In mathematics, the Poisson summation formula is an equation that relates the Fourier series coefficients of the periodic summation of a function to values of the function's continuous Fourier transform. Consequently, the periodic summation of a function is completely defined by discrete samples of the original function's Fourier transform. And conversely, the periodic summation of a function's Fourier transform is completely defined by discrete samples of the original function. The Poisson summation formula was discovered by Siméon Denis Poisson and is sometimes called Poisson resummation.
For a smooth, complex valued function s ( x ) {\displaystyle s(x)} on R {\displaystyle \mathbb {R} } which decays at infinity with all derivatives (Schwartz function), the simplest version of the Poisson summation formula states that
where S {\displaystyle S} is the Fourier transform of s {\displaystyle s} , i.e., S ( f ) ≜ ∫ − ∞ ∞ s ( x ) e − i 2 π f x d x . {\textstyle S(f)\triangleq \int _{-\infty }^{\infty }s(x)\ e^{-i2\pi fx}\,dx.} The summation formula can be restated in many equivalent ways, but a simple one is the following. Suppose that f ∈ L 1 ( R n ) {\displaystyle f\in L^{1}(\mathbb {R} ^{n})} (L1 for L1 space) and Λ {\displaystyle \Lambda } is a unimodular lattice in R n {\displaystyle \mathbb {R} ^{n}} . Then the periodization of f {\displaystyle f} , which is defined as the sum f Λ ( x ) = ∑ λ ∈ Λ f ( x + λ ) , {\textstyle f_{\Lambda }(x)=\sum _{\lambda \in \Lambda }f(x+\lambda ),} converges in the L 1 {\displaystyle L^{1}} norm of R n / Λ {\displaystyle \mathbb {R} ^{n}/\Lambda } to an L 1 ( R n / Λ ) {\displaystyle L^{1}(\mathbb {R} ^{n}/\Lambda )} function having Fourier series f Λ ( x ) ∼ ∑ λ ′ ∈ Λ ′ f ^ ( λ ′ ) e 2 π i λ ′ x {\displaystyle f_{\Lambda }(x)\sim \sum _{\lambda '\in \Lambda '}{\hat {f}}(\lambda ')e^{2\pi i\lambda 'x}} where Λ ′ {\displaystyle \Lambda '} is the dual lattice to Λ {\displaystyle \Lambda } . (Note that the Fourier series on the right-hand side need not converge in L 1 {\displaystyle L^{1}} or otherwise.)