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Root of unity
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==Orthogonality== From the summation formula follows an [[orthogonality]] relationship: for {{math|1=''j'' = 1,ββ¦β,β''n''}} and {{math|1=''jβ²'' = 1,ββ¦β,β''n''}} :<math>\sum_{k=1}^{n} \overline{z^{j\cdot k}} \cdot z^{j'\cdot k} = n \cdot\delta_{j,j'}</math> where {{mvar|δ}} is the [[Kronecker delta]] and {{mvar|z}} is any primitive {{mvar|n}}th root of unity. The {{math|''n'' Γ ''n''}} [[matrix (mathematics)|matrix]] {{mvar|U}} whose {{math|(''j'',β''k'')}}th entry is :<math>U_{j,k} = n^{-\frac{1}{2}}\cdot z^{j\cdot k}</math> defines a [[discrete Fourier transform]]. Computing the inverse transformation using [[Gaussian elimination]] requires {{math|''[[big-O notation|O]]''(''n''<sup>3</sup>)}} operations. However, it follows from the orthogonality that {{mvar|U}} is [[unitary matrix|unitary]]. That is, :<math>\sum_{k=1}^{n} \overline{U_{j,k}} \cdot U_{k,j'} = \delta_{j,j'},</math> and thus the inverse of {{mvar|U}} is simply the complex conjugate. (This fact was first noted by [[Carl Friedrich Gauss|Gauss]] when solving the problem of [[trigonometric interpolation]].) The straightforward application of {{mvar|U}} or its inverse to a given vector requires {{math|''O''(''n''<sup>2</sup>)}} operations. The [[fast Fourier transform]] algorithms reduces the number of operations further to {{math|''O''(''n''βlogβ''n'')}}.
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