Fourier transforms 

A Fourier series (/ˈfʊrieɪ, iər/^{[1]}) is an expansion of a periodic function into a sum of trigonometric functions. The Fourier series is an example of a trigonometric series, but not all trigonometric series are Fourier series.^{[2]} By expressing a function as a sum of sines and cosines, many problems involving the function become easier to analyze because trigonometric functions are well understood. For example, Fourier series were first used by Joseph Fourier to find solutions to the heat equation. This application is possible because the derivatives of trigonometric functions fall into simple patterns. Fourier series cannot be used to approximate arbitrary functions, because most functions have infinitely many terms in their Fourier series, and the series do not always converge. Wellbehaved functions, for example smooth functions, have Fourier series that converge to the original function. The coefficients of the Fourier series are determined by integrals of the function multiplied by trigonometric functions, described in Common forms of the Fourier series below.
The study of the convergence of Fourier series focus on the behaviors of the partial sums, which means studying the behavior of the sum as more and more terms from the series are summed. The figures below illustrate some partial Fourier series results for the components of a square wave.
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 frequencydomain representation that reveals the amplitudes of the summed sine waves.
Fourier series are closely related to the Fourier transform, which can be used to find the frequency information for functions that are not periodic. Periodic functions can be identified with functions on a circle, for this reason Fourier series are the subject of Fourier analysis on a circle, usually denoted as or . The Fourier transform is also part of Fourier analysis, but is defined for functions on .
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 realvalued functions of real arguments, and used the sine and cosine functions in the decomposition. Many other Fourierrelated transforms have since been defined, extending his initial idea to many applications and birthing an area of mathematics called Fourier analysis.
The Fourier series can be represented in different forms. The sinecosine form, exponential form, and amplitudephase form are expressed here for a periodic function with periodicity of P.
The Fourier series coefficients^{[3]} are defined by the integrals:

(Eq. 1) 
It is notable that is the average value of the function . This is a property that extends to similar transforms such as the Fourier transform.^{[A]}
With these coefficients defined the Fourier series is:

(Eq. 2) 
Many others use the symbol, because it is not always true that the sum of the Fourier series is equal to . It can fail to converge entirely, or converge to something that differs from . While these situations can occur, their differences are rarely a problem in science and engineering, and authors in these disciplines will sometimes write Eq. 2 with replaced by .
The integer index in the Fourier series coefficients is the number of cycles the corresponding or from the series make in the function's period . Therefore the terms corresponding to and have:
Consider a sawtooth function:
In this case, the Fourier coefficients are given by
It can be shown that the Fourier series converges to at every point where is differentiable, and therefore:

(Eq.8) 
When is an odd multiple of , the Fourier series converges to 0, which is the halfsum of the left and rightlimit of s at . This is a particular instance of the Dirichlet theorem for Fourier series.
This example leads to a solution of the Basel problem.
It is possible to simplify the integrals for the Fourier series coefficients by using Euler's formula.
With the definitions

(Eq. 3) 
By substituting equation Eq. 1 into Eq. 3 it can be shown that:^{[4]}
Given the complex Fourier series coefficients, it is possible to recover and from the formulas
With these definitions the Fourier series is written as:

(Eq. 4) 
This is the customary form for generalizing to complexvalued functions. Negative values of correspond to negative frequency. (Also see Fourier transform § Negative frequency).
The Fourier series in amplitudephase form is:

(Eq. 5) 
Clearly Eq. 5 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 nonsinusoidal functions because of the potentially infinite number of terms ().
The coefficients and can be understood and derived in terms of the crosscorrelation between and a sinusoid at frequency . For a general frequency and an analysis interval the crosscorrelation function:

