Series and Integrals

When you press the SIN or LOG key on your scientific calculator, it almost instantly returns a numerical value. What is really happening is that the microprocessor inside the calculator is summing a series representation of that function, similar to the series we have already encountered for , , and in Chapter 3 and 4. Power series (sums of powers of ) and other series of functions are very important mathematical tools, both for computation and for deriving theoretical results.

An *arithmetic progression* is a sequence such as 1, 4, 7, 10, 13, 16. As shown in Section 1.2, the sum of an arithmetic progression with terms is given by

(7.1)

where is the first term, is the constant difference between terms, and is the last term.

A *geometric progression* is a sequence which increases or decreases by a common factor , for example, 1, 3, 9, 27, 81, … or 1, 1/2, 1/4, 1/8, 1/16, … The sum of a geometric progression is given by,

(7.2)

When , the sum can be carried to infinity to give

(7.3)

We had already found from the binomial theorem that

(7.4)

By applying the binomial theorem successively to , then to each you can show that

(7.5)

Therefore serves as a *generating function* for the Fibonacci numbers:

(7.6)

An infinite geometric series inspired by one of Zeno’s paradoxes is

(7.7)

Zeno’s paradox of motion claims that if you shoot an arrow, it can never reach its target. First it has to travel half way, then half way again—meaning 1/4 of the distance—then continue with an *infinite* number of steps, each taking it closer. Since infinity is so large, you’ll never get there! What we now understand that Zeno possibly didn’t (some scholars believe that his argument was meant to be satirical) was that an infinite number of decreasing terms can add up to a finite quantity.

The integers 1 to to add up to

(7.8)

while the sum of the squares is given by

(7.9)

and the sum of the cubes by

(7.10)

Almost all functions can be represented by *power series* of the form

(7.11)

where might be a factor such as , , or . The case and provides the most straightforward class of power series. We are already familiar with the series for the exponential function:

(7.12)

as well as the sine and cosine:

(7.13)

(7.14)

Recall also the binomial expansion:

(7.15)

Useful results can be obtained when power series are differentiated or integrated term by term. This is a valid procedure under very general conditions.

Consider, for example, the binomial expansion

(7.16)

Making use of the known integral

(7.17)

we obtain a series for the arctangent

(7.18)

With , this gives a famous series for

(7.19)

usually attributed to Gregory and Leibniz.

A second example begins with another binomial expansion

(7.20)

We can again evaluate the integral

(7.21)

This gives a series representation for the natural logarithm:

(7.22)

For , this gives another famous series

(7.23)

As a practical matter, the convergence of this series is excruciatingly slow. It takes about 1000 terms to get correct to three significant figures, .

It is relatively simple to multiply two power series. Note that

(7.24)

Inversion of a power series means, in principle, solving for the expansion variable as a function of the sum of the series. Consider the special case of a series in the form

(7.25)

The constants and in the more general case can be absorbed into the variable and the remaining constants. Then can be expanded in powers of in the form

(7.26)

By a recursive procedure, carried out only as far as , we obtain:

(7.27)

The *partial sum* of an infinite series is the sum of the first terms:

(7.28)

The series is *convergent* if

(7.29)

where is a finite quantity. A *necessary* condition for convergence, thus a preliminary test, is that

(7.30)

Several tests for convergence that are usually covered in introductory calculus courses. The *comparison test*: if a series is known to converge and for all , then the series is also convergent. The *ratio test*: if then the series converges. There are more sensitive ratio tests in the case that the limit approaches 1, but you will rarely need these outside of math courses. The most useful test for convergence is the *integral test*. This is based on turning things around using our original definition of an integral as the limit of a sum. The sum can be approximated by an integral by turning the discrete variable into a continuous variable . If the integral

(7.31)

is finite, then the original series converges.

