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Fourier series

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**Fourier series**({{IPAc-en|Ëˆ|f|ÊŠr|i|eÉª|,_|-|i|É™r}}){{Dictionary.com|Fourier}} is a periodic function composed of harmonically related sinusoids, combined by a weighted summation. With appropriate weights, 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). As such, the summation is a

**synthesis**of another function. The discrete-time Fourier transform is an example of synthesis. The process of deriving the weights that describe a given function is a form of Fourier

**analysis**. For functions on unbounded intervals, the analysis and synthesis analogies are Fourier transform and inverse transform.

## History{{anchor|Historical development}}

{{see also|Fourier analysis#History}}The Fourier series is named in honour of Jean-Baptiste Joseph Fourier (1768â€“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.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 here, pg.s 209 & 210, ). Fourier introduced the series for the purpose of solving the heat equation in a metal plate, publishing his initial results in his 1807*MÃ©moire sur la propagation de la chaleur dans les corps solides*(

*Treatise on the propagation of heat in solid bodies*), and publishing his

*ThÃ©orie analytique de la chaleur*(

*Analytical theory of heat*) in 1822. The

*MÃ©moire*introduced Fourier analysis, specifically Fourier series. Through Fourier's research the fact was established that an arbitrary (continuous)John Stillwell, "Logic and Philosophy of mathematics in the nineteenth century,"

*Routledge History of Philosophy*Volume VII (2013) p. 204. 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.Florian Cajori,

*A History of Mathematics*(1893) p. 283. 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 DirichletJOURNAL, Lejeune-Dirichlet, P., List of important publications in mathematics#Sur la convergence des sÃ©ries trigonomÃ©triques qui servent Ã reprÃ©senter une fonction arbitraire entre des limites donnÃ©es, Sur la convergence des sÃ©ries trigonomÃ©triques qui servent Ã reprÃ©senter une fonction arbitraire entre des limites donnÃ©es, French, On the convergence of trigonometric series which serve to represent an arbitrary function between two given limits, Journal fÃ¼r die reine und angewandte Mathematik, 4, 1829, 157â€“169, and Bernhard RiemannWEB,weblink Ueber die Darstellbarkeit einer Function durch eine trigonometrische Reihe, German, Habilitationsschrift, GÃ¶ttingen; 1854. Abhandlungen der GÃ¶ttingen Academy of Sciences, KÃ¶niglichen Gesellschaft der Wissenschaften zu GÃ¶ttingen, vol. 13, 1867''. Published posthumously for Riemann by Richard Dedekind, About the representability of a function by a trigonometric series, 19 May 2008,weblink" title="web.archive.org/web/20080520085248weblink">weblink 20 May 2008, no, D. Mascre, Bernhard Riemann: Posthumous Thesis on the Representation of Functions by Trigonometric Series (1867). Landmark Writings in Western Mathematics 1640â€“1940, Ivor Grattan-Guinness (ed.); pg. 492. Elsevier, 20 May 2005. Accessed 7 Dec 2012.Theory of Complex Functions: Readings in Mathematics, by Reinhold Remmert; pg 29. Springer, 1991. Accessed 7 Dec 2012. 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,BOOK, Marc, Nerlove, David M., Grether, Jose L., Carvalho, 1995, Analysis of Economic Time Series. Economic Theory, Econometrics, and Mathematical Economics, Elsevier, 0-12-515751-7, thin-walled shell theory,BOOK, Wilhelm, Flugge, 1957, Statik und Dynamik der Schalen, Springer-Verlag, Berlin, etc.

## Definition

A Fourier series is a representation of a periodic function as a sum of orthogonal sinusoids, each with an integer number of cycles in the period of the function. In general, the series has an infinite number of such*harmonics*. We begin with a simple example and then generalize it.Consider a function, s(x), that is integrable on the interval x in [0,1]. The analysis process determines the weights (known as

**Fourier coefficients**), indexed by integer n, which is also the number of cycles of the nth harmonic in the analysis interval:

a_n = 2 int_0^1 s(x)cos(2pi n x);dxquad text{and}quad b_n = 2 int_0^1 s(x)sin(2pi n x);dx,

which for *n*= 0 reduces to just: a_0 = 2 int_0^1 s(x);dxquad text{and}quad b_0 =0.Then the synthesis process (the actual Fourier series) is:

s(x) sim frac{1}{2}a_0+sum_{n=1}^infty[a_ncos(2pi n x)+b_nsin(2pi n x)],

where "sim" indicates that the series may or may not converge or exactly equate to s(x) for all x in [0,1]. A single point discontinuity is an example. But for the "well-behaved" functions typical of physical processes, equality is customarily assumed.### A more general definition

The example given above uses the**sine-cosine form**of Fourier series. In this section we revisit that one and two other forms:

**amplitude-phase**and

**exponential**.Again consider s(x), a real-valued function, integrable on an interval [ x_0 , x_0 + P], for real numbers x_0 and P. We wish to represent s in that interval as an infinite sum, or series, of harmonically related sinusoidal functions. Outside the interval, the series is periodic with period P (frequency 1/P). It follows that if s also has that property, the approximation is valid on the entire real line. We begin with a finite summation (or

*partial sum*), in the

**amplitude-phase**form

**:**{{Gallery|width=150 | height=150 |lines=2 |align=right< 0)

**:**{{Equation box 1|indent =|title=< x < pi,

s(x + 2pi k) = s(x), quad mathrm{for } -pi < x < pi text{ and } k in mathbb{Z} .

