Alphabeta Math
LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Roots-of-unity orthogonality and radix-two FFT cost

Statement

Let M=2q for an integer q≥0, and let ω=e−2πi/M (with ω=1 when M=1). Define the forward DFT matrix F∈CM×M by Fkj=ωjk, where ∗ denotes conjugate transpose. Then F∗F=MI, so the inverse DFT is M−1F∗. The radix-two procedure defined on this page computes either transform exactly in O(Mlog⁡(2+M)) complex arithmetic operations and O(M) space. Consequently, for every integer n≥1, two polynomials of degree less than n can be multiplied exactly in O(nlog⁡(2+n)) complex arithmetic operations and O(n) space by zero-padding to a radix-two transform length at least 2n−1.

Facts & Assumptions

Given: An integer q≥0, M=2q, and the root ω=e−2πi/M.

[A1]

For M>1, ωM=1 and ωM/2=−1; for M=1, the DFT is the identity on one coordinate.

[F1]

Radix-two discrete Fourier transform: for M>1, split the even and odd input subsequences, recursively compute their length-M/2 DFTs using ω2, and combine as Ek+ωkOk and Ek−ωkOk for 0≤k<M/2.

Proof

1.1A1algebra

For 0≤j,ℓ<M, the (j,ℓ) entry of F∗F is ∑k=0M−1ω(ℓ−j)k. If j=ℓ, this sum is M. Otherwise ωℓ−j≠1 and the finite geometric-sum identity gives ∑k=0M−1ω(ℓ−j)k=(1−ωM(ℓ−j))/(1−ωℓ−j)=0. Thus F∗F=MI, which gives the asserted inverse.

2.1A1F1step 1.1algebra

Splitting the DFT sum into even and odd indices gives Ek+ωkOk for 0≤k<M/2. For frequency k+M/2, the even factor is unchanged because ωjM=1, while the odd factor changes sign because ωM/2=−1. Therefore the outputs in [F1] are exactly the defining DFT sums. Induction on q, starting with M=1, proves exactness of the recursion.

3.1F1step 2.1algebra

At length M>1, forming both half-length transforms and combining their M outputs costs at most cM arithmetic operations for an absolute constant c. Hence T(M)≤2T(M/2)+cM and T(1)=O(1); expanding this recurrence for q=log⁡2M levels gives T(M)=O(Mlog⁡(2+M)). The input, output, and one level of butterflies require O(M) workspace; the inverse has the same cost plus coordinatewise scaling by 1/M.

4.1A1step 1.1step 3.1algebra∎

Let f,g have degree less than n, and choose M′=2⌈log⁡2(2n−1)⌉. Pad their coefficient lists to length M′, apply the forward DFT, multiply corresponding transformed coordinates, and apply the inverse DFT. For coefficient index s, the inverse-transform formula expands it as ∑j,ℓfjgℓ M′−1∑k=0M′−1ω(j+ℓ−s)k; step 1.1 makes the inner sum one exactly when j+ℓ≡s(modM′) and zero otherwise. Thus the result is cyclic convolution modulo zM′−1. The ordinary product has degree at most 2n−2<M′, so no coefficient wraps around and the result is the exact polynomial product. Since 2n−1≤M′<2(2n−1) when n>1 (and M′=1 when n=1), step 3.1 gives O(nlog⁡(2+n)) operations and O(n) space.

Remarks

The proofs use only finite sums and arithmetic in C; no choice principle is needed.

Depends on

Used by

Dependency tree · one level

1 result within one dependency step of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources