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 for an integer , and let (with when ). Define the forward DFT matrix by , where denotes conjugate transpose. Then , so the inverse DFT is . The radix-two procedure defined on this page computes either transform exactly in complex arithmetic operations and space. Consequently, for every integer , two polynomials of degree less than can be multiplied exactly in complex arithmetic operations and space by zero-padding to a radix-two transform length at least .
Statement references
Facts & Assumptions
Given: An integer , , and the root .
For , and ; for , the DFT is the identity on one coordinate.
Radix-two discrete Fourier transform: for , split the even and odd input subsequences, recursively compute their length- DFTs using , and combine as and for .
Proof
For , the entry of is . If , this sum is . Otherwise and the finite geometric-sum identity gives . Thus , which gives the asserted inverse.
Splitting the DFT sum into even and odd indices gives for . For frequency , the even factor is unchanged because , while the odd factor changes sign because . Therefore the outputs in [F1] are exactly the defining DFT sums. Induction on , starting with , proves exactness of the recursion.
At length , forming both half-length transforms and combining their outputs costs at most arithmetic operations for an absolute constant . Hence and ; expanding this recurrence for levels gives . The input, output, and one level of butterflies require workspace; the inverse has the same cost plus coordinatewise scaling by .
Let have degree less than , and choose . Pad their coefficient lists to length , apply the forward DFT, multiply corresponding transformed coordinates, and apply the inverse DFT. For coefficient index , the inverse-transform formula expands it as ; step 1.1 makes the inner sum one exactly when and zero otherwise. Thus the result is cyclic convolution modulo . The ordinary product has degree at most , so no coefficient wraps around and the result is the exact polynomial product. Since when (and when ), step 3.1 gives operations and space.
Remarks
The proofs use only finite sums and arithmetic in ; no choice principle is needed.
Depends on
Used by
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Sources
- Gu, Goel, and Ré, Efficiently Modeling Long Sequences with Structured State Spaces, Appendix C.2 (standard reference, not scraped)
- Pan, Fast Approximate Computations with Cauchy Matrices and Polynomials, §3.3 (standard reference, not scraped)