A Fourier series (/ˈfʊrieɪ,-iər/) is a summation of harmonically related sinusoidal functions, also known as components or harmonics. The result of the summation is a periodic function whose functional form is determined by the choices of cycle length (or period), the number of components, and their amplitudes and phase parameters. With appropriate choices, one cycle (or period) of the summation can be made to approximate an arbitrary function in that interval (or the entire function if it too is periodic). The number of components is theoretically infinite, in which case the other parameters can be chosen to cause the series to converge to almost any well behaved periodic function (see Pathological and Dirichlet–Jordan test). The components of a particular function are determined by analysis techniques described in this article. Sometimes the components are known first, and the unknown function is synthesized[A]
by a Fourier series. Such is the case of a discrete-time Fourier transform.
A square wave (represented as the blue dot) is approximated by its sixth partial sum (represented as the purple dot), formed by summing the first six terms (represented as arrows) of the square wave's Fourier series. Each arrow starts at the vertical sum of all the arrows to its left (i.e. the previous partial sum).
The first four partial sums of the Fourier series for a square wave. As more harmonics are added, the partial sums converge to (become more and more like) the square wave.
Function (in red) is a Fourier series sum of 6 harmonically related sine waves (in blue). Its Fourier transform is a frequency-domain representation that reveals the amplitudes of the summed sine waves.
Another analysis technique (not covered here), suitable for both periodic and non-periodic functions, is the Fourier transform, which provides a frequency-continuum of component information. But when applied to a periodic function all components have zero amplitude, except at the harmonic frequencies. The inverse Fourier transform is a synthesis process (like the Fourier series), which converts the component information (often known as the frequency domain representation) back into its time domain representation.
Since Fourier's time, many different approaches to defining and understanding the concept of Fourier series have been discovered, all of which are consistent with one another, but each of which emphasizes different aspects of the topic. Some of the more powerful and elegant approaches are based on mathematical ideas and tools that were not available in Fourier's time. Fourier originally defined the Fourier series for real-valued functions of real arguments, and used the sine and cosine functions as the basis set for the decomposition. Many other Fourier-related transforms have since been defined, extending his initial idea to many applications and birthing an area of mathematics called Fourier analysis.
This section describes the analysis process that derives the parameters of a Fourier series that approximates a known function, An example of synthesizing an unknown function from known parameters is discrete-time Fourier transform.
The harmonic's frequency is and in reciprocal units of .
Fig 1. The top graph shows a non-periodic function s(x) in blue defined only over the red interval from 0 to P. The function can be analyzed over this interval to produce the Fourier series in the bottom graph. The Fourier series is always a periodic function, even if original function s(x) wasn't.
Clearly Eq.1 can represent functions that are just a sum of one or more of the harmonic frequencies. The remarkable thing, for those not yet familiar with this concept, is that it can also represent the intermediate frequencies and/or non-sinusoidal functions because of the potentially infinite number of terms ().
Fig 2. The blue curve is the cross-correlation of a square wave and a cosine function, as the phase lag of the cosine varies over one cycle. The amplitude and phase lag at the maximum value are the polar coordinates of one harmonic in the Fourier series expansion of the square wave. The corresponding rectangular coordinates can be determined by evaluating the cross-correlation at just two phase lags separated by 90º.
The coefficients and can be understood and derived in terms of the cross-correlation between and a sinusoid at frequency . For a general frequency and an analysis interval the cross-correlation function:
is essentially a matched filter, with template.[C] The maximum of is a measure of the amplitude of frequency in the function , and the value of at the maximum determines the phase of that frequency. Figure 2 is an example, where is a square wave (not shown), and frequency is the harmonic. It is also an example of deriving the maximum from just two samples, instead of searching the entire function. That is made possible by a trigonometric identity:
In engineering applications, the Fourier series is generally presumed to converge almost everywhere (the exceptions being at discrete discontinuities) since the functions encountered in engineering are better-behaved than the functions that mathematicians can provide as counter-examples to this presumption. In particular, if is continuous and the derivative of (which may not exist everywhere) is square integrable, then the Fourier series of converges absolutely and uniformly to . If a function is square-integrable on the interval , then the Fourier series converges to the function at almost every point. It is possible to define Fourier coefficients for more general functions or distributions, in such cases convergence in norm or weak convergence is usually of interest.