In this case, the Fourier coefficients are given by
begin{align}

a_n & = frac{1}{pi}int_{-pi}^{pi}s(x) cos(nx),dx = 0, quad n ge 0. [4pt]b_n & = frac{1}{pi}int_{-pi}^{pi}s(x) sin(nx), dx[4pt]&= -frac{2}{pi n}cos(npi) + frac{2}{pi^2 n^2}sin(npi)[4pt]&= frac{2,(-1)^{n+1}}{pi n}, quad n ge 1.end{align}It can be proven that Fourier series converges to s(x) at every point x where s is differentiable, and therefore:{{NumBlk|:|begin{align}s(x) &= frac{a_0}{2} + sum_{n=1}^infty left[a_ncosleft(nxright)+b_nsinleft(nxright)right] [4pt]&=frac{2}{pi}sum_{n=1}^infty frac{(-1)^{n+1}}{n} sin(nx), quad mathrm{for} quad x - pi notin 2 pi mathbb{Z}.end{align}< x le P . - F[n], G[n] designate the Fourier series coefficients (exponential form) of f and g as defined in equation {{EquationNote|Eq.5}}.

The first four partial sums of the Fourier series for a square wave|File:SquareWaveFourierArrows%2Crotated.gif
}}(File:Fourier series and transform.gif|frame|right|Function s(x) (in red) is a sum of six sine functions of different amplitudes and harmonically related frequencies. Their summation is called a Fourier series. The Fourier transform, S(f) (in blue), which depicts amplitude vs frequency, reveals the 6 frequencies (at odd harmonics) and their amplitudes (1/odd number).){{Equation box 1|indent =|title= | {{EquationRef|Eq.1}}}}|cellpadding= 6|border|border colour = #0073CF|background colour=#F5FFFA}}Here, triangleq means "equal by definition," sometimes denoted := or stackrel{text{def}}{=}.s_N(x) is a periodic function with period P. The coefficients A_n and varphi_n denote the amplitude and phase of the nth oscillation. The sine-cosine form follows from this trigonometric identity:
sinleft(tfrac{2pi nx}{P}+varphi_nright) equiv sin(varphi_n) cosleft(tfrac{2pi nx}{P}right) + cos(varphi_n) sinleft(tfrac{2pi nx}{P}right),
and these definitions: a_0 triangleq A_0,quad a_n triangleq A_n sin(varphi_n),quad b_n triangleq A_n cos(varphi_n)(and conversely: A_n=sqrt{a_n^2+b_n^2} quad varphi_n = operatorname{arctan2}(a_n,b_n) ):{{Equation box 1|indent =|title= | |begin{align}s_N(x) &= a_0/2 + sum_{n=1}^N left(a_n cosleft(tfrac{2pi nx}{P}right) + b_n sinleft(tfrac{2pi nx}{P}right)right).end{align} | Eq.2}}}}|cellpadding= 6|border|border colour = #0073CF|background colour=#F5FFFA}}The exponential form follows from this identity (where asterisk denotes complex conjugation):
## frac{1}{2i}cdot e^{i left(tfrac{2pi nx}{P}+varphi_nright)}+left(frac{1}{2i}cdot e^{i left(tfrac{2pi nx}{P}+varphi_nright)}right)^*&=left(frac{1}{2i} e^{i varphi_n}right) cdot e^{i tfrac{2pi (+n)x}{P}}
+left(frac{1}{2i} e^{i varphi_n}right)^* cdot e^{i tfrac{2pi (-n)x}{P}},
end{align}and these definitions: c_0 triangleq A_0/2,quad c_n triangleq frac{A_n}{2i} e^{i varphi_n} (text{for } n > 0),quad c_n triangleq c_{|n|}^* (text{for } n | |begin{align}s_N(x) &= sum_{n=-N}^N c_ncdot e^{i tfrac{2pi nx}{P}}.end{align} | Eq.3}}}}|cellpadding= 6|border|border colour = #0073CF|background colour=#F5FFFA}}When the coefficients are computed as follows:BOOK, Dorf, Richard C., Ronald J., Tallarida, Pocket Book of Electrical Engineering Formulas, CRC Press, 1, 1993-07-15, Boca Raton,FL, 171â€“174, 0849344735, {{Equation box 1|indent =|title= | |begin{align}a_n &= frac{2}{P}int_{x_0}^{x_0+P} s(x)cdot cosleft(tfrac{2pi nx}{P}right) dxb_n &= frac{2}{P}int_{x_0}^{x_0+P} s(x)cdot sinleft(tfrac{2pi nx}{P}right) dx,end{align} | Eq.4}}}}|cellpadding= 6|border|border colour = #0073CF|background colour=#F5FFFA}}or:{{Equation box 1|indent =|title= | {{EquationRef|Eq.5}}}}|cellpadding= 6|border|border colour = #0073CF|background colour=#F5FFFA}}s_N(x) approximates s(x) on [x_0, x_0+P], and the approximation improves as {{math|N â†’ âˆž}}. The infinite sum, s_{infty}(x), is called the Fourier series representation of s.## Complex-valued functionsIf s(x) is a complex-valued function of a real variable x,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:
c_{_{Rn}} = frac{1}{P}int_{x_0}^{x_0+P} operatorname{Re}{s(x)}cdot e^{-i tfrac{2pi nx}{P}} dx and c_{_{In}} = frac{1}{P}int_{x_0}^{x_0+P} operatorname{Im}{s(x)}cdot e^{-i tfrac{2pi nx}{P}} dx
## sum_{n-N}^N left(c_{_{Rn}}+icdot c_{_{In}}right) cdot e^{ i tfrac{2pi nx}{P}}.Defining c_n triangleq c_{_{Rn}}+icdot c_{_{In}} yields:{{Equation box 1|indent =|title= | |s_N(x) = sum_{n=-N}^N c_n cdot e^{ i tfrac{2pi nx}{P}}. | Eq.6}}}}|cellpadding= 6|border|border colour = #0073CF|background colour=#F5FFFA}}This is identical to {{EquationNote|Eq.3}} except c_n and c_{-n} are no longer complex conjugates. The formula for c_n is also unchanged (see {{EquationNote|Eq.5}}):
## Other common notationsThe notation c_n is inadequate for discussing the Fourier coefficients of several different functions. Therefore, it is customarily replaced by a modified form of the function (s, in this case), such as hat{s}(n) or S[n], and functional notation often replaces subscripting:
begin{align}
