 4x2 ⎞ ⎛ 4x2 ⎞ ⎛ 4x2 ⎞ cos x = ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎜1 − π ⎠ ⎝ 9π ⎠ ⎝ 25π 2 ⎟⎠ ⎝

38.3.

⎛ x2 ⎞ ⎛ x2 ⎞ ⎛ x2 ⎞ sinh x = x ⎜1 + 2 ⎟ ⎜1 + 2 ⎟ ⎜1 + 2 ⎟ ⎝ π ⎠ ⎝ 4π ⎠ ⎝ 9π ⎠

38.4.

⎛ 4x2 ⎞ ⎛ 4x2 ⎞ ⎛ 4x2 ⎞ cosh x = ⎜1 + 2 ⎟ ⎜1 + 2 ⎟ ⎜1 + π ⎠ ⎝ 9π ⎠ ⎝ 25π 2 ⎟⎠ ⎝

38.5.

1 ⎫ ⎧⎛ x ⎫ ⎧⎛ x ⎫ ⎧⎛ x = xeγ x ⎨ 1 + ⎞ e − x ⎬ ⎨ 1 + ⎞ e − x/2 ⎬ ⎨ 1 + ⎞ e − x/3 ⎬ 1 2 3 Γ( x ) ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎭⎩ ⎭⎩ ⎭ ⎩

See also 25.11. ⎛ x2 ⎞ ⎛ x2 ⎞ ⎛ x2 ⎞ J 0 ( x ) = ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎝ 1 ⎠ ⎝ 2 ⎠ ⎝ 3 ⎠ where 1, 2, 3,… are the positive roots of J0(x) = 0.

38.6.

⎛ x2 ⎞ ⎛ x2 ⎞ ⎛ x2 ⎞ J1( x ) = x ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎜1 − 2 ⎟ ⎝ 1 ⎠ ⎝ 2 ⎠ ⎝ 3 ⎠ where 1, 2, 3,… are the positive roots of J1(x) = 0.

38.7.

38.8.

sin x x x x x = cos cos cos cos 2 4 8 16 x

38.9.

π 2 2 4 4 6 6 = i i i i i i 2 1 3 3 5 5 7

This is called Wallis’ product.

207

Section XI: Probability and Statistics

39

DESCRIPTIVE STATISTICS

The numerical data x1, x2,… will either come from a random sample of a larger population or from the larger population itself. We distinguish these two cases using different notation as follows: n = number of items in a sample, N = number of items in the population, x = (read: x-bar) = sample mean, s2 = sample variance, s = sample standard deviation,

m (read: mu) = population mean, s 2 = population variance, s = population standard deviation

Note that Greek letters are used with the population and are called parameters, whereas Latin letters are used with the samples and are called statistics. First we give formulas for the data coming from a sample. This is followed by formulas for the population. Grouped Data Frequently, the sample data are collected into groups (grouped data). A group refers to a set of numbers all with the same value xi, or a set (class) of numbers in a given interval with class value xi. In such a case, we assume there are k groups with fi denoting the number of elements in the group with value or class value xi. Thus, the total number of data items is 39.1.

n = ∑ fi

As usual, Σ will denote a summation over all the values of the index, unless otherwise specified. Accordingly, some of the formulas will be designated as (a) or as (b), where (a) indicates ungrouped data and (b) indicates grouped data.

Measures of Central Tendency Mean (Arithmetic Mean) The arithmetic mean or simply mean of a sample x1, x2,…, xn, frequently called the “average value,” is the sum of the values divided by the number of values. That is: 39.2(a). Sample mean: 39.2(b). Sample mean:

x= x=

x1 + x 2 + + x n Σ xi = n n f1 x1 + f2 x 2 + + fk x k Σ fi xi = f1 + f2 + + fk Σ fi

Median Suppose that the data x1, x2,…, xn are now sorted in increasing order. The median of the data, denoted by M or Median

is defined to be the “middle value.” That is:



209

when n is odd and n = 2k + 1, ⎧x k +1 ⎪ 39.3(a). Median = ⎨ x + x k +1 ⎪ k when n is even and n = 2k. ⎩ 2 The median of grouped data is obtained by first finding the cumulative frequency function Fs. Specifically, we define Fs = f1 + f2 + + fs

that is, Fs is the sum of the frequencies up to fs. Then: 39.3(b.1).

⎧x j +1 ⎪ Median = ⎨ x + x j +1 ⎪ j ⎩ 2

when n = 2k + 1 (odd) and Fj < k + 1 ≤ Fj +1 when n = 2k (even), and Fj = k.

Finding the median of data arranged in classes is more complicated. First one finds the median class m, the class with the median value, and then one linearly interpolates in the class using the formula 39.3(b.2).

Median = Lm + c

(n/2) − Fm −1 fm

where Lm denotes the lower class boundary of the median class and c denotes its class width (length of the class interval). Mode The mode is the value or values which occur most often. Namely: 39.4. Mode xm = numerical value that occurs the most number of times The mode is not defined if every xm occurs the same number of times, and when the mode is defined it may not be unique. Weighted and grand means Suppose that each xi is assigned a weight wi ≥ 0. Then: 39.5. Weighted Mean x w =

w1x1 + w2 x 2 + + wk x k Σ wi xi = Σ wi w1 + w2 + + wk

Note that 39.2(b.1) is a special case of 39.4 where the weight wi of xi is its frequency. Suppose that there are k sample sets and that each sample set has ni elements and a mean x. Then the grand mean, denoted by xi is the “mean of the means” where each mean is weighted by the number of elements in its sample. Specifically: 39.6. Grand Mean x =

n1 x1 + n2 x 2 + + nk x k Σ ni xi = n1 + n2 + + nk Σ ni

Geometric and Harmonic Means The geometric mean (G.M.) and harmonic mean (H.M.) are defined as follows: 39.7(a). G.M. = n x1 x 2 x n f f f 39.7(b). G.M. = n x1 x 2 x k 1

2

k


210

39.8(a).

H.M. =

39.8(b).

H.M. =

n n = 1/x1 + 1/x 2 + + 1/x n Σ (1/xi ) n n = f1 /x1 + f2 /x 2 + + fk /x k Σ ( fk /xi )

Relation Between Arithmetic, Geometric, and Harmonic Means 39.9. H.M. ≤ G.M. ≤ x The equality sign holds only when all the sample values are equal. Midrange The midrange is the average of the smallest value x1 and the largest value xn. That is: 39.10. midrange: mid =

x1 + x n 2

Population Mean The formula for the population mean m follows: 39.11(a). Population mean:

x1 + x 2 + + x N Σ xi = N N

μ=

39.11(b). Population mean: μ =

f1 x1 + f2 x 2 + + fk x k Σ fi xi = f1 + f2 + + fk Σ fi

(Recall that N denotes the number of elements in a population.) Observe that the formula for the population mean m is the same as the formula for the sample mean x. On the other hand, the formula for the population standard deviation s is not the same as the formula for the sample standard deviation s. (This is the main reason we give separate formulas for m and x. )

Measures of Dispersion Sample Variance and Standard Deviation Here the sample set has n elements with mean x. 39.12(a). Sample variance:

s2 =

Σ( xi − x )2 Σ xi2 − (Σ xi )2 /n = n −1 n −1

39.12(b). Sample variance:

s2 =

Σfi ( xi − x )2 Σfi xi2 − (Σ fi xi )2 / Σ fi = ( Σ fi ) − 1 ( Σ fi ) − 1 s = Variance = s 2

39.13. Sample standard deviation: EXAMPLE 39.1:

Consider the following frequency distribution: xi

1

2

3

4

5

6

fi

8

14

7

12

3

1

Then n = Σ fi = 45 and Σ fi xi = 126. Hence, by 39.2(b), Mean x =

Σ xi fi 126 = = 2.8 Σ fi 45


211

Also, n – 1 = 44 and Σ fi xi2 = 430. Hence, by 39.12(b) and 39.13, s2 =

430 − (126)2 /45 ≈ 1.75 and s = 1.32 44

We find the median M, first finding the cumulative frequencies: F1 = 8,

F2 = 22,

F3 = 29,

F4 = 41,

F5 = 44,

F6 = 45 = n

Here n is odd, and (n + 1)/2 = 23. Hence, Median M = 23rd value = 3 The value 2 occurs most often, hence Mode = 2 M.D. and R.M.S. Here M.D. stands for mean deviation and R.M.S. stands for root mean square. As previously, x is the mean of the data and, for grouped data, n = Σ fi. 1 x −x n i

39.14(a).

M.D. =

39.15(a).

R.M.S. =

1 (Σ xi2 ) n

1 f x −x n i i

39.14(b).

M.D. =

39.15(b).

R.M.S. =

1 (Σ fi xi2 ) n

Measures of Position (Quartiles and Percentiles) Now we assume that the data x1, x2,…, xn are arranged in increasing order. 39.16. Sample range: xn – x1. There are three quartiles: the first or lower quartile, denoted by Q1 or QL; the second quartile or median, denoted by Q2 or M; and the third or upper quartile, denoted by Q3 or QU. These quartiles (which essentially divide the data into “quarters”) are defined as follows, where “half” means n/2 when n is even and (n-1)/2 when n is odd: 39.17. QL(= Q1) = median of the first half of the values. M (= Q2 ) = median of the values. QU (= Q3) = median of the second half of the values. 39.18. Five-number summary: [L, QL, M, QU, H] where L = x1 (lowest value) and H = xn (highest value). 39.19. Innerquartile range: QU – QL 39.20. Semi-innerquartile range:

Q=

QU − QL 2

The kth percentile, denoted by Pk, is the number for which k percent of the values are at most Pk and (100–k) percent of the values are greater than Pk. Specifically: 39.21. Pk = largest xs such that Fs ≤ k/100. Thus, QL = 25th percentile, M = 50th percentile, QU = 75th percentile.


212

Higher-Order Statistics 39.22. The rth moment: (a) mr =

1 Σ xir , n

(b) mr =

1 Σ f xr n i i

39.23. The rth moment about the mean x: (a)

μr =

1 Σ ( x i − x )r , n

(b)

μr =

1 Σ ( fi x i − x ) r n

μr =

1 Σ fi x i − x n

39.24. The rth absolute moment about mean x: (a)

μr =

r 1 Σ xi − x , n

(b)

r

39.25. The rth moment in standard z units about z = 0:

αr =

1 r Σz , n i

x −x 1 Σ f z r where zi = i σ n i i

(b)

αr =

39.26. Coefficient of skewness:

γ1 =

μ3 = α3 σ3

39.27. Momental skewness:

μ3 2σ 3

39.28. Coefficient of kurtosis:

α4 =

39.29. Coefficient of excess (kurtosis):

α4 − 3 =

39.30. Quartile coefficient of skewness:

QU − 2 xˆ + QL Q3 − 2Q2 + Q1 = QU − QL Q3 − Q1

(a)

Measures of Skewness and Kurtosis

μ4 σ4 μ4 −3 σ4

Population Variance and Standard Deviation Recall that N denotes the number of values in the population. 39.31. Population variance: σ 2 =

Σ ( xi − x )2 Σ xi2 − (Σ xi )2 /n = N N

39.32. Population standard deviation: σ = Variance = σ 2

Bivariate Data The following formulas apply to a list of pairs of numerical values: ( x1, y1), ( x 2 , y2 ), ( x3, y3),… , ( x n , yn )

where the first values correspond to a variable x and the second to a variable y. The primary objective is to determine whether there is a mathematical relationship, such as a linear relationship, between the data. The scatterplot of the data is simply a picture of the pairs of values as points in a coordinate plane.