Four partial sums (Fourier series) of lengths 1, 2, 3, and 4 terms, showing how the approximation to a square wave improves as the number of terms increases (animation)
Four partial sums (Fourier series) of lengths 1, 2, 3, and 4 terms, showing how the approximation to a sawtooth wave improves as the number of terms increases (animation)
Example of convergence to a somewhat arbitrary function. Note the development of the "ringing" (Gibbs phenomenon) at the transitions to/from the vertical sections.
If is a complex-valued function of a real variable both components (real and imaginary part) are real-valued functions that can be represented by a Fourier series. The two sets of coefficients and the partial sum are given by:
This is identical to Eq.5 except and are no longer complex conjugates. The formula for is also unchanged:
Other common notationsEdit
The notation is inadequate for discussing the Fourier coefficients of several different functions. Therefore, it is customarily replaced by a modified form of the function (, in this case), such as or , and functional notation often replaces subscripting:
In engineering, particularly when the variable represents time, the coefficient sequence is called a frequency domain representation. Square brackets are often used to emphasize that the domain of this function is a discrete set of frequencies.
Another commonly used frequency domain representation uses the Fourier series coefficients to modulate a Dirac comb:
The constructed function is therefore commonly referred to as a Fourier transform, even though the Fourier integral of a periodic function is not convergent at the harmonic frequencies.[E]
The Fourier series is named in honor of Jean-Baptiste Joseph Fourier (1768–1830), who made important contributions to the study of trigonometric series, after preliminary investigations by Leonhard Euler, Jean le Rond d'Alembert, and Daniel Bernoulli.[F] Fourier introduced the series for the purpose of solving the heat equation in a metal plate, publishing his initial results in his 1807 Mémoire sur la propagation de la chaleur dans les corps solides (Treatise on the propagation of heat in solid bodies), and publishing his Théorie analytique de la chaleur (Analytical theory of heat) in 1822. The Mémoire introduced Fourier analysis, specifically Fourier series. Through Fourier's research the fact was established that an arbitrary (at first, continuous and later generalized to any piecewise-smooth) function can be represented by a trigonometric series. The first announcement of this great discovery was made by Fourier in 1807, before the French Academy. Early ideas of decomposing a periodic function into the sum of simple oscillating functions date back to the 3rd century BC, when ancient astronomers proposed an empiric model of planetary motions, based on deferents and epicycles.
The heat equation is a partial differential equation. Prior to Fourier's work, no solution to the heat equation was known in the general case, although particular solutions were known if the heat source behaved in a simple way, in particular, if the heat source was a sine or cosine wave. These simple solutions are now sometimes called eigensolutions. Fourier's idea was to model a complicated heat source as a superposition (or linear combination) of simple sine and cosine waves, and to write the solution as a superposition of the corresponding eigensolutions. This superposition or linear combination is called the Fourier series.
This immediately gives any coefficient ak of the trigonometrical series for φ(y) for any function which has such an expansion. It works because if φ has such an expansion, then (under suitable convergence assumptions) the integral
can be carried out term-by-term. But all terms involving for j ≠ k vanish when integrated from −1 to 1, leaving only the term.
In these few lines, which are close to the modern formalism used in Fourier series, Fourier revolutionized both mathematics and physics. Although similar trigonometric series were previously used by Euler, d'Alembert, Daniel Bernoulli and Gauss, Fourier believed that such trigonometric series could represent any arbitrary function. In what sense that is actually true is a somewhat subtle issue and the attempts over many years to clarify this idea have led to important discoveries in the theories of convergence, function spaces, and harmonic analysis.
When Fourier submitted a later competition essay in 1811, the committee (which included Lagrange, Laplace, Malus and Legendre, among others) concluded: ...the manner in which the author arrives at these equations is not exempt of difficulties and...his analysis to integrate them still leaves something to be desired on the score of generality and even rigour.
