This and the next chapter are concerned with the formalism of probably the most widely used mathematical technique in the physical sciences, namely the calculus. The current chapter deals with the process of differentiation whilst Chapter 4 is concerned with its inverse process, integration. The topics covered are essential for the remainder of the book; once studied, the contents of the two chapters serve as reference material, should that be needed. Readers who have had previous experience of differentiation and integration should ensure full familiarity by looking at the worked examples in the main text and by attempting the problems at the ends of the two chapters.

Also included in this chapter is a section on curve sketching. Most of the mathematics needed as background to this important skill for applied physical scientists was covered in the first two chapters, but delaying our main discussion of it until the end of this chapter allows the location and characterisation of turning points to be included amongst the techniques available.

Differentiation

Differentiation is the process of determining how quickly or slowly a function varies, as the quantity on which it depends, its argument, is changed. More specifically, it is the procedure for obtaining an expression (numerical or algebraic) for the rate of change of the function with respect to its argument.

This chapter introduces space vectors and their manipulation. Firstly we deal with the description and algebra of vectors, then we consider how vectors may be used to describe lines, planes and spheres, and finally we look at the practical use of vectors in finding distances. The calculus of vectors will be developed in a later chapter; this chapter gives only some basic rules.

Scalars and vectors

The simplest kind of physical quantity is one that can be completely specified by its magnitude, a single number, together with the units in which it is measured. Such a quantity is called a scalar, and examples include temperature, time and density.

A vector is a quantity that requires both a magnitude (≥ 0) and a direction in space to specify it completely; we may think of it as an arrow in space. A familiar example is force, which has a magnitude (strength) measured in newtons and a direction of application. The large number of vectors that are used to describe the physical world include velocity, displacement, momentum and electric field. Vectors can also be used to describe quantities such as angular momentum and surface elements (a surface element has a magnitude, defined by its area, and a direction defined by the normal to its tangent plane); in such cases their definitions may seem somewhat arbitrary (though in fact they are standard) and not as physically intuitive as for vectors such as force.

Differential equations are the group of equations that contain derivatives. There are several different types of differential equations, but here we will be considering only the simplest types. As its name suggests, an ordinary differential equation (ODE) contains only ordinary derivatives (no partial derivatives) and describes the relationship between these derivatives of the dependent variable, usually called y, with respect to the independent variable, usually called x. The solution to such an ODE is therefore a function of x and is written y(x). For an ODE to have a closed-form solution, it must be possible to express y(x) in terms of the standard elementary functions such as x 2, exp x, In x, sin x, etc. The solutions of some differential equations cannot, however, be written in closed form, but only as an infinite series that carry no special names.

Ordinary differential equations may be separated conveniently into different categories according to their general characteristics. The primary grouping adopted here is by the order of the equation. The order of an ODE is simply the order of the highest derivative it contains. Thus, equations containing dy/dx, but no higher derivatives, are called first order, those containing d 2 y/dx 2 are called second order and so on. In this chapter we consider first-order equations and some of the more straightforward equations of second order.

As indicated at the start of the previous chapter, the differential calculus and its complement, the integral calculus, together form the most widely used tool for the analysis of physical systems. The link that connects the two is that they both deal with the effects of vanishingly small changes in related quantities; one seeks to obtain the ratio of two such changes, the other uses such a ratio to calculate the variation in one of the quantities resulting from a change in the other.

Any change in the value of any one property (or variable) of a physical system almost always results in the values of some or all of its other properties being altered; in general, the size of each consequential change depends upon the current values of all of the variables. As a result, during a finite change in any one of the values, that of x say, those associated with all of the other variables are continuously changing, making computation of the final situation difficult, if not impossible. The solution to this difficulty is provided by the integral calculus, which allows only vanishingly small changes, and, after any such change in one variable, brings all the other values ‘up to date’ (by infinitesimal amounts) before allowing any further change.

All scientists will know the importance of experiment and observation and, equally, be aware that the results of some experiments depend to a degree on chance. For example, in an experiment to measure the heights of a random sample of people, we would not be in the least surprised if all the heights were found to be different; but, if the experiment were repeated often enough, we would expect to find some sort of regularity in the results. Statistical methods are concerned with the analysis of real experimental data of this sort.

In this final chapter we discuss the subject of probability, which is the theoretical basis for most statistical methods. Our development of probability will be with an eye to its eventual applications in statistics, with little emphasis on the axioms and theorems approach favoured by most pure mathematicians.

