# Fourier inversion theorem

Last updated

In mathematics, the Fourier inversion theorem says that for many types of functions it is possible to recover a function from its Fourier transform. Intuitively it may be viewed as the statement that if we know all frequency and phase information about a wave then we may reconstruct the original wave precisely.

Mathematics includes the study of such topics as quantity, structure (algebra), space (geometry), and change. It has no generally accepted definition.

The Fourier transform (FT) decomposes a function of time into its constituent frequencies. This is similar to the way a musical chord can be expressed in terms of the volumes and frequencies of its constituent notes. The term Fourier transform refers to both the frequency domain representation and the mathematical operation that associates the frequency domain representation to a function of time. The Fourier transform of a function of time is itself a complex-valued function of frequency, whose magnitude (modulus) represents the amount of that frequency present in the original function, and whose argument is the phase offset of the basic sinusoid in that frequency. The Fourier transform is not limited to functions of time, but the domain of the original function is commonly referred to as the time domain. There is also an inverse Fourier transform that mathematically synthesizes the original function from its frequency domain representation.

In physics and mathematics, the phase of a periodic function of some real variable is the relative value of that variable within the span of each full period.

## Contents

The theorem says that if we have a function ${\displaystyle f:\mathbb {R} \rightarrow \mathbb {C} }$ satisfying certain conditions, and we use the convention for the Fourier transform that

${\displaystyle ({\mathcal {F}}f)(\xi ):=\int _{\mathbb {R} }e^{-2\pi iy\cdot \xi }\,f(y)\,dy,}$

then

${\displaystyle f(x)=\int _{\mathbb {R} }e^{2\pi ix\cdot \xi }\,({\mathcal {F}}f)(\xi )\,d\xi .}$

In other words, the theorem says that

${\displaystyle f(x)=\int _{\mathbb {R} }\int _{\mathbb {R} }e^{2\pi i(x-y)\cdot \xi }\,f(y)\,dy\,d\xi .}$

This last equation is called the Fourier integral theorem.

Another way to state the theorem is to note that if ${\displaystyle R}$ is the flip operator i.e. ${\displaystyle (Rf)(x):=f(-x)}$, then

${\displaystyle {\mathcal {F}}^{-1}={\mathcal {F}}R=R{\mathcal {F}}.}$

The theorem holds if both ${\displaystyle f}$ and its Fourier transform are absolutely integrable (in the Lebesgue sense) and ${\displaystyle f}$ is continuous at the point ${\displaystyle x}$. However, even under more general conditions versions of the Fourier inversion theorem hold. In these cases the integrals above may not converge in an ordinary sense.

An absolutely integrable function is a function whose absolute value is integrable, meaning that the integral of the absolute value over the whole domain is finite.

In mathematics, the integral of a non-negative function of a single variable can be regarded, in the simplest case, as the area between the graph of that function and the x-axis. The Lebesgue integral extends the integral to a larger class of functions. It also extends the domains on which these functions can be defined.

## Statement

In this section we assume that ${\displaystyle f}$ is an integrable continuous function. Use the convention for the Fourier transform that

${\displaystyle ({\mathcal {F}}f)(\xi ):=\int _{\mathbb {R} ^{n}}e^{-2\pi iy\cdot \xi }\,f(y)\,dy.}$

Furthermore, we assume that the Fourier transform is also integrable.

### Inverse Fourier transform as an integral

The most common statement of the Fourier inversion theorem is to state the inverse transform as an integral. For any integrable function ${\displaystyle g}$ and all ${\displaystyle x\in \mathbb {R} ^{n}}$ set

${\displaystyle {\mathcal {F}}^{-1}g(x):=\int _{\mathbb {R} ^{n}}e^{2\pi ix\cdot \xi }\,g(\xi )\,d\xi .}$

Then for all ${\displaystyle x\in \mathbb {R} ^{n}}$ we have

${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}g)(x)=g(x).}$

### Fourier integral theorem

The theorem can be restated as

${\displaystyle f(x)=\int _{\mathbb {R} ^{n}}\int _{\mathbb {R} ^{n}}e^{2\pi i(x-y)\cdot \xi }\,f(y)\,dy\,d\xi .}$

If f is real valued then by taking the real part of each side of the above we obtain

${\displaystyle f(x)=\int _{\mathbb {R} ^{n}}\int _{\mathbb {R} ^{n}}\cos(2\pi (x-y)\cdot \xi )\,f(y)\,dy\,d\xi .}$

### Inverse transform in terms of flip operator

For any function ${\displaystyle g}$ define the flip operator [note 1] ${\displaystyle R}$ by

${\displaystyle Rg(x):=g(-x).}$

${\displaystyle {\mathcal {F}}^{-1}f:=R{\mathcal {F}}f={\mathcal {F}}Rf.}$

It is immediate from the definition of the Fourier transform and the flip operator that both ${\displaystyle R{\mathcal {F}}f}$ and ${\displaystyle {\mathcal {F}}Rf}$ match the integral definition of ${\displaystyle {\mathcal {F}}^{-1}f}$, and in particular are equal to each other and satisfy ${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)=f(x)}$.

Note also that since ${\displaystyle Rf=R{\mathcal {F}}^{-1}{\mathcal {F}}f=RR{\mathcal {FF}}f}$ we have ${\displaystyle R={\mathcal {F}}^{2}}$ and

${\displaystyle {\mathcal {F}}^{-1}={\mathcal {F}}^{3}.}$

### Two-sided inverse

The form of the Fourier inversion theorem stated above, as is common, is that

${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)=f(x).}$

In other words, ${\displaystyle {\mathcal {F}}^{-1}}$ is a left inverse for the Fourier transform. However it is also a right inverse for the Fourier transform i.e.

${\displaystyle {\mathcal {F}}({\mathcal {F}}^{-1}f)(\xi )=f(\xi ).}$

Since ${\displaystyle {\mathcal {F}}^{-1}}$ is so similar to ${\displaystyle {\mathcal {F}}}$, this follows very easily from the Fourier inversion theorem (changing variables ${\displaystyle \zeta :=-\zeta }$):

{\displaystyle {\begin{aligned}f&={\mathcal {F}}^{-1}({\mathcal {F}}f)(x)\\[6pt]&=\int _{\mathbb {R} ^{n}}\int _{\mathbb {R} ^{n}}e^{2\pi ix\cdot \xi }\,e^{-2\pi iy\cdot \xi }\,f(y)\,dy\,d\xi \\[6pt]&=\int _{\mathbb {R} ^{n}}\int _{\mathbb {R} ^{n}}e^{-2\pi ix\cdot \zeta }\,e^{2\pi iy\cdot \zeta }\,f(y)\,dy\,d\zeta \\[6pt]&={\mathcal {F}}({\mathcal {F}}^{-1}f)(x).\end{aligned}}}

Alternatively, this can be seen from the relation between ${\displaystyle {\mathcal {F}}^{-1}f}$ and the flip operator and the associativity of function composition, since

In mathematics, function composition is an operation that takes two functions f and g and produces a function h such that h(x) = g(f ). In this operation, the function g is applied to the result of applying the function f to x. That is, the functions f : XY and g : YZ are composed to yield a function that maps x in X to g(f ) in Z.

${\displaystyle f={\mathcal {F}}^{-1}({\mathcal {F}}f)={\mathcal {F}}R{\mathcal {F}}f={\mathcal {F}}({\mathcal {F}}^{-1}f).}$

## Conditions on the function

When used in physics and engineering, the Fourier inversion theorem is often used under the assumption that everything "behaves nicely". In mathematics such heuristic arguments are not permitted, and the Fourier inversion theorem includes an explicit specification of what class of functions is being allowed. However, there is no "best" class of functions to consider so several variants of the Fourier inversion theorem exist, albeit with compatible conclusions.

