Mathematical Methods For Physics And Engineering Book Pdf
The third edition of this highly acclaimed undergraduate textbook is suitable for teaching all the mathematics for an undergraduate course in any of the physical sciences. As well as lucid descriptions of all the topics and many worked examples, it contains over 800 exercises. New stand-alone chapters give a systematic account of the 'special functions' of physical science, cover an extended range of practical applications of complex variables, and give an introduction to quantum operators. Further tabulations, of relevance in statistics and numerical integration, have been added. In this edition, half of the exercises are provided with hints and answers and, in a separate manual available to both students and their teachers, complete worked solutions. The remaining exercises have no hints, answers or worked solutions and can be used for unaided homework; full solutions are available to instructors on a password-protected web site, www.cambridge.org/9780521679718. Contains all the mathematical material likely to be needed for any undergraduate course in the physical sciences Maintains the method and clarity of presentation that has been much praised in earlier editions Over 800 exercises: half with complete solutions available; half suitable for unaided homework - the only book at this level to have fully-worked solutions to ALL of its problems
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Mathematical methods
for physics and engineering
A comprehensive guide
Second edition
K. F. Riley, M. P. Hobson and S. J. Bence
published by the press syndicate of the univers ity of cambridge
The Pitt Building, Trumpington Street, Cambridge, United Kingdom
cambridge university press
The Edinburgh Building, Cambridge CB2 2RU, UK
40 West 20th Street, New York, NY 10011–4211, USA
477 Williamstown Road, Port Melbourne, VIC 3207, Australia
Ruiz de Al arc ´
on 13, 28014 Madrid, Spain
Dock House, The Waterfront, Cape Town 8001, South Africa
http://www.cambridge.org
First edition c
Cambridge University Press 1998
Second edition c
Ken Riley, Mike Hobson, Stephen Bence 2002
This book is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without
the written permission of Cambridge University Press.
First published 1998
Reprinted 1998 (with minor corrections), 2000 (twice), 2001
Second edition published 2002
Printed in the United Kingdom at the University Press, Cambridge
Typ ese t in T E
X Monotype Times [epc ]
A catalogue record for this book is available from the British Library
Library of Congress Cataloguing in Publication data
Riley, K. F. (Kenneth Franklin), 1936–
Mathematical methods for physics and engineering / Ken Riley,
Mike Hobson, and Stephen Bence.
p. cm.
Includes bibliographical references and index.
ISBN 0 521 81372 7 (HB) – ISBN 0 521 89067 5 (PB)
1. Mathematical analysis. I. Hobson, M. P. (Michael Paul), 1967– .
II. Bence, S. J. (Stephen John), 1972– . III. Title.
QA300.R495 2002
515 .1–dc21 2002018922 CIP
ISBN 0 521 81372 7 hardback
ISBN 0 521 89067 5 paperback
Contents
Preface to the second edition xix
Preface to the first edition xxi
1 Preliminary algebra 1
1.1 Simple functions and equations 1
Polynomial equations; factorisation; properties of roots
1.2 Trigonometric identities 10
Single angle; compound-angles; double- and half-angle identities
1.3 Coordinate geometry 15
1.4 Partial fractions 18
Complications and special cases
1.5 Binomial expansion 25
1.6 Properties of binomial coefficients 27
1.7 Some particular methods of proof 30
Proof by induction; proof by contradiction; necessary and sufficient
conditions
1.8 Exercises 36
1.9 Hints and answers 39
2 Preliminary calculus 42
2.1 Differentiation 42
Differentiation from first principles; products; the chain rule; quotients;
implicit differentiation; logarithmic differentiation; Leibnitz' theorem;
special points of a function; curvature; theorems of differentiation
v
CONTENTS
2.2 Integration 60
Integration from first principles; the inverse of differentiation; by inspection;
sinusoidal functions; logarithmic integration; using partial fractions;
substitution method; integration by parts; reduction formulae; infinite
and improper integrals; plane polar coordinates; integral inequalities;
applications of integration
2.3 Exercises 77
2.4 Hints and answers 82
3 Complex numbers and hyperbolic functions 86
3.1 The need for complex numbers 86
3.2 Manipulation of complex numbers 88
Addition and subtraction; modulus and argument; multiplication; complex
conjugate; division
3.3 Polar representation of complex numbers 95
Multiplication and division in polar form
3.4 de Moivre's theorem 98
trigonometric identities; finding the n th roots of unity; solving polynomial
equations
3.5 Complex logarithms and complex powers 102
3.6 Applications to differentiation and integration 104
3.7 Hyperbolic functions 105
Definitions; hyperbolic–trigonometric analogies; identities of hyperbolic
functions; solving hyperbolic equations; inverses of hyperbolic functions;
calculus of hyperbolic functions
3.8 Exercises 112
3.9 Hints and answers 116
4 Series and limits 118
4.1 Series 118
4.2 Summation of series 119
Arithmetic series; geometric series; arithmetico-geometric series; the
difference method; series involving natural numbers; transformation of series
4.3 Convergence of infinite series 127
Absolute and conditional convergence; series containing only real positive
terms; alternating series test
4.4 Operations with series 134
4.5 Power series 134
Convergence of power series; operations with power series
4.6 Taylor series 139
Taylor's theorem; approximation errors; standard Maclaurin series
4.7 Evaluation of limits 144
vi
CONTENTS
4.8 Exercises 147
4.9 Hints and answers 152
5 Partial differentiation 154
5.1 Definition of the partial derivative 154
5.2 The total differential and total derivative 156
5.3 Exact and inexact differentials 158
5.4 Useful theorems of partial differentiation 160
5.5 The chain rule 160
5.6 Change of variables 161
5.7 Taylor's theorem for many-variable functions 163
5.8 Stationary values of many-variable functions 165
5.9 Stationary values under constraints 170
5.10 Envelopes 176
5.11 Thermodynamic relations 179
5.12 Differentiation of integrals 181
5.13 Exercises 182
5.14 Hints and answers 188
6 Multiple integrals 190
6.1 Double integrals 190
6.2 Triple integrals 193
6.3 Applications of multiple integrals 194
Areas and volumes; masses, centres of mass and centroids; Pappus'
theorems; moments of inertia; mean values of functions
6.4 Change of variables in multiple integrals 202
Change of variables in double integrals; evaluation of the integral I=
∞
−∞ e −x2 dx; change of variables in triple integrals; general properties of
Jacobians
6.5 Exercises 210
6.6 Hints and answers 214
7 Vector algebra 216
7.1 Scalars and vectors 216
7.2 Addition and subtraction of vectors 217
7.3 Multiplication by a scalar 218
7.4 Basis vectors and components 221
7.5 Magnitude of a vector 222
7.6 Multiplication of vectors 223
Scalar product; vector product; scalar triple product; vector triple product
vii
CONTENTS
7.7 Equations of lines, planes and spheres 230
7.8 Using vectors to find distances 233
Point to line; point to plane; line to line; line to plane
7.9 Reciprocal vectors 237
7.10 Exercises 238
7.11 Hints and answers 244
8 Matrices and vector spaces 246
8.1 Vector spaces 247
Basis vectors; inner product; some useful inequalities
8.2 Linear operators 252
8.3 Matrices 254
8.4 Basic matrix algebra 255
Matrix addition; multiplication by a scalar; matrix multiplication
8.5 Functions of matrices 260
8.6 The transpose of a matrix 260
8.7 The complex and Hermitian conjugates of a matrix 261
8.8 The trace of a matrix 263
8.9 The determinant of a matrix 264
Properties of determinants
8.10 The inverse of a matrix 268
8.11 The rank of a matrix 272
8.12 Special types of square matrix 273
Diagonal; triangular; symmetric and antisymmetric; orthogonal; Hermitian
and anti-Hermitian; unitary; normal
8.13 Eigenvectors and eigenvalues 277
Of a normal matrix; of Hermitian and anti-Hermitian matrices; of a unitary
matrix; of a general square matrix
8.14 Determination of eigenvalues and eigenvectors 285
Degenerate eigenvalues
8.15 Change of basis and similarity transformations 288
8.16 Diagonalisation of matrices 290
8.17 Quadratic and Hermitian forms 293
Stationary properties of the eigenvectors; quadratic surfaces
8.18 Simultaneous linear equations 297
Range; null space; N simultaneous linear equations in N unknowns;
singular value decomposition
8.19 Exercises 312
8.20 Hints and answers 319
9 Normal modes 322
9.1 Typical oscillatory systems 323
viii
CONTENTS
9.2 Symmetry and normal modes 328
9.3 Rayleigh–Ritz method 333