s_infty(x) &= sum_{n=-infty}^infty hat{s}(n)cdot e^{i,2pi nx/P} [6pt]&= sum_{n=-infty}^infty S[n]cdot e^{j,2pi nx/P} && scriptstyle mathsf{common engineering notation}end{align}In engineering, particularly when the variable x 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:
S(f) triangleq sum_{n=-infty}^infty S[n]cdot delta left(f-frac{n}{P}right),
where f represents a continuous frequency domain. When variable x has units of seconds, f has units of hertz. The "teeth" of the comb are spaced at multiples (i.e. harmonics) of 1/P, which is called the fundamental frequency. s_{infty}(x) can be recovered from this representation by an inverse Fourier transform:
begin{align}
mathcal{F}^{-1}{S(f)} &= int_{-infty}^infty left( sum_{n=-infty}^infty S[n]cdot delta left(f-frac{n}{P}right)right) e^{i 2 pi f x},df, [6pt]&= sum_{n=-infty}^infty S[n]cdot int_{-infty}^infty deltaleft(f-frac{n}{P}right) e^{i 2 pi f x},df, [6pt]&= sum_{n=-infty}^infty S[n]cdot e^{i,2pi nx/P} triangleq s_infty(x).end{align}The constructed function S(f) 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.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 mathcal{F} left{ e^{i frac{2pi nx}{P} } right} is a Dirac delta function, which is an example of a distribution.## ConvergenceIn engineering applications, the Fourier series is generally presumed to converge everywhere except at discontinuities, since the functions encountered in engineering are more well behaved than the ones that mathematicians can provide as counter-examples to this presumption. In particular, if s is continuous and the derivative of s(x) (which may not exist everywhere) is square integrable, then the Fourier series of s converges absolutely and uniformly to s(x).BOOK, Fourier Series, Georgi P. Tolstov, Courier-Dover, 1976, 0-486-63317-9,weblink If a function is square-integrable on the interval [x_0,x_0+P], then the Fourier series converges to the function at almost every point. Convergence of Fourier series also depends on the finite number of maxima and minima in a function which is popularly known as one of the Dirichlet's condition for Fourier series. See Convergence of Fourier series. 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.Fourier_series_square_wave_circles_animation.gif|Another visualisation of an approximation of a square wave by taking the first 1, 2, 3 and 4 terms of its Fourier series. (An interactive animation can be seen here)Fourier_series_sawtooth_wave_circles_animation.gif|A visualisation of an approximation of a sawtooth wave of the same amplitude and frequency for comparisonExample_of_Fourier_Convergence.gif |Example of convergence to a somewhat arbitrary function. Note the development of the "ringing" (Gibbs phenomenon) at the transitions to/from the vertical sections.## Examples## Example 1: a simple Fourier seriesFile:sawtooth pi.svg|thumb|right|400px|Plot of the sawtooth wavesawtooth wave](File:Periodic identity function.gif|thumb|right|400px|Animated plot of the first five successive partial Fourier series)We now use the formula above to give a Fourier series expansion of a very simple function. Consider a sawtooth wave
s(x) = frac{x}{pi}, quad mathrm{for } -pi
| Eq.7}}}}When x=pi, the Fourier series converges to 0, which is the half-sum of the left- and right-limit of s at x=pi. This is a particular instance of the Dirichlet theorem for Fourier series.(File:Fourier heat in a plate.png|thumb|right|Heat distribution in a metal plate, using Fourier's method)This example leads us to a solution to the Basel problem.## Example 2: Fourier's motivationThe Fourier series expansion of our function in Example 1 looks more complicated than the simple formula s(x)=x/ pi, 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 side measures pi meters, with coordinates (x,y) in [0,pi] times [0,pi]. 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 y=pi, is maintained at the temperature gradient T(x,pi)=x degrees Celsius, for x in (0,pi), then one can show that the stationary heat distribution (or the heat distribution after a long period of time has elapsed) is given by
T(x,y) = 2sum_{n=1}^infty frac{(-1)^{n+1}}{n} sin(nx) {sinh(ny) over sinh(npi)}.