Heat distribution in a metal plate, using Fourier's method
The Fourier series expansion of the sawtooth function (above) looks more complicated than the simple formula , so it is not immediately apparent why one would need the Fourier series. While there are many applications, Fourier's motivation was in solving the heat equation. For example, consider a metal plate in the shape of a square whose sides measure meters, with coordinates . If there is no heat source within the plate, and if three of the four sides are held at 0 degrees Celsius, while the fourth side, given by , is maintained at the temperature gradient degrees Celsius, for in , then one can show that the stationary heat distribution (or the heat distribution after a long period of time has elapsed) is given by
Here, sinh is the hyperbolic sine function. This solution of the heat equation is obtained by multiplying each term of Eq.6 by . While our example function seems to have a needlessly complicated Fourier series, the heat distribution is nontrivial. The function cannot be written as a closed-form expression. This method of solving the heat problem was made possible by Fourier's work.
Complex Fourier series animationEdit
Complex Fourier series tracing the letter 'e'. (The Julia source code that generates the frames of this animation is here in Appendix B.)
An example of the ability of the complex Fourier series to trace any two dimensional closed figure is shown in the adjacent animation of the complex Fourier series tracing the letter 'e' (for exponential). Note that the animation uses the variable 't' to parameterize the letter 'e' in the complex plane, which is equivalent to using the parameter 'x' in this article's subsection on complex valued functions.
In the animation's back plane, the rotating vectors are aggregated in an order that alternates between a vector rotating in the positive (counter clockwise) direction and a vector rotating at the same frequency but in the negative (clockwise) direction, resulting in a single tracing arm with lots of zigzags. This perspective shows how the addition of each pair of rotating vectors (one rotating in the positive direction and one rotating in the negative direction) nudges the previous trace (shown as a light gray dotted line) closer to the shape of the letter 'e'.
In the animation's front plane, the rotating vectors are aggregated into two sets, the set of all the positive rotating vectors and the set of all the negative rotating vectors (the non-rotating component is evenly split between the two), resulting in two tracing arms rotating in opposite directions. The animation's small circle denotes the midpoint between the two arms and also the midpoint between the origin and the current tracing point denoted by '+'. This perspective shows how the complex Fourier series is an extension (the addition of an arm) of the complex geometric series which has just one arm. It also shows how the two arms coordinate with each other. For example, as the tracing point is rotating in the positive direction, the negative direction arm stays parked. Similarly, when the tracing point is rotating in the negative direction, the positive direction arm stays parked.
In between the animation's back and front planes are rotating trapezoids whose areas represent the values of the complex Fourier series terms. This perspective shows the amplitude, frequency, and phase of the individual terms of the complex Fourier series in relation to the series sum spatially converging to the letter 'e' in the back and front planes. The audio track's left and right channels correspond respectively to the real and imaginary components of the current tracing point '+' but increased in frequency by a factor of 3536 so that the animation's fundamental frequency (n=1) is a 220 Hz tone (A220).
When the real and imaginary parts of a complex function are decomposed into their even and odd parts, there are four components, denoted below by the subscripts RE, RO, IE, and IO. And there is a one-to-one mapping between the four components of a complex time function and the four components of its complex frequency transform:
From this, various relationships are apparent, for example:
The transform of a real-valued function (sRE + sRO) is the even symmetric function SRE + iSIO. Conversely, an even-symmetric transform implies a real-valued time-domain.
The transform of an imaginary-valued function (isIE + isIO) is the odd symmetric function SRO + iSIE, and the converse is true.
The transform of an even-symmetric function (sRE + isIO) is the real-valued function SRE + SRO, and the converse is true.
The transform of an odd-symmetric function (sRO + isIE) is the imaginary-valued function iSIE + iSIO, and the converse is true.
is also -periodic, with Fourier series coefficients:
A doubly infinite sequence in is the sequence of Fourier coefficients of a function in if and only if it is a convolution of two sequences in . See 
We say that belongs to
if is a 2π-periodic function on which is times differentiable, and its derivative is continuous.