9.4 Exercises 335
9.5 Hints and answers 338
10 Vector calculus 340
10.1 Differentiation of vectors 340
Composite vector expressions; differential of a vector
10.2 Integration of vectors 345
10.3 Space curves 346
10.4 Vector functions of several arguments 350
10.5 Surfaces 351
10.6 Scalar and vector fields 353
10.7 Vector operators 353
Gradient of a scalar field; divergence of a vector field; curl of a vector field
10.8 Vector operator formulae 360
Vector operators acting on sums and products; combinations of grad, div
and curl
10.9 Cylindrical and spherical polar coordinates 363
10.10 General curvilinear coordinates 370
10.11 Exercises 375
10.12 Hints and answers 381
11 Line, surface and volume integrals 383
11.1 Line integrals 383
Evaluating line integrals; physical examples; line integrals with respect to
ascalar
11.2 Connectivity of regions 389
11.3 Green's theorem in a plane 390
11.4 Conservative fields and potentials 393
11.5 Surface integrals 395
Evaluating surface integrals; vector areas of surfaces; physical examples
11.6 Volume integrals 402
Volumes of three-dimensional regions
11.7 Integral forms for grad, div and curl 404
11.8 Divergence theorem and related theorems 407
Green's theorems; other related integral theorems; physical applications
11.9 Stokes' theorem and related theorems 412
Related integral theorems; physical applications
11.10 Exercises 415
11.11 Hints and answers 420
ix
CONTENTS
12 Fourier series 421
12.1 The Dirichlet conditions 421
12.2 The Fourier coefficients 423
12.3 Symmetry considerations 425
12.4 Discontinuous functions 426
12.5 Non-periodic functions 428
12.6 Integration and differentiation 430
12.7 Complex Fourier series 430
12.8 Parseval's theorem 432
12.9 Exercises 433
12.10 Hints and answers 437
13 Integral transforms 439
13.1 Fourier transforms 439
The uncertainty principle; Fraunhofer diffraction; the Dirac δ -function;
relation of the δ -function to Fourier transforms; properties of Fourier
transforms; odd and even functions; convolution and deconvolution;
correlation functions and energy spectra; Parseval's theorem; Fourier
transforms in higher dimensions
13.2 Laplace transforms 459
Laplace transforms of derivatives and integrals; other properties of Laplace
transforms
13.3 Concluding remarks 465
13.4 Exercises 466
13.5 Hints and answers 472
14 First-order ordinary differential equations 474
14.1 General form of solution 475
14.2 First-degree first-order equations 476
Separable-variable equations; exact equations; inexact equations, integrating
factors; linear equations; homogeneous equations; isobaric equations;
Bernoulli's equation; miscellaneous equations
14.3 Higher-degree first-order equations 486
Equations soluble for p ;forx;fory ; Clairaut's equation
14.4 Exercises 490
14.5 Hints and answers 494
15 Higher-order ordinary differential equations 496
15.1 Linear equations with constant coefficients 498
Finding the complementary function yc ( x) ; finding the particular integral
yp (x); constructing the general solution y c(x )+y p (x); linear recurrence
relations; Laplace transform method
x
CONTENTS
15.2 Linear equations with variable coefficients 509
The Legendre and Euler linear equations; exact equations; partially
known complementary function; variation of parameters; Green's functions;
canonical form for second-order equations
15.3 General ordinary differential equations 524
Dependent variable absent; independent variable absent; non-linear exact
equations; isobaric or homogeneous equations; equations homogeneous in x
or y alone; equations having y= Aex as a solution
15.4 Exercises 529
15.5 Hints and answers 535
16 Series solutions of ordinary differential equations 537
16.1 Second-order linear ordinary differential equations 537
Ordinary and singular points
16.2 Series solutions about an ordinary point 541
16.3 Series solutions about a regular singular point 544
Distinct roots not differing by an integer; repeated root of the indicial
equation; distinct roots differing by an integer
16.4 Obtaining a second solution 549
The Wronskian method; the derivative method; series form of the second
solution
16.5 Polynomial solutions 554
16.6 Legendre's equation 555
General solution for integer ; properties of Legendre polynomials
16.7 Bessel's equation 564
General solution for non-integer ν ; general solution for integer ν ; properties
of Bessel functions
16.8 General remarks 575
16.9 Exercises 575
16.10 Hints and answers 579
17 Eigenfunction methods for differential equations 581
17.1 Sets of functions 583
Some useful inequalities
17.2 Adjoint and Hermitian operators 587
xi
CONTENTS
17.3 The properties of Hermitian operators 588
Reality of the eigenvalues; orthogonality of the eigenfunctions; construction
of real eigenfunctions
17.4 Sturm–Liouville equations 591
Valid boundary conditions; putting an equation into Sturm–Liouville form
17.5 Examples of Sturm–Liouville equations 593
Legendre's equation; the associated Legendre equat ion; Bessel's equation;
the simple harmonic equation; Hermite's equation; Laguerre's equation;
Chebyshev's equation
17.6 Superposition of eigenfunctions: Green's functions 597
17.7 A useful generalisation 601
17.8 Exercises 602
17.9 Hints and answers 606
18 Partial differential equations: general and particular solutions 608
18.1 Important partial differential equations 609
The wave equation; the diffusion equation; Laplace's equation; Poisson's
equation; Schr¨
odinger's equation
18.2 General form of solution 613
18.3 General and particular solutions 614
First-order equations; inhomogeneous equations and problems; second-order
equations
18.4 The wave equation 626
18.5 The diffusion equation 628
18.6 Characteristics and the existence of solutions 632
First-order equations; second-order equations
18.7 Uniqueness of solutions 638
18.8 Exercises 640
18.9 Hints and answers 644
19 Partial differential equations: separation of variables
and other methods 646
19.1 Separation of variables: the general method 646
19.2 Superposition of separated solutions 650
19.3 Separation of variables in polar coordinates 658
Laplace's equation in polar coordinates; spherical harmonics; other
equations in polar coordinates; solution by expansion; separation of
variables for inhomogeneous equations
19.4 Integral transform methods 681
19.5 Inhomogeneous problems – Green's functions 686
Similarities to Green's functions for ordinary differential equations; general
boundary-value problems; Dirichlet problems; Neumann problems
xii
CONTENTS
19.6 Exercises 702
19.7 Hints and answers 708
20 Complex variables 710
20.1 Functions of a complex variable 711
20.2 The Cauchy–Riemann relations 713
20.3 Power series in a complex variable 716
20.4 Some elementary functions 718
20.5 Multivalued functions and branch cuts 721
20.6 Singularities and zeroes of complex functions 723
20.7 Complex potentials 725
20.8 Conformal transformations 730
20.9 Applications of conformal transformations 735
20.10 Complex integrals 738
20.11 Cauchy's theorem 742
20.12 Cauchy's integral formula 745
20.13 Taylor and Laurent series 747
20.14 Residue theorem 752
20.15 Location of zeroes 754
20.16 Integrals of sinusoidal functions 758
20.17 Some infinite integrals 759
20.18 Integrals of multivalued functions 762
20.19 Summation of series 764
20.20 Inverse Laplace transform 765
20.21 Exercises 768
20.22 Hints and answers 773
21 Tensors 776
21.1 Some notation 777
21.2 Change of basis 778
21.3 Cartesian tensors 779
21.4 First- and zero-order Cartesian tensors 781
21.5 Second- and higher-order Cartesian tensors 784
21.6 The algebra of tensors 787
21.7 The quotient law 788
21.8 The tensors δij and ijk 790
21.9 Isotropic tensors 793
21.10 Improper rotations and pseudotensors 795
21.11 Dual tensors 798
21.12 Physical applications of tensors 799
21.13 Integral theorems for tensors 803
21.14 Non-Cartesian coordinates 804
xiii
CONTENTS
21.15 The metric tensor 806
21.16 General coordinate transformations and tensors 809
21.17 Relative tensors 812
21.18 Derivatives of basis vectors and Christoffel symbols 814
21.19 Covariant differentiation 817
21.20 Vector operators in tensor form 820
21.21 Absolute derivatives along curves 824
21.22 Geodesics 825
21.23 Exercises 826
21.24 Hints and answers 831
22 Calculus of variations 834
22.1 The Euler–Lagrange equation 835
22.2 Special cases 836
Fdoes not contain yexplicitly; F does not contain x explicitly
22.3 Some extensions 840
Several dependent variables; several independent variables; higher-order
derivatives; variable end-points
22.4 Constrained variation 844
22.5 Physical variational principles 846
Fermat's principle in optics; Hamilton's principle in mechanics
22.6 General eigenvalue problems 849
22.7 Estimation of eigenvalues and eigenfunctions 851