A general result is that any decreasing alternating series, such as (7.23), converges. *Alternating* refers to the alternation of plus and minus signs. How about the analogous series with all plus signs?:

(7.32)

After 1000 terms the sum equals 7.485. It might appear that, with sufficient patience, the series will eventually converge to a finite quantity. Not so, however! The series is divergent and sums to infinity. This can be seen by applying the integral test:

(7.33)

A finite series of the form

(7.34)

is called a *harmonic series*. Using the same approximation by an integral, we estimate that this sum is approximately equal to . The difference between and was shown by Euler to approach a constant as :

(7.35)

where (sometimes denoted ) is known as the *Euler-Mascheroni constant*. It comes up frequently in mathematics, for example, the integral

(7.36)

An alternating series is said to be *absolutely convergent* if the corresponding sum of absolute values, , is also convergent. This is *not* true for the alternating harmonic series (7.23). Such a series is said to be *conditionally convergent*. Conditionally convergent series must be treated with extreme caution. A theorem due to Riemann states that, by a suitable rearrangement of terms, a conditionally convergent series may be made to equal any desired value, or to diverge. Consider, for example, the series (7.20) for when :

(7.37)

Different ways of grouping the terms of the series give different answers. Thus , while . But actually equals 1/2. Be very careful!

There is a systematic procedure for deriving power-series expansions for all well-behaved functions. Assuming that a function can be represented in a power series about , we write

(7.38)

Clearly, when ,

(7.39)

The first derivative of is given by

(7.40)

and, setting , we obtain

(7.41)

The second derivative is given by

(7.42)

with

(7.43)

With repeated differentiation, we find

(7.44)

where

(7.45)

We have therefore determined the coefficients in terms of derivatives of evaluated at and the expansion (7.38) can be given more explicitly by

(7.46)

If the expansion is carried out around , rather than 0, the result generalizes to

(7.47)

This result is known as *Taylor’s theorem* and the expansion is a *Taylor series*. The case , given by Eq. (7.46), is sometimes called a *Maclaurin series*.

In order for a Taylor series around to be valid it is necessary for all derivatives to exist. The function is then said to be *analytic* at . A function which is not analytic at one point can still be analytic at other points. For example, is not analytic at but *is* at . The series (7.22) is equivalent to an expansion of around .

We can now systematically derive all the series we obtained previously by various other methods. For example, given , we find

(7.48)

so that (7.47) gives

(7.49)

which is the binomial expansion. This result is seen to be valid even for noninteger values of . In the latter case we should express (7.49) in terms of the gamma function as follows:

(7.50)

The series for is easy to derive because for all . Therefore, as we have already found

(7.51)

The Taylor series for is also straightforward since successive derivatives cycle among , , , and . Since and , the series expansion contains only odd powers of with alternating signs:

(7.52)

Analogously, the expansion for the cosine is given by

(7.53)

Euler’s theorem can then be deduced by comparing the series for these three functions.

The answer to Problem 7.4.1 is the series

(7.54)

This series provides a generating function for the *Bernoulli numbers*, , whereby

(7.55)

Explicitly,

(7.56)

Bernoulli numbers find application in number theory, in numerical analysis, and in expansions of several functions related to and . A symbolic relation which can be used to evaluate Bernoulli numbers mimics the binomial expansion:

(7.57)

where is to be replaced by .

We can obtain an expansion for in the following steps involving the Bernoulli numbers:

(7.58)

But

(7.59)

Therefore

(7.60)

and, more explicitly,

(7.61)

Somewhat analogous to the definition of Bernoulli numbers are the *Euler numbers*. These can be obtained from the generating function:

(7.62)

The first few Euler numbers are

(7.63)

with all equal to 0 for odd indices . We find directly that

(7.64)

and thus obtain the expansion

(7.65)

The Euler-Maclaurin sum formula provides a powerful method for evaluating some difficult summations. It actually represents a more precise connection between Riemann sums and their corresponding integrals. It can be used to approximate finite sums and even infinite series using integrals with some additional terms involving Bernoulli numbers. It can be shown that

(7.66)

The value of a function is called an *indeterminate form* at some point if its limit as apparently approaches one of the forms , or . Two examples are the combinations and as . (As we used to say in high school, such sick functions had to be sent to L’Hôspital to be cured.) To be specific, let us consider a case for which

(7.67)

If and are both expressed in Taylor series about ,

(7.68)

If and both equal 0 but and are finite, the limit in (7.67) is given by

(7.69)

a result known as *L’Hôpital’s rule*. In the event that one or both first derivatives also vanishes, the lowest order nonvanishing derivatives in the numerator and denominator determine the limit.