213

Correlation Coefficient A numerical indicator of a linear relationship between variables x and y is the sample correlation coefficient r of x and y, defined as follows: r=

39.33. Sample correlation coefficient:

Σ ( xi − x )( yi − y ) Σ ( xi − x )2 Σ ( yi − y )2

We assume that the denominator in Formula 39.33 is not zero. An alternative formula for computing r follows: 39.34.

r=

Σ xi yi − (Σ xi )(Σ yi)/n Σ xi2 − (Σ xi )2 /n Σ yi2 − (Σ yi)2 /n

Properties of the correlation coefficient r follow: 39.35. (1) –1 r 1 or, equivalently, r 1. (2) r is positive or negative according as y tends to increase or decrease as x increases. (3) The closer |r| is to 1, the stronger the linear relationship between x and y. The sample covariance of x and y is denoted and defined as follows: sxy =

39.36. Sample covariance:

Σ ( xi − x )( yi − y ) n −1

Using the sample covariance, Formula 39.33 can be written in the compact form: 39.37.

r=

sxy sx s y

where sx and sy are the sample standard deviations of x and y, respectively. EXAMPLE 39.2:

Consider the following data: x y

50 2.5

45 5.0

40 6.2

38 7.4

32 8.3

40 4.7

55 1.8

The scatterplot of the data appears in Fig. 39-1. The correlation coefficient r for the data may be obtained by first constructing the table in Fig. 39-2. Then, by Formula 39.34 with n = 7,

r=

1431.8 − (300)(35.9) / 7 ≈ −0.9562 13, 218 − (300)2 / 7 218.67 + (35.9)2 / 7

Here r is close to –1, and the scatterplot in Fig. 39-1 does indicate a strong negative linear relationship between x and y.


214

Fig. 39-1

Fig. 39-2

Regression Line Consider a given set of n data points Pi (xi, yi). Any (nonvertical) line L may be defined by an equation of the form y = a + bx Let yi * denote the y value of the point on L corresponding to xi; that is, let yi* = a + bxi . Now let di = yi − yi* = yi − (a + bxi )

that is, di is the vertical (directed) distance between the point Pi and the line L. The squares error between the line L and the data points is defined by 39.38.

Σ di2 = d12 + d 22 + + d n2

The least-squares line or the line of best fit or the regression line of y on x is, by definition, the line L whose squares error is as small as possible. It can be shown that such a line L exists and is unique. The constants a and b in the equation y = a + bx of the line L of best fit can be obtained from the following two normal equations, where a and b are the unknowns and n is the number of points: 39.39.

⎧⎪ na + (Σ xi ) b = Σ yi ⎨ ⎪⎩(Σ xi )a + (Σ xi2 )b = Σ xi yi

The solution of the above normal equations follows: 39.40.

b=

n Σ xi yi − (Σ xi )(Σ yi ) rs y ; = n Σ xi2 − (Σ xi )2 sx

a=

Σ yi Σ xi −b = y − bx n n

The second equation tells us that the point ( x , y ) lies on L, and the first equation tells us that the point ( x + sx , y + rs y ) also lies on L. EXAMPLE 39.3: Suppose we want the line L of best fit for the data in Example 39.2. Using the table in Fig. 39-2 and n = 7, we obtain the normal equations

7a + 300 b = 35.9 300 a + 13, 218b = 1431.8

Substitution in 39.40 yields b=

7(1431.8) − (300)(35.9) = − 0.2 2959 7(13, 218) − (300)2

a=

35.9 300 − (−0.2959) = 17.8100 7 7


215

Thus, the line L of best fit is y = 17.8100 – 0.2959x The graph of L appears in Fig. 39-3.

Fig. 39-3

Curve Fitting Suppose that n data points Pi (xi, yi) are given, and that the data (using the scatterplot or the correlation coefficient r) do not indicate a linear relationship between the variables x and y, but do indicate that some other standard (well-known) type of curve y = f (x) approximates the data. Then the particular curve C that one uses to approximate that data, called the best-fitting or least-squares curve, is the curve in the collection which minimizes the squares error sum Σ di2 = d12 + d 22 + + d n2 where di = yi – f(xi). Three such types of curve are discussed as follows. Polynomial function of degree m: y = a0 + a1 x + a2 x 2 + + am x m The coefficients a0 , a1, a2 ,… , am of the best-fitting polynomial can be obtained by solving the following system of m + 1 normal equations: na0 + a1Σ xi + a2 Σ xi2 + + am Σ xim = Σ yi

39.41.

a0 Σ xi + a1 Σ xi2 + a2 Σ xi3 + + am Σ xim +1 = Σ xi yi .................................................................................... a0 Σ xim + a1 Σ xim +1 + a2 Σ xim + 2 + + am Σ xi2 m = Σ xim yi

Exponential curve:

y = ab x or log y = log a + (log b) x

The exponential curve is used if the scatterplot of log y verses x indicates a linear relationship. Then log a and log b are obtained from transformed data points. Namely, the best-fit line L for data points P′(xi, log yi) is 39.42.

⎧⎪ na ′ + (Σ xi ) b ′ = Σ (log yi ) ⎨ ⎪⎩(Σ xi ) a ′ + (Σ xi2 ) b ′ = Σ ( xi log yi )

Then a = antilog a′, b = antilog b′. EXAMPLE 39.4:

Consider the following data which indicates exponential growth: x y

1 6

2 18

3 55

4 160

5 485

6 1460


216

Thus, we seek the least-squares line L for the following data: x log y

1 0.7782

2 1.2553

3 1.7404

4 2.2041

5 2.6857

6 3.1644

Using the normal equation 39.42 for L, we get a ′ = 0.3028, b ′ = 0.4767

The antiderivatives of a′ and b′ yield, approximately, a = 2.0,

b = 3.0

Hence, y = 2(3 ) is the required exponential curve C. The data points and C are depicted in Fig. 39-4. x

Fig. 39-4

Power function: y = axb or log y = log a + b log x The power curve is used if the scatterplot of log y verses log x indicates a linear relationship. The log a and b are obtained from transformed data points. Namely, the best-fit line L for transformed data points P′(log xi, log yi) is 39.43.

⎧⎪ na ′ + Σ (log xi )b = Σ (log yi ) ⎨ ⎪⎩Σ (log xi )a ′ + Σ (log xi )2 b = Σ (log xi log yi )

Then a = antilog a′.

40

PROBABILITY

Sample Spaces and Events Let S be a sample space which consists of the possible outcomes of an experiment where the events are subsets of S. The sample space S itself is called the certain event, and the null set ∅ is called the impossible event. It would be convenient if all subsets of S could be events. Unfortunately, this may lead to contradictions when a probability function is defined on the events. Thus, the events are defined to be a limited collection C of subsets of S as follows. DEFINITION 40.1: properties:

The class C of events of a sample space S form a σ-field. That is, C has the following three

(i) S ∈ C. (ii) If A1, A2, … belong to C, then their union A1 ∪ A2 ∪ A3 ∪ … belongs to C. (iii) If A ∈ C, then its complement Ac ∈ C.

Although the above definition does not mention intersections, DeMorgan’s law (40.3) tells us that the complement of a union is the intersection of the complements. Thus, the events form a collection that is closed under unions, intersections, and complements of denumerable sequences. If S is finite, then the class of all subsets of S form a σ-field. However, if S is nondenumerable, then only certain subsets of S can be the events. In fact, if B is the collection of all open intervals on the real line R, then the smallest σ-field containing B is the collection of Borel sets in R. If Condition (ii) in Definition 40.1 of a σ-field is replaced by finite unions, then the class of subsets of S is called a field. Thus a σ-field is a field, but not visa versa. First, for completeness, we list basic properties of the set operations of union, intersection, and complement. 40.1.

Sets satisfy the properties in Table 40-1.

TABLE 40-1

Laws of the Algebra of Sets

Idempotent laws:

(1a) A ∪ A = A

Associative laws:

(2a)

(A ∪ B) ∪ C = A ∪ (B ∪ C)

Commutative laws: (3a) A ∪ B = B ∪ A

(1b) A ∩ A = A (2b) (A ∩ B) ∩ C = A ∩ (B ∩ C) (3b) A ∩ B = B ∩ A

Distributive laws: (4a) A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C) (4b) A ∩ (B ∪ C) = (A ∩ B) ∪ (A ∩ B) Identity laws:

(5a) A ∪ ∅ = A (6a) A ∪ U = U

Involution law:

(7)

(5b) A ∩ U = A (6b) A ∩ ∅ = ∅

(AC)C = A

Complement laws: (8a) A ∪ Ac = U (9a) Uc = ∅

(8b) A ∩ Ac = ∅ (9b) ∅c = U

DeMorgan’s laws: (10a) (A ∪ B)c = Ac ∩ Bc

(10b) (A ∩ B)c = Ac ∪ Bc

217

PROBABILITY

218

40.2. The following are equivalent: (i) A ⊆ B, (ii) A ∩ B = A, (iii) A ∩ B = B. Recall that the union and intersection of any collection of sets is defined as follows: ∪j Aj = {x | there exists j such that x ∈ Aj} 40.3.

and

∩j Aj = {x | for every j we have x ∈ Aj}

(Generalized DeMorgan’s Law) (10a)'(∪j Aj)c = ∩j Ajc; (10b)'(∩j Aj)c = ∪j Ajc

Probability Spaces and Probability Functions DEFINITION 40.2: Let P be a real-valued function defined on the class C of events of a sample space S. Then P is called a probability function, and P(A) is called the probability of an event A, when the following axioms hold:

Axiom [P1] For every event A, P(A) ≥ 0. Axiom [P2] For the certain event S, P(S) = 1. Axiom [P3] For any sequence of mutually exclusive (disjoint) events A1, A2, …,

P(A1 ∪ A2 ∪ …) = P(A1) + P(A2) + … The triple (S, C, P), or simply S when C and P are understood, is called a probability space. Axiom [P3] implies an analogous axiom for any finite number of sets. That is: Axiom [P3'] For any finite collection of mutually exclusive events A1, A2, …, An, P(A1 ∪ A2 ∪ … ∪ An) = P(A1) + P(A2) + … + P(An) In particular, for two disjoint events A and B, we have P(A ∪ B ) = P(A) + P(B). The following properties follow directly from the above axioms. 40.4.

(Complement rule) P(Ac) = 1 – P(A). Thus, P(∅) = 0.

40.5.

(Difference Rule) P(A\B) = P(A) – P(A ∩ B).

40.6.

(Addition Rule) P(A ∪ B) = P(A) + P(B) – P(A ∩ B).

40.7. For n ≥ 2, P 40.8.

(∪

n j =1

)

Aj ≤

∑

n j =1

P( Aj )

(Monoticity Rule) If A ⊆ B, then P(A) ≤ P(B).

Limits of Sequences of Events 40.9. (Continuity) Suppose A1, A2, … form a monotonic increasing (decreasing) sequence of events; that is, Aj ⊆ Aj+1 (Aj ⊇ Aj+1). Let A = ∪ j Aj (A = ∩ j Aj). Then lim P(An) exists and lim P(An) = P(A) For any sequence of events A1, A2, …, we define lim inf An =

∪ ∩ +∞

+∞

k=1

j=k

Aj

and

lim sup An =

∩ ∪ +∞

+∞

k=1

j=k

Aj

If lim inf An = lim sup An, then we call this set lim An. Note lim An exists when the sequence is monotonic.