Here, sinh is the hyperbolic sine function. This solution of the heat equation is obtained by multiplying each term of {{EquationNote|Eq.7}} by sinh(ny)/sinh(npi). While our example function s(x) seems to have a needlessly complicated Fourier series, the heat distribution T(x,y) is nontrivial. The function T cannot be written as a closed-form expression. This method of solving the heat problem was made possible by Fourier's work.## Other applicationsAnother application of this Fourier series is to solve the Basel problem by using Parseval's theorem. The example generalizes and one may compute Î¶(2n), for any positive integer n.## Beginnings{{cquote|varphi(y)=a_0cosfrac{pi y}{2}+a_1cos 3frac{pi y}{2}+a_2cos5frac{pi y}{2}+cdots.Multiplying both sides by cos(2k+1)frac{pi y}{2}, and then integrating from y=-1 to y=+1 yields:
a_k=int_{-1}^1varphi(y)cos(2k+1)frac{pi y}{2},dy.
| 30px | MÃ©moire sur la propagation de la chaleur dans les corps solides. (1807)HTTP://GALLICA.BNF.FR/ARK:/12148/BPT6K33707.IMAGE.R=OEUVRES+DE+FOURIER.F223.PAGINATION.LANGFR >TITLE=GALLICA â€“ FOURIER, JEAN-BAPTISTE-JOSEPH (1768â€“1830). OEUVRES DE FOURIER. 1888, PP. 218â€“219 | PUBLISHER=GALLICA.BNF.FR | ACCESSDATE=2014-08-08, 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.}}This immediately gives any coefficient ak of the trigonometrical series for Ï†(y) for any function which has such an expansion. It works because if Ï† has such an expansion, then (under suitable convergence assumptions) the integral
begin{align}
a_k&=int_{-1}^1varphi(y)cos(2k+1)frac{pi y}{2},dy &= int_{-1}^1left(acosfrac{pi y}{2}cos(2k+1)frac{pi y}{2}+a'cos 3frac{pi y}{2}cos(2k+1)frac{pi y}{2}+cdotsright),dyend{align}can be carried out term-by-term. But all terms involving cos(2j+1)frac{pi y}{2} cos(2k+1)frac{pi y}{2} for {{nowrap|j ≠ k}} vanish when integrated from âˆ’1 to 1, leaving only the kth 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|date=November 2012}}## Birth of harmonic analysisSince 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 at the time Fourier completed his original work. Fourier originally defined the Fourier series for real-valued functions of real arguments, and using the sine and cosine functions as the basis set for the decomposition.Many other Fourier-related transforms have since been defined, extending the initial idea to other applications. This general area of inquiry is now sometimes called harmonic analysis. A Fourier series, however, can be used only for periodic functions, or for functions on a bounded (compact) interval.## Extensions## Fourier series on a squareWe can also define the Fourier series for functions of two variables x and y in the square [-pi,pi]times[-pi,pi]:## Fourier series of Bravais-lattice-periodic-functionThe three-dimensional Bravais lattice is defined as the set of vectors of the form:
mathbf{R} = n_1mathbf{a}_1 + n_2mathbf{a}_2 + n_3mathbf{a}_3
where n_i are integers and mathbf{a}_i are three linearly independent vectors. Assuming we have some function, f(mathbf{r}), such that it obeys the following condition for any Bravais lattice vector mathbf{R} : f(mathbf{r}) = f(mathbf{R}+mathbf{r}), 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 a Fourier series of the potential then when applying Bloch's theorem. First, we may write any arbitrary vector mathbf{r} in the coordinate-system of the lattice:
mathbf{r} = x_1frac{mathbf{a}_{1}}{a_1}+ x_2frac{mathbf{a}_{2}}{a_2}+ x_3frac{mathbf{a}_{3}}{a_3},
where a_i := |mathbf{a}_i|.Thus we can define a new function,
g(x_1,x_2,x_3) := f(mathbf{r}) = f left (x_1frac{mathbf{a}_{1}}{a_1}+x_2frac{mathbf{a}_{2}}{a_2}+x_3frac{mathbf{a}_{3}}{a_3} right ).
This new function, g(x_1,x_2,x_3), is now a function of three-variables, each of which has periodicity a1, a2, a3 respectively:
g(x_1,x_2,x_3) = g(x_1+a_1,x_2,x_3) = g(x_1,x_2+a_2,x_3) = g(x_1,x_2,x_3+a_3).
If we write a series for g on the interval [0, a1] for x1, we can define the following:
h^mathrm{one}(m_1, x_2, x_3) := frac{1}{a_1}int_0^{a_1} g(x_1, x_2, x_3)cdot e^{-i 2pi frac{m_1}{a_1} x_1}, dx_1
And then we can write:
g(x_1, x_2, x_3)=sum_{m_1=-infty}^infty h^mathrm{one}(m_1, x_2, x_3) cdot e^{i 2pi frac{m_1}{a_1} x_1}
Further defining:
g(x_1, x_2, x_3)=sum_{m_1=-infty}^infty sum_{m_2=-infty}^infty h^mathrm{two}(m_1, m_2, x_3) cdot e^{i 2pi frac{m_1}{a_1} x_1} cdot e^{i 2pi frac{m_2}
{a_2} x_2}Finally applying the same for the third coordinate, we define:
g(x_1, x_2, x_3)=sum_{m_1=-infty}^infty sum_{m_2=-infty}^infty sum_{m_3=-infty}^infty h^mathrm{three}(m_1, m_2, m_3) cdot e^{i 2pi frac{m_1}{a_1} x_1} cdot e^{i 2pi frac{m_2}{a_2} x_2}cdot e^{i 2pi frac{m_3}{a_3} x_3}
Re-arranging:
g(x_1, x_2, x_3)=sum_{m_1, m_2, m_3 in Z } h^mathrm{three}(m_1, m_2, m_3) cdot e^{i 2pi left( frac{m_1}{a_1} x_1+ frac{m_2}{a_2} x_2 + frac{m_3}{a_3} x_3right)}.