### Schwartz functions

The Fourier inversion theorem holds for all Schwartz functions (roughly speaking, smooth functions that decay quickly and whose derivatives all decay quickly). This condition has the benefit that it is an elementary direct statement about the function (as opposed to imposing a condition on its Fourier transform), and the integral that defines the Fourier transform and its inverse are absolutely integrable. This version of the theorem is used in the proof of the Fourier inversion theorem for tempered distributions (see below).

### Integrable functions with integrable Fourier transform

The Fourier inversion theorem holds for all continuous functions that are absolutely integrable (i.e. ${\displaystyle L^{1}(\mathbb {R} ^{n})}$) with absolutely integrable Fourier transform. This includes all Schwartz functions, so is a strictly stronger form of the theorem than the previous one mentioned. This condition is the one used above in the statement section.

A slight variant is to drop the condition that the function ${\displaystyle f}$ be continuous but still require that it and its Fourier transform be absolutely integrable. Then ${\displaystyle f=g}$ almost everywhere where g is a continuous function, and ${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)=g(x)}$ for every ${\displaystyle x\in \mathbb {R} ^{n}}$.

### Integrable functions in one dimension

Piecewise smooth; one dimension

If the function is absolutely integrable in one dimension (i.e. ${\displaystyle f\in L^{1}(\mathbb {R} )}$) and is piecewise smooth then a version of the Fourier inversion theorem holds. In this case we define

${\displaystyle {\mathcal {F}}^{-1}g(x):=\lim _{R\to \infty }\int _{-R}^{R}e^{2\pi ix\xi }\,g(\xi )\,d\xi .}$

Then for all ${\displaystyle x\in \mathbb {R} }$

${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)={\frac {1}{2}}(f(x_{-})+f(x_{+})),}$

i.e. ${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)}$ equals the average of the left and right limits of ${\displaystyle f}$ at ${\displaystyle x}$. Note that at points where ${\displaystyle f}$ is continuous this simply equals ${\displaystyle f(x)}$.

A higher-dimensional analogue of this form of the theorem also holds, but according to Folland (1992) is "rather delicate and not terribly useful".

Piecewise continuous; one dimension

If the function is absolutely integrable in one dimension (i.e. ${\displaystyle f\in L^{1}(\mathbb {R} )}$) but merely piecewise continuous then a version of the Fourier inversion theorem still holds. In this case the integral in the inverse Fourier transform is defined with the aid of a smooth rather than a sharp cut off function; specifically we define

${\displaystyle {\mathcal {F}}^{-1}g(x):=\lim _{R\to \infty }\int _{\mathbb {R} }\varphi (\xi /R)\,e^{2\pi ix\xi }\,g(\xi )\,d\xi ,\qquad \varphi (\xi ):=e^{-\xi ^{2}}.}$

The conclusion of the theorem is then the same as for the piecewise smooth case discussed above.

Continuous; any number of dimensions

If ${\displaystyle f}$ is continuous and absolutely integrable on ${\displaystyle \mathbb {R} ^{n}}$ then the Fourier inversion theorem still holds so long as we again define the inverse transform with a smooth cut off function i.e.

${\displaystyle {\mathcal {F}}^{-1}g(x):=\lim _{R\to \infty }\int _{\mathbb {R} ^{n}}\varphi (\xi /R)\,e^{2\pi ix\cdot \xi }\,g(\xi )\,d\xi ,\qquad \varphi (\xi ):=e^{-\vert \xi \vert ^{2}}.}$

The conclusion is now simply that for all ${\displaystyle x\in \mathbb {R} ^{n}}$

${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)=f(x).}$
No regularity condition; any number of dimensions

If we drop all assumptions about the (piecewise) continuity of ${\displaystyle f}$ and assume merely that it is absolutely integrable, then a version of the theorem still holds. The inverse transform is again defined with the smooth cut off, but with the conclusion that