PROBABILITY

40.10.

219

For any sequence Aj of events in a probability space, P(lim inf An) ≤ lim inf P(An) ≤ lim sup P(An) ≤ P(lim sup An) Thus, if lim An exists, then P(lim An) = lim P(An).

40.11.

For any sequence Aj of events in a probability space, P(∪j Aj) ≤

∑

j

P(Aj).

40.12. (Borel-Cantelli Lemma) Suppose Aj is any sequence of events in a probability space. Furthermore, +∞ suppose ∑ n=1 P(An) < +∞. Then P(lim sup An) = 0. 40.13. (Extension Theorem) Let F be a field of subsets of S. Let P be a function on F satisfying Axioms P1, P2, and P3¢. Then there exists a unique probability function P* on the smallest σ-field containing F such that P* is equal to P on F.

Conditional Probability DEFINITION 40.3: Let E be an event with P(E) > 0. The conditional probability of an event A given E is denoted and defined as follows:

P(A|E) =

40.14.

P( A ∩ E ) P(E )

(Multiplication Theorem for Conditional Probability) P(A ∩ B) = P(A)P(B|A). This theorem can be genealized as follows:

40.15. P(A1 ∩ … ∩ An) = P(A1)P(A2|A1)P(A3|A1 ∩ A2) … P(An|A1 ∩ … ∩ An-1) EXAMPLE 40.1: A lot contains 12 items of which 4 are defective. Three items are drawn at random from the lot one after the other. Find the probabiliy that all three are nondefective.

The probability that the first item is nondefective is 8/12. Assuming the first item is nondefective, the probability that the second item is nondefective is 7/11. Assuming the first and second items are nondefective, the probability that the third item is nondefective is 6/10. Thus, p=

8 7 6 14 ⋅ ⋅ = 12 11 10 55

Stochastic Processes and Probability Tree Diagrams A (finite) stochastic process is a finite sequence of experiments where each experiment has a finite number of outcomes with given probabilities. A convenient way of describing such a process is by means of a probability tree diagram, illustrated below, where the multiplication theorem (40.14) is used to compute the probability of an event which is represented by a given path of the tree. EXAMPLE 40.2: Let X, Y, Z be three coins in a box where X is a fair coin, Y is two-headed, and Z is weighted so the probability of heads is 1/3. A coin is selected at random and is tossed. (a) Find P(H), the probability that heads appears. (b) Find P(X|H), the probability that the fair coin X was picked if heads appears.

PROBABILITY

220

The probability tree diagram corresponding to the two-step stochastic process appears in Fig. 40-1a. (a) Heads appears on three of the paths (from left to right); hence, P(H) =

1 1 1 1 1 11 ⋅ + ⋅1 + ⋅ = 3 2 3 3 3 18

(b) X and heads H appear only along the top path; hence P(X ∩ H) =

1 1 1 P( X ∩ H ) 1/ 6 3 ⋅ = and so P(X|H) = = = 3 2 6 P( H ) 11 / 18 11

1/2

3%

H A

X 1/2

1/3 1/3 o

D

50%

T

30%

1 H

Y

N

o

4%

D

B N

1/3

1/3

H

2/3

T

20%

Z

5%

D

C N (b)

(a)

Fig. 40-1

Law of Total Probability and Bayes’ Theorem Here we assume E is an event in a sample space S, and A1, A2, … An are mutually disjoint events whose union is S; that is, the events A1, A2, …, An form a partition of S. 40.16.

(Law of Total Probability) P(E) = P(A1)P(E|A1) + P(A2)P(E|A2) + … + P(An)P(E|An)

40.17. (Bayes’ Formula) For k = 1, 2, …, n, P(Ak|E) =

P( Ak )P(E | Ak ) P( Ak )P(E | Ak ) = P(E ) P( A1 )P(E | A1 ) + P( A2 )P(E | A2 ) + ⋅ ⋅ ⋅ + P( An )P(E | An )

EXAMPLE 40.3: Three machines, A, B, C, produce, respectively, 50%, 30%, and 20% of the total number of items in a factory. The percentages of defective output of these machines are, respectively, 3%, 4%, and 5%. An item is randomly selected.

(a) Find P(D), the probability the item is defective. (b) If the item is defective, find the probability it came from machine: (i) A, (ii) B, (iii) C. (a) By 40.16 (Total Probability Law), P(D) = P(A)P(D|A) + P(B)P(D|B) + P(C)P(D|C) = (0.50)(0.03) + (0.30)(0.04) + (0.20)(0.05) = 3.7% P( A)P( D | A) (0.50)(0.03) (b) By 40.17 (Bayes’ rule), (i) P(A|D) = = = 40.5%. Similarly, 0.037 P( D) P ( B ) P ( D | B) P(C )P( D | C ) = 32.5%; (iii) P(C|D) = = 27.0% (ii) P(B|D) = P( D) P( D)

PROBABILITY

221

Alternately, we may consider this problem as a two-step stochastic process with a probability tree diagram, as in Fig. 40-1(b). We find P(D) by adding the three probability paths to D: (0.50)(0.03) + (0.30)(0.04) + (0.20)(0.05) = 3.7% We find P(A|D) by dividing the top path to A and D by the sum of the three paths to D. (0.50)(0.03)/0.037 = 40.5% Similarly, we find P(B|D) = 32.5% and P(C|D) = 27.0%.

Independent Events DEFINITION 40.4:

Events A and B are independent if P(A ∩ B) = P(A)P(B).

40.18. The following are equivalent: (i) P(A ∩ B) = P(A)P(B), (ii) P(A|B) = P(A), (iii) P(B|A) = P(B). That is, events A and B are independent if the occurrence of one of them does not influence the occurrence of the other. EXAMPLE 40.4: Consider the following events for a family with children where we assume the sample space S is an equiprobable space:

E = {children of both sexes},

F = {at most one boy}

(a) Show that E and F are independent events if a family has three children. (b) Show that E and F are dependent events if a family has two children. (a) Here S = {bbb, bbg, bgb, bgg, gbb, gbg, ggb, ggg}. So: E = {bbg, bgb, bgg, gbb, gbg, ggb}, P(E) = 6/8 = 3/4, F = {bgg, gbg, ggb, ggg}, P(F) = 4/8 = 1/2 E ∩ F = {bgg, gbg, ggb}, P(E ∩ F) = 3/8 Therefore, P(E)P(F) = (3/4)(1/2) = 3/8 = P(E ∩ F). Hence, E and F are independent. (b) Here S = {bb, bg, gb, gg}. So: E = {bg, gb}, P(E) = 2/4 = 1/2, F = {bg, gb, gg}, P(F) = 3/4 E ∩ F = {bg, gb}, P(E ∩ F) = 2/4 = 1/2 Therefore, P(E)P(F) = (1/2)(3/4) = 3/8 ≠ P(E ∩ F). Hence, E and F are dependent. DEFINITION 40.5: and

For n > 2, the events A1, A2, …, An are independent if any proper subset of them is independent

P(A1 ∩ A2 ∩ … ∩ An) = P(A1)P(A2) … P(An) Observe that induction is used in this definition. DEFINITION 40.6: A collection {Aj | j ∈ J} of events is independent if, for any n > 0, the sets Aj , Aj , …, Aj are inn 1 2 dependent.

The concept of independent repeated trials, when S is a finite set, is formalized as follows.

PROBABILITY

222

DEFINITION 40.7: Let S be a finite probability space. The probability space of n independent trials or repeated trials, denoted by Sn, consists of ordered n-tuples (s1, s2, …, sn) of elements of S with the probability of an n-tuple defined by

P((s1, s2, …, sn)) = P(s1)P(s2) … P(sn) EXAMPLE 40.5: Suppose whenever horses a, b, c race together, their respective probabilities of winning are 20%, 30%, and 50%. That is, S = {a, b, c} with P(a) = 0.2, P(b) = 0.3, and P(c) = 0.5.

They race three times. Find the probability that (a) the same horse wins all three times (b) each horse wins once (a) Writing xyz for (x, y, z), we seek the probability of the event A = {aaa, bbb, ccc}. Here, P(aaa) = (0.2)3 = 0.008, P(bbb) = (0.3)3 = 0.027, P(ccc) = (0.5)3 = 0.125 Thus, P(A) = 0.008 + 0.027 + 0.125 = 0.160. (b) We seek the probability of the event B = {abc, acb, bac, bca, cab, cba}. Each element in B has the same probability (0.2)(0.3)(0.5) = 0.03. Thus, P(B) = 6(0.03) = 0.18.

41

RANDOM VARIABLES

Consider a probability space (S, C, P). DEFINITION 41.1. A random variable X on the sample space S is a function from S into the set R of real numbers such that the preimage of every interval of R is an event of S.

If S is a discrete sample space in which every subset of S is an event, then every real-valued function on S is a random variable. On the other hand, if S is uncountable, then certain real-valued functions on S may not be random variables. Let X be a random variable on S, where we let RX denote the range of X; that is, RX = {x | there exists s ∈S for which X(s) = x} There are two cases that we treat separately. (i) X is a discrete random variable; that is, RX is finite or countable. (ii) X is a continuous random variable; that is, RX is a continuum of numbers such as an interval or a union of intervals. Let X and Y be random variables on the same sample space S. Then, as usual, X + Y, X + k, kX, and XY (where k is a real number) are the functions on S defined as follows (where s is any point in S): (X + Y)(s) = X(s) + Y(s), (X + k)(s) = X(s) + k,

(kX)(s) = kX(s), (XY)(s) = X(s)Y(s).

More generally, for any polynomial, exponential, or continuous function h(t), we define h(X) to be the function on S defined by [h(X)](s) = h[X(s)] One can show that these are also random variables on S. The following short notation is used: P(X = xi) P(a ≤ X ≤ b μ X or E(X) or simply μ σ X or Var(X) or simply σ 2 σ X or simply σ

denotes the probability that X = xi. denotes the probability that X lies in the closed interval [a, b]. denotes the mean or expectation of X. denotes the variance of X. denotes the standard deviation of X.

2

Sometimes we let Y be a random variable such that Y = g(X), that is, where Y is some function of X.

Discrete Random Variables Here X is a random variable with only a finite or countable number of values, say RX = {x1, x2, x3, …}where, say, x1 < x2, < x3 < …. Then X induces a function f(x) on RX as follows: f(xi) = P(X = xi) = P({s ∈S | X(s) = xi}) The function f(x) has the following properties: (i) f(xi) ≥ 0

and

(ii) Σ i f(xi) = 1

Thus, f defines a probability function on the range RX of X. The pair (xi, f(xi)), usually given by a table, is called the probability distribution or probability mass function of X.

223

RANDOM VARIABLES

224

Mean 41.1.

μ X = E(X) = Σxif(xi) Here, Y = g(X).

41.2.

μY = E(Y) = Σg(xi) f(xi)

Variance and Standard Deviation 41.3.

