Convolution

1 min read Last updated Sat Jun 27 2026 08:46:52 GMT+0000 (Coordinated Universal Time)

A mathematical operation that combines 2 functions to produce a third expressing how the shape of one is modified by the other. Denoted by *.

(fg)(t)=f(τ)g(tτ)dτ(f * g)(t) = \int_{-\infty}^{\infty} f(\tau) \, g(t - \tau) \, \text{d}\tau

Slides one function over another, multiplying and integrating at each shift.

Properties

  • Commutative
    fg=gff * g = g * f
  • Associative
    f(gh)=(fg)hf * (g * h) = (f * g) * h
  • Distributive
    f(g+h)=(fg)+(fh)f * (g + h) = (f * g) + (f * h)
  • Identity
    fδ=ff * \delta = f, where δ\delta is the Dirac delta function

Convolution Theorem

Laplace

The Laplace transform of a convolution is the product of the individual Laplace transforms.

Suppose L{f}=F\mathcal{L}\{f\} = F and L{g}=G\mathcal{L}\{g\} = G:

L{fg}=F(s)G(s)\mathcal{L}\{f * g\} = F(s) \, G(s)

Useful for solving ODEs: multiply in the ss-domain, then apply the inverse Laplace transform.

Fourier

F{fg}=F(ω)G(ω)\mathcal{F}\{f * g\} = F(\omega) \cdot G(\omega)

Convolution in the time domain equals pointwise multiplication in the frequency domain. Used extensively in signal processing and filter design.

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