22.8 Adjustment of parameters 854
22.9 Exercises 856
22.10 Hints and answers 860
23 Integral equations 862
23.1 Obtaining an integral equation from a differential equation 862
23.2 Types of integral equation 863
23.3 Operator notation and the existence of solutions 864
23.4 Closed-form solutions 865
Separable kernels; integral transform methods; differentiation
23.5 Neumann series 872
23.6 Fredholm theory 874
23.7 Schmidt–Hilbert theory 875
23.8 Exercises 878
23.9 Hints and answers 882
24 Group theory 883
24.1 Groups 883
Definition of a group; examples of groups
xiv
CONTENTS
24.2 Finite groups 891
24.3 Non-Abelian groups 894
24.4 Permutation groups 898
24.5 Mappings between groups 901
24.6 Subgroups 903
24.7 Subdividing a group 905
Equivalence relations and classes; congruence and cosets; conjugates and
classes
24.8 Exercises 912
24.9 Hints and answers 915
25 Representation theory 918
25.1 Dipole moments of molecules 919
25.2 Choosing an appropriate formalism 920
25.3 Equivalent representations 926
25.4 Reducibility of a representation 928
25.5 The orthogonality theorem for irreducible representations 932
25.6 Characters 934
Orthogonality property of characters
25.7 Counting irreps using characters 937
Summation rules for irreps
25.8 Construction of a character table 942
25.9 Group nomenclature 944
25.10 Product representations 945
25.11 Physical applications of group theory 947
Bonding in molecules; matrix elements in quantum mechanics; degeneracy
of normal modes; breaking of degeneracies
25.12 Exercises 955
25.13 Hints and answers 959
26 Probability 961
26.1 Venn diagrams 961
26.2 Probability 966
Axioms and theorems; conditional probability; Bayes' theorem
26.3 Permutations and combinations 975
26.4 Random variables and distributions 981
Discrete random variables; continuous random variables
26.5 Properties of distributions 985
Mean; mode and median; variance and standard deviation; moments;
central moments
26.6 Functions of random variables 992
xv
CONTENTS
26.7 Generating functions 999
Probability generating functions; moment generating functions; characteris-
tic functions; cumulant generating functions
26.8 Important discrete distributions 1009
Binomial; geometric; negative binomial; hypergeometric; Poisson
26.9 Important continuous distributions 1021
Gaussian; log-normal; exponential; gamma; chi-squared; Cauchy; Breit–
Wigner; uniform
26.10 The central limit theorem 1036
26.11 Joint distributions 1038
Discrete bivariate; continuous bivariate; marginal and conditional distribu-
tions
26.12 Properties of joint distributions 1041
Means; variances; covariance and correlation
26.13 Generating functions for joint distributions 1047
26.14 Transformation of variables in joint distributions 1048
26.15 Important joint distributions 1049
Multinominal; multivariate Gaussian
26.16 Exercises 1053
26.17 Hints and answers 1061
27 Statistics 1064
27.1 Experiments, samples and populations 1064
27.2 Sample statistics 1065
Averages; variance and standard deviation; moments; covariance and
correlation
27.3 Estimators and sampling distributions 1072
Consistency, bias and efficiency; Fisher's inequality; standard errors;
confidence limits
27.4 Some basic estimators 1086
Mean; variance; standard deviation; moments; covariance and correlation
27.5 Maximum-likelihood method 1097
ML estimator; transformation invariance and bias; efficiency; errors and
confidence limits; Bayesian interpretation; large-N behaviour; extended
ML method
27.6 The method of least squares 1113
Linear least squares; non-linear least squares
27.7 Hypothesis testing 1119
Simple and composite hypotheses; statistical tests; Neyman–Pearson;
generalised likelihood-ratio; Student's t ;Fisher'sF ; goodness of fit
27.8 Exercises 1140
27.9 Hints and answers 1145
xvi
CONTENTS
28 Numerical methods 1148
28.1 Algebraic and transcendental equations 1149
Rearrangement of the equation; linear interpolation; binary chopping;
Newton–Raphson method
28.2 Convergence of iteration schemes 1156
28.3 Simultaneous linear equations 1158
Gaussian elimination; Gauss–Seidel iteration; tridiagonal matrices
28.4 Numerical integration 1164
Trapezium rule; Simpson's rule; Gaussian integration; Monte Carlo methods
28.5 Finite differences 1179
28.6 Differential equations 1180
Difference equations; Taylor series solutions; prediction and correction;
Runge–Kutta methods; isoclines
28.7 Higher-order equations 1188
28.8 Partial differential equations 1190
28.9 Exercises 1193
28.10 Hints and answers 1198
Appendix Gamma, beta and error functions 1201
A1.1 The gamma function 1201
A1.2 The beta function 1203
A1.3 The error function 1204
Index 1206
xvii
1
Preliminary algebra
This opening chapter reviews the basic algebra of which a working knowledge is
presumed in the rest of the book. Many students will be familiar with much, if
not all, of it, but recent changes in what is studied during secondary education
mean that it cannot be taken for granted that they will already have a mastery
of all the topics presented here. The reader may assess which areas need further
study or revision by attempting the exercises at the end of the chapter. The main
areas covered are polynomial equations and the related topic of partial fractions,
curve sketching, coordinate geometry, trigo nometric identities and the notions of
proof by induction or contradiction.
1.1 Simple functions and equations
It is normal practice when starting the mathematical investigation of a physical
problem to assign an algebraic symbol to the quantity whose value is sought, either
numerically or as an explicit algebraic expression. For the sake of definiteness, in
this chapter we will use x to denote this quantity most of the time. Subsequent
steps in the analysis involve applying a combination of known laws, consistency
conditions and (possibly) given constraints to derive one or more equations
satisfied by x . These equations may take many forms, ranging from a simple
polynomial equation to, say, a partial differential equation with several boundary
conditions. Some of the more complicated possibilities are treated in the later
chapters of this book, but for the present we will be concerned with techniques
for the solution of relatively straightforward algebraic equations.
1.1.1 Polynomials and polynomial equations
Firstly we consider the simplest type of equation, a polynomial equation ,inwhich
apolynomial expression in x , denoted by f (x ), is set equal to zero and thereby
1
PRELIMINARY ALGEBRA
forms an equation which is satisfied by particular values of x , called the roots of
the equation:
f( x)= an xn + an−1 x n−1+ ···+ a1 x+ a0 =0 .(1.1)
Here n is an integer > 0, called the degree of both the polynomial and the
equation, and the known coefficients a 0 ,a
1,...,a
nare real quantities with a n=0.
Equations such as (1.1) arise frequently in physical problems, the coefficients ai
being determined by the physical properties of the system under study. What is
needed is to find some or all of the roots of (1.1), i.e. the x -values, αk ,thatsatisfy
f( αk )=0;here k is an index that, as we shall see later, can take up to n different
values, i.e. k =1 ,2 ,...,n . The roots of the polynomial equation can equally well
be described as the zeroes of the polynomial. When they are real , they correspond
to the points at which a graph of f (x ) crosses the x -axis. Roots that are complex
(see chapter 3) do not have such a graphical interpretation.
For polynomial equations containing powers of x greater than x 4 general
methods do not exist for obtaining explicit expressions for the roots αk . Even
for n =3andn = 4 the prescriptions for obtaining the roots are sufficiently
complicated that it is usually preferable to obtain exact or approximate values
by other methods. Only for n =1andn = 2 can closed-form solutions be given.
These results will be well known to the reader, but they are given here for the
sake of completeness. For n = 1, (1.1) reduces to the linear equation
a1 x+ a0 = 0; (1.2)
the solution (root) is α 1 =−a 0 /a 1 .Forn = 2, (1.1) reduces to the quadratic
equation
a2 x2 + a1 x+ a0 = 0; (1.3)
the two roots α 1 and α 2 are given by
α1,2= − a 1 ± a 2
1−4a 2 a 0
2a 2
.(1.4)
When discussing specifically quadratic equations, as opposed to more general
polynomial equations, it is usual to write the equation in one of the two notations
ax2 +bx +c =0 ,ax
2+2 bx + c=0 ,(1.5)
with respective explicit pairs of solutions
α1,2= − b±√ b 2 − 4 ac
2a,α
1, 2=−b±√ b 2 −ac
a.(1.6)
Of course, these two notations are entirely equivalent and the only important
2
1.1 SIMPLE FUNCTIONS AND EQUATIONS
point is to associate each form of answer with the corresponding form of equation;
most people keep to one form, to avoid any possible confusion.