σ X 2 = Var(X) = Σ(xi – m)2 f(xi) = E((X – m)2) Alternately, Var(X) = s 2 may be obtained as follows:

41.4. Var(X) = Sxi2f(xi) – m2 = E(X2) – m2 41.5. σX =

Var ( X ) =

E(X 2 ) − μ 2

REMARK: Both the variance Var(X) = s 2 and the standard deviation s measure the weighted spread of the values xi

about the mean m; however, the standard deviation has the same units as m. EXAMPLE 41.1:

Suppose X has the following probability distribution:

x

2

f(x)

0.1

4

6

8

0.2

0.3

0.4

Then: m = E(X) = Σxif(xi) = 2(0.1) + 4(0.2) + 6(0.3) + 8(0.4) = 6 E(X2) = Σxi2 f(xi) = 22(0.1) + 42(0.2) + 62(0.3) + 82(0.4) = 40 s 2 = Var(X) = E(X2) − m2 = 40 − 36 = 4 s=

Var ( X ) =

4 =2

Continuous Random Variable Here X is a random variable with a continuum number of values. Then X determines a function f(x), called the density function of X, such that (i) f(x) ≥ 0

and

(ii)

∫

∞ −∞

f(x) dx =

Furthermore, P(a ≤ X ≤ b) =

Mean 41.6.

μ X = E(X) =

∫

∞ −∞

xf(x) dx

Here, Y = g(X). 41.7.

μY = E(Y) =

∫

∞ −∞

g(x) f(x) dx

∫

b a

f(x) dx

∫

R

f ( x ) dx = 1

RANDOM VARIABLES

225

Variance and Standard Deviation ∞

41.8. σ X 2 = Var(X) = ∫ (x − m)2 f(x)dx = E((X − m)2) −∞ Alternately, Var(X) = s 2 may be obtained as follows: 41.9. Var(X) = 41.10. s X =

∫

∞ −∞

x2f(x)dx − m2 = E(X2) − m2

Var ( X ) =

E(X 2 ) − μ 2

Let X be the continuous random variable with the following density function:

EXAMPLE 41.2:

⎧(1 / 2) x f(x) = ⎨ ⎩ 0

if 0 ≤ x ≤ 2 elsewhere

Then: E(X) = ∫ E(X2) =

∞ −∞

∫

∞ −∞

2

xf(x) dx =

∫

x2f(x) dx =

⎡x3 ⎤ 1 2 4 x dx = ⎢ 6 ⎥ = 2 3 ⎣ ⎦0

2 0

∫

2 0

1 3 x dx = 2

s 2 = Var(X) = E(X2) − m2 = 2 − s=

Var ( X ) =

2

⎡x4 ⎤ ⎢⎣ 8 ⎥⎦ = 2 0

16 2 = 9 9

2 1 2 = 9 3

Cumulative Distribution Function The cumulative distribution function F(x) of a random variable X is the function F:R → R defined by 41.11. F(a) = P(X ≤ a) The function F is well-defined since the inverse of the interval (−∞, a] is an event. The function F(x) has the following properties: 41.12. 41.13.

F(a) ≤ F(b) whenever a ≤ b. lim F(x) = 0

x→−∞

and

lim F(x) = 1

x→+∞

That is, F(x) is monotonic, and the limit of F to the left is 0 and to the right is 1. If X is the discrete random variable with distribution f(x), then F(x) is the following step function: 41.14. F(x) =

∑ f(xi) xi ≤ x

If X is a continuous random variable, then the density funcion f(x) of X can be obtained from the cummulative distribution function F(x) by differentiation. That is, d F(x) = F′(x) dx Accordingly, for a continuous random variable X,

41.15. f(x) =

41.16.

F(x) =

∫

x −∞

f(t) dt

RANDOM VARIABLES

226

Standardized Random Variable The standardized random variable Z of a random variable X with mean m and standard deviation s > 0 is defined by X−μ σ Properties of such a standardized random variable Z follow:

41.17.

Z=

μ Z = E(Z) = 0

σZ = 1

and

Consider the random variable X in Example 41.1 where μ X = 6 and σ X = 2.

EXAMPLE 41.3:

The distribution of Z = (X – 6)/2 where f(z) = f(x) follows: Z

−2

−1

0

1

f(Z)

0.1

0.2

0.3

0.4

Then: E(Z) = Σ zif(zi) = (−2)(0.1) + (−1)(0.2) + 0(0.3) + 1(0.4) = 0 E(Z2) = Σ zi2 f(zi) = (−2)2(0.1) + (−1)2(0.2) + 02(0.3) + 12(0.4) = 1 Var(Z) = 1 − 02 = 1 and sZ = Var ( X ) = 1

Probability Distributions

∑

⎛ n⎞ t n-t ⎜⎝ t ⎟⎠ p q

41.18.

Binomial Distribution: Φ(x) =

41.19.

Poisson Distribution: Φ(x) =

41.20.

Hypergeometric Distribution: Φ(x) =

41.21.

Normal Distribution: Φ(x) =

41.22.

t≤x

∑

t≤x

λ t e −λ t!

1 2π

Student’s t Distribution: Φ(x) =

∫

∑

t≤x

x

⎛r⎞ ⎛ s ⎞ ⎜⎝ t⎟⎠ ⎜⎝ n − t⎟⎠ z ⎛r + s⎞ ⎜⎝ n ⎟⎠

e − t / 2 dt 2

−∞

⎛ n + 1⎞ Γ⎜ ⎝ 2 ⎟⎠ nπ Γ (n/2) 1

2 41.23. c (Chi Square) Distribution: Φ(x) =

2

n/2

∫

x −∞

⎛ t 2⎞ ⎜⎝1 + ⎟⎠ n

− ( n +1)/ 2

dt

x 1 t(n - 2)/2e-t/2 dt ∫ Γ (n/2) 0

⎛ n1 + n 2 ⎞ n /2 n Γ⎜ ⎟ n1 n 2 ⎝ 2 ⎠ F Distribution: Φ(x) = Γ (n 1 /2)Γ (n2 /2) 1

41.24.

p > 0, q > 0, p + q = 1

2

/2

∫

x 0

t (n

1

/ 2 ) −1

(n 2 + n 1 t ) − ( n + n 1

2

)/ 2

dt

Section XII: Numerical Methods

42

INTERPOLATION

Lagrange Interpolation Two-point formula 42.1.

x − x0 x − x1 + f ( x1 ) x 0 − x1 x1 − x 0

p ( x ) = f ( x0 )

where p (x) is a linear polynomial interpolating two points ( x 0 , f ( x 0 )), ( x1 , f ( x1 )), x 0 ≠ x1

General formula 42.2.

p ( x ) = f ( x 0 ) Ln , 0 ( x ) + f ( x1 ) Ln ,1 ( x ) + + f ( x n ) Ln , n ( x )

where Ln , k =

n

∏

i=0,i≠ k

x − xi x k − xi

and where p(x) is an nth-order polynomial interpolating n + 1 points ( x k , f ( x k )), k = 0, 1, … , n; and

xi ≠ x j for i ≠ j

Remainder formula Suppose f ( x ) ∈ Cn+1[a, b]. Then there is a ξ ( x ) ∈ (a, b) such that: 42.3.

f (x) = p (x) +

f n+1 (ξ ( x )) ( x − x 0 )( x − x1 ) ( x − x n ) (n + 1)!

Newton’s Interpolation First-order divided-difference formula 42.4.

f [ x 0 , x1 ] =

f ( x1 ) − f ( x 0 ) x1 − x 0

Two-point interpolatory formula 42.5.

p( x ) = f ( x 0 ) + f [ x 0 , x1 ]( x − x 0 )

where p(x) is a linear polynomial interpolating two points ( x 0 , f ( x 0 )), ( x1 , f ( x1 )), x 0 ≠ x1


INTERPOLATION

228

Second-order divided-difference formula f [ x1 , x 2 ] − f [ x 0 , x1 ] 42.6. f [ x 0 , x1 , x 2 ] = x2 − x0 Three-point interpolatory formula 42.7.

p ( x ) = f ( x 0 ) + f [ x 0 , x1 ]( x − x 0 ) + f [ x 0 , x1 , x 2 ]( x − x 0 )( x − x1 )

where p(x) is a quadrant polynomial interpolating three points ( x 0 , f ( x 0 )), ( x1 , f ( x1 )), ( x 2 , f ( x3 ))

General kth-order divided-difference formula 42.8.

f [ x 0 , x1 ,… , x k } =

f [ x1 , x 2 ,… , x k ] − f [ x 0 , x1 ,… , x k −1 ] xk − x0

General interpolatory formula 42.9.

p ( x ) = f ( x 0 ) + f [ x 0 , x1 ]( x − x 0 ) + + f [ x 0 , x1 ,… , x n ]( x − x 0 )( x − x1 ) ( x − x n−1 )

where p(x) is an nth-order polynomial interpolating n + 1 points ( x k , f ( x k )), k = 0, 1,… , n; and

xi ≠ x j for i ≠ j

Remainder formula Suppose f ( x ) ∈ Cn+1[a, b]. Then there is a ξ ( x ) ∈ (a, b) such that 42.10.

f ( x ) = p( x ) +

f n+1 (ξ ( x )) ( x − x 0 )( x − x1 ) ( x − x n ) (n + 1)!

Newton’s Forward-Difference Formula First-order forward-difference at x0 42.11.

Δf ( x 0 ) = f ( x1 ) − f ( x 0 )

Second-order forward difference at x0 42.12.

Δ 2 f ( x 0 ) = Δf ( x1 ) − Δf ( x 0 )

General kth-order forward difference at x0 42.13.

Δ k f ( x 0 ) = Δ k −1 f ( x1 ) − Δ k −1 f ( x 0 )

Binomial coefficient 42.14.

⎛ s⎞ s(s − 1) (s − k + 1) ⎜⎝ k⎟⎠ = k!

Newton’s forward-difference formula ⎛ n⎞ p( x ) = ∑ ⎜ ⎟ Δ k f ( x 0 ) ⎝ k⎠ k =0 n

42.15.

where p(x) is an nth-order polynomial interpolating n + 1 equal spaced points ( x k , f ( x k )), x k = x 0 + kh k = 0, 1, … , n

INTERPOLATION

229

Newton’s Backward-Difference Formula First-order backward difference at xn 42.16.

∇f ( x n ) = f ( x n ) − f ( x n−1 )

Second-order backward difference at xn 42.17.

∇ 2 f ( x n ) = ∇f ( x n ) − ∇f ( x n−1 )

General kth-order backward difference at xn 42.18.

∇ k f ( x n ) = ∇ k −1 f ( x n ) − ∇ k −1 f ( x n−1 )

Newton’s backward-difference formula ⎛− n p( x ) = ∑ (−1) k ⎜ ⎞⎟ ∇ k f ( x n ) ⎝ k⎠ k =0 n

42.19.

where p(x) is an nth-order polynomial interpolating n + 1 equal spaced points ( x k , f ( x k )), x k = x 0 + kh

k = 0, 1, … , n

Hermite Interpolation Two-point basis polynomials 42.20.

x − x 0 ⎞ ( x − x1 )2 x − x1 ⎞ ( x − x 0 )2 ⎛ ⎛ H1,1 = ⎜1 − 2 H1, 0 = ⎜1 − 2 ⎟ 2 , x 0 − x1 ⎠ ( x 0 − x1 ) x1 − x 0 ⎟⎠ ( x1 − x 0 )2 ⎝ ⎝ ( x − x 0 )2 ( x − x1 )2 Hˆ 1, 0 = ( x − x 0 ) Hˆ 1,1 = ( x − x1 ) 2 , ( x 0 − x1 ) ( x1 − x 0 )2

Two-point interpolatory formula 42.21.