If , then the Fourier coefficients of the derivative can be expressed in terms of the Fourier coefficients of the function , via the formula .
If , then . In particular, since for a fixed we have as , it follows that tends to zero, which means that the Fourier coefficients converge to zero faster than the kth power of n for any .
One of the interesting properties of the Fourier transform which we have mentioned, is that it carries convolutions to pointwise products. If that is the property which we seek to preserve, one can produce Fourier series on any compact group. Typical examples include those classical groups that are compact. This generalizes the Fourier transform to all spaces of the form L2(G), where G is a compact group, in such a way that the Fourier transform carries convolutions to pointwise products. The Fourier series exists and converges in similar ways to the [−π,π] case.
An alternative extension to compact groups is the Peter–Weyl theorem, which proves results about representations of compact groups analogous to those about finite groups.
If the domain is not a group, then there is no intrinsically defined convolution. However, if is a compactRiemannian manifold, it has a Laplace–Beltrami operator. The Laplace–Beltrami operator is the differential operator that corresponds to Laplace operator for the Riemannian manifold . Then, by analogy, one can consider heat equations on . Since Fourier arrived at his basis by attempting to solve the heat equation, the natural generalization is to use the eigensolutions of the Laplace–Beltrami operator as a basis. This generalizes Fourier series to spaces of the type , where is a Riemannian manifold. The Fourier series converges in ways similar to the case. A typical example is to take to be the sphere with the usual metric, in which case the Fourier basis consists of spherical harmonics.
Locally compact Abelian groupsEdit
The generalization to compact groups discussed above does not generalize to noncompact, nonabelian groups. However, there is a straightforward generalization to Locally Compact Abelian (LCA) groups.
This generalizes the Fourier transform to or , where is an LCA group. If is compact, one also obtains a Fourier series, which converges similarly to the case, but if is noncompact, one obtains instead a Fourier integral. This generalization yields the usual Fourier transform when the underlying locally compact Abelian group is .
Fourier series on a squareEdit
We can also define the Fourier series for functions of two variables and in the square :
Aside from being useful for solving partial differential equations such as the heat equation, one notable application of Fourier series on the square is in image compression. In particular, the JPEG image compression standard uses the two-dimensional discrete cosine transform, a discrete form of the Fourier cosine transform, which uses only cosine as the basis function.
For two-dimensional arrays with a staggered appearance, half of the Fourier series coefficients disappear, due to additional symmetry.
Fourier series of Bravais-lattice-periodic-functionEdit
A three-dimensional Bravais lattice is defined as the set of vectors of the form:
where are integers and are three linearly independent vectors. Assuming we have some function, , such that it obeys the condition of periodicity for any Bravais lattice vector , , we could make a Fourier series of it. This kind of function can be, for example, the effective potential that one electron "feels" inside a periodic crystal. It is useful to make the Fourier series of the potential when applying Bloch's theorem. First, we may write any arbitrary position vector in the coordinate-system of the lattice:
where meaning that is defined to be the magnitude of , so is the unit vector directed along .
Thus we can define a new function,
This new function, , is now a function of three-variables, each of which has periodicity , , and respectively:
This enables us to build up a set of Fourier coefficients, each being indexed by three independent integers . In what follows, we use function notation to denote these coefficients, where previously we used subscripts. If we write a series for on the interval for , we can define the following:
And then we can write:
We can write once again as:
Finally applying the same for the third coordinate, we define:
We write as:
Now, every reciprocal lattice vector can be written (but does not mean that it is the only way of writing) as , where are integers and are reciprocal lattice vectors to satisfy ( for , and for ). Then for any arbitrary reciprocal lattice vector and arbitrary position vector in the original Bravais lattice space, their scalar product is:
So it is clear that in our expansion of , the sum is actually over reciprocal lattice vectors:
we can solve this system of three linear equations for , , and in terms of , and in order to calculate the volume element in the original rectangular coordinate system. Once we have , , and in terms of , and , we can calculate the Jacobian determinant:
which after some calculation and applying some non-trivial cross-product identities can be shown to be equal to:
(it may be advantageous for the sake of simplifying calculations, to work in such a rectangular coordinate system, in which it just so happens that is parallel to the x axis, lies in the xy-plane, and has components of all three axes). The denominator is exactly the volume of the primitive unit cell which is enclosed by the three primitive-vectors , and . In particular, we now know that
We can write now as an integral with the traditional coordinate system over the volume of the primitive cell, instead of with the , and variables:
writing for the volume element ; and where is the primitive unit cell, thus, is the volume of the primitive unit cell.