If the value of the quantity appearing under the square root sign is positive
then both roots are real; if it is negative then the roots form a complex conjugate
pair, i.e. they are of the form p± iq with p and q real (see chapter 3); if it has
zero value then the two roots are equal and special considerations usually arise.
Thus linear and quadratic equations can be dealt with in a cut-and-dried way.
We now turn to methods for obtaining partial information about the roots of
higher-degree polynomial equations. In some circumstances the knowledge that
an equation has a root lying in a certain range, or that it has no real roots at all,
is all that is actually required. For example, in the design of electronic circuits
it is necessary to know whether the current in a proposed circuit will break
into spontaneous oscillation. To test this, it is sufficient to establish whether a
certain polynomial equation, whose coefficients are determined by the physical
parameters of the circuit, has a root with a positive real part (see chapter 3);
complete determination of all the roots is not needed for this purpose. If the
complete set of roots of a polynomial equation is required, it can usually be
obtained to any desired accuracy by numerical methods such as those described
in chapter 28.
There is no explicit step-by-step approach to finding the roots of a general
polynomial equation such as (1.1). In most cases analytic methods yield only
information about the roots, rather than their exact values. To explain the relevant
techniques we will consider a particular example, 'thinking aloud' on paper and
expanding on special points about methods and lines of reasoning. In more
routine situations such comment would be absent and the whole process briefer
and more tightly focussed.
Example: the cubic case
Let us investigate the roots of the equation
g( x)=4 x3 +3 x2 −6 x−1 = 0 (1.7)
or, in an alternative phrasing, investigate the zeroes of g (x ). We note first of all
that this is a cubic equation. It can be seen that for x large and positive g (x)
will be large and positive and, equally, that for x large and negative g (x ) will
be large and negative. Therefore, intuitively (or, more formally, by continuity)
g( x) must cross the x-axis at least once and so g( x) = 0 must have at least one
real root. Furthermore, it can be shown that if f (x )isann th-degree polynomial
then the graph of f (x ) must cross the x -axis an even or odd number of times
as x varies between −∞ and +∞ , according to whether n itself is even or odd.
Thus a polynomial of odd degree always has at least one real root, but one of
even degree may have no real root. A small complication, discussed later in this
section, occurs when repeated roots arise.
3
PRELIMINARY ALGEBRA
Having established that g (x ) = 0 has at least one real root, we may ask how
many real roots it could have. To answer this we need one of the fundamental
theorems of algebra, mentioned above:
An n th-degree polynomial equation has exactly n roots.
It should be noted that this does not imply that there are nreal roots (only that
there are not more than n ); some of the roots may be of the form p +iq.
To make the above theorem plausible and to see what is meant by repeated
roots, let us suppose that the n th-degree polynomial equation f (x ) = 0, (1.1), has
rroots α1 ,α
2,...,α
r, considered distinct for the moment. That is, we suppose that
f( αk )=0fork =1 ,2 ,...,r ,sothatf (x ) vanishes only when x is equal to one of
the r values αk . But the same can be said for the function
F( x)= A( x− α1 )(x− α 2 )··· (x− αr ), (1.8)
in which A is a non-zero constant; F (x ) can clearly be multiplied out to form a
polynomial expression.
We now call upon a second fundamental result in algebra: that if two poly-
nomial functions f (x )andF (x ) have equal values for all values of x , then their
coefficients are equal on a term-by-term basis. In other words, we can equate
the coefficients of each and every power of x in the two expressions (1.8) and
(1.1); in particular we can equate the coefficients of the highest power of x .From
this we have Axr ≡an xn and thus that r =n and A =an .Asr is both equal
to n and to the number of roots of f (x ) = 0, we conclude that the n th-degree
polynomial f (x )=0has n roots. (Although this line of reasoning may make the
theorem plausible, it does not constitute a proof since we have not shown that it
is permissible to write f (x ) in the form of equation (1.8).)
We next note that the condition f (αk )=0for k =1 ,2 ,...,r, could also be met
if (1.8) were replaced by
F( x)= A( x− α1 ) m 1 (x− α 2) m 2 ···(x− αr ) m r ,(1.9)
with A =an . In (1.9) the mk are integers ≥ 1 and are known as the multiplicities
of the roots, mk being the multiplicity of αk . Expanding the right-hand side (RHS)
leads to a polynomial of degree m 1 +m 2+···+mr . This sum must be equal to n.
Thus, if any of the mk is greater than unity then the number of distinct roots, r ,
is less than n ; the total number of roots remains at n , but one or more of the αk
counts more than once. For example, the equation
F( x)= A( x− α1 )2(x− α2 )3( x− α3 )( x− α4 )=0
has exactly seven roots, α 1 being a double root and α 2 a triple root, whilst α 3 and
α4 are unrepeated (simple )roots.
We can now say that our particular equation (1.7) has either one or three real
roots but in the latter case it may be that not all the roots are distinct. To decide
4
1.1 SIMPLE FUNCTIONS AND EQUATIONS
xx
φ1 (x )φ 2 (x )
β1 β1
β2
β2
Figure 1.1 Two curves φ 1 (x )and φ 2 (x ), both with zero derivatives at the
same values of x , but with different numbers of real solutions to φi (x )=0.
how many real roots the equation has, we need to anticipate two ideas from the
next chapter. The first of these is the notion of the derivative of a function, and
the second is a result known as Rolle's theorem.
The derivative f (x ) of a function f (x ) measures the slope of the tangent to
the graph of f (x ) at that value of x (see figure 2.1 in the next chapter). For
the moment, the reader with no prior knowledge of calculus is asked to accept
that the derivative of axn is naxn−1 , so that the derivative g (x )ofthecurve
g( x)=4 x3 +3 x2 −6 x−1isgivenbyg (x )=12 x2 +6 x−6. Similar expressions
for the derivatives of other polynomials are used later in this chapter.
Rolle's theorem states that if f (x ) has equal values at two different values of x
then at some point between these two x -values its derivative is equal to zero; i.e.
the tangent to its graph is parallel to the x -axis at that point (see figure 2.2).
Having briefly mentioned the derivative of a function and Rolle's theorem, we
now use them to establish whether g (x ) has one or three real zeroes. If g (x )=0
does have three real roots αk ,i.e.g (αk )=0fork =1 ,2,3, then it follows from
Rolle's theorem that between any consecutive pair of them (say α 1 and α 2 )there
must be some real value of x at which g (x ) = 0. Similarly, there must be a further
zero of g (x ) lying between α 2 and α 3 . Thus a necessary condition for three real
roots of g (x )=0isthatg (x ) = 0 itself has two real roots.
However, this condition on the number of roots of g (x ) = 0, whilst necessary,
is not sufficient to guarantee three real roots of g (x )=0.Thiscanbeseenby
inspecting the cubic curves in figure 1.1. For each of the two functions φ 1 (x )and
φ2 (x ), the derivative is equal to zero at both x =β 1 and x =β 2 . Clearly, though,
φ2 (x ) = 0 has three real roots whilst φ 1 (x ) = 0 has only one. It is easy to see that
the crucial difference is that φ 1 (β 1 )andφ 1 (β 2 ) have the same sign, whilst φ 2 (β 1)
and φ 2 (β 2 ) have opposite signs.
5
PRELIMINARY ALGEBRA
It will be apparent that for some equations, φ (x )=0say, φ (x ) equals zero
at a value of x for which φ (x ) is also zero. Then the graph of φ (x )justtouches
the x -axis. When this happens the value of x so found is, in fact, a double real
root of the polynomial equation (corresponding to one of the mk in (1.9) having
the value 2) and must be counted twice when determining the number of real
roots.
Finally, then, we are in a position to decide the number of real roots of the
equation
g( x)=4 x3 +3 x2 −6 x−1=0 .