H 3 ( x ) = f ( x 0 ) H1, 0 + f ( x1 ) H1,1 + f ′( x 0 ) Hˆ 1, 0 + f ′( x1 ) Hˆ 1,1

where H3(x) is a third-order polynomial, agrees with f (x) and its first-order derivatives at two points, i.e., H 3 ( x 0 ) = f ( x 0 ), H 3′ ( x 0 ) = f ′( x 0 ),

H 3 ( x1 ) = f ( x1 ), H 3′ ( x1 ) = f ′( x1 )

General basis polynomials 42.22.

x − xj ⎞ 2 ⎛ H n , j = ⎜1 − 2 L ( x ), Hˆ n , j = ( x − x j ) L2n , j ( x ) Ln′, j ( x j )⎟⎠ n , j ⎝

where Ln , j =

n

∏

i=0,i≠ j

x − xi x j − xi

INTERPOLATION

230

General interpolatory formula 42.23.

n

n

j=0

j=0

H 2 n+1 ( x ) = ∑ f ( x j ) H n , j ( x ) + ∑ f ′( x j ) Hˆ n , j ( x )

where H 2 n+1 ( x ) is a (2n + 1)th-order polynomial, agrees with f(x) and its first order derivatives at n + 1 points, i.e., H 2 n+1 ( x k ) = f ( x k ), H 2′n+1 ( x k ) = f ′( x k )

Remainder formula Suppose f ( x ) ∈ C2 n+ 2 [a, b]. Then there is a ξ ( x ) ∈ (a, b) such that 42.24.

f ( x ) = H 2 n+1 ( x ) +

f 2 n+ 2 (ξ ( x )) ( x − x 0 )2 ( x − x1 )2 ( x − x n )2 (2n + 2)!

k = 0, 1, … , n

43

QUADRATURE

Trapezoidal Rule Trapezoidal rule 43.1.

∫

b a

f ( x ) dx ~

b−a [ f (a) + f (b)] 2

Composite trapezoidal rule

43.2.

∫

b a

f ( x ) dx ~

n −1 ⎞ h⎛ f (a) + 2∑ f (a + ih) + f (b)⎟ ⎜ 2⎝ ⎠ i =1

where h = (b − a)/n is the grid size.

Simpson’s Rule Simpson’s rule 43.3.

∫

b a

f ( x ) dx ~

b−a ⎡ ⎤ ⎛ a + b⎞ f (a) + 4 f + f (b)⎥ 6 ⎢⎣ ⎝ 2 ⎠ ⎦

Composite Simpson’s rule 43.4.

∫

b a

f ( x ) dx ~

n/2 n/2 ⎞ h⎛ f ( x ) + f ( x ) + f ( x 2i −1 ) + f ( x n )⎟ 2 4 ∑ ∑ 0 2 i − 2 3 ⎜⎝ ⎠ i=2 i =1

where n even, h = (b − a)/n, xi = a + ih, i = 0, 1, … , n.

Midpoint Rule Midpoint rule 43.5.

∫

b a

f ( x ) dx ~ (b − a) f

⎛ a + b⎞ ⎝ 2 ⎠

Composite midpoint rule 43.6.

∫

b a

n/2

f ( x ) dx ~ 2h∑ f ( x 2i ) i=0

where n even, h = (b − a)/(n + 2), xi = a + (i − 1)h, i = −1, 0, … , n + 1.

231

QUADRATURE

232

Gaussian Quadrature Formula Legendre polynomial 43.7.

Pn ( x ) =

1 dn [( x 2 − 1)n ] 2n n ! dx n

Abscissa points and weight formulas The abscissa points x k( n ) and weight coefficient ω k( n ) are defined as follows: 43.8.

x k( n ) = the kth zero of the Legendre polynomial Pn(x)

43.9.

ω k( n ) =

2Pn′( x k( n ) )2 1 − x k( n ) 2

Tables for Gauss-Legendre abscissas and weights appear in Fig. 43-1. Gauss-Legendre formula in interval (–1, 1) 43.10.

∫

1 −1

n

f ( x ) dx = ∑ ω k( n ) f ( x k( n ) ) + Rn k =1

Gauss-Legendre formula in general interval (a, b) 43.11.

∫

b a

f ( x ) dx =

b − a n (n) ⎛ a + b b − a⎞ ωk f + x k( n ) + Rn 2 ∑ 2 2 ⎠ ⎝ k =1

Remainder formula 43.12.

Rn =

(b − a)2 n+1 (n !)4 ( 2 n ) f (ξ ) (2n + 1)[(2n)!]3

for some a < ξ < b.

Fig. 43-1

44

SOLUTION of NONLINEAR EQUATIONS

Here we give methods to solve nonlinear equations which come in two forms: 44.1.

Nonlinear equation: f (x) = 0

44.2. Fixed point nonlinear equation: x = g(x) One can change from 44.1 to 44.2 or from 44.2 to 44.1 by settting: g( x ) = f ( x ) + x or

f ( x ) = g( x ) − x

Since the methods are iterative, there are two types of error estimates: 44.3.

| f ( x n ) | < or | x n+1 − x n | <

for some preassigned > 0.

Bisection Method The following theorem applies: Intermediate Value Theorem: Suppose f is continuous on an interval [a, b] and f (a) f (b) < 0. Then there is a root x* to f (x) = 0 in (a, b). The bisection method approximates one such solution x*. 44.4.

Bisection method: Initial step: Set a0 = a and b0 = b. Repetitive step: (a) Set cn = (an + bn )/2. (b) If f (an ) f (cn ) < 0, then set an+1 = an and bn+1 = cn ; else set an+1 = cn and bn+1 = bn .

Newton’s Method Newton method 44.5.

x n+1 = x n −

f ( xn ) f ′( x n )

Quadratic convergence 44.6.

| x n+1 − x ∗ | f ′′( x ∗ ) = ∗ 2 n→∞ | x − x | 2( f ′( x ∗ ))2 n

lim

where x* is a root of the nonlinear equation 44.1.

233

SOLUTION OF NONLINEAR EQUATIONS

234

Secant Method Secant method 44.7.

x n+1 = x n −

( x n − x n−1 ) f ( x n ) f ( x n ) − f ( x n−1 )

Rate of convergence 44.8.

| x n+1 − x ∗ | f ′′( x ∗ ) = n→∞ | x − x ∗ || x − x ∗ | 2( f ′( x ∗ ))2 n n −1

lim

where x* is a root of the nonlinear equation 44.1.

Fixed-Point Iteration The following definition and theorem apply: Definition: A function g from (a, b) to (a, b) is called a contraction mapping if | g ( x ) − g ( y) | L | x − y |

for any x , y ∈ (a, b)

where L < 1 is a positive constant. Fixed-point theorem: Suppose that g is a contraction mapping on (a, b). Then g has a unique fixed point in (a, b). Given such a contraction mapping g, the following method may be used. Fixed-point iteration 44.9.

x n+1 = g( x n )

45

NUMERICAL METHODS for ORDINARY DIFFERENTIAL EQUATIONS

Here we give methods to solve the following initial-value problem of an ordinary differential equation:

45.1.

⎧⎪dx = f ( x , t ) ⎨ dt ⎩⎪x (t0 ) = x 0

The methods will use a computational grid: 45.2.

tn = t0 + nh

where h is the grid size.

First-Order Methods Forward Euler method (first-order explicit method) 45.3.

x (t + h) = x (t ) + hf ( x (t ), t )

Backward Euler method (first-order implicit method) 45.4.

x (t + h) = x (t ) + hf ( x (t + h), t + h)

Second-Order Methods Mid-point rule (second-order explicit method)

45.5.

⎧ * h ⎪⎪x = x (t ) + 2 f ( x (t ), t ) ⎨ ⎛ h⎞ ⎪x (t + h) = x (t ) + hf ⎜ x * , t + ⎟ 2⎠ ⎝ ⎪⎩

Trapezoidal rule (second-order implicit method) 45.6.

h x (t + h) = x (t ) + { f ( x (t ), t ) + f ( x (t + h), t + h)} 2

Heun’s method (second-order explicit method)

45.7.

⎧x * = x (t ) + hf ( x (t ), t ) ⎪ h ⎨ * ⎪⎩x (t + h) = x (t ) + 2 { f ( x (t ), t ) + f ( x , t + h)}

235

NUMERICAL METHODS FOR ORDINARY DIFFERENTIAL EQUATIONS

236

Single-Stage High-Order Methods Fourth-order Runge–Kutta method (fourth-order explicit method) 45.8.

x (t + h) = x (t ) +

1 (F + 2F2 + 2F3 + F4 ) 6 1

where F ⎛ F1 = hf ( x , t ), F2 = hf ⎜ x + 1 , t + 2 ⎝

F h⎞ ⎛ , F3 = hf ⎜ x + 2 , t + 2⎟⎠ 2 ⎝

h⎞ , F4 = hf ( x + F3 , t + h) 2⎟⎠

Multi-Step High-Order Methods Adams-Bashforth two-step method 45.9.

x (t + h) = x (t ) + h

1 ⎛3 f ( x (t ), t ) − f ( x (t − h), t − h)⎞ 2 ⎝2 ⎠

Adams-Bashforth three-step method 45.10.

x (t + h) = x (t ) + h

5 4 ⎛ 23 f ( x (t − 2h), t − 2h)⎞ f ( x (t ), t ) − f ( x (t − h), t − h) + 12 3 ⎝ 12 ⎠

Adams-Bashforth four-step method 45.11.

⎛ 55 ⎞ 59 37 9 x (t + h) = x (t ) + h ⎜ f ( x (t ), t ) − f ( x (t − h), t − h) + f ( x (t − 2h), t − 2h) − f ( x (t − 3h), t − 3h)⎟ 24 24 24 ⎝ 24 ⎠

Milne’s method 45.12.

x (t + h) = x (t − 3h) + h

8 4 ⎛8 f ( x (t ), t ) − f ( x (t − h), t − h) + f ( x (t − 2h), t − 2h)⎞ 3 3 ⎝3 ⎠

Adams-Moulton two-step method 45.13.

x (t + h) = x (t ) + h

2 1 ⎛5 f ( x (t + h), t + h) + f ( x (t ), t ) − f ( x (t − h), t − h)⎞ 3 12 ⎝ 12 ⎠

Adams-Moulton three-step method

45.14.

⎛3 ⎞ 5 1 19 x (t + h) = x (t ) + h ⎜ f ( x (t + h), t + h) + f ( x (t ), t ) − f ( x (t − h), t − h) + f ( x (t − 2h), t − 3h)⎟ 24 24 24 ⎝8 ⎠

46

NUMERICAL METHODS for PARTIAL DIFFERENTIAL EQUATIONS

Finite-Difference Method for Poisson Equation The following is the Poisson equation in a domain (a, b) × (c, d): 46.1.

∇ 2u = f ,

∇2 =

∂2 ∂2 2 + ∂x ∂y 2

Boundary condition: 46.2.

u ( x , y) = g ( x , y)

for x = a, b

or

y = c, d

Computation grid: 46.3.

xi = a + iΔx

for i = 0, 1, … , n

y j = c + jΔy

for j = 0, 1, … , m

where Δx = (b − a)/n and Δy = (d − c)/m are grid sizes for x and y variables, respectively. Second-order difference approximation 46.4.