Now, every reciprocal lattice vector can be written as mathbf{K} = ell_1mathbf{g}_1 + ell_2mathbf{g}_2 + ell_3mathbf{g}_3, where l_i are integers and mathbf{g}_i are the reciprocal lattice vectors, we can use the fact that mathbf{g_i} cdot mathbf{a_j}=2pidelta_{ij} to calculate that for any arbitrary reciprocal lattice vector mathbf{K} and arbitrary vector in space mathbf{r}, their scalar product is:
mathbf{K} cdot mathbf{r} = left ( ell_1mathbf{g}_1 + ell_2mathbf{g}_2 + ell_3mathbf{g}_3 right ) cdot left (x_1frac{mathbf{a}_1}{a_1}+ x_2frac{mathbf{a}_2}{a_2} +x_3frac{mathbf{a}_3}{a_3} right ) = 2pi left( x_1frac{ell_1}{a_1}+x_2frac{ell_2}{a_2}+x_3frac{ell_3}{a_3} right ).
And so it is clear that in our expansion, the sum is actually over reciprocal lattice vectors:
f(mathbf{r})=sum_{mathbf{K}} h(mathbf{K}) cdot e^{i mathbf{K} cdot mathbf{r}},
where
h(mathbf{K}) = frac{1}{a_3} int_0^{a_3} dx_3 , frac{1}{a_2}int_0^{a_2} dx_2 , frac{1}{a_1}int_0^{a_1} dx_1 , fleft(x_1frac{mathbf{a}_1}{a_1} + x_2frac{mathbf{a}_2}{a_2} + x_3frac{mathbf{a}_3}{a_3} right)cdot e^{-i mathbf{K} cdot mathbf{r}}.
Assuming
mathbf{r} = (x,y,z) = x_1frac{mathbf{a}_1}{a_1}+x_2frac{mathbf{a}_2}{a_2}+x_3frac{mathbf{a}_3}{a_3},
we can solve this system of three linear equations for x, y, and z in terms of x_1, x_2 and x_3 in order to calculate the volume element in the original cartesian coordinate system. Once we have x, y, and z in terms of x_1, x_2 and x_3, we can calculate the Jacobian determinant:
begin{vmatrix}
dfrac{partial x_1}{partial x} & dfrac{partial x_1}{partial y} & dfrac{partial x_1}{partial z} [12pt]dfrac{partial x_2}{partial x} & dfrac{partial x_2}{partial y} & dfrac{partial x_2}{partial z} [12pt]dfrac{partial x_3}{partial x} & dfrac{partial x_3}{partial y} & dfrac{partial x_3}{partial z}end{vmatrix}which after some calculation and applying some non-trivial cross-product identities can be shown to be equal to:
frac{a_1 a_2 a_3}{mathbf{a_1}cdot(mathbf{a_2} times mathbf{a_3})}
(it may be advantageous for the sake of simplifying calculations, to work in such a cartesian coordinate system, in which it just so happens that mathbf{a_1} is parallel to the x axis, mathbf{a_2} lies in the x-y plane, and mathbf{a_3} 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 mathbf{a_1}, mathbf{a_2} and mathbf{a_3}. In particular, we now know that
dx_1 , dx_2 , dx_3 = frac{a_1 a_2 a_3}{mathbf{a_1}cdot(mathbf{a_2} times mathbf{a_3})} cdot dx , dy , dz.
We can write now h(mathbf{K}) as an integral with the traditional coordinate system over the volume of the primitive cell, instead of with the x_1, x_2 and x_3 variables:
h(mathbf{K}) = frac{1}{mathbf{a_1}cdot(mathbf{a_2} times mathbf{a_3})}int_{C} dmathbf{r} f(mathbf{r})cdot e^{-i mathbf{K} cdot mathbf{r}}
And C is the primitive unit cell, thus, mathbf{a_1}cdot(mathbf{a_2} times mathbf{a_3}) is the volume of the primitive unit cell.## Hilbert space interpretationIn the language of Hilbert spaces, the set of functions {e_n=e^{inx}: n in mathbb{Z}} is an orthonormal basis for the space L^2([-pi,pi]) of square-integrable functions on [-pi,pi]. This space is actually a Hilbert space with an inner product given for any two elements f and g by
langle f,, g rangle ;triangleq ; frac{1}{2pi}int_{-pi}^{pi} f(x)overline{g(x)},dx.
The basic Fourier series result for Hilbert spaces can be written as
f=sum_{n=-infty}^infty langle f,e_n rangle , e_n.
(File:Fourier series integral identities.gif|thumb|400px|right|Sines and cosines form an orthonormal set, as illustrated above. The integral of sine, cosine and their product is zero (green and red areas are equal, and cancel out) when m, n or the functions are different, and pi only if m and n are equal, and the function used is the same.)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:
int_{-pi}^{pi} cos(mx), cos(nx), dx = frac{1}{2}int_{-pi}^{pi} cos((n-m)x)+cos((n+m)x), dx = pi delta_{mn}, quad m, n ge 1, ,
int_{-pi}^{pi} sin(mx), sin(nx), dx = frac{1}{2}int_{-pi}^{pi} cos((n-m)x)-cos((n+m)x), dx = pi delta_{mn}, quad m, n ge 1
(where Î´mn is the Kronecker delta), and
int_{-pi}^{pi} cos(mx), sin(nx), dx = frac{1}{2}int_{-pi}^{pi} sin((n+m)x)+sin((n-m)x), dx = 0;,
furthermore, the sines and cosines are orthogonal to the constant function 1. An orthonormal basis for L^2([-pi,pi]) consisting of real functions is formed by the functions 1 and sqrt{2} cos (nx), sqrt{2} sin (nx) with n = 1, 2,... The density of their span is a consequence of the Stoneâ€“Weierstrass theorem, but follows also from the properties of classical kernels like the FejÃ©r kernel.## Properties## Table of basic propertiesThis table shows some mathematical operations in the time domain and the corresponding effect in the Fourier series coefficients. Notation:- z^{} is the complex conjugate of z.
- f(x),g(x) designate P-periodic functions defined on 0
| ||

