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TheoremStatement: AI-adaptedProof: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
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Second-order convolution bias for an interior C² field

Statement

Let f∈C²(R^d) have bounded Hessian and define S_εf(x)=∫f(x−z)W_ε(z)dz. Then for every ε>0 and x∈R^d,

∣Sεf(x)−f(x)∣≤m22ε2∥D2f∥∞.

Facts & Assumptions

Given: The kernel family, a C² function f with bounded Hessian, and ε>0.

[F1]

Scaling identities for kernel moments and derivatives and Scaled symmetric compactly supported kernel family: ∫W_ε=1, ∫zW_ε=0, and ∫|z|²W_ε=ε²m₂; the kernel definition gives W_ε≥0.

[F2]

Multivariable Taylor formula with a Lagrange remainder along a line segment: for a C² function along a line segment, the degree-one Taylor formula has a second-derivative remainder at an interior point of that segment.

[F3]

Linearity, monotonicity, the absolute-value estimate and coordinate-slice additivity for the Riemann integral in Rm: on a bounding rectangle, integration is linear and satisfies the absolute-value estimate and monotonicity.

Proof

1.1givenF2

For each x,z apply [F2] with increment −z. It gives f(x−z)=f(x)−∇f(x)·z+R(x,z), where |R(x,z)|≤(1/2)||D²f||∞|z|².

2.1F1F3step 1.1∎

Integrate this identity against the nonnegative W_ε. Linearity in [F3] and [F1] make the constant term integrate to f(x) and the linear term zero. The absolute-value estimate and monotonicity in [F3], together with W_ε≥0, bound the remainder by (1/2)||D²f||∞∫|z|²W_ε=(m₂/2)ε²||D²f||∞.

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