( Dx2 + Dy2 )u( xi , y j ) = f ( xi , y j )

where Dx2 u( xi , y j ) =

u( xi +1 , y j ) − 2u( xi , y j ) + u( xi −1 , y j ) Δx 2

Dy2 u( xi , y j ) =

u( xi , y j +1 ) − 2u( xi , y j ) + u( xi , y j −1 ) Δy 2

Computational boundary condition 46.5.

u( x 0 , y j ) = g(a, y j ),

u( x n , y j ) = g(b, y j )

for j = 1, 2, … , m

u( xi , y0 ) = g( xi , c),

u( xi , ym ) = g( xi , d )

for i = 1, 2, … , n

Finite-Difference Method for Heat Equation The following is the heat equation in a domain (a, b) × (c, d ) × (0, T ) : 46.6.

∂u = ∇ 2u ∂t

237

NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS

238


u ( x , y , t ) = g ( x , y)

for x = a, b

or

y = c, d

Initial condition: 46.8.

u( x , y, 0) = u0 ( x , y)

Computational grid: 46.9.

xi = a + iΔx

for i = 0,1, … , n

y j = c + jΔy

for j = 0,1, … , m

t k = kΔt

for k = 0,1, … ,

where Δx = (b − a)/n, Δy = (d − c)/m, and Δt are grid sizes for x, y and t variables, respectively. Computational boundary condition 46.10.

u( x 0 , y j ) = g(a, y j ), u( x n , y j ) = g(b, y j )

for j = 1, 2, … , m

u( xi , y0 ) = g( xi , c), u( xi , ym ) = g( xi , d )

for i = 1, 2, … , n

Computational initial condition 46.11.

u( xi , y j , 0) = u0 ( xi , y j )

for i = 1, 2, … , n; j = 0, 1, … , m

Forward Euler method with stability condition 46.12.

u( xi , y j , t k +1 ) = u( xi , y j , t k ) + Δt ( Dx2 + Dy2 )u( xi , y j , t k )

46.13.

2Δt 2Δt + 1 Δx 2 Δy 2

Backward Euler method (unconditional stable) 46.14.

u( xi , y j , t k +1 ) = u( xi , y j , t k ) + Δt ( Dx2 + Dy2 )u( xi , y j , t k +1 )

Crank-Nicholson method (unconditional stable) 46.15.

u( xi , y j , t k +1 ) = u( xi , y j , t k ) + Δt ( Dx2 + Dy2 ){u( xi , y j , t k ) + u( xi , y j , t k +1 )}/2

Finite-Difference Method for Wave Equation The following is a wave equation in a domain (a, b) × (c, d ) × (0, T ): 46.16.

∂2u = A2 ∇ 2 u ∂t 2

where A is a constant representing the speed of the wave.

NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS


u ( x , y, t ) = g ( x , y)

for x = a, b or y = c, d

Initial condition: 46.18.

u( x , y, 0) = u0 ( x , y),

∂u u( x , y, 0) = u1 ( x , y) ∂t

Computational grids: 46.19.

xi = a + iΔx

for i = 0, 1, … , n

y j = c + jΔy

for j = 0, 1, … , m

t k = kΔt

for k = −1, 0, 1, …

where Δx = (b − a)/n, Δy = (d − c)/m, and Δt are the grid sizes for x, y, and t variables, respectively. A second-order finite-difference approximation 46.20.

u( xi , y j , t k +1 ) = 2u( xi , y j , t k ) − u( xi , y j , t k −1 ) + Δt 2 A2 ( Dx2 + Dy2 )u( xi , y j , t k )

Computational boundary condition 46.21.

u( x 0 , y j ) = g(a, y j ), u( x n , y j ) = g(b, y j )

for j = 1, 2, … , m

u( xi , y0 ) = g( xi , c), u( xi , ym ) = g( xi , d )

for i = 1, 2, … , n

Computational initial condition 46.22.

u( xi , y j , t0 ) = u0 ( xi , y j )

for i = 1, 2, … , n; j = 0, 1, … , m

u( xi , y j , t −1 ) = u0 ( xi , y j ) + Δt 2 u1 ( xi , y j )

Stability condition 46.23.

Δt A min(Δx , Δx )

for i = 1, 2, … , n; j = 0, 1, … , m

239

ITERATION METHODS for LINEAR SYSTEMS

47

Iteration Methods for Poisson Equation The finite-difference approximation to the Poisson equation follows: 47.1.

⎧ui +1, j + ui −1, j + ui , j +1 + ui , j −1 − 4ui , j = fi , j ⎪⎪ for j = 1, 2, … , n − 1 ⎨u0 , j = un , j = 0 ⎪ for i = 1, 2, … , n − 1 ⎪⎩ui ,0 = ui ,n = 0

for i, j = 1, 2, … , n − 1

Three iteration methods for solving the system follow: Jacobi method 47.2.

uik,+j 1 =

1 k (u + uik−1, j + uik, j +1 + uik, j −1 − fi , j ) 4 i +1, j

Gauss-Seidel method 47.3.

uik,+j 1 =

1 k (u + uik−+11, j + uik, j +1 + uik,+j −11 − fi , j ) 4 i +1, j

Successive-overrelaxation (SOR) method 47.4.

1 k ⎧ * * k * ⎪ui , j = (ui +1, j + ui −1, j + ui , j +1 + ui , j −1 − fi , j ) 4 ⎨ ⎪⎩uik,+j 1 = (1 − ω )uik, j + ω ui*, j

Iteration Methods for General Linear Systems Consider the linear system 47.5. Ax = b where A is an n × n matrix and x and b are n-vectors. We assume the coefficient matrix A is partitioned as follows: 47.6. A = D – L – U where D = diag (A), L is the negative of the strictly lower triangular part of A, and U is the negative of the strictly upper triangular part of A.

240

ITERATION METHODS FOR LINEAR SYSTEMS

Four iteration methods for solving the system follow: Richardson method 47.7.

x k +1 = (I − A) x k + b

Jacobi method 47.8.

Dx k +1 = ( L + U ) x k + b

Gauss-Seidel method 47.9.

( D − L ) x k +1 = Ux k + b

Successive-overrelaxation (SOR) method 47.10.

( D − ω L ) x k +1 = ω (Ux k + b) + (1 − ω ) Dx k

241


PART B

TABLES

Copyright © 2009, 1999, 1968 by The McGraw-Hill Companies, Inc. Click here for terms of use.


Section I: Logarithmic, Trigonometric, Exponential Functions

1

FOUR PLACE COMMON LOGARITHMS log 10 N or log N


1

246

FOUR PLACE COMMON LOGARITHMS log 10 N or log N (Continued)

2

Sin x (x in degrees and minutes)

247

3

248

Cos x (x in degrees and minutes)

4

Tan x (x in degrees and minutes)

249

5

250

CONVERSION OF RADIANS TO DEGREES, MINUTES, AND SECONDS OR FRACTIONS OF DEGREES

6

CONVERSION OF DEGREES, MINUTES, AND SECONDS TO RADIANS

251

7

NATURAL OR NAPIERIAN LOGARITHMS log e x or ln x

ln 10 = 2.30259 2 ln 10 = 4.60517 3 ln 10 = 6.90776

252

4 ln 10 = 9.21034 5 ln 10 = 11.51293 6 ln 10 = 13.81551

7 ln 10 = 16.11810 8 ln 10 = 18.42068 9 ln 10 = 20.72327

7

NATURAL OR NAPIERIAN LOGARITHMS log e x or ln x(Continued)

253

8

254

EXPONENTIAL FUNCTIONS ex

9

EXPONENTIAL FUNCTIONS e –x

255

10

256

EXPONENTIAL, SINE, AND COSINE INTEGRALS Ei( x ) =

∫

∞ −u x

e du, Si( x ) = u

∫

x 0

sin u du, Ci( x ) = u

∫

∞ x

cos u du u

Section II: Factorial and Gamma Function, Binomial Coefficients

11

FACTORIAL n n ! = 1 i 2 i 3 ii n


12

258

GAMMA FUNCTION Γ( x ) =

∫

∞

x

t x −1e − t dt

for 1 x 2

[For other values use the formula Γ(x + 1) = x Γ(x)]

13

BINOMIAL COEFFICIENTS n! n(n − 1) (n − k + 1) ⎛ n ⎞ ⎛ n⎞ =⎜ , 0! = 1 ⎜⎝ k⎟⎠ = k !(n − k )! = k! ⎝ n − k⎟⎠

Note that each number is the sum of two numbers in the row above; one of these numbers is in the same column and the other is in the preceding column (e.g., 56 = 35 + 21). The arrangement is often called Pascal’s triangle (see 3.6, page 8).

259

13

BINOMIAL COEFFICIENTS n! n(n − 1) (n − k + 1) ⎛ n ⎞ ⎛ n⎞ =⎜ , 0 ! = 1 (Continued) ⎜⎝ k⎟⎠ = k !(n − k )! = k! ⎝ n − k⎟⎠

⎛ n⎞ ⎛ n ⎞ . For k > 15 use the fact that ⎜ ⎟ = ⎜ ⎝ k⎠ ⎝ n − k⎟⎠

260

Section III: Bessel Functions

14

15

BESSEL FUNCTIONS J 0 (x)

BESSEL FUNCTIONS J 1 (x)


16

17

262

BESSEL FUNCTIONS Y 0 (x)

BESSEL FUNCTIONS Y 1 (x)

18

19

BESSEL FUNCTIONS I 0 (x)

BESSEL FUNCTIONS I 1 (x)

263

20

21

264

BESSEL FUNCTIONS K 0 (x)

BESSEL FUNCTIONS K 1 (x)

22

23

BESSEL FUNCTIONS Ber(x)

BESSEL FUNCTIONS Bei(x)

265

24

25

266

BESSEL FUNCTIONS Ker(x)

BESSEL FUNCTIONS Kei(x)

26

VALUES FOR APPROXIMATE ZEROS OF BESSEL FUNCTIONS

The following table lists the first few positive roots of various equations. Note that for all cases listed the successive large roots differ approximately by π = 3.14159. . . .

267

Section IV: Legendre Polynomials

27

LEGENDRE POLYNOMIALS P n (x) [P 0 (x)=1, P 1 (x)=x]


28

LEGENDRE POLYNOMIALS P n (cos ␪) [P 0 (cos ␪)=1]

269

Section V: Elliptic Integrals

29

COMPLETE ELLIPTIC INTEGRALS OF FIRST AND SECOND KINDS K=

∫

π /2 0

dθ

1 − k sin θ 2

2

, E =∫

π /2 0

1 − k 2 sin 2 θ dθ , k = sin ψ


30

31

INCOMPLETE ELLIPTIC INTEGRAL OF THE FIRST KIND F (k , φ ) =

∫

φ 0

dθ , k = sin ψ 1 − k 2 sin 2 θ

INCOMPLETE ELLIPTIC INTEGRAL OF THE SECOND KIND E (k , φ ) =

∫

φ 0

1 − k 2 sin 2 θ dθ , k = sin ψ

271

Section VI: Financial Tables

32

COMPOUND AMOUNT : (1 + r) n If a principal P is deposited at interest rate r (in decimals) compounded annually, then at the end of n years the accumulated amount A = P(1 + r)n.