p. 610}} | |||||||||||||||||

p. 610}} | |||||||||||||||||

PUBLISHER=SPRINGER | ISBN=1402062710, {{rp|p. 610}} | ||||||||||||||||

p. 610}} |

### Symmetry properties

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**:**{{Citation | last =Proakis | first =John G. | last2 =Manolakis | first2 =Dimitri G. | title =Digital Signal Processing: Principles, Algorithms and Applications | place =New Jersey | publisher =Prentice-Hall International | year =1996 | edition =3 | page =291 | language =English | id =sAcfAQAAIAAJ | isbn =9780133942897}}

**:**

- The transform of a real-valued function ({{math|
*f*{{sub|{{sub|RE}}}}+*f*{{sub|{{sub|RO}}}}}}) is the even symmetric function {{math|F{{sub|{{sub|RE}}}}+*i*F{{sub|{{sub|IO}}}}}}. Conversely, an even-symmetric transform implies a real-valued time-domain. - The transform of an imaginary-valued function ({{math|
*i**f*{{sub|{{sub|IE}}}}+*i**f*{{sub|{{sub|IO}}}}}}) is the odd symmetric function {{math|F{{sub|{{sub|RO}}}}+*i*F{{sub|{{sub|IE}}}}}}, and the converse is true. - The transform of an even-symmetric function ({{math|
*f*{{sub|{{sub|RE}}}}+*i**f*{{sub|{{sub|IO}}}}}}) is the real-valued function {{math|F{{sub|{{sub|RE}}}}+ F{{sub|{{sub|RO}}}}}}, and the converse is true. - The transform of an odd-symmetric function ({{math|
*f*{{sub|{{sub|RO}}}}+*i**f*{{sub|{{sub|IE}}}}}}) is the imaginary-valued function {{math|*i*F{{sub|{{sub|IE}}}}+*i*F{{sub|{{sub|IO}}}}}}, and the converse is true.

### Riemannâ€“Lebesgue lemma

If f is integrable, lim_{|n|rightarrow infty}hat{f}(n)=0, lim_{nrightarrow +infty}a_n=0 and lim_{nrightarrow +infty}b_n=0. This result is known as the Riemannâ€“Lebesgue lemma.### Derivative property

We say that f belongs to C^k(mathbb{T}) if f is a 2{{pi}}-periodic function on mathbb{R} which is k times differentiable, and its*k*th derivative is continuous.

- If f in C^1(mathbb{T}), then the Fourier coefficients widehat{f'}(n) of the derivative f' can be expressed in terms of the Fourier coefficients widehat{f}(n) of the function f, via the formula widehat{f'}(n) = in widehat{f}(n).
- If f in C^k(mathbb{T}), then widehat{f^{(k)}}(n) = (in)^k widehat{f}(n). In particular, since widehat{f^{(k)}}(n) tends to zero, we have that |n|^kwidehat{f}(n) tends to zero, which means that the Fourier coefficients converge to zero faster than the
*k*th power of*n*.

### Parseval's theorem

If f belongs to L^2([-pi,pi]), then sum_{n=-infty}^infty |hat{f}(n)|^2 = frac{1}{2pi}int_{-pi}^{pi} |f(x)|^2 , dx.### Plancherel's theorem

If c_0,, c_{pm 1},, c_{pm 2},ldots are coefficients and sum_{n=-infty}^infty |c_n|^2 < infty then there is a unique function fin L^2([-pi,pi]) such that hat{f}(n) = c_n for every n.### Convolution theorems

- The first convolution theorem states that if f and g are in L^1([-pi,pi]), the Fourier series coefficients of the 2{{pi}}-periodic convolution of f and g are given by:

[widehat{f*_{2pi}g}](n) = 2picdot hat{f}(n)cdothat{g}(n),The scale factor is always equal to the period, 2{{pi}} in this case.

where:

begin{align}

- The second convolution theorem states that the Fourier series coefficients of the product of f and g are given by the discrete convolution of the hat f and hat g sequences:

[widehat{fcdot g}](n) = [hat{f}*hat{g}](n).