(Eq. 6) 
is essentially a matched filter, with template .^{[B]} Here denotes If is periodic, is arbitrary, often chosen to be or But in general, the Fourier series can also be used to represent a nonperiodic function on just a finite interval, as depicted in Fig.1.
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:
Combining this with Eq. 6 gives:
which introduces the definitions of and .^{[5]} And we note for later reference that and can be simplified:
Therefore and are the rectangular coordinates of a vector with polar coordinates and
Fourier series can also be applied to functions that are not necessarily periodic. The simplest extension occurs when the function is defined only in a fixed interval . In this case the integrals defining the Fourier coefficients can be taken over this interval. In this case all of the convergence results will be the same as for the periodic extension of to the whole real line. In particular, it may happen that for a continuous function there is a discontinuity in the periodic extension of at and . In this case, it is possible to see Gibbs phenomenon at the end points of the interval.
For functions which have compact support, meaning that values of are defined everywhere but identically zero outside some fixed interval , the Fourier series can be taken on any interval containing the support .
For both the cases above, it is sometimes desirable to take an even or odd reflection of the function, or extend it by zero in the case the function is only defined on a finite interval. This allows one to prescribe desired properties for the Fourier coefficients. For example, by making the function even you ensure . This is often known as a cosine series. One may similarly arrive at a sine series.
In the case where the function doesn't have compact support and is defined on entire real line, one can use the Fourier transform. Fourier series can be taken for a truncated version of the function or to the periodic summation.
Frequently when describing how Fourier series behave, authors introduce the partial sum operator for a function .^{[6]}

(Eq. 8) 
Where are the Fourier coefficients of . Unlike series in calculus, it is important that the partial sums are taken symmetrically for Fourier series, otherwise convergence results may not hold.
Main article: Convergence of Fourier series 
A proof that a Fourier series is a valid representation of any periodic function (that satisfies the Dirichlet conditions) is overviewed in § Fourier theorem proving convergence of Fourier series.
In engineering applications, the Fourier series is generally assumed to converge except at jump discontinuities since the functions encountered in engineering are betterbehaved than functions encountered in other disciplines. 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 .^{[7]} If a function is squareintegrable on the interval , then the Fourier series converges to the function at almost everywhere. It is possible to define Fourier coefficients for more general functions or distributions, in which case point wise convergence often fails, and convergence in norm or weak convergence is usually studied.
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.
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:
where represents a continuous frequency domain. When variable has units of seconds, has units of hertz. The "teeth" of the comb are spaced at multiples (i.e. harmonics) of , which is called the fundamental frequency. can be recovered from this representation by an inverse Fourier transform:
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.^{[C]}
See also: Fourier analysis § History 
The Fourier series is named in honor of JeanBaptiste 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.^{[D]} 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^{[8]} and later generalized to any piecewisesmooth^{[9]}) 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.^{[10]} 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.
From a modern point of view, Fourier's results are somewhat informal, due to the lack of a precise notion of function and integral in the early nineteenth century. Later, Peter Gustav Lejeune Dirichlet^{[11]} and Bernhard Riemann^{[12]}^{[13]}^{[14]} expressed Fourier's results with greater precision and formality.
Although the original motivation was to solve the heat equation, it later became obvious that the same techniques could be applied to a wide array of mathematical and physical problems, and especially those involving linear differential equations with constant coefficients, for which the eigensolutions are sinusoids. The Fourier series has many such applications in electrical engineering, vibration analysis, acoustics, optics, signal processing, image processing, quantum mechanics, econometrics,^{[15]} shell theory,^{[16]} etc.
Joseph Fourier wrote:^{[dubious – discuss]}
Multiplying both sides by , and then integrating from to yields:
— Joseph Fourier, Mémoire sur la propagation de la chaleur dans les corps solides. (1807)^{[17]}^{[E]}
This immediately gives any coefficient a_{k} 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
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.^{[citation needed]}
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 closedform expression. This method of solving the heat problem was made possible by Fourier's work.
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 nonrotating 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).
Another application is to solve the Basel problem by using Parseval's theorem. The example generalizes and one may compute ζ(2n), for any positive integer n.
Some common pairs of periodic functions and their Fourier series coefficients are shown in the table below.
Time domain 
Plot  Frequency domain (sinecosine form) 
Remarks  Reference 