${\displaystyle {\mathcal {F}}^{-1}({\mathcal {F}}f)(x)=f(x)}$

for almost every ${\displaystyle x\in \mathbb {R} ^{n}.}$ [1]

### Square integrable functions

In this case the Fourier transform cannot be defined directly as an integral since it may not be absolutely convergent, so it is instead defined by a density argument (see the Fourier transform article). For example, putting

${\displaystyle g_{k}(\xi ):=\int _{\{y\in \mathbb {R} ^{n}:\left\vert y\right\vert \leq k\}}e^{-2\pi iy\cdot \xi }\,f(y)\,dy,\qquad k\in \mathbb {N} ,}$

we can set ${\displaystyle \textstyle {\mathcal {F}}f:=\lim _{k\to \infty }g_{k}}$ where the limit is taken in the ${\displaystyle L^{2}}$-norm. The inverse transform may be defined by density in the same way or by defining it in terms of the Fourier transform and the flip operator. We then have

${\displaystyle f(x)={\mathcal {F}}({\mathcal {F}}^{-1}f)(x)={\mathcal {F}}^{-1}({\mathcal {F}}f)(x)}$

in the mean squared norm. In one dimension (and one dimension only), it can also be shown that it converges for almost every x∈ℝ- this is Carleson's theorem, but is much harder to prove than convergence in the mean squared norm.

### Tempered distributions

The Fourier transform may be defined on the space of tempered distributions ${\displaystyle {\mathcal {S}}'(\mathbb {R} ^{n})}$ by duality of the Fourier transform on the space of Schwartz functions. Specifically for ${\displaystyle f\in {\mathcal {S}}'(\mathbb {R} ^{n})}$ and for all test functions ${\displaystyle \varphi \in {\mathcal {S}}(\mathbb {R} ^{n})}$ we set

${\displaystyle \langle {\mathcal {F}}f,\varphi \rangle :=\langle f,{\mathcal {F}}\varphi \rangle ,}$

where ${\displaystyle {\mathcal {F}}\varphi }$ is defined using the integral formula. If ${\displaystyle f\in L^{1}(\mathbb {R} ^{n})\cap L^{2}(\mathbb {R} ^{n})}$ then this agrees with the usual definition. We may define the inverse transform ${\displaystyle {\mathcal {F}}^{-1}\colon {\mathcal {S}}'(\mathbb {R} ^{n})\to {\mathcal {S}}'(\mathbb {R} ^{n})}$, either by duality from the inverse transform on Schwartz functions in the same way, or by defining it in terms of the flip operator (where the flip operator is defined by duality). We then have

${\displaystyle {\mathcal {F}}{\mathcal {F}}^{-1}={\mathcal {F}}^{-1}{\mathcal {F}}=\operatorname {Id} _{{\mathcal {S}}'(\mathbb {R} ^{n})}.}$

## Relation to Fourier series

When considering the Fourier series of a function it is conventional to rescale it so that it acts on ${\displaystyle [0,2\pi ]}$ (or is ${\displaystyle 2\pi }$-periodic). In this section we instead use the somewhat unusual convention taking ${\displaystyle f}$ to act on ${\displaystyle [0,1]}$, since that matches the convention of the Fourier transform used here.

The Fourier inversion theorem is analogous to the convergence of Fourier series. In the Fourier transform case we have

${\displaystyle f\colon \mathbb {R} ^{n}\to \mathbb {C} ,\quad {\hat {f}}\colon \mathbb {R} ^{n}\to \mathbb {C} ,}$
${\displaystyle {\hat {f}}(\xi ):=\int _{\mathbb {R} ^{n}}e^{-2\pi iy\cdot \xi }\,f(y)\,dy,}$
${\displaystyle f(x)=\int _{\mathbb {R} ^{n}}e^{2\pi ix\cdot \xi }\,{\hat {f}}(\xi )\,d\xi .}$

In the Fourier series case we instead have

${\displaystyle f\colon [0,1]^{n}\to \mathbb {C} ,\quad {\hat {f}}\colon \mathbb {Z} ^{n}\to \mathbb {C} ,}$
${\displaystyle {\hat {f}}(k):=\int _{[0,1]^{n}}e^{-2\pi iy\cdot k}\,f(y)\,dy,}$
${\displaystyle f(x)=\sum _{k\in \mathbb {Z} ^{n}}e^{2\pi ix\cdot k}\,{\hat {f}}(k).}$

In particular, in one dimension ${\displaystyle k\in \mathbb {Z} }$ and the sum runs from ${\displaystyle -\infty }$ to ${\displaystyle \infty }$.

