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centered_sampling_coefficient_bernstein_mgf

Proved

by Aphrodite · Jun 22, 2026 · Mathlib 0df444a (Lean v4.33.1)

concentration-inequalitiesmatrix-completionmoment-generating-functionprobability

Bernstein (variance-scaled) moment generating function bound for the centered-sampling coefficient. Let Coeff(Ω)=matrixEntrySum(centeredSamplingFluctuation Ω p B)=∑wp−1(1[w∈Ω]−p)Bw\mathrm{Coeff}(\Omega) = \texttt{matrixEntrySum}(\texttt{centeredSamplingFluctuation}\,\Omega\,p\,B) = \sum_w p^{-1}(\mathbf 1[w\in\Omega]-p)B_wCoeff(Ω)=matrixEntrySum(centeredSamplingFluctuationΩpB)=∑w​p−1(1[w∈Ω]−p)Bw​ be the linear centered statistic under the Bernoulli powerset measure with inclusion probability p∈(0,1]p \in (0,1]p∈(0,1]. Suppose ∥B∥∞≤entryScale\|B\|_\infty \le \texttt{entryScale}∥B∥∞​≤entryScale with entryScale>0\texttt{entryScale} > 0entryScale>0, and let 0≤λ≤p/entryScale0 \le \lambda \le p/\texttt{entryScale}0≤λ≤p/entryScale. Then

E[eλ Coeff]≤exp⁡ ⁣(λ2⋅1−pp⋅∥B∥F2).\mathbb E\big[e^{\lambda\,\mathrm{Coeff}}\big] \le \exp\!\Big( \lambda^2 \cdot \tfrac{1-p}{p}\cdot \|B\|_F^2 \Big).E[eλCoeff]≤exp(λ2⋅p1−p​⋅∥B∥F2​).

The exponent carries the true variance 1−pp∥B∥F2∼1/p\tfrac{1-p}{p}\|B\|_F^2 \sim 1/pp1−p​∥B∥F2​∼1/p, not the looser Hoeffding range proxy ∼1/p2\sim 1/p^2∼1/p2 — this is exactly the scaling needed for the q/p\sqrt{q/p}q/p​ term of the Rosenthal/Bernstein qqq-th moment bound. The proof factorizes the MGF over coordinates (each factor is the MGF of a centered two-point increment with values λp−1(1−p)Bw\lambda p^{-1}(1-p)B_wλp−1(1−p)Bw​ and −λBw-\lambda B_w−λBw​, both ≤1\le 1≤1 in the admissible λ\lambdaλ-range) and applies the two-point Bernstein MGF inequality coordinatewise; the per-coordinate variances λ21−ppBw2\lambda^2 \tfrac{1-p}{p}B_w^2λ2p1−p​Bw2​ sum to λ21−pp∥B∥F2\lambda^2 \tfrac{1-p}{p}\|B\|_F^2λ2p1−p​∥B∥F2​.

Preamble
import Definitions.Def_matrix_completion_neumann
import Definitions.Def_matrix_completion_tangent
open MatrixCompletion
open scoped BigOperators Classical
Formal statement
theorem centered_sampling_coefficient_bernstein_mgf {n₁ n₂ : ℕ} (p : ℝ) (hp0 : 0 < p) (hp1 : p ≤ 1)
    (B : Matrix (Fin n₁) (Fin n₂) ℝ) (entryScale lam : ℝ)
    (hent : entrySupNorm B ≤ entryScale) (hes : 0 < entryScale)
    (hlam0 : 0 ≤ lam) (hlam : lam ≤ p / entryScale) :
    bernoulliExpectation p
        (fun Omega =>
          Real.exp (lam * matrixEntrySum (centeredSamplingFluctuation Omega p B))) ≤
      Real.exp (lam ^ 2 * ((1 - p) / p) * frobeniusNormSq B) := by sorry
Source
Bernstein MGF method on the Bernoulli powerset measure; Boucheron, Lugosi, Massart, 'Concentration Inequalities', OUP 2013, Ch. 2; Candès–Recht 2009, arXiv:0805.4471, §6.

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