- A doubly infinite sequence left {c_n right }_{n in Z} in c_0(mathbb{Z}) is the sequence of Fourier coefficients of a function in L^1([0,2pi]) if and only if it is a convolution of two sequences in ell^2(mathbb{Z}). See WEB,weblink fa.functional analysis - Characterizations of a linear subspace associated with Fourier series, MathOverflow, 2010-11-19, 2014-08-08,

### Compact groups

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 [âˆ’{{pi}},{{pi}}] case.An alternative extension to compact groups is the Peterâ€“Weyl theorem, which proves results about representations of compact groups analogous to those about finite groups.

### Riemannian manifolds

File:F orbital.png|thumb|right|The atomic orbitals of chemistry are partially described by spherical harmonics, which can be used to produce Fourier series on the spheresphereIf the domain is not a group, then there is no intrinsically defined convolution. However, if X is a compact Riemannian manifold, it has a Laplaceâ€“Beltrami operator. The Laplaceâ€“Beltrami operator is the differential operator that corresponds to Laplace operator for the Riemannian manifold X. Then, by analogy, one can consider heat equations on X. Since Fourier arrived at his basis by attempting to solve the heat equation, the natural generalization is to use the eigensolutions of the Laplaceâ€“Beltrami operator as a basis. This generalizes Fourier series to spaces of the type L^2(X), where X is a Riemannian manifold. The Fourier series converges in ways similar to the [-pi,pi] case. A typical example is to take X to be the sphere with the usual metric, in which case the Fourier basis consists of spherical harmonics.### Locally compact Abelian groups

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 L^1(G) or L^2(G), where G is an LCA group. If G is compact, one also obtains a Fourier series, which converges similarly to the [-pi,pi] case, but if G is noncompact, one obtains instead a Fourier integral. This generalization yields the usual Fourier transform when the underlying locally compact Abelian group is mathbb{R}.## Table of common Fourier series

Some common pairs of periodic functions and their Fourier Series coefficients are shown in the table below. The following notation applies:- f(x) designates a periodic function defined on 0 < x le T .
- a_0, a_n, b_n designate the Fourier Series coefficients (sine-cosine form) of the periodic function f as defined in {{EquationNote|Eq.4}}.

frac{-4A}{pi}frac{1}{1-n^2} & quad n text{ even}

0 & quad n text{ odd}

end{cases}

b_n = & 0end{align}|Full-wave rectified sine PUBLISHER=VIEWEG+TEUBNER VERLAG ISBN=3834807575, {{rp|p. 193}}|f(x)=begin{cases}A sinleft(frac{2pi}{T}xright) & quad text{for } 0 le x < T/2 end{cases}250px|center)|begin{align}a_0 = & frac{2A}{pi}a_n = & begin{cases}
0 & quad n text{ odd}

end{cases}

frac{-2A}{pi}frac{1}{1-n^2} & quad n text{ even}

0 & quad n text{ odd}

end{cases}

b_n = & begin{cases}
0 & quad n text{ odd}

end{cases}

frac{A}{2} & quad n=1

0 & quad n > 1

end{cases}

end{align}|Half-wave rectified sinep. 193}}|f(x)=begin{cases}A & quad text{for } 0 le x < D cdot T end{cases}250px|center)|begin{align}a_0 = & 2ADa_n = & frac{A}{n pi} sin left( 2 pi n D right)b_n = & frac{2A}{n pi} left( sin left( pi n D right) right) ^2end{align}| 0 le D le 1||f(x)=frac{Ax}{T} quad text{for } 0 le x < T250px|center)|begin{align}a_0 = & Aa_n = & 0b_n = & frac{-A}{n pi}end{align}| p. 192}}|f(x)=A-frac{Ax}{T} quad text{for } 0 le x < T250px|center)|begin{align}a_0 = & Aa_n = & 0b_n = & frac{A}{n pi}end{align}| p. 192}}|f(x)=frac{4A}{T^2}left( x-frac{T}{2} right)^2 quad text{for } 0 le x < T250px|center)|begin{align}a_0 = & frac{2A}{3}a_n = & frac{4A}{pi^2 n^2}b_n = & 0end{align}| p. 193}}0 & quad n > 1

end{cases}

## Approximation and convergence of Fourier series

An important question for the theory as well as applications is that of convergence. In particular, it is often necessary in applications to replace the infinite series sum_{-infty}^infty by a finite one,
f_N(x) = sum_{n=-N}^N hat{f}(n) e^{inx}.

This is called a *partial sum*. We would like to know, in which sense does f_N(x) converge to f(x) as N rightarrow infty.

### Least squares property

We say that p is a trigonometric polynomial of degree N when it is of the form
p(x)=sum_{n=-N}^N p_n e^{inx}.

Note that f_N is a trigonometric polynomial of degree N. Parseval's theorem implies that**Theorem.**The trigonometric polynomial f_N is the unique best trigonometric polynomial of degree N approximating f(x), in the sense that, for any trigonometric polynomial p neq f_N of degree N, we have

|f_N - f|_2 < |p - f|_2,

where the Hilbert space norm is defined as:
| g |_2 = sqrt{{1 over 2pi} int_{-pi}^pi |g(x)|^2 , dx}.