To evaluate the limit of , let and . In this case, . But , so . Also, , for all . Therefore

(7.70)

This is also consistent with the approximation that for . For the limit of , let , . Now and . We find therefore that

(7.71)

Taylor series expansions, as described above, provide a very general method for representing a large class of mathematical functions. For the special case of *periodic functions*, a powerful alternative method is expansion in an infinite sum of sines and cosines, known as a *trigonometric series* or *Fourier series*. A periodic function is one which repeats in value when its argument is increased by multiples of a constant , called the *period* or *wavelength*. For example,

(7.72)

as shown in Figure 7.1. For convenience, let us consider the case . Sine and cosine are definitive examples of functions periodic in since

(7.73)

The functions and , with =integer, are also periodic in (as well as in ).

A function periodic in can be expanded as follows:

(7.74)

Writing the constant term as is convenient, as will be seen shortly. The coefficients can be determined by making use of the following definite integrals:

(7.75)

(7.76)

(7.77)

These integrals are expressed compactly with use of the *Kronecker delta*, defined as follows:

(7.78)

Two functions are said to be *orthogonal* if the definite integral of their product equals zero. (Analogously, two vectors and whose scalar product equals zero are said to be orthogonal—meaning perpendicular, in that context.) The set of functions is termed an *orthogonal set* in an interval of width .

The nonvanishing integrals in (7.75) and (7.76) follow easily from the fact that and have average values of over an integral number of wavelengths. Thus

(7.79)

The orthogonality relations (7.75)–(7.77) enable us to determine the Fourier expansion coefficients and . Consider the integral , with expanded using (7.74). By virtue of the orthogonality relations, only one term in the expansion survives integration:

(7.80)

Solving for we find

(7.81)

Note that the case correctly determines , by virtue of the factor in Eq. (7.74). Analogously, the coefficients are given by

(7.82)

In about half of textbooks, the limits of integration in Eqs. (7.81) and (7.82) are chosen as rather than . This is just another way to specify one period of the function and the same results are obtained in either case.

As an illustration, let us calculate the Fourier expansion for a *square wave*, defined as follows:

(7.83)

A square-wave oscillator is often used to test the frequency response of an electronic circuit. The Fourier coefficients can be computed using (7.81) and (7.82). First we find

(7.84)

Thus all the cosine contributions equal zero since is symmetrical about . The square wave is evidently a Fourier sine series, with only nonvanishing coefficients. We find

(7.85)

giving

(7.86)

The Fourier expansion for a square wave can thus be written

(7.87)

For , Eq. (7.87) reduces to (7.19), the Gregory-Leibniz series for :

(7.88)

A Fourier series carried through terms is called a *partial sum*. Figure 7.2 shows the function and the partial sums , and . Note how the higher partial sums overshoot near the points of discontinuity . This is known as the *Gibbs phenomenon*. As at these points.

The general conditions for a periodic function to be representable by a Fourier series are the following. The function must have a finite number of maxima and minima and a finite number of discontinuities between 0 and . Also, must be finite. If these conditions are fulfilled then the Fourier series (7.74) with coefficients given by (7.81) and (7.82) converges to at points where the function is continuous. At discontinuities , the Fourier series converges to the midpoint of the jump, .

Recall that sines and cosines can be expressed in terms of complex exponential functions, according to Eqs. (4.60) and (4.61). Accordingly, a Fourier series can be expressed in a more compact form:

(7.89)

where the coefficients might be complex numbers. The orthogonality relations for complex exponentials are given by

(7.90)

These determine the complex Fourier coefficients:

(7.91)

If is a real function, then for all .

For functions with a periodicity different from , the variable can be replaced by . The formulas for Fourier series are then modified as follows:

(7.92)

with

(7.93)

and

(7.94)

For complex Fourier series,

(7.95)

with

(7.96)

Many of the applications of Fourier analysis involve the time-frequency domain. A time-dependent signal can be expressed

(7.97)

where the are frequencies expressed in radians per second.

