Fourier series

A Fourier series (/ˈfʊri, -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. Well-behaved 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.

Fourier series are closely related to the Fourier transform, which can be used to find the frequency information for functions that are not periodic. The Fourier transform can be derived as the Fourier series of an aperiodic function (which periodicity ); the Fourier series of this function becomes a Riemann sum, eventually to be the Fourier transform as a Riemann integral.[3] 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 real-valued functions of real arguments, and used the sine and cosine functions in 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.

Common forms of the Fourier seriesEdit

The Fourier series can be represented in different forms. The sine-cosine form, exponential form, and amplitude-phase form are expressed here for a periodic function   with periodicity of P.

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 Fourier series can be thought of as analyzing the periodic extension (bottom graph) of the original function. The Fourier series is always a periodic function, even if original function s(x) wasn't.

Sine-cosine formEdit

The Fourier series coefficients[4] are defined by the integrals:

Fourier series coefficients





(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:

Fourier series





(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:

  • a wavelength equal to   and having the same units as  .
  • a frequency equal to   and having reciprocal units as  .


Plot of the sawtooth wave, a periodic continuation of the linear function   on the interval  
Animated plot of the first five successive partial Fourier series

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:







When   is an odd multiple of  , the Fourier series converges to 0, which is the half-sum of the left- and right-limit 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.

Exponential formEdit

It is possible to simplify the integrals for the Fourier series coefficients by using Euler's formula.

With the definitions

Complex Fourier series coefficients





(Eq. 3)

By substituting equation Eq. 1 into Eq. 3 it can be shown that:[5]

Complex Fourier series coefficients


Given the Complex Fourier series coefficients, it is possible to recover   and   from the formulas

Complex Fourier series coefficients


With these definitions the Fourier series is written as:

Fourier series, exponential form





(Eq. 4)

This is the customary form for generalizing to Complex-valued functions. Negative values of   correspond to negative frequency. (Also see Fourier transform § Negative frequency).

Amplitude-phase formEdit

The Fourier series in amplitude-phase form is:

Fourier series, amplitude-phase form





(Eq. 5)

  • Its   harmonic is  .
  •   is the   harmonic's amplitude and   is its phase shift.
  • The fundamental frequency of   is the term for when   equals 1, and can be referred to as the   harmonic.
  •   is sometimes called the   harmonic or DC component. It is the mean value of  .

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 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:






(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 non-periodic 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:

Equivalence of polar and rectangular forms





(Eq. 7)

Combining this with Eq. 6 gives:


which introduces the definitions of   and  .[6]  And we note for later reference that   and   can be simplified:

The derivative of   is zero at the phase of maximum correlation.
And the correlation peak value is:

Therefore   and   are the rectangular coordinates of a vector with polar coordinates   and  

Extensions to non-periodic functionsEdit

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.

Partial Sum OperatorEdit

Frequently when describing how Fourier series behave, authors introduce the partial sum operator   for a function  .[7]






(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.


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 better-behaved 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  .[8] If a function is square-integrable 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.

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:


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]


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.[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[9] and later generalized to any piecewise-smooth[10]) 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.[11] 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[12] and Bernhard Riemann[13][14][15] 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,[16] shell theory,[17] etc.


Joseph Fourier wrote:[dubious ]


Multiplying both sides by  , and then integrating from   to   yields:


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 jk 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.[citation needed]

Fourier's motivationEdit

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[19] 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).

Other applicationsEdit

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.

Table of common Fourier seriesEdit

Some common pairs of periodic functions and their Fourier series coefficients are shown in the table below.

  •   designates a periodic function with period  .
  •   designate the Fourier series coefficients (sine-cosine form) of the periodic function  .
Time domain
Plot Frequency domain (sine-cosine form)
Remarks Reference
    Full-wave rectified sine [20]: p. 193 
    Half-wave rectified sine [20]: p. 193 
    [20]: p. 192 
    [20]: p. 192 
    [20]: p. 193 

Table of basic propertiesEdit

This table shows some mathematical operations in the time domain and the corresponding effect in the Fourier series coefficients. Notation:

  • Complex conjugation is denoted by an asterisk.
  •   designate  -periodic functions or functions defined only for  
  •   designate the Fourier series coefficients (exponential form) of   and  
Property Time domain Frequency domain (exponential form) Remarks Reference
Time reversal / Frequency reversal     [21]: p. 610 
Time conjugation     [21]: 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       [21]: p. 610 
Shift in frequency / Modulation in time       [21]: p. 610 

Symmetry propertiesEdit

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:[22]


From this, various relationships are apparent, for example:

  • The transform of a real-valued function (sRE + sRO) is the even symmetric function SRE + i SIO. Conversely, an even-symmetric transform implies a real-valued time-domain.
  • The transform of an imaginary-valued function (i sIE + i sIO) is the odd symmetric function SRO + i SIE, and the converse is true.
  • The transform of an even-symmetric function (sRE + i sIO) is the real-valued function SRE + SRO, and the converse is true.
  • The transform of an odd-symmetric function (sRO + i sIE) is the imaginary-valued function i SIE + i SIO, and the converse is true.