## Applications

In applications of the Fourier transform the Fourier inversion theorem often plays a critical role. In many situations the basic strategy is to apply the Fourier transform, perform some operation or simplification, and then apply the inverse Fourier transform.

More abstractly, the Fourier inversion theorem is a statement about the Fourier transform as an operator (see Fourier transform on function spaces). For example, the Fourier inversion theorem on ${\displaystyle f\in L^{2}(\mathbb {R} ^{n})}$ shows that the Fourier transform is a unitary operator on ${\displaystyle L^{2}(\mathbb {R} ^{n})}$.

## Properties of inverse transform

The inverse Fourier transform is extremely similar to the original Fourier transform: as discussed above, it differs only in the application of a flip operator. For this reason the properties of the Fourier transform hold for the inverse Fourier transform, such as the Convolution theorem and the Riemann–Lebesgue lemma.

Tables of Fourier transforms may easily be used for the inverse Fourier transform by composing the looked-up function with the flip operator. For example, looking up the Fourier transform of the rect function we see that

${\displaystyle f(x)=\operatorname {rect} (ax)\quad \Rightarrow \quad ({\mathcal {F}}f)(\xi )={\frac {1}{|a|}}\operatorname {sinc} \left({\frac {\xi }{a}}\right)\!,}$

so the corresponding fact for the inverse transform is

${\displaystyle g(\xi )=\operatorname {rect} (a\xi )\quad \Rightarrow \quad ({\mathcal {F}}^{-1}g)(x)={\frac {1}{|a|}}\operatorname {sinc} \left(-{\frac {x}{a}}\right)\!.}$

## Proof

The proof uses a few facts, given ${\displaystyle f(y)}$ and ${\displaystyle {\mathcal {F}}f(\xi )=\int _{\mathbb {R} ^{n}}e^{-2\pi iy\cdot \xi }f(y)\,dy}$.

1. If ${\displaystyle x\in \mathbb {R} ^{n}}$ and ${\displaystyle g(\xi )=e^{2\pi \mathrm {i} x\cdot \xi }\psi (\xi )}$, then ${\displaystyle ({\mathcal {F}}g)(y)=({\mathcal {F}}\psi )(y-x)}$.
2. If ${\displaystyle \varepsilon \in \mathbb {R} }$ and ${\displaystyle \psi (\xi )=\varphi (\varepsilon \xi )}$, then ${\displaystyle ({\mathcal {F}}\psi )(y)=({\mathcal {F}}\varphi )(y/\varepsilon )/|\varepsilon |}$.
3. For ${\displaystyle f,g\in L^{1}(\mathbb {R} ^{n})}$, Fubini's theorem implies that ${\displaystyle \textstyle \int g(x,\xi )\cdot ({\mathcal {F}}f)(\xi )\,d\xi =\int ({\mathcal {F}}g)(x,y)\cdot f(y)\,dy}$.
4. Define ${\displaystyle \varphi (\xi )=e^{-\pi \vert \xi \vert ^{2}}}$; then ${\displaystyle ({\mathcal {F}}\varphi )(y)=\varphi (y)}$.
5. Define ${\displaystyle \varphi _{\varepsilon }(y)=\varphi (y/\varepsilon )/\varepsilon ^{n}}$. Then with ${\displaystyle \ast }$ denoting convolution, ${\displaystyle \varphi _{\varepsilon }}$ is an approximation to the identity: for any continuous ${\displaystyle f\in L^{1}(\mathbb {R} ^{n})}$ and point ${\displaystyle x\in \mathbb {R} ^{n}}$, ${\displaystyle \lim _{\varepsilon \to 0}(\varphi _{\varepsilon }\ast f)(x)=f(x)}$ (where the convergence is pointwise).

