Alphabeta Math
LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
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Correctly scaled bounded product approximation

Statement

Fix M>0. For every 0<ε<M2, there is a ReLU network PM,ε on [−M,M]2 that satisfies ∣PM,ε(x,y)−xy∣≤ε and PM,ε(x,0)=PM,ε(0,y)=0. Its depth, number of weights, and number of computation units are at most CM(1+log⁡(M2/ε)), where CM depends only on M.

Facts & Assumptions

Given: M>0, 0<ε<M2, and the square networks in Logarithmic-size ReLU approximation of the square.

[F1]

For every 0<δ<1, a ReLU network Qδ satisfies Qδ(0)=0, ∥Qδ(u)−u2∥[0,1]≤δ, and has O(1+log⁡(1/δ)) depth, weights, and units (Logarithmic-size ReLU approximation of the square).

Proof

1.1givenF1algebraconstruct

Set δ=ε/(6M2) and, for ∣x∣,∣y∣≤M, put PM,ε(x,y)=2M2[Qδ ⁣(∣x+y∣2M)−Qδ ⁣(∣x∣2M)−Qδ ⁣(∣y∣2M)]. All three arguments lie in [0,1]. Replacing Qδ(u) by u2 gives 2M2[((x+y)2−x2−y2)/(4M2)]=xy, so the absolute error is at most 2M2(3δ)=ε.

2.1step 1.1F1algebra∎

Since Qδ(0)=0, substituting x=0 makes the first and third terms equal and the second zero; substituting y=0 makes the first and second terms equal and the third zero. The formula is globally defined, so this cancellation holds for every real value of the other coordinate. Absolute value is represented by ∣z∣=ρ(z)+ρ(−z), and the three square networks run in parallel followed by an affine combination, so the depth, weights, and units are O(1+log⁡(1/δ))=O(1+log⁡(M2/ε)), since 0<δ<1/6 and log⁡(1/δ)=log⁡6+log⁡(M2/ε).

Depends on

Used by

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Sources