Other propertiesEdit

Riemann–Lebesgue lemmaEdit

If   is integrable,  ,   and   This result is known as the Riemann–Lebesgue lemma.

Parseval's theoremEdit

If   belongs to   (periodic over an interval of length  ) then:  

If   belongs to   (periodic over an interval of length  ), and   is of a finite-length   then:[23]

for  , then  

and for  , then  

Plancherel's theoremEdit

If   are coefficients and   then there is a unique function   such that   for every  .

Convolution theoremsEdit

Given  -periodic functions,   and   with Fourier series coefficients   and    

  • The pointwise product:
    is also  -periodic, and its Fourier series coefficients are given by the discrete convolution of the   and   sequences:
  • The periodic convolution:
    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 [24]

Derivative propertyEdit

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  .

Compact groupsEdit

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.

The atomic orbitals of chemistry are partially described by spherical harmonics, which can be used to produce Fourier series on the sphere.

Riemannian manifoldsEdit

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.

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.[25]

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:


Further defining:


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:

(where δmn is the Kronecker delta), and
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.[26][27][28][29]

The earlier Eq.7

is a trigonometric polynomial of degree   that can be generally expressed as:

Least squares propertyEdit

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:

where the Hilbert space norm is defined as:

Convergence theoremsEdit

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  :

Theorem — If  , then   converges to   uniformly (and hence also pointwise.)

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 T-periodic function need not converge pointwise.[citation needed] 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).

See alsoEdit


  1. ^ Some authors define   differently so that the same integral can be used to define   and  . This changes Eq. 2 so that the first term needs to be divided by 2, and is no longer the average value.
  2. ^ The scale factor   which could be inserted later, results in a series that converges to   instead of  
  3. ^ 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.
  4. ^ These three did some important early work on the wave equation, especially D'Alembert. Euler's work in this area was mostly comtemporaneous/ in collaboration with Bernoulli, although the latter made some independent contributions to the theory of waves and vibrations. (See Fetter & Walecka 2003, pp. 209–210).
  5. ^ 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.


  1. ^ "Fourier". Unabridged (Online). n.d.
  2. ^ Zygmund, A. (2002). Trigonometric Series (3nd ed.). Cambridge, UK: Cambridge University Press. ISBN 0-521-89053-5.
  3. ^ Khare, Kedar; Butola, Mansi; Rajora, Sunaina (2023). "Chapter 2.3 Fourier Transform as a Limiting Case of Fourier Series". Fourier Optics and Computational Imaging (2nd ed.). Springer. pp. 13–14. doi:10.1007/978-3-031-18353-9. ISBN 978-3-031-18353-9.
  4. ^ Haberman, Richard (1987). Elementary Applied Partial Differential Equations (2nd ed.). Englewood Cliffs, New Jersey: Prentice Hall. p. 77. ISBN 0-13-252875-4.
  5. ^ Pinkus, Allan; Zafrany, Samy (1997). Fourier Series and Integral Transforms (1st ed.). Cambridge, UK: Cambridge University Press. pp. 42–44. ISBN 0-521-59771-4.
  6. ^ Dorf, Richard C.; Tallarida, Ronald J. (1993). Pocket Book of Electrical Engineering Formulas (1st ed.). Boca Raton,FL: CRC Press. pp. 171–174. ISBN 0849344735.
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Further readingEdit

  • William E. Boyce; Richard C. DiPrima (2005). Elementary Differential Equations and Boundary Value Problems (8th ed.). New Jersey: John Wiley & Sons, Inc. ISBN 0-471-43338-1.
  • Joseph Fourier, translated by Alexander Freeman (2003). The Analytical Theory of Heat. Dover Publications. ISBN 0-486-49531-0. 2003 unabridged republication of the 1878 English translation by Alexander Freeman of Fourier's work Théorie Analytique de la Chaleur, originally published in 1822.
  • Enrique A. Gonzalez-Velasco (1992). "Connections in Mathematical Analysis: The Case of Fourier Series". American Mathematical Monthly. 99 (5): 427–441. doi:10.2307/2325087. JSTOR 2325087.
  • Fetter, Alexander L.; Walecka, John Dirk (2003). Theoretical Mechanics of Particles and Continua. Courier. ISBN 978-0-486-43261-8.
  • Felix Klein, Development of mathematics in the 19th century. Mathsci Press Brookline, Mass, 1979. Translated by M. Ackerman from Vorlesungen über die Entwicklung der Mathematik im 19 Jahrhundert, Springer, Berlin, 1928.
  • Walter Rudin (1976). Principles of mathematical analysis (3rd ed.). New York: McGraw-Hill, Inc. ISBN 0-07-054235-X.
  • A. Zygmund (2002). Trigonometric Series (third ed.). Cambridge: Cambridge University Press. ISBN 0-521-89053-5. The first edition was published in 1935.

External linksEdit

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