First note that, since, by assumption, ${\displaystyle {\mathcal {F}}f\in L^{1}(\mathbb {R} ^{n})}$, then it follows by the dominated convergence theorem that

${\displaystyle \int _{\mathbb {R} ^{n}}e^{2\pi ix\cdot \xi }({\mathcal {F}}f)(\xi )\,d\xi =\lim _{\varepsilon \to 0}\int _{\mathbb {R} ^{n}}e^{-\pi \varepsilon ^{2}|\xi |^{2}+2\pi ix\cdot \xi }({\mathcal {F}}f)(\xi )\,d\xi .}$

Define ${\displaystyle g(\xi )=e^{-\pi \varepsilon ^{2}\vert \xi \vert ^{2}+2\pi \mathrm {i} x\cdot \xi }}$. Applying facts 1, 2 and 4, repeatedly for multiple integrals if necessary, we obtain

${\displaystyle ({\mathcal {F}}g)(x,y)={\frac {1}{\varepsilon ^{n}}}e^{-{\frac {\pi }{\varepsilon ^{2}}}|x-y|^{2}}.}$

Using fact 3 on ${\displaystyle f}$ and ${\displaystyle g}$, we have

${\displaystyle \int _{\mathbb {R} ^{n}}e^{-\pi \varepsilon ^{2}|\xi |^{2}+2\pi ix\cdot \xi }({\mathcal {F}}f)(\xi )\,d\xi =\int _{\mathbb {R} ^{n}}{\frac {1}{\varepsilon ^{n}}}e^{-{\frac {\pi }{\varepsilon ^{2}}}|x-y|^{2}}f(y)\,dy=(\varphi _{\varepsilon }*f)(x),}$

the convolution of ${\displaystyle f}$ with an approximate identity. But since ${\displaystyle f\in L^{1}(\mathbb {R} ^{n})}$, fact 5 says that

${\displaystyle \lim _{\varepsilon \to 0}(\varphi _{\varepsilon }*f)(x)=f(x).}$

Putting together the above we have shown that

${\displaystyle \int _{\mathbb {R} ^{n}}e^{2\pi ix\cdot \xi }({\mathcal {F}}f)(\xi )\,d\xi =f(x).\qquad \square }$

## Notes

1. An operator is a transformation that maps functions to functions. The flip operator, the Fourier transform, the inverse Fourier transform and the identity transform are all examples of operators.

## Related Research Articles

In mathematics, the Dirac delta function is a generalized function or distribution introduced by the physicist Paul Dirac. It is used to model the density of an idealized point mass or point charge as a function equal to zero everywhere except for zero and whose integral over the entire real line is equal to one. As there is no function that has these properties, the computations made by the theoretical physicists appeared to mathematicians as nonsense until the introduction of distributions by Laurent Schwartz to formalize and validate the computations. As a distribution, the Dirac delta function is a linear functional that maps every function to its value at zero. The Kronecker delta function, which is usually defined on a discrete domain and takes values 0 and 1, is a discrete analog of the Dirac delta function.

In mathematics, the convolution theorem states that under suitable conditions the Fourier transform of a convolution of two signals is the pointwise product of their Fourier transforms. In other words, convolution in one domain equals point-wise multiplication in the other domain. Versions of the convolution theorem are true for various Fourier-related transforms. Let and be two functions with convolution .

In mathematics, a Fourier series is a periodic function composed of harmonically related sinusoids, combined by a weighted summation. With appropriate weights, one cycle of the summation can be made to approximate an arbitrary function in that interval. As such, the summation is a synthesis of another function. The discrete-time Fourier transform is an example of Fourier series. 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.