33

PRESENT VALUE OF AN AMOUNT : (1 ⴙ r) ⴚn The present value P which will amount to A in n years at an interest rate of r (in decimals) compounded annually is P = A(1 + r)n.

273

AMOUNT OF AN ANNUITY :

34

274

(1 + r ) n - 1 r

If a principal P is deposited at the end of each year at interest rate r (in decimals) compounded annually, then at the end of n years the accumulated amount is ⎡(1 − r )n − 1⎤ . The process is often called an annuity. P⎢ r ⎣ ⎦⎥

PRESENT VALUE OF AN ANNUITY :

35

1 - (1 + r ) - n r

An annuity in which the yearly payment at the end of each of n years is A at an interest rate r (in decimals) compounded annually has present value ⎡1 − (1 + r ) − n ⎤ . A⎢ ⎥⎦ r ⎣

275

Section VII: Probability and Statistics

36 NOTE:

AREAS UNDER THE STANDARD NORMAL CURVE from −∞ to x x 1 Φ( x ) = e − t / 2 dt ∫ −∞ 2π 2

erf (x) = 2Φ(x 2 ) − 1


37

ORDINATES OF THE STANDARD NORMAL CURVE y=

1 −x e 2π

2

/2

277

38

278

PERCENTILE VALUES (t p ) FOR STUDENT'S t DISTRIBUTION with n degrees of freedom (shaded area = p)

39

PERCENTILE VALUES (␹ 2p ) FOR ␹ 2 (CHI-SQUARE) DISTRIBUTION with n degrees of freedom (shaded area = p)

279

40

280

95th PERCENTILE VALUES FOR THE F DISTRIBUTION n1 = degrees of freedom for numerator n2 = degrees of freedom for denominator (shaded area = .95)

41

99th PERCENTILE VALUES FOR THE F DISTRIBUTION n1 = degrees of freedom for numerator n2 = degrees of freedom for denominator (shaded area = .99)

281

42

282

RANDOM NUMBERS

Index of Special Symbols and Notations The following list show special symbols and notations together with pages on which they are defined or first appear. Cases where a symbol has more than one meaning will be clear from the context.

Symbols Bern(x), Bein(x) B(m, n) Bb C(x) Ci(x) e1, e2, e3 erf(x) erfc(x) E = E(k, p /2) E = E(k, f) Ei(x) En E(X) f [x0, x1, ..., xk] F(a), F(x) F(a, b; c; x) F(k, f ) g, g−1 G. M. h1, h2, h3 Hn(x) Hn(1)(x), Hn(2)(x) H. M. i, j, k In(x) Jn(x) K = F(k, p /2) Kern(x), Kein(x) Kn(x) ln x or loge x log x or log10 x Ln(x) Lnm(x) l, l−1 M.D. P(A/E) Pn(x) Pnm(x) QU, M, QL Qn(x) Qnm(x) r R.M.S. s s2 sxy Si(x) S(x) Tn(x)

Ber and Bei functions, 157 beta function, 152 Bernoulli numbers, 142 Fresnel cosine integral, 204 cosine integral, 204 unit vectors in curvilinear coordinates, 127 error function, 203 complementary error function, 203 complete elliptic integral of the second kind, 198 incomplete elliptic integral of the second kind, 198 exponential integral, 203 Euler number, 142 mean or expectation of random variable X, 223 divided distance formula, 287, 288 cumulative distribution function, 209 hypergeometric function, 178 incomplete elliptic integral of the first kind, 198 Fourier transform and inverse Fourier transform, 194 geometric mean, 209 scale factors in curvilinear coordinates, 127 Hermite polynomial, 169 Hankel functions of the first and second kind, 155 harmonic mean, 210 unit vectors in rectangular coordinates, 120 modified Bessel function of the first kind, 155 Bessel function of the first kind, 153 complete elliptic integral of the first kind, 198 Ker and Kei functions, 158 modified Bessel function of the second kind, 156 natural logarithm of x, 53 common logarithm of x, 53 Laguerre polynomials, 171 associated Laguerre polynomials, 173 Laplace transform and inverse Laplace transform, 180 mean deviation conditional probability of A given E, 219 Legendre polynomials, 164 associated Legendre polynomials, 173 quartiles, 211 Legendre functions of second kind, 167 associated Legendre functions of second kind, 168 sample correlation coefficient, 213 root-mean-square, 211 sample standard deviation, 208 sample variance, 210 sample covariance, 213 Sine integral, 203 Fresnel sine integral, 204 Chebyshev polynomials of first kind, 175


INDEX OF SPECIAL SYMBOLS AND NOTATIONS

284

Un(x) Var(X) x, x xk(n) Yn(x) Z

Chebyshev polynomials of second kind, 176 variance of random variable X, 224 sample mean, grand mean, 208, 209 kth zero of Legendre polynomial Pn(x), 232 Bessel function of second kind, 153 standardized random variable, 226

Greek Symbols ar g Γ(x) ζ(x) m q

rth moment in standard units, 212 Euler’s constant, 4 gamma function, 149 Rieman zeta function, 204 population mean, 208 coordinate: cylindrical 37, polar, 11, 24; spherical, 38

p f Φ (p) Φ (x) s s2

pi, 3 spherical coordinate, 38 1 1 1 sum 1 + + + + , Φ(0) = 0, 154 p 2 3 probability distribution function, 226 population standard deviation, 223 population variance, 223

Notations A~B |A|

A is asymptotic to B or A/B approaches 1, 151 ⎧ A if A ≥ 0 absolute value of A = ⎨ ⎩− A if A < 0

n!

factorial n, 7

⎛ n⎞ ⎜⎝ k⎟⎠ dy ⎫ = f ( x ) ⎪ dx ⎬ d2y y = 2 = f ( x ), etc.⎪ dx ⎭ p d Dp = p dx ∂f ∂f ∂2 f , , , etc. ∂x ∂x ∂x ∂y ∂( x , y, z ) ∂(u1 , u2 , u3 )

binomial coefficients, 8

y =

∫ f ( x )dx b

∫a f ( x )dx ∫C A i dr

AiB A×B ∇ ∇2 = ∇ i ∇ ∇4 = ∇2 (∇2)

derivatives of y or f(x) with respect to x, 62

pth derivative with respect to x, 64 partial derivatives, 65 Jacobian, 128 indefinite integral, 67 definite integral, 108 line integral of A along C, 124 dot product of A and B, 120 cross product of A and B, 121 del operator, 122 Laplacian operator, 123 biharmonic operator, 123

Index Adams-Bashforth methods, 236 Adams-Moulton methods, 236 Addition formula: Bessel functions, 163 Hermite polynomials, 170 Addition rule (probability) 208 Addition of vectors, 119 Algebra of sets, 217 Algebraic equations, solutions of, 13 Alphabet, Greek, 3 Analytic geometry, plane, 22–33 solid, 34–40 Annuity table, 274 Anti-derivative, 67 Anti-logarithms, 53 Arithmetic: mean, 208 series, 134 Arithmetic-geometric series, 134 Associated Laguerre polynomials, 173 (See also Laguerre polynomials) Associated Legendre functions, 164 (See also Legendre functions) of the first kind, 168 of the second kind, 168 Asymptotic expansions or formulas: Bernoulli numbers, 143 Bessel functions, 160 Backward difference formulas, 228 Her and Bei functions, 157 Bayes formula, 220 Bernoulli numbers, 142 asymptotic formula, 143 series, 143 Bernoulli’s differential equation, 116 Bessel functions, 153–164 graphs, 159 integral representation, 161 modified, 155 recurrence formulas, 154, 157 series, orthogonal, 161 tables, 261–267 Bessel’s differential equation, 118, 153 general solution, 154 modified differential equation, 155 Best fit, line of, 214 Beta function, 152 Biharmonic operator, 123 Binomial: coefficients, 7, 228, 259 distribution, 226 formula, 7 series, 136 Bipolar coordinates, 131 Bisection method, 223 Bivariate data, 212 Carioid, 29 Cassini, ovals of, 32 Catalan’s constant, 200 Catenary, 29 Cauchy or Euler differential equation, 117

Cauchy’s form of remainder in Taylor series, 134 Cauchy-Schwarz inequality, 205 for integrals, 206 Central tendency, 208 Chain rule for derivatives, 67 Chebyshev polynomials, 175 of the first kind, 175 of the second kind, 176 recurrence formula, 175 Chebyshev’s differential equation, 175 general solution, 177 Chebyshev’s inequality, 206 Chi-square distribution, 226 table of values, 279 Circle, 17, 25 Coefficient: of excess (kurtosis), 212 of skewness, 212 Coefficients: binomial, 7 multinomial, 9 Complementary error function, 203 Complex: conjugate, 10 numbers, 10 logarithm of, 55 plane, 10 Components of a vector, 120 Compound amount, 262 Confocal: ellipsdoidal coordinates, 133 paraboloidal coordinates, 133 Conical coordinates, 129 Conics, 25 (See also Ellipse, Parabola, Hyperbola) Conjugate, complex, 10 Constant of integration, 67 Constants, 3 series of, 134 Continuous random variable, 224 Convergence, interval of, 138. Conversion factors, 15 Convolution theorem, Fourier transform, 194 Coordinates, 127 bipolar, 131 confocal ellipsoidal, 133 confocal paraboloidal, 133 conical, 132 curvilinear, 127 cylindrical, 129 elliptic cylindrical, 130 oblate spheroidal, 131 paraboloidal, 130 prolate spheroidal, 131 spherical, 129 toroidal, 132 Correlation coefficient, 213 Cosine, 43 graph of, 46 table of values, 245 Cosine integral, 203, 256 Cosines, law of, 51 Covariance, 213 Cross or vector product, 121 Cubic equation, solution of, 13


INDEX

286

Cumulative distribution function, 225 Curl, 123 Curve fitting, 215 Curvilinear coordinates, 134 Cycloid, 28 Cylindrical coordinates, 37, 129 Definite integrals, 108–116 approximate formula, 109 definition of, 108 Degrees, conversion to radians, 251 Del operator, 122 DeMoivre’s theorem, 11 Derivatives, 62–66 chain rule for, 62 higher, 64 Leibniz’s rule, 64 of vectors, 122 Deviation: mean, 210 standard, 210 Differential equations, numerical methods for solution: ordinary, 235–236 partial, 237–240 Differentials, 65, 66 Differentiation, 62–66 (See also Derivatives) Direction numbers, 34 cosines, 34 Discrete random variable, 223 Distributions, probability, 226 Divergence, 122, 128 theorem, 126 Divided-difference formula (general), 228 Dot or scalar product, 120 Double integrals, 125 Eccentricity, 25 Ellipse, 18, 25 Ellipsoid, 39 Elliptic cylinder, 41 Elliptic cylindrical coordinates, 130 Elliptic functions, 198–202 Jacobi’s, 199 series expansion, 200 Elliptic integrals, 198–199 table of values, 270–271 Epicycloid, 30 Equality of vectors, 119 Equations, algebraic, 13 Error functions, 203 Euler: constant, 4 differential equation, 117 methods, 235 numbers, 142 Euler-Maclaurin summation formula, 137 Exact differential equation, 116 Excess, coefficient of kurtosis, 212 Exponential curve (least-squares), 215 Exponential function, 53–54 series for, 139 table of values, 254–255 Exponential integral, 203, 256 Exponents, 53 F distribution, 226 table of values, 280–281 Factorial n, 7 table of values, 257 Factors, special, 5 Financial tables, 272–275