The equation g (x )=0,with g (x )=12 x2 +6 x−6, is a quadratic equation with
explicit solutions§
β1,2 =− 3±√ 9+72
12 ,
so that β 1 =− 1andβ 2 =1
2. The corresponding values of g( x)areg (β 1 )=4and
g( β2 )=− 11
4, which are of opposite sign. This indicates that 4x 3 +3 x2 −6 x−1=0
has three real roots, one lying in the range − 1<x< 1
2and the others one on
each side of that range.
The techniques we have developed above have been used to tackle a cubic
equation, but they can be applied to polynomial equations f ( x)=0ofdegree
greater than 3. However, much of the analysis centres around the equation
f (x ) = 0 and this, itself, being then a polynomial equation of degree 3 or more
either has no closed-form general solution or one that is complicated to evaluate.
Thus the amount of information that can be obtained about the roots of f (x )=0
is correspondingly reduced.
A more general case
To illustrate what can (and cannot) be done in the more general case we now
investigate as far as possible the real roots of
f( x)= x7 +5 x6 + x4 − x3 +x2 −2=0 .
The following points can be made.
(i) This is a seventh-degree polynomial equation; therefore the number of
realrootsis1,3,5or7.
(ii) f (0) is negative whilst f (∞ )=+ ∞, so there must be at least one positive
root.
§The two roots β1 ,β
2are written as β 1, 2. By convention β 1refers to the upper symbol in ±,β 2to
the lower symbol.
6
1.1 SIMPLE FUNCTIONS AND EQUATIONS
(iii) The equation f (x ) = 0 can be written as x (7x 5 +30 x4 +4 x2 −3 x+2)= 0
and thus x = 0 is a root. The derivative of f (x ), denoted by f (x ), equals
42x 5 + 150x 4 +12 x2 −6 x+2. That f (x ) is zero whilst f (x ) is positive
at x = 0 indicates (subsection 2.1.8) that f (x ) has a minimum there. This,
together with the facts that f (0) is negative and f (∞ )=∞ , implies that
the total number of real roots to the right of x = 0 must be odd. Since
the total number of real roots must be odd, the number to the left must
be even (0, 2, 4 or 6).
This is about all that can be deduced by simple analytic methods in this case,
although some further progress can be made in the ways indicated in exercise 1.3.
There are, in fact, more sophisticated tests that examine the relative signs of
successive terms in an equation such as (1.1), and in quantities derived from
them, to place limits on the numbers and positions of roots. But they are not
prerequisites for the remainder of this book and will not be pursued further
here.
We conclude this section with a worked example which demonstrates that the
practical application of the ideas developed so far can be both short and decisive.
For what values of k , if any, does
f( x)= x3 −3x 2 +6 x+ k=0
have three real roots?
Firstly we study the equation f (x )=0,i.e.3 x2 −6 x+ 6 = 0. This is a quadratic equation
but, using (1.6), because 62 <4× 3× 6, it can have no real roots. Therefore, it follo ws
immediately that f (x ) has no maximum or minimum; consequently f (x ) = 0 cannot have
more than one real root, whatever the value of k .
1.1.2 Factorising polynomials
In the previous subsection we saw how a polynomial with r given distinct zeroes
αk could be constructed as the product of factors containing those zeroes:
f( x)= an (x− α 1) m 1 (x− α 2) m 2 ···(x− αr ) m r
=an xn +an−1 x n−1 +···+a 1 x +a 0 , (1.10)
with m 1 +m 2+···+mr =n , the degree of the polynomial. It will cause no loss of
generality in what follows to suppose that all the zeroes are simple, i.e. all mk =1
and r =n , and this we will do.
Sometimes it is desirable to be able to reverse this process, in particular when
one exact zero has been found by some method and the remaining zeroes are to
be investigated. Suppose that we have located one zero, α ; it is then possible to
write (1.10) as
f( x)=( x− α)f1 (x), (1.11)
7
PRELIMINARY ALGEBRA
where f 1 (x ) is a polynomial of degree n− 1. How can we find f 1(x )? The procedure
is much more complicated to describe in a general form than to carry out for
an equation with given numerical coefficients ai . If such manipulations are too
complicated to be carried out mentally, they could be laid out along the lines of
an algebraic 'long division' sum. However, a more compact form of calculation
is as follows. Write f 1 (x )as
f1 (x )= bn−1 x n−1 +bn−2 x n−2 +bn−3 x n−3 +··· + b1 x+ b0 .
Substitution of this form into (1.11) and subsequent comparison of the coefficients
of xp for p =n ,n− 1, ... , 1, 0 with those in the second line of (1.10) generates
the series of equations
bn−1= an,
bn−2 − αb n−1= an−1 ,
bn−3 − αb n−2= an−2 ,
.
.
.
b0 −αb1 = a1,
−αb0 =a 0 .
These can be solved successively for the bj , starting either from the top or from
the bottom of the series. In either case the final equation used serves as a check;
if it is not satisfied, at least one mistake has been made in the computation –
or α is not a zero of f (x ) = 0. We now illustrate this procedure with a worked
example.
Determine by inspection the simple roots of the equation
f( x)=3 x4 −x3 −10x 2 −2x +4=0
and hence, by factorisation, find the rest of its roots.
From the pattern of coefficients it can be seen that x =− 1 is a solution to the equation.
We therefore write
f( x)=( x+1)( b3 x3 + b2 x2 + b1 x+ b0 ) ,
where
b3 =3 ,
b2 +b 3 =−1,
b1 +b 2 =−10,
b0 +b 1 =−2,
b0 =4 .
These equations give b 3 =3 ,b
2=−4,b
1=−6,b
0= 4 (check) and so
f( x)=( x+1) f1 (x )=( x+ 1)(3 x3 −4 x2 −6 x+4) .
8
1.1 SIMPLE FUNCTIONS AND EQUATIONS
We now note that f 1 (x )=0ifx is set equal to 2. Thus x− 2isafactoroff 1 (x ), which
therefore can be written as
f1 (x )=( x−2) f2 ( x)=( x−2)( c2 x2 + c1 x+ c0 )
with
c2 =3 ,
c1 −2 c2 =−4 ,
c0 −2 c1 =−6 ,
−2c 0 =4 .
These equations determine f 2 (x )as3 x2 +2 x−2. Since f2 ( x) = 0 is a quadratic equation,
its solutions can be written explicitly as
x=− 1±√ 1+6
3.
Thus the four roots of f (x )=0are−1,2, 1
3(−1+ √ 7) and 1
3(−1 −√ 7).
1.1.3 Properties of roots
From the fact that a polynomial equation can be written in any of the alternative
forms
f( x)= an xn + an−1 x n−1+ ···+ a1 x+ a0 =0 ,
f( x)= an (x− α1 ) m 1 ( x− α2 ) m 2 ··· ( x− αr ) m r =0 ,
f( x)= an (x− α1 )( x− α2 )··· ( x− αn )=0 ,
it follows that it must be possible to express the coefficients ai in terms of the
roots αk . To take the most obvious example, comparison of the constant terms
(formally the coefficient of x 0 ) in the first and third expressions shows that
an (−α 1 )(− α2 )··· (− αn )=a0,
or, using the product notation,
n
k=1
αk =( −1)n a 0
an
.(1.12)
Only slightly less obvious is a result obtained by comparing the coefficients of
xn−1 in the same two expressions of the polynomial:
n
k=1
αk =− a n−1
an
.(1.13)
Comparing the coefficients of other powers of x yields further results, though
they are of less general use than the two just given. One such, which the reader
may wish to derive, is
n
j=1
n
k>j
αjαk = a n−2
an
.(1.14)
9
PRELIMINARY ALGEBRA
In the case of a quadratic equation these root properties are used sufficiently
often that they are worth stating explicitly, as follows. If the roots of the quadratic
equation ax 2 +bx +c = 0 are α 1 and α 2 then
α1 + α2 =− b
a,
α1 α2 = c
a.
If the alternative standard form for the quadratic is used, b is replaced by 2b in
both the equation and the first of these results.
Find a cubic equation whose roots are − 4, 3 and 5 .
From results (1.12) – (1.14) we can compute that, arbitrarily setting a 3 =1,
−a2 =
3
k=1
αk =4 ,a
1=
3
j=1
3
k>j
αjαk =−17 ,a
0=( −1)3
3
k=1
αk =60 .
Thus a possible cubic equation is x 3 +( −4)x 2 +( −17)x + (60) = 0. Of course, any multiple
of x 3 −4x 2 −17x + 60 = 0 will do just as well.