When a tuning fork is struck, it emits a tone which can be represented by a sinusoidal wave—one having the shape of a sine or cosine. For tuning musical instruments, a fork might be machined to produce a pure tone at 440 Hz, which corresponds to A above middle C. (Middle C would then have a frequency of 278.4375 Hz.) The graph in Figure 7.3 shows the variation of air pressure (or density) with time for a sound wave, as measured at a single point. represents the deviation from the undisturbed atmospheric pressure . The maximum variation of above or below is called the *amplitude* of the wave. The time between successive maxima of the wave is called the *period*. Since the argument of the sine or cosine varies between and in one period, the form of the wave could be the function

(7.98)

Psi () is a very common symbol for wave amplitude. The *frequency*, defined by

(7.99)

gives the number of oscillations per second, conventionally expressed in *hertz* (Hz). An alternative measure of frequency is the number of *radians per second*, . Since one cycle corresponds to radians,

(7.100)

The upper strip in Figure 7.3 shows the profile of the sound wave at a single instant of time. The pressure or density of the air also has a sinusoidal shape. At some given instant of time the deviation of pressure from the undisturbed presssure might be represented by

(7.101)

where is the *wavelength* of the sound, the distance between successive pressure maxima. Sound consists of *longitudinal* waves, in which the wave amplitude varies in the direction parallel to the wave’s motion. By contrast, electromagnetic waves, such as light, are *transverse*, with their electric and magnetic fields oscillating *perpendicular* to the direction of motion. The speed of light in vacuum, m/s. The speed of sound in air is much slower, typically around 350 m/s (1100 ft/s or 770 miles/hr—known as *Mach 1* for jet planes). As you know, thunder is the sound of lightning. You see the lightning essentially instantaneously but thunder takes about 5 s to travel 1 mile. You can calculate how far away a storm is by counting the number of seconds between the lightning and the thunder. A wave (light or sound) traveling at a speed moves a distance of one wavelength in the time of one period . This implies the general relationship between frequency and wavelength

(7.102)

valid for all types of wave phenomena. A trumpet playing the same sustained note produces a much richer sound than a tuning fork, as shown in Figure 7.4. *Fourier analysis* of a musical tone shows a superposition of the fundamental frequency augmented by *harmonics* or *overtones*, which are integer multiples of .

Recall that the Kronecker delta , defined in Eq. (7.78), pertains to the discrete variables and . A useful application enables the reduction of a summation to a single term:

(7.103)

The analog of the Kronecker delta for continuous variables is the *Dirac deltafunction*, which has the defining property

(7.104)

which includes the normalization condition

(7.105)

Evidently

(7.106)

The approach to is sufficiently tame, however, that the integral has a finite value.

A simple representation for the deltafunction is the limit of a normalized Gaussian as the standard deviation approaches zero:

(7.107)

The Dirac deltafunction is shown pictorially in Figure 7.5. The deltafunction is the limit of a function which becomes larger and larger in an interval which becomes narrower and narrower. (Some university educators bemoaning increased specialization contend that graduate students are learning more and more about less and less until they eventually wind up knowing everything about nothing—the ultimate deltafunction!)

Differentiation of a function at a finite discontinuity produces a deltafunction. Consider, for example, the *Heaviside unit step function*:

(7.108)

Sometimes (for ) is defined as . The derivative of the Heaviside function is clearly equal to zero when . In addition

(7.109)

We can thus identify

(7.110)

The deltafunction can be generalized to multiple dimensions. In three dimensions, the defining relation for a deltafunction can be expressed

(7.111)

For example, the limit of a continuous distribution of electrical charge shrunken to a point charge at can be represented by

(7.112)

The potential energy of interaction between two continuous charge distributions is given by

(7.113)

If the distribution is reduced to a point charge at , this reduces to

(7.114)

If the analogous thing then happens to , the formula reduces to the Coulomb potential energy between two point charges

(7.115)

Fourier series are ideal for periodic functions, sums over frequencies which are integral multiples of some . For a more general class of functions which are not simply periodic, *all* possible frequency contributions must be considered. This can be accomplished by replacing a discrete Fourier series by a continuous integral. The coefficients (or, equivalently, and ) which represent the relative weight of each harmonic will turn into a *Fourier transform*, which measures the contribution of a frequency in a continuous range of . In the limit as , a complex Fourier series (7.95) generalizes to a *Fourier integral*. The discrete variable can be replaced by a continuous variable , such that

(7.116)

with the substitution

(7.117)

Correspondingly, Eq. (7.96) becomes

(7.118)

where is called the Fourier transform of —alternatively written , , , or sometimes simply . A Fourier-transform pair and can also be defined more symmetrically by writing:

(7.119)

Fourier integrals in the time-frequency domain have the form

(7.120)

Figure 7.4 is best described as a Fourier transform of a trumpet tone since the spectrum of frequencies consists of peaks of finite width.