Finite-difference methods for solution of: heat equation, 237 Poisson equation, 237 wave equation, 238 First-order divided-difference formula, 227 Five number summary [L, QL, M, QH, H ], 211 Fixed-point iteration, 234 Folium of Descartes, 31 Forward difference formulas, 228 Fourier series, 144–146 Fourier transform, 193 convolution of, 194 cosine, 194, 197 Parseval’s identity for, 193 sine, 194, 196 tables, 195–199 Fourier’s integral theorem, 193 Fresnel sine and cosine integral, 204 Frullani’s integral, 115 Gamma function, 149, 150 relation to beta function, 152 table of values, 258 Gauss’ theorem, 126 Gauss-Legendre formula, 232 Gauss-Seidel method, 230 Gaussian quadrature formula, 231 Generating functions, 157, 165, 168, 169, 171, 173, 175, 176 Geometric: mean (G.M.), 209 series, 134 Geometry, 16–21 analytic, 22–40 Gradient, 122, 128 Grand mean, 209 Greek alphabet, 3 Green’s theorem, 126 Griggsian logarithms, 53 Half angle formulas, 48 Half rectified sine wave function, 191 Hankel functions, 155 Harmonic mean, 209 Heat equation, 237 Heaviside’s unit function, 192 Hermite: interpolation,229 polynomials, 169–170 Hermite’s differential equation, 169 Heun’s method, 235 Holder’s inequality, 205 for integrals, 206 Homogeneous differential equation, 116 linear second order, 117 Hyperbola, 25 Hyperbolic functions, 56–61 graphs of, 59 inverse, 59–61 series for, 140 Hyperboloid, 39 Hypergeometric: differential equation, 178 distribution, 226 functions, 178 Hypocycloid, 28, 30 Imaginary part of a complex number, 10 Indefinite integrals, 67–107 definition of, 67 tables of, 71–107 transformation of, 69 Independent events, 221

INDEX

Inequalities, 205 Infinite products, 207 Integral calculus, fundamental theorem, 108 Integrals: definite (see Definite integrals) improper, 108 indefinite (see Indefinite integrals) line, 124 multiple, 125 surface, 125 Integration, 64 (See also Integrals) constant of, 67 general rules, 67–69 Integration by parts, 67 generalized, 69 Intercepts, 22 Interest, 272–275 Intermediate Value Theorem, 233 Interpolation, 227 Hermite, 229 Interpolatory formula (general), 228 Interquartile range, 211 Interval of convergence, 138 Inverse: hyperbolic functions, 59–61 Laplace transforms, 180 trigonometric functions, 49–51 Iteration methods, 240 for general linear systems, 240 for Poisson equation, 240 Jacobi method, 240 Jacobi’s elliptic functions, 199 Jacobian, 128 Ker and Kei functions, 158–159 Kurtosis, 212 Lagrange: form of remainder, 138 interpolation, 227 Laguerre polynomials, 172 generating function for, 173 recurrence formula, 192 Laguerre’s associated differential equation, 170 Laguerre’s differential equation, 172 Landen’s transformation, 199 Laplace transform, 180–192 complex inversion formula for, 180 definition of, 180 inverse, 180 tables of, 181–192 Laplacian, 123, 128 Least-squares: curve, 215 line, 214 Legendre functions, 164–168 of the second kind, 166 Legendre polynomial, 164–165, 232 generating function for, 164 recurrence formula for, 166 tables of values for, 269 Legendre’s associated differential equation, 168 Legendre’s differential equation, 118, 164 Leibniz’s rule, 64 Lemniscate, 28 Limacon of Pascal, 32 Line, 22, 35 of best fit, 214 regression, 214 Line integral, 124

287

Logarithmic functions, 53–55 (See also Logarithms) series for, 139 table of values, 245–246, 252–253 Logarithms, 53–55 of complex numbers, 55 Griggsian, 53 Maclaurin series, 138 Mean, 208 continuous random variable, 224 deviation (M.D.), 211 discrete random variable, 223 geometric, 209 grand, 209 harmonic, 209 population, 212 weighted. 209 Mean value theorem, for definite integrals, 108 generalized, 109 Median, 208 Midpoint rule, 231, 235 Midrange, 210 Milne’s method, 236 Minkowski’s inequality, 206 for integrals, 206 Mode, 209 Modified Bessel functions, 155–157 generating function for, 157 graphs of, 159 recurrence formulas for, 157 Modulus of a complex number, 11 Moment, rth, 212 Momental skewness, 212 Moments of inertia, 41 Monoticity Rule (Probability), 218 Mutinomial coefficients, 9 Multiple, integrals, 125 Napier’s rules, 52 Natural logarithms and antilogarithms, 53 tables of, 252–253 Neumann’s function, 153 Newton’s: backward-difference formula, 228 forward-difference formula, 228 interpolation, 227 method, 233 Nonhomogeneous differential equation, linear second order, 117 Nonlinear equations, solution of, 233 Normal curve, 276–277 distribution, 226 Normal equations for least-squares line, 214 Null function, 189 Numbers: Bernoulli, 142 Euler, 142 Numerical methods for partial differential equations, 237–239 Oblate spheroidal coordinates, 131 Orthogonal curvilinear coordinates, 127–128 formulas involving, 128 Orthogonality: Chebyshev’s polynomials, 176 Laguerre polynomials, 172 Legendre polynomials, 165 Ovals of Cassini, 32

INDEX

288

Parabola, 25 segment of, 18 Parabolic cylindrical coordinates, 129 Paraboloid, 40 Paraboloidal coordinates, 130 Parallelepiped, 19 Parallelogram, 7 Parameter, 208 Parseval’s identity for: Fourier series, 144 Fourier transform, 194 Partial: derivatives, 65 differential equations, numerical methods, 237 Pascal’s triangle, 8 Percentile, kth, 211 Periods of elliptic functions, 200 Plane analytic geometry, formulas from, 22–27 Plane, complex, 10 Poisson: distribution, 226 equation, 237 summation formula, 137 Polar: coordinates, 24 form of a complex number, 11 Polygon, regular, 17 Polynomial function (least-squares), 214 Polynomials: Chebyshev’s, 175 Laguerre, 171 Legendre, 164 Population, 208 mean 210 standard deviation, 212 variance, 212 Power function (least-squares), 214 Power series, 138–141 reversion of 141 Powers, sums of, 134 Present value, of an amount, 273 of an annuity, 275 Probability, 217 distribution, 223 function, 218 tables, 276 Products, infinite, 207 special, 5 Pulse function, 192 Pyramid, volume of, 20 Quadrants, 43 Quadratic convergence, 233 Quadratic equation, solution of, 103 Quadrature, 231–232 Quartic equation, solution of, 13 Quartile coefficient of skewness, 212 Quartiles [QL, M, QU], 211 Radians, 4, 44 table of conversion to degrees, 250 Random numbers table, 282 Random variable, 223–226 standardized, 226 Range, sample, 210 Real part of a complex number, 10 Reciprocals of powers, sums of, 135 Rectangle, 13 Rectangular coordinate system, 120 Rectangular coordinates, 24 transformation to polar coordinates, 24 Rectangular formula, 109 Rectified sine wave function, 191

Recurrence or recursion formulas: Bessel functions, 154 Chebyshev’s polynomials, 175 gamma function, 149 Hermite polynomials, 169 Laguerre polynomials, 171 Legendre polynomials, 165 Regression line, 214 Regular polygon, 17 Remainder: Cauchy’s form, 13 Lagrange form, 138 Remainder formula: Gauss-Legendre interpolation, 232 Hermite interpolation, 230 Lagrange interpolation, 227 Reversion of power series, 141 Richardson method, 240 Riemann zeta function, 204 Right circular cone, 20 Rochigue’s formula: Laguerre polynomials, 171 Legendre’s polynomials, 164 Root mean square (R.M.S.), 211 Roots of complex numbers, 11 Rose, 29 Rotation, 24, 37 Runge-Kutta method, 236 Sample, 208 covariance, 213 Saw tooth wave function, 191 Scalar, 119 multiplication of vectors, 119 Scalar or dot product, 120 Scale factors, 127 Scatterplot, 212 Schwarz (Cauchy-Schwarz) inequality, 205 for integrals, 206 Secant method, 233 Second-order differential equation, 117 Second-order divided-difference formula, 228 Sector of a circle, 17 Segment: of circle, 18 of parabola, 18 Semi-interquartile range, 211 Separation of variables, 116 Series, arithmetic, 134 arithmetic-geometric, 134 binomial, 188 of constants, 134 Fourier, 144–148 geometric, 134 power, 138 of sums of powers, 134 Taylor, 138–141 Simpson’s formula, 109, 231 Sine, 43 graph of, 46 table of values, 247 Sine integral, 88 table of values, 264 Sines, law of, 51 Skewness, 212 Solid analytic geometry, 34–40 Solutions of algebraic equations, 13–14 SOR (successive-overrelaxation) method, 240 Sphere, equations of, 38 surface area, 19 volume, 21 Spherical coordinates, 38, 129 Spherical triangle, 51

INDEX

Spiral of Archimedes, 33 Square wave function, 191 Squares error, 215 Standard deviation, 210 continuous random variable, 225 discrete random variable, 224 population, 212 sample, 210 Standardized random variable, 215 Statistics, 208–216 tables, 276–281 Step function, 192 Stirling’s formula, 150 Stochastic process, 219 Stokes’ theorem, 126 Student’s t distribution, 226 table of, 298 Successive-overrelaxation (SOR) method, 240 Summation formula: Euler-Maclaurin, 137 Poisson, 137 Surface integrals, 125 Tangent function, 43 graph of, 46 table of values, 249 Tangents, law of, 51, 52 Taylor series, 138–141 two variables, 141 Three-point interpolatory formula, 228 Toroidal coordinates, 132 Torus, surface area, volume, 18 Total probability, Law of, 220 Tractrix, 31 Transformation: Jacobian of, 128 of coordinates, 24, 36–37, 128 of integrals, 70, 128 Translation of coordinates: in a plane, 24 in space, 36 Trapezoid, area, perimeter, 16 Trapezoidal rule (formula), 109, 231, 235 Tree diagrams, Probability, 219

289

Triangle inequality, 205 Triangular wave function, 191 Trigonometric functions, 43–52 definition of, 43 graphs of, 46 inverse, 49–50 series for, 139 tables of, 247–249 Triple integrals, 125 Trochoid, 30 Two-point formula, 228 Two-point interpolatory formula, 228 Unit function, Heaviside’s, 192 Unit normal to the surface, 125 Unit vector, 120 Variance, 210 continuous random variable, 225 discrete random variable, 224 population, 210 sample, 210 Vector analysis, 119–133 Vector or cross-product, 121 Vectors, 119 derivatives of, 122 integrals involving, 124 unit, 119 Volume integrals, 125 Wallis’ product, 207 Wave equation, 238 Weber’s function, 153 Weighted mean, 209 Witch of Agnesi, 31 x-intercept, 22 y-intercept, 22 Zero vector, 119 Zeros of Bessel functions, 267 Zeta function of Riemann, 204

Schaum's Mathematical Handbook of Formulas and Tables

Mathematical Handbook of Formulas and Tables

Mathematical handbook of formulas and tables