Fullwave rectified sine  ^{[19]}^{: p. 193 }  
Halfwave rectified sine  ^{[19]}^{: p. 193 }  
^{[19]}^{: p. 192 }  
^{[19]}^{: p. 192 }  
^{[19]}^{: p. 193 } 
This table shows some mathematical operations in the time domain and the corresponding effect in the Fourier series coefficients. Notation:
Property  Time domain  Frequency domain (exponential form)  Remarks  Reference 

Linearity  
Time reversal / Frequency reversal  ^{[20]}^{: p. 610 }  
Time conjugation  ^{[20]}^{: p. 610 }  
Time reversal & conjugation  
Real part in time  
Imaginary part in time  
Real part in frequency  
Imaginary part in frequency  
Shift in time / Modulation in frequency  ^{[20]}^{: p. 610 }  
Shift in frequency / Modulation in time  ^{[20]}^{: p. 610 } 
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 onetoone mapping between the four components of a complex time function and the four components of its complex frequency transform:^{[21]}
From this, various relationships are apparent, for example:
If is integrable, , and This result is known as the Riemann–Lebesgue lemma.
Main article: Parseval's theorem 
If belongs to (periodic over an interval of length ) then:
An extension of Parseval's theorem to ; If belongs to (periodic over an interval of length ), and is of a finitelength then:^{[22]}
for , then
and for , then
Main article: Plancherel theorem 
If are coefficients and then there is a unique function such that for every .
Main article: Convolution theorem § Periodic convolution (Fourier series coefficients) 
Given periodic functions, and with Fourier series coefficients and
We say that belongs to if is a 2πperiodic function on which is times differentiable, and its derivative is continuous.
Main articles: Compact group, Lie group, and Peter–Weyl theorem 
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 L^{2}(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.
Main articles: Laplace operator and Riemannian manifold 
If the domain is not a group, then there is no intrinsically defined convolution. However, if is a compact Riemannian 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.
Main article: Pontryagin duality 
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 .
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 twodimensional discrete cosine transform, a discrete form of the Fourier cosine transform, which uses only cosine as the basis function.
For twodimensional arrays with a staggered appearance, half of the Fourier series coefficients disappear, due to additional symmetry.^{[24]}
A threedimensional Bravais lattice is defined as the set of vectors of the form:
Thus we can define a new function,
This new function, , is now a function of threevariables, 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:
Further defining:
We can write once again as:
Finally applying the same for the third coordinate, we define:
We write as:
Rearranging:
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:
where
Assuming
(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 xyplane, and has components of all three axes). The denominator is exactly the volume of the primitive unit cell which is enclosed by the three primitivevectors , 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:
Main article: Hilbert space 
In the language of Hilbert spaces, the set of functions is an orthonormal basis for the space of squareintegrable functions on . This space is actually a Hilbert space with an inner product given for any two elements and by:
The basic Fourier series result for Hilbert spaces can be written as
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:
Main article: Convergence of Fourier series 
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.^{[25]}^{[26]}^{[27]}^{[28]}
The earlier Eq.7
Parseval's theorem implies that:
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:
See also: Gibbs phenomenon 
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:
proves uniform convergence.
Many other results concerning the convergence of Fourier series are known, ranging from the moderately simple result that the series converges at if is differentiable at , to Lennart Carleson's much more sophisticated result that the Fourier series of an function actually converges almost everywhere.
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 Tperiodic function need not converge pointwise.^{[citation needed]} The uniform boundedness principle yields a simple nonconstructive proof of this fact.
In 1922, Andrey Kolmogorov published an article titled Une série de FourierLebesgue divergente presque partout in which he gave an example of a Lebesgueintegrable function whose Fourier series diverges almost everywhere. He later constructed an example of an integrable function whose Fourier series diverges everywhere (Katznelson 1976).