Noether's theorem states that every differentiable symmetry of the action of a physical system has a corresponding conservation law. The theorem was proven by mathematician Emmy Noether in 1915 and published in 1918, after a special case was proven by E. Cosserat & F. Cosserat in 1909. The action of a physical system is the integral over time of a Lagrangian function, from which the system's behavior can be determined by the principle of least action. This theorem only applies to continuous and smooth symmetries over physical space.

In mathematics, the uniform boundedness principle or Banach–Steinhaus theorem is one of the fundamental results in functional analysis. Together with the Hahn–Banach theorem and the open mapping theorem, it is considered one of the cornerstones of the field. In its basic form, it asserts that for a family of continuous linear operators whose domain is a Banach space, pointwise boundedness is equivalent to uniform boundedness in operator norm.

In mathematics, the group algebra is any of various constructions to assign to a locally compact group an operator algebra, such that representations of the algebra are related to representations of the group.

In mathematics, the Plancherel theorem is a result in harmonic analysis, proven by Michel Plancherel in 1910. It states that the integral of a function's squared modulus is equal to the integral of the squared modulus of its frequency spectrum. That is, if is a function on the real line, and is its frequency spectrum, then

In mathematics, the Radon transform is the integral transform which takes a function f defined on the plane to a function Rf defined on the (two-dimensional) space of lines in the plane, whose value at a particular line is equal to the line integral of the function over that line. The transform was introduced in 1917 by Johann Radon, who also provided a formula for the inverse transform. Radon further included formulas for the transform in three dimensions, in which the integral is taken over planes. It was later generalized to higher-dimensional Euclidean spaces, and more broadly in the context of integral geometry. The complex analog of the Radon transform is known as the Penrose transform. The Radon transform is widely applicable to tomography, the creation of an image from the projection data associated with cross-sectional scans of an object.

In Fourier analysis, a multiplier operator is a type of linear operator, or transformation of functions. These operators act on a function by altering its Fourier transform. Specifically they multiply the Fourier transform of a function by a specified function known as the multiplier or symbol. Occasionally, the term multiplier operator itself is shortened simply to multiplier. In simple terms, the multiplier reshapes the frequencies involved in any function. This class of operators turns out to be broad: general theory shows that a translation-invariant operator on a group which obeys some regularity conditions can be expressed as a multiplier operator, and conversely. Many familiar operators, such as translations and differentiation, are multiplier operators, although there are many more complicated examples such as the Hilbert transform.

In mathematics, the Riemann–Lebesgue lemma, named after Bernhard Riemann and Henri Lebesgue, is of importance in harmonic analysis and asymptotic analysis.

In mathematics, in particular in algebraic geometry and differential geometry, Dolbeault cohomology is an analog of de Rham cohomology for complex manifolds. Let M be a complex manifold. Then the Dolbeault cohomology groups depend on a pair of integers p and q and are realized as a subquotient of the space of complex differential forms of degree (p,q).

In mathematical analysis an oscillatory integral is a type of distribution. Oscillatory integrals make rigorous many arguments that, on a naive level, appear to use divergent integrals. It is possible to represent approximate solution operators for many differential equations as oscillatory integrals.

In probability theory and statistics, the characteristic function of any real-valued random variable completely defines its probability distribution. If a random variable admits a probability density function, then the characteristic function is the Fourier transform of the probability density function. Thus it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions. There are particularly simple results for the characteristic functions of distributions defined by the weighted sums of random variables.

In mathematical analysis, Fourier integral operators have become an important tool in the theory of partial differential equations. The class of Fourier integral operators contains differential operators as well as classical integral operators as special cases.

The Sokhotski–Plemelj theorem is a theorem in complex analysis, which helps in evaluating certain integrals. The real-line version of it is often used in physics, although rarely referred to by name. The theorem is named after Julian Sochocki, who proved it in 1868, and Josip Plemelj, who rediscovered it as a main ingredient of his solution of the Riemann-Hilbert problem in 1908.