### Convergence

{{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 f belongs to L^2 ( left[ -pi,pi right] ), then f_{infty} converges to f in L^2 ( left[ -pi,pi right] ), that is, |f_N - f|_2 converges to 0 as N rightarrow infty.We have already mentioned that if f is continuously differentiable, then (icdot n) hat{f}(n) is the

*n*th Fourier coefficient of the derivative f'. It follows, essentially from the Cauchyâ€“Schwarz inequality, that f_{infty} is absolutely summable. The sum of this series is a continuous function, equal to f, since the Fourier series converges in the mean to f:

**Theorem.**If f in C^1(mathbb{T}), then f_{infty} converges to f uniformly (and hence also pointwise.)This result can be proven easily if f is further assumed to be C^2, since in that case n^2hat{f}(n) tends to zero as n rightarrow infty. More generally, the Fourier series is absolutely summable, thus converges uniformly to f, provided that f satisfies a HÃ¶lder condition of order alpha > 1/2. In the absolutely summable case, the inequality sup_x |f(x) - f_N(x)| le sum_{|n| > N} |hat{f}(n)| 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 x if f is differentiable at x, to Lennart Carleson's much more sophisticated result that the Fourier series of an L^2 function actually converges almost everywhere.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".BOOK

, Circuits, signals, and systems

, William McC. Siebert

, MIT Press

, 1985

, 978-0-262-19229-3

, 402

,weblink

, BOOK

, Advances in Electronics and Electron Physics

, L. Marton, Claire Marton

, Academic Press

, 1990

, 978-0-12-014650-5

, 369

,weblink

, BOOK

, Solid-state spectroscopy

, Hans Kuzmany

, Springer

, 1998

, 978-3-540-63913-8

, 14

,weblink

, BOOK

, Brain and perception

, Karl H. Pribram, Kunio Yasue, Mari Jibu

, Lawrence Erlbaum Associates

, 1991

, 978-0-89859-995-4

, 26

,weblink

,

, William McC. Siebert

, MIT Press

, 1985

, 978-0-262-19229-3

, 402

,weblink

, BOOK

, Advances in Electronics and Electron Physics

, L. Marton, Claire Marton

, Academic Press

, 1990

, 978-0-12-014650-5

, 369

,weblink

, BOOK

, Solid-state spectroscopy

, Hans Kuzmany

, Springer

, 1998

, 978-3-540-63913-8

, 14

,weblink

, BOOK

, Brain and perception

, Karl H. Pribram, Kunio Yasue, Mari Jibu

, Lawrence Erlbaum Associates

, 1991

, 978-0-89859-995-4

, 26

,weblink

,

### Divergence

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 sÃ©rie 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 {{harv|Katznelson|1976}}.

## See also

- ATS theorem
- Dirichlet kernel
- Discrete Fourier transform
- Fast Fourier transform
- FejÃ©r's theorem
- Fourier analysis
- Fourier sine and cosine series
- Fourier transform
- Gibbs phenomenon
- Laurent series â€“ the substitution
*q*=*e**ix*transforms a Fourier series into a Laurent series, or conversely. This is used in the*q*-series expansion of the*j*-invariant. - Multidimensional transform
- Spectral theory
- Sturmâ€“Liouville theory

## Notes

## References

{{Reflist|30em}}### Further reading

- BOOK, William E. Boyce, Richard C. DiPrima, Elementary Differential Equations and Boundary Value Problems, 8th, John Wiley & Sons, Inc., New Jersey, 2005, 0-471-43338-1,
- BOOK, Joseph Fourier, translated by Alexander Freeman, The Analytical Theory of Heat, Dover Publications, 2003, 0-486-49531-0, 2003 unabridged republication of the 1878 English translation by Alexander Freeman of Fourier's work
*ThÃ©orie Analytique de la Chaleur*, originally published in 1822. - JOURNAL, Enrique A. Gonzalez-Velasco, Connections in Mathematical Analysis: The Case of Fourier Series, American Mathematical Monthly, 99, 1992, 427â€“441, 5, 10.2307/2325087, 2325087,
- JOURNAL, Katznelson, Yitzhak, An introduction to harmonic analysis, Second corrected, Dover Publications, Inc, 1976, New York, harv, 0-486-63331-4,
- Felix Klein,
*Development of mathematics in the 19th century*. Mathsci Press Brookline, Mass, 1979. Translated by M. Ackerman from*Vorlesungen Ã¼ber die Entwicklung der Mathematik im 19 Jahrhundert*, Springer, Berlin, 1928. - BOOK, Walter Rudin, Walter Rudin, Principles of mathematical analysis, 3rd, McGraw-Hill, Inc., New York, 1976, 0-07-054235-X,
- BOOK, A. Zygmund, Antoni Zygmund, Trigonometric series, third, Cambridge University Press, Cambridge, 2002, 0-521-89053-5, The first edition was published in 1935.

## External links

- {{springer|title=Fourier series|id=p/f041090}}
- {{MathWorld | urlname= FourierSeries | title= Fourier Series}}
- {{webarchive |url=https://web.archive.org/web/20011205152434weblink |date=December 5, 2001 |title=Joseph Fourier â€“ A site on Fourier's life which was used for the historical section of this article }}

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