1.2 Trigonometric identities
So many of the applications of mathematics to physics and engineering are
concerned with periodic, and in particular sinusoidal, behaviour that a sure and
ready handling of the corresponding mathematical functions is an essential skill.
Even situations with no obvious periodicity are often expressed in terms of
periodic functions for the purposes of analysis. Later in this book whole chapters
are devoted to developing the techniques involved, but as a necessary prerequisite
we here establish (or remind the reader of) some standard identities with which he
or she should be fully familiar, so that the manipulation of expressions containing
sinusoids becomes automatic and reliable. So as to emphasise the angular nature
of the argument of a sinusoid we will denote it in this section by θ rather than x .
1.2.1 Single-angle identities
We give without proof the basic identity satisfied by the sinusoidal functions sin θ
and cos θ , namely
cos2 θ+sin
2θ=1 .(1.15)
If sin θ and cos θ have been defined geometrically in terms of the coordinates of
a point on a circle, a reference to the name of Pythagoras will suffice to establish
this result. If they have been defined by means of series (with θ expressed in
radians) then the reader should refer to Euler's equation (3.23) on page 96, and
note that eiθ has unit modulus if θ is real.
10
1.2 TRIGONOMETRIC IDENTITIES
x
y
x
y
O
A
B
P
T
N
R
M
Figure 1.2 Illustration of the compound-angle identities. Refer to the main
text for details.
Other standard single-angle formulae derived from (1.15) by dividing through
by various powers of sin θ and cos θ are
1+tan
2θ=sec
2θ, (1.16)
cot2 θ+1=cosec 2 θ. (1.17)
1.2.2 Compound-angle identities
The basis for building expressions for the sinusoidal functions of compound
angles are those for the sum and difference of just two angles, since all other
cases can be built up from these, in principle. Later we will see that a study of
complex numbers can provide a more efficient approach in some cases.
To prove the basic formulae for the sine and cosine of a compound angle
A+ Bin terms of the sines and cosines of Aand B, we consider the construction
shown in figure 1.2. It shows two sets of axes, Oxy and Ox y ,with a common
origin but rotated with respect to each other through an angle A . The point
Plies on the unit circle centred on the common origin Oand has coordinates
cos(A +B ), sin(A +B ) with respect to the axes Oxy and coordinates cos B, sin B
with respect to the axes Ox y .
Parallels to the axes Oxy (dotted lines) and Ox y (broken lines) have been
drawn through P . Further parallels (MR and RN )totheOx y axes have been
11
PRELIMINARY ALGEBRA
drawn through R , the point (0, sin(A +B )) in the Oxy system. That all the angles
marked with the symbol • are equal to A follows from the simple geometry of
right-angled triangles and crossing lines.
We now determine the coordinates of P in terms of lengths in the figure,
expressing those lengths in terms of both sets of coordinates:
(i) cos B =x =TN +NP =MR +NP
=OR sin A + RP cos A = sin(A +B )sinA +cos( A+ B)cosA;
(ii) sin B =y =OM − TM =OM − NR
=OR cos A− RP sin A = sin(A +B )cosA− cos(A +B )sinA.
Now, if equation (i) is multiplied by sin A and added to equation (ii) multiplied
by cos A , the result is
sin A cos B +cosA sin B = sin(A +B )(sin2 A +cos
2A )=sin( A+ B).
Similarly, if equation (ii) is multiplied by sin A and subtracted from equation (i)
multiplied by cos A , the result is
cos Acos B− sin A sin B =cos( A+ B)(cos2 A+sin
2A )=cos( A+ B).
Corresponding graphically based results can be derived for the sines and cosines
of the difference of two angles; however, they are more easily obtained by setting
Bto − Bin the previous results and remembering that sin Bbecomes − sin B
whilst cos B is unchanged. The four results may be summarised by
sin(A± B )=sin Acos B±cos Asin B(1.18)
cos(A± B )=cos Acos B∓sin Asin B. (1.19)
Standard results can be deduced from these by setting one of the two angles
equal to π or to π/ 2:
sin(π− θ )=sin θ, cos( π− θ)=− cos θ, (1.20)
sin 1
2π−θ =cos θ, cos 1
2π−θ =sin θ, (1.21)
From these basic results many more can be derived. An immediate deduction,
obtained by taking the ratio of the two equations (1.18) and (1.19) and then
dividing both the numerator and denominator of this ratio by cos A cos B ,is
tan(A± B )= tan A± tan B
1∓ tan Atan B. (1.22)
One application of this result is a test for whether two lines on a graph
are orthogonal (perpendicular); more generally, it determines the angle between
them. The standard notation for a straight-line graph is y =mx +c ,inwhichm
is the slope of the graph and c is its intercept on the y -axis. It should be noted
that the slope m is also the tangent of the angle the line makes with the x -axis.
12
1.2 TRIGONOMETRIC IDENTITIES
Consequently the angle θ 12 between two such straight-line graphs is equal to the
difference in the angles they individually make with the x -axis, and the tangent
of that angle is given by (1.22):
tan θ 12 = tan θ 1 − tan θ2
1+tanθ 1 tan θ2
=m 1 −m2
1+m 1 m2
.(1.23)
For the lines to be orthogonal we must have θ 12 =π/ 2, i.e. the final fraction on
the RHS of the above equation must equal ∞ ,andso
m1 m2 =− 1 .(1.24)
A kind of inversion of equations (1.18) and (1.19) enables the sum or difference
of two sines or cosines to be expressed as the product of two sinusoids; the
procedure is typified by the following. Adding together the expressions given by
(1.18) for sin(A +B ) and sin(A− B ) yields
sin(A +B )+sin( A− B)=2sin Acos B.
If we now write A +B =C and A− B =D , this becomes
sin C +sinD =2sin C+D
2 cos C−D
2 . (1.25)
In a similar way each of the following equations can be derived:
sin C− sin D =2cos C+D
2 sin C−D
2 , (1.26)
cos C +cosD =2cos C+D
2 cos C−D
2 , (1.27)
cos C− cos D = − 2sin C +D
2 sin C−D
2 . (1.28)
The minus sign on the right of the last of these equations should be noted; it may
help to avoid overlooking this 'oddity' to recall that if C>D then cos C< cos D .
1.2.3 Double- and half-angle identities
Double-angle and half-angle identities are needed so often in practical calculations
that they should be committed to memory by any physical scientist. They can be
obtained by setting B equal to A in results (1.18) and (1.19). When this is done,
13
PRELIMINARY ALGEBRA
and use made of equation (1.15), the following results are obtained:
sin 2 θ =2 sin θcos θ, (1.29)
cos 2θ =cos
2θ−sin 2θ
=2cos
2θ−1
=1− 2sin
2θ, (1.30)
tan 2θ =2tanθ
1− tan2 θ. (1.31)
A further set of identities enables sinusoidal functions of θ to be expressed
as polynomial functions of a variable t =tan( θ/2). They are not used in their
primary role until the next chapter, but we give a derivation of them here for
reference.
If t =tan( θ/2), then it follows from (1.16) that 1+ t 2 =sec
2(θ/2) and cos(θ/2) =
(1 + t 2 )− 1/2 , whilst sin(θ/ 2) = t (1 + t 2)− 1/2 . Now, using (1.29) and (1.30), we may
write:
sin θ =2sinθ
2cos θ
2= 2t
1+t 2 , (1.32)
cos θ =cos
2θ
2− sin 2 θ
2= 1−t2
1+t 2 , (1.33)
tan θ =2t
1−t 2 . (1.34)
It can be further shown that the derivative of θ with respect to t takes the
algebraic form 2/ (1 + t 2 ). This completes a package of results that enables
expressions involving sinusoids, particularly when they appear as integrands, to
be cast in more convenient algebraic forms. The proof of the derivative property
and examples of use of the above results are given in subsection (2.2.7).
We conclude this section with a worked example which is of such a commonly
occurring form that it might be considered a standard procedure.
Solve for θ the equation
asin θ+ bcos θ= k,
where a, b and k are given real quantities.
To solve this equation we make use of result (1.18) by setting a =K cos φ and b =K sin φ
for suitable values of K and φ . We then have
k= Kcos φsin θ+ Ksin φcos θ= Ksin( θ+ φ) ,
with
K2 =a 2 +b 2 and φ =tan
−1b
a.
Whether φ lies in 0 ≤φ≤π or in −π<φ< 0 has to be determined by the individual
signs of a and b . The solution is thus
θ=sin
−1k
K− φ,
14
1.3 COORDINATE GEOMETRY
with K and φ as given above. Notice that there is no real solution to the original equation
if |k |> |K | =( a2 +b 2 )1/2 .