In mathematics, the FBI transform or Fourier–Bros–Iagolnitzer transform is a generalization of the Fourier transform developed by the French mathematical physicists Jacques Bros and Daniel Iagolnitzer in order to characterise the local analyticity of functions on Rn. The transform provides an alternative approach to analytic wave front sets of distributions, developed independently by the Japanese mathematicians Mikio Sato, Masaki Kashiwara and Takahiro Kawai in their approach to microlocal analysis. It can also be used to prove the analyticity of solutions of analytic elliptic partial differential equations as well as a version of the classical uniqueness theorem, strengthening the Cauchy–Kowalevski theorem, due to the Swedish mathematician Erik Albert Holmgren (1872–1943).

In mathematics, the Plancherel theorem for spherical functions is an important result in the representation theory of semisimple Lie groups, due in its final form to Harish-Chandra. It is a natural generalisation in non-commutative harmonic analysis of the Plancherel formula and Fourier inversion formula in the representation theory of the group of real numbers in classical harmonic analysis and has a similarly close interconnection with the theory of differential equations. It is the special case for zonal spherical functions of the general Plancherel theorem for semisimple Lie groups, also proved by Harish-Chandra. The Plancherel theorem gives the eigenfunction expansion of radial functions for the Laplacian operator on the associated symmetric space X; it also gives the direct integral decomposition into irreducible representations of the regular representation on L2(X). In the case of hyperbolic space, these expansions were known from prior results of Mehler, Weyl and Fock.

In mathematics, singular integral operators of convolution type are the singular integral operators that arise on Rn and Tn through convolution by distributions; equivalently they are the singular integral operators that commute with translations. The classical examples in harmonic analysis are the harmonic conjugation operator on the circle, the Hilbert transform on the circle and the real line, the Beurling transform in the complex plane and the Riesz transforms in Euclidean space. The continuity of these operators on L2 is evident because the Fourier transform converts them into multiplication operators. Continuity on Lp spaces was first established by Marcel Riesz. The classical techniques include the use of Poisson integrals, interpolation theory and the Hardy–Littlewood maximal function. For more general operators, fundamental new techniques, introduced by Alberto Calderón and Antoni Zygmund in 1952, were developed by a number of authors to give general criteria for continuity on Lp spaces. This article explains the theory for the classical operators and sketches the subsequent general theory.

In mathematics, singular integral operators on closed curves arise in problems in analysis, in particular complex analysis and harmonic analysis. The two main singular integral operators, the Hilbert transform and the Cauchy transform, can be defined for any smooth Jordan curve in the complex plane and are related by a simple algebraic formula. The Hilbert transform is an involution and the Cauchy transform an idempotent. The range of the Cauchy transform is the Hardy space of the bounded region enclosed by the Jordan curve. The theory for the original curve can be deduced from that on the unit circle, where, because of rotational symmetry, both operators are classical singular integral operators of convolution type. The Hilbert transform satisfies the jump relations of Plemelj and Sokhotski, which express the original function as the difference between the boundary values of holomorphic functions on the region and its complement. Singular integral operators have been studied on various classes of functions, including Hőlder spaces, Lp spaces and Sobolev spaces. In the case of L2 spaces—the case treated in detail below—other operators associated with the closed curve, such as the Szegő projection onto Hardy space and the Neumann–Poincaré operator, can be expressed in terms of the Cauchy transform and its adjoint.

## References

• Folland, G. B. (1992). Fourier Analysis and its Applications. Belmont, CA, USA: Wadsworth. ISBN   0-534-17094-3.
• Folland, G. B. (1995). Introduction to Partial Differential Equations (2nd ed.). Princeton, USA: Princeton Univ. Press. ISBN   978-0-691-04361-6.
1. "DMat0101, Notes 3: The Fourier transform on L^1". I Woke Up In A Strange Place. 2011-03-10. Retrieved 2018-02-12.