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The signed-square ReLU differential need not be locally Lipschitz
Statement refuted
Refuted claim: the differential of the signed-square ReLU feature map is locally Lipschitz near the parameter .
For input law with density on , define as an element of . This feature map is Fréchet differentiable near , but its differential is not locally Lipschitz there.
Statement references
Facts & Assumptions
Given: The signed-square feature and Gaussian density in the Statement.
The Gaussian density is continuous, positive on every real interval, and has finite second moment, so the feature belongs to and the kink hyperplane has measure zero. Signed-square ReLU feature parameterization
For , the preactivation is and its unique zero is . Signed-square ReLU feature parameterization
Counterexample
Given: , , and [F1]–[F2] in the Statement.
On a small neighborhood of with , the root is a smooth function of parameters. For an increment , The candidate differential is . Outside the interval between and , the two preactivations have the same sign; there the ReLU remainder is either zero or , whose norm is by the finite second moment in [F1]. The interval between the two roots has length and lies in a fixed bounded neighborhood of ; on it the pointwise remainder is , so its norm is . Thus the full remainder is , proving Fréchet differentiability and the displayed differential.
At the derivative in direction is . At it is . For , their squared distance is because the interval lies in , where and , and has length . The operator norm difference of the two differentials is at least their difference on the unit vector . Dividing by the parameter distance gives a quotient at least , which diverges as . Therefore the differential is not locally Lipschitz at .
Source note
Chizat and Bach's Lemma D.5 establishes differentiability under a finite-second-moment density assumption. The proposition's application does not establish local Lipschitzness of this differential, which is a separate feature-energy hypothesis.
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Sources
- Lénaïc Chizat and Francis Bach, On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport (standard reference, not scraped)