1.3 Coordinate geometry
We have already mentioned the standard form for a straight-line graph, namely
y= mx + c, (1.35)
representing a linear relationship between the independent variable x and the
dependent variable y .Theslopem is equal to the tangent of the angle the line
makes with the x -axis whilst c is the intercept on the y -axis.
An alternative form for the equation of a straight line is
ax + by + k=0 ,(1.36)
to which (1.35) is clearly connected by
m=− a
band c=− k
b.
This form treats x and y on a more symmetrical basis, the intercepts on the two
axes being −k/a and −k/b respectively.
A power relationship between two variables, i.e. one of the form y =Axn ,c
an
also be cast into straight-line form by taking the logarithms of both sides. Whilst
it is normal in mathematical work to use natural logarithms (to base e , written
ln x ), for practical investigations logarithms to base 10 are often employed. In
either case the form is the same, but it needs to be remembered which has been
used when recovering the value of A from fitted data. In the mathematical (base
e) form, the power relationship becomes
ln y =n ln x +lnA. (1.37)
Now the slope gives the power n , whilst the intercept on the ln y axis is ln A,
which yields A , either by exponentiation or by taking antilogarithms.
The other standard coordinate forms of two-dimensional curves that students
should know and recognise are those concerned with the conic sections – so called
because they can all be obtained by taking suitable sections across a (double)
cone. Because the conic sections can take many different orientations and scalings
their general form is complex,
Ax2 +By 2+Cxy +Dx +Ey +F =0 ,(1.38)
but each can be represented by one of four generic forms, an ellipse, a parabola, a
hyperbola or, the degenerate form, a pair of straight lines. If they are reduced to
15
PRELIMINARY ALGEBRA
their standard representations, in which axes of symmetry are made to coincide
with the coordinate axes, the first three take the forms
(x− α )2
a2 + ( y−β)2
b2 = 1 (ellipse), (1.39)
(y− β )2 =4 a( x− α) (parabola), (1.40)
(x− α )2
a2 − ( y−β)2
b2 = 1 (hyperbola). (1.41)
Here, (α, β ) gives the position of the 'centre' of the curve, usually taken as
the origin (0, 0) when this does not conflict with any imposed conditions. The
parabola equation given is that for a curve symmetric about a line parallel to
the x -axis. For one symmetrical about a parallel to the y -axis the equation would
read (x− α)2 =4 a( y− β).
Of course, the circle is the special case of an ellipse in which b =a and the
equation takes the form
(x− α )2 +( y− β)2 = a 2 .(1.42)
The distinguishing characteristic of this equation is that when it is expressed in
the form (1.38) the coefficients of x 2 and y 2 are equal and that of xy is zero; this
property is not changed by any reorientation or scaling and so acts to identify a
general conic as a circle.
Definitions of the conic sections in terms of geometrical properties are also
available; for example, a parabola can be defined as the locus of a point that
is always at the same distance from a given straight line (the directrix )asitis
from a given point (the focus ). When these properties are expressed in Cartesian
coordinates the above equations are obtained. For a circle, the defining property
is that all points on the curve are a distance a from (α, β ); (1.42) expresses this
requirement very directly. In the following worked example we derive the equation
for a parabola.
Find the equation of a parabola that has the line x=− a as its directrix and the point
(a, 0) as its focus.
Figure 1.3 shows the situation in Cartesian coordinates. Expressing the defining requirement
that PN and PF are equal in length gives
(x +a )=[( x− a)2 +y 2 ]1/2 ⇒ ( x+ a)2 =( x− a)2 + y2
which, on expansion of the squared terms, immediately gives y 2 =4 ax. This is (1.40) with
αand βboth set equal to zero.
Although the algebra is more complicated, the same method can be used to
derive the equations for the ellipse and the hyperbola. In these cases the distance
from the fixed point is a definite fraction, e , known as the eccentricity ,ofthe
distance from the fixed line. For an ellipse 0 <e< 1, for a circle e =0,andfora
hyperbola e> 1. The parabola corresponds to the case e =1.
16
1.3 COORDINATE GEOMETRY
x
y
O
P
F
N
x=− a
(a, 0)
(x, y )
Figure 1.3 Construction of a parabola using the point (a, 0) as the focus and
the line x =−a as the directrix.
The values of a and b (with a≥ b ) in equation (1.39) for an ellipse are related
to e through
e2 = a 2 − b2
a2
and give the lengths of the semi-axes of the ellipse. If the ellipse is centred on
the origin, i.e. α =β = 0, then the focus is (−ae, 0) and the directrix is the line
x=− a/e.
For each conic section curve, although we have two variables, x and y ,theyare
not independent, since if one is given then the other can be determined. However,
determining y when x is given, say, involves solving a quadratic equation on each
occasion, and so it is convenient to have parametric representations of the curves.
A parametric representation allows each point on a curve to be associated with
a unique value of a single parameter t . The simplest parametric representations
for the conic sections are as given below, though that for the hyperbola uses
hyperbolic functions, not formally introduced until chapter 3. That they do give
valid parameterizations can be verified by substituting them into the standard
forms (1.39)–(1.41); in each case the standard form is reduced to an algebraic or
trigonometric identity.
x= α+ acos φ, y= β+ bsin φ(ellipse),
x= α+ at2 ,y =β +2 at (parabola),
x= α+ acosh φ, y = β+ bsinh φ(hyperbola).
As a final example illustrating several topics from this section we now prove
17
PRELIMINARY ALGEBRA
the well-known result that the angle subtended by a diameter at any point on a
circle is a right angle.
Taking the diameter to be the line joining Q=( −a, 0) and R =( a, 0) and the point Pto
be any point on the circle x2 +y 2 =a 2 , prove that angle QP R is a right angle.
If P is the point (x, y ), the slope of the line QP is
m1 = y−0
x−( −a)= y
x+ a.
That of RP is
m2 = y−0
x−( a)= y
x− a.
Thus
m1 m2 = y 2
x2 − a2 .
But, since P is on the circle, y 2 =a 2 −x 2 and consequently m 1 m 2 =− 1. From result (1.24)
this implies that QP and RP are orthogonal and that QP R is therefore a right angle. Note
that this is true for any point P on the circle.
1.4 Partial fractions
In subsequent chapters, and in particular when we come to study integration
in chapter 2, we will need to express a function f (x ) that is the ratio of two
polynomials in a more manageable form. To remove some potential complexity
from our discussion we will assume that all the coefficients in the polynomials
are real, although this is not an essential simplification.
The behaviour of f (x ) is crucially determined by the location of the zeroes of
its denominator, i.e. if f (x ) is written as f (x )=g (x)/h(x ) where both g (x )and
h( x) are polynomials,§ then f ( x) changes extremely rapidly when xis close to
those values αi that are the roots of h( x ) = 0. To make such behaviour explicit,
we write f (x ) as a sum of terms such as A/ (x− α )n ,inwhichA is a constant, α is
one of the αi that satisfy h(αi )=0andn is a positive integer. Writing a function
in this way is known as expressing it in partial fractions.
Suppose, for the sake of definiteness, that we wish to express the function
f( x)= 4 x+2
x2 +3 x+2
§It is assumed that the ratio has been reduced so that g(x )andh(x ) do not contain any common
factors, i.e. there is no value of x that makes both vanish at the same time. We may also assume
without any loss of generality that the coefficient of the highest power of x in h (x ) has been made
equal to unity, if necessary, by dividing both numerator and denominator by the coefficient of this
highest power.
18
1.4 PARTIAL FRACTIONS
in partial fractions, i.e. to write it as
f( x)= g( x)
h( x)= 4 x+2
x2 +3 x+2 = A1
(x− α 1)n 1 + A 2
(x− α 2)n 2 +··· .
(1.43)
The first question that arises is that of how many terms there should be on
the right-hand side (RHS). Although some complications occur when h( x )has
repeated roots (these are considered below) it is clear that f (x ) only becomes
infinite at the two values of x ,α 1 and α 2 ,thatmake h( x ) = 0. Consequently the
RHS can only become infinite at the same two values of x and therefore contains
only two partial fractions – these are the ones shown explicitly. This argument
can be trivially extended (again temporarily ignoring the possibility of repeated
roots of h( x )) to show that if h( x ) is a polynomial of degree n then there should be
nterms on the RHS, each containing a different root αi of the equation h(αi )=0.