We first discuss the terminology required, with particular reference to the convenient graphical representation of experimental results as Venn diagrams. The concepts of random variables and distributions of random variables are then introduced. It is here that the connection with statistics is made; we assert that the results of many experiments are random variables and that those results have some sort of regularity, represented by a distribution. Finally, the defining equations for some important distributions, together with some useful quantities that characterise them, are introduced and discussed.

Since Mathematical Methods for Physics and Engineering by Riley, Hobson and Bence (Cambridge: Cambridge University Press, 1998), hereafter denoted by MMPE, was first published, the range of material it covers has increased with each subsequent edition (2002 and 2006). Most of the additions have been in the form of introductory material covering polynomial equations, partial fractions, binomial expansions, coordinate geometry and a variety of basic methods of proof, though the third edition of MMPE also extended the range, but not the general level, of the areas to which the methods developed in the book could be applied. Recent feedback suggests that still further adjustments would be beneficial. In so far as content is concerned, the inclusion of some additional introductory material such as powers, logarithms, the sinusoidal and exponential functions, inequalities and the handling of physical dimensions, would make the starting level of the book better match that of some of its readers.

To incorporate these changes, and others aimed at increasing the user-friendliness of the text, into the current third edition of MMPE would inevitably produce a text that would be too ponderous for many students, to say nothing of the problems the physical production and transportation of such a large volume would entail.

In Chapters 3 and 4 we discussed functions f of only one variable x, which were usually written f(x). Certain constants and parameters may also have appeared in the definition of f, e.g. f(x) = ax + 2 contains the constant 2 and the parameter a, but only x was considered as a variable and only the derivatives f (n)(x) = dnf/dxn were defined.

However, we can equally well consider functions that depend on more than one variable, e.g. the function f(x, y) = x 2 + 3xy, which depends on the two variables x and y. For any pair of values x, y, the function f(x, y) has a well-defined value, e.g. f(2, 3) = 22. This notion can clearly be extended to functions dependent on more than two variables. For the n-variable case, we write f(x 1, x 2, …, xn ) for a function that depends on the variables x 1, x 2, …, xn . When n = 2, x 1 and x 2 correspond to the variables x and y used above.

Functions of one variable, like f(x), can be represented by a graph on a plane sheet of paper, and it is apparent that functions of two variables can, with a little effort, be represented by a surface in three-dimensional space.

In Chapter 9 we discussed the algebra of vectors and in Chapter 10 we considered how to transform one vector into another using a linear operator. In this chapter and the next we discuss the calculus of vectors, i.e. the differentiation and integration both of vectors describing particular bodies, such as the velocity of a particle, and of vector fields, in which a vector is defined as a function of the coordinates throughout some volume (one-, two- or three-dimensional). Since the aim of this chapter is to develop methods for handling multi-dimensional physical situations, we will assume throughout that the functions with which we have to deal have sufficiently amenable mathematical properties, in particular that they are continuous and differentiable.

Differentiation of vectors

Let us consider a vector a that is a function of a scalar variable u. By this we mean that with each value of u we associate a vector a(u). For example, in Cartesian coordinates a(u) = ax (u)i + ay (u)j + az (u)k, where ax (u), ay (u) and az (u) are scalar functions of u and are the components of the vector a(u) in the x-, y- and z-directions respectively. We note that if a(u) is continuous at some point u = u 0 then this implies that each of the Cartesian components ax (u), ay (u) and az (u) is also continuous there.

The first two chapters of this book review the basic arithmetic, algebra and geometry of which a working knowledge is presumed in the rest of the text; many students will have at least some familiarity with much, if not all, of it. However, the considerable choice now available in what is to be studied for secondary-education examination purposes means that none of it can be taken for granted. The reader may make a preliminary assessment of which areas need further study or revision by first attempting the problems at the ends of the chapters. Unlike the problems associated with all other chapters, those for the first two are divided into named sections and each problem deals almost exclusively with a single topic.

This opening chapter explains the basic definitions and uses associated with some of the most common mathematical procedures and tools; these are the components from which the mathematical methods developed in more advanced texts are built. So as to keep the explanations as free from detailed mathematical working as possible – and, in some cases, because results from later chapters have to be anticipated – some justifications and proofs have been placed in appendices. The reader who chooses to omit them on a first reading should return to them after the appropriate material has been studied.