Another representation of the deltafunction, useful in the manipulation of Fourier transforms, is defined by the limit:

(7.121)

For , the function equals , which approaches . For the sine function oscillates with ever-increasing frequency as . The positive and negative contributions cancel so that the function becomes essentially equivalent to 0 under the integral sign. Finally, since

(7.122)

for finite values of k, Eq. (7.121) is suitably normalized to represent a deltafunction. The significance of this last representation is shown by the integral

(7.123)

This shows that the Fourier transform of a complex monochromatic wave is a deltafunction .

An important result for Fourier transforms can be derived using (7.123). Using the symmetrical form for the Fourier integral (7.119), we can write

(7.124)

being careful to use the dummy variable in the second integral on the right. The integral over on the right then gives

(7.125)

Following this by integration over we obtain

(7.126)

a result known as *Parseval’s theorem*. A closely related result is *Plancherel’s theorem*:

(7.127)

where and are the symmetric Fourier transforms of and , respectively.

The *convolution* of two functions is defined by

(7.128)

The Fourier transform of a convolution integral can be found by substituting the symmetric Fourier transforms and and using the deltafunction formula (7.125). The result is the *convolution theorem*, which can be expressed very compactly as

(7.129)

In an alternative form, .

For Bessel functions and other types of special functions to be introduced later, it is possible to construct *orthonormal sets* of basis functions which satisfy the orthogonality and normalization conditions:

(7.130)

with respect to integration over appropriate limits. (If the functions are real, complex conjugation is unnecessary.) An arbitrary function in the same domain as the basis functions can be expanded in an expansion analogous to a Fourier series

(7.131)

with the coefficients determined by

(7.132)

If (7.132) is substituted into (7.131), with the appropriate use of a dummy variable, we obtain

(7.133)

The last quantity in square brackets has the same effect as the deltafunction . The relation known as *closure*

(7.134)

is, in a sense, complementary to the orthonormality condition (7.130).

Generalized Fourier series find extensive application in mathematical physics, particularly quantum mechanics.

In certain circumstances, a *divergent* series can be used to determine approximate values of a function as . Consider, as an example, the complementary error function , defined in Eq. (6.95):

(7.135)

Noting that

(7.136)

Equation (7.135) can be integrated by parts to give

(7.137)

Integrating by parts again:

(7.138)

Continuing the process, we obtain

(7.139)

This is an instance of an *asymptotic series*, as indicated by the equivalence symbol rather than an equal sign. The series in brackets is actually divergent. However, a finite number of terms gives an approximation to for large values of . The omitted terms, when expressed in their original form as an integral, as in Eqs. (7.137) or (7.138), approach zero as .

Consider the general case of an asymptotic series

(7.140)

with a partial sum

(7.141)

The condition for to be an asymptotic representation for can be expressed

(7.142)

A convergent series approaches for a given as , where is the number of terms in the partial sum . By contrast, an asymptotic series approaches as for a given .

The *exponential integral* is another function defined as a definite integral which cannot be evaluated in a simple closed form. In the usual notation

(7.143)

By repeated integration by parts, the following asymptotic series for the exponential integral can be derived:

(7.144)

Finally, we will derive an asymptotic expansion for the gamma function, applying a technique known as the *method of steepest descents*. Recall the integral definition:

(7.145)

For large the integrand will be very sharply peaked around . It is convenient to write

(7.146)

A Taylor series expansion of about gives

(7.147)

noting that . The integral (7.145) can then be approximated as follows:

(7.148)

For large , we introduce a negligible error by extending the lower integration limit to . The integral can then be done exactly to give *Stirling’s formula*

(7.149)

or in terms of the factorial

(7.150)

This is consistent with the well-known approximation for the natural logarithm:

(7.151)

A more complete asymptotic expansion for the gamma function will have the form

(7.152)

Making use of the recursion relation

(7.153)

it can be shown that and .

You will notice that, as we go along, we are leaving more and more computational details for you to work out on your own. Hopefully, your mathematical facility is improving at a sufficient rate to keep everything understandable.

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