A second general question concerns the appropriate values of the ni .Thisis
answered by putting the RHS over a common denominator, which will clearly
have to be the product (x− α 1 )n 1(x− α 2)n 2 ··· . Comparison of the highest power
of x in this new RHS with the same power in h( x )showsthatn 1 +n 2+··· =n.
This result holds whether or not h( x ) = 0 has repeated roots and, although we
do not give a rigorous proof, strongly suggests the following correct conclusions.
•The number of terms on the RHS is equal to the number of distinct roots of
h( x) = 0, each term having a different root αi in its denominator (x− αi ) n i .
•If αi is a multiple root of h( x ) = 0 then the value to be assigned to ni in (1.43) is
that of mi when h( x ) is written in the product form (1.9). Further, as discussed
on p. 23, Ai has to be replaced by a polynomial of degree mi − 1. This is also
formally true for non-repeated roots, since then both mi and ni are equal to
unity.
Returning to our specific example we note that the denominator h( x ) has zeroes
at x =α 1 =− 1andx =α 2=− 2; these x -values are the simple (non-repeated)
roots of h( x ) = 0. Thus the partial fraction expansion will be of the form
4x +2
x2 +3 x+2 = A1
x+1 + A2
x+2 .(1.44)
We now list several methods available for determining the coefficients A 1 and
A2 . We also remind the reader that, as with all the explicit examples and techniques
described, these methods are to be considered as models for the handling of any
ratio of polynomials, with or without characteristics that make it a special case.
(i) The RHS can be put over a common denominator, in this case (x +1)(x +2),
and then the coefficients of the various powers of x can be equated in the
19
PRELIMINARY ALGEBRA
numerators on both sides of the equation. This leads to
4x +2= A 1 (x +2)+A 2 (x +1) ,
4=A 1 +A 2 2=2 A1 +A2.
Solving the simultaneous equations for A 1 and A 2 gives A 1 =− 2and
A2 =6 .
(ii) A second method is to substitute two (or more generally n ) different
values of x into each side of (1.44) and so obtain two (or n ) simultaneous
equations for the two (or n )constantsAi . To justify this practical way of
proceeding it is necessary, strictly speaking, to appeal to method (i) above,
which establishes that there are unique values for A 1 and A 2 valid for
all values of x . It is normally very convenient to take zero as one of the
values of x , but of course any set will do. Suppose in the present case that
we use the values x =0and x = 1 and substitute in (1.44). The resulting
equations are
2
2= A 1
1+ A 2
2,
6
6= A 1
2+ A 2
3,
which on solution give A 1 =− 2andA 2 = 6, as before. The reader can
easily verify that any other pair of values for x (except for a pair that
includes α 1 or α 2 ) gives the same values for A 1 and A 2 .
(iii) The very reason why method (ii) fails if x is chosen as one of the roots
αi of h( x) = 0 can be made the basis for determining the values of the Ai
corresponding to non-multiple roots without having to solve simultaneous
equations. The method is conceptually more difficult than the other meth-
ods presented here, and needs results from the theory of complex variables
(chapter 20) to justify it. However, we give a practical 'cookbook' recipe
for determining the coefficients.
(a) To determine the coefficient Ak , imagine the denominator h( x )
written as the product (x− α 1 )(x− α 2 )··· (x− αn ), with any m -fold
repeated root giving rise to m factors in parentheses.
(b) Now set x equal to αk and evaluate the expression obtained after
omitting the factor that reads αk − αk .
(c) Divide the value so obtained into g (αk ); the result is the required
coefficient Ak .
For our specific example we find that in step (a) that h( x )=( x+1)( x+2)
and that in evaluating A 1 step (b) yields − 1 + 2, i.e. 1. Since g (− 1) =
4(− 1) + 2 = − 2, step (c) gives A 1 as (− 2)/ (1), i.e in agreement with our
other evaluations. In a similar way A 2 is evaluated as (− 6)/(− 1) = 6.
20
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In this work, we focus on the numerical analysis of the propagation of plane-waves in one-dimensional periodic lossy dielectric media, which constitute the building block of dielectric frequency-selective surfaces (DFSSs). To this end, a full-vectorial modal method was used, in which discontinuities of some components of the electromagnetic fields have to be evaluated, and we propose a numerical improvement in the evaluation of some integrals appearing in the developed formulation. Some confusion may exist in the evaluation of the cited integrals due to the discontinuous nature of the dielectric function and its transverse gradient. Therefore, some considerations are given in order to solve these integrals accurately for the general case of a relative dielectric permittivity function defined as a sum of lossy dielectric slabs. We particularize our study to a dielectric frequency-selective surface (DFSS), for which the periodic dielectric medium can be defined as constant functions inside an homogeneous region, whose contours define the discontinuities. Thus, the relative dielectric permittivity can be expressed in terms of the Heaviside or step function. In this way, the above-mentioned integrals can be correctly evaluated in the discontinuity, obtaining good results with the employed vectorial modal method for both the propagation constant and the electromagnetic fields obtained in the periodic dielectric medium constituting the DFSS. These results are compared with those obtained with a less accurate evaluation of the cited integrals, when an approximation made by other authors is used.
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Passivity is a fundamental concept that constitutes a necessary condition for any quantum system to attain thermodynamic equilibrium, and for a notion of temperature to emerge. While extensive work has been done that exploits this, the transition from passivity at a single-shot level to the completely passive Gibbs state is technically clear but lacks a good over-arching intuition. Here, we reformulate passivity for quantum systems in purely geometric terms. This description makes the emergence of the Gibbs state from passive states entirely transparent. Beyond clarifying existing results, it also provides novel analysis for non-equilibrium quantum systems. We show that, to every passive state, one can associate a simple convex shape in a 2 -dimensional plane, and that the area of this shape measures the degree to which the system deviates from the manifold of equilibrium states. This provides a novel geometric measure of athermality with relations to both ergotropy and β --athermality.
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Lithium-ion cells may experience rapid degradation in later life, especially with more extreme usage protocols. The onset of rapid degradation is called the 'knee point', and forecasting it is important for the safe and economically viable use for batteries. We propose a data-driven method that uses automated feature selection to produce inputs for a Gaussian process regression model that estimates changes in battery health, from which the entire capacity fade trajectory, knee point and end of life may be predicted. The feature selection procedure flexibly adapts to varying inputs and prioritises those that impact degradation. For the datasets considered, it was found that calendar time and time spent in specific voltage regions had a strong impact on degradation rate. The approach produced median root mean square errors on capacity estimates under 1%, and also produced median knee point and end of life prediction errors of 2.6% and 1.3% respectively.
Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.
The uniaxial orientational order in a macromolecular system is usually specified using the Hermans factor which is equivalent to the second moment of the system's orientation distribution function (ODF) expanded in terms of Legendre polynomials. In this work, we show that for aligned materials that are two‐dimensional (2D) or have a measurable 2D intensity distribution, such as carbon nanotube (CNT) textiles, the Hermans factor is not appropriate. The ODF must be expanded in terms of Chebyshev polynomials and therefore, its second moment is a better measure of orientation in 2D. We also demonstrate that both orientation parameters (Hermans in three dimensional (3D) and Chebyshev in 2D) depend not only on the respective full‐width‐at‐half‐maximum of the peaks in the ODF but also on the shape of the fitted functions. Most importantly, we demonstrate a method to rapidly estimate the Chebyshev orientation parameter from a sample's 2D Fourier power spectrum, using an analysis program written in Python which is available for open access. As validation examples, we use digital photographs of dry spaghetti as well as scanning electron microscopy images of direct‐spun carbon nanotube fibers, proving the technique's applicability to a wide variety of fibers and images.
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We report the phototransferred thermoluminescence of tanzanite induced by 470 nm blue light. A glow curve measured after irradiation to 120 Gy and heating at 1oC s⁻¹ shows three nominal peaks at 86oC (peak I) and two secondary peaks at 160oC and 320oC. The latter two peaks are collocated peaks with each one consisting of multiple components. Phototransferred thermoluminescence is only reproduced at peak I. Although this work addresses phototransfer, the system studied is unconventional in that the phototransferred TL is associated with an indeterminate number of donors. A matrix-based method has been used to analyse the time-response profiles of the phototransferred thermoluminescence. Competition effects, where putative donor electron traps act in competition with acceptor electron traps, have been studied using empirical models.
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Mathematical Methods For Physics And Engineering Book Pdf
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