Hilbert space interpretationEdit
In the language of Hilbert spaces, the set of functions is an orthonormal basis for the space of square-integrable functions on . This space is actually a Hilbert space with an inner product given for any two elements and by:
where is the complex conjugate of
The basic Fourier series result for Hilbert spaces can be written as
Sines and cosines form an orthogonal set, as illustrated above. The integral of sine, cosine and their product is zero (green and red areas are equal, and cancel out) when , or the functions are different, and π only if and are equal, and the function used is the same. They would form an orthonormal set, if the integral equaled 1 (that is, each function would need to be scaled by ).
This corresponds exactly to the complex exponential formulation given above. The version with sines and cosines is also justified with the Hilbert space interpretation. Indeed, the sines and cosines form an orthogonal set:
furthermore, the sines and cosines are orthogonal to the constant function . An orthonormal basis for consisting of real functions is formed by the functions and , with n= 1,2,.... The density of their span is a consequence of the Stone–Weierstrass theorem, but follows also from the properties of classical kernels like the Fejér kernel.
Fourier theorem proving convergence of Fourier seriesEdit
These theorems, and informal variations of them that don't specify the convergence conditions, are sometimes referred to generically as Fourier's theorem or the Fourier theorem.
Theorem — The trigonometric polynomial is the unique best trigonometric polynomial of degree approximating , in the sense that, for any trigonometric polynomial of degree , we have:
where the Hilbert space norm is defined as:
Because of the least squares property, and because of the completeness of the Fourier basis, we obtain an elementary convergence result.
Theorem — If belongs to (an interval of length ), then converges to in , that is, converges to 0 as .
We have already mentioned that if is continuously differentiable, then is the Fourier coefficient of the derivative . It follows, essentially from the Cauchy–Schwarz inequality, that is absolutely summable. The sum of this series is a continuous function, equal to , since the Fourier series converges in the mean to :
This result can be proven easily if is further assumed to be , since in that case tends to zero as . More generally, the Fourier series is absolutely summable, thus converges uniformly to , provided that satisfies a Hölder condition of order . In the absolutely summable case, the inequality:
Since Fourier series have such good convergence properties, many are often surprised by some of the negative results. For example, the Fourier series of a continuous T-periodic function need not converge pointwise. The uniform boundedness principle yields a simple non-constructive proof of this fact.
In 1922, Andrey Kolmogorov published an article titled Une série de Fourier-Lebesgue divergente presque partout in which he gave an example of a Lebesgue-integrable function whose Fourier series diverges almost everywhere. He later constructed an example of an integrable function whose Fourier series diverges everywhere (Katznelson 1976).
^In this context, synthesis is the creation of a complex waveform by summation of simpler waveforms.
^Some texts define P=2π to simplify the sinusoid's argument at the expense of generality.
^The scale factor which could be inserted later, results in a series that converges to instead of
^Some authors define differently than Rather their scale factor is just and that of course changes Eq.4 accordingly.
^Since the integral defining the Fourier transform of a periodic function is not convergent, it is necessary to view the periodic function and its transform as distributions. In this sense is a Dirac delta function, which is an example of a distribution.
^These words are not strictly Fourier's. Whilst the cited article does list the author as Fourier, a footnote indicates that the article was actually written by Poisson (that it was not written by Fourier is also clear from the consistent use of the third person to refer to him) and that it is, "for reasons of historical interest", presented as though it were Fourier's original memoire.
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^Vanishing of Half the Fourier Coefficients in Staggered Arrays
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