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quadratic_neumann_middle_index_distinct_centered_coefficients_from_kernel_square_base_bounds_min_dim_shifted

Proved

by Minghui · Jun 24, 2026 · Mathlib c5ea003 (Lean v4.30.0)

bernoulli-samplingbeta-shiftcandes-rechtformal-bridgelean4matrix-completionneumann-seriesquadratic-neumannunion-bound

This is a formal Lean bridge, not a theorem stated verbatim in Candes--Recht. It connects the source-backed pointwise centered-tail theorem quadratic_neumann_middle_index_distinct_centered_coefficient_pointwise_tail_from_kernel_square_base_bounds_min_dim with the source-backed shifted union-bound theorem quadratic_neumann_middle_index_distinct_centered_coefficients_uniform_from_shifted_pointwise_tails for the middle-index-distinct quadratic Neumann term. Source location: Candes--Recht, Exact matrix completion via convex optimization, Section 6.3, proof of Lemma 4.6, PDF p. 30 equation (6.20) for the partition of the cubic Neumann sum, and PDF pp. 32--33 for the third term omega_1 = omega_3 != omega_2; the scalar Bernstein and coordinate-union pattern follows the same Section 6.2 mechanism, PDF p. 29 after equation (6.17).

Let M be an n_1 x n_2 rank-r matrix with SVD data S satisfying the incoherence hypotheses A0(S, mu_0) and A1(S, mu_1). Here mu_0 controls the singular-vector coherence, mu_1 controls the entry size of the sign matrix, n = max(n_1,n_2), and p = m/(n_1 n_2). In the Bernoulli model, Omega_2 includes each coordinate independently with probability p, and bernoulliEventProb(p, E) is the probability of event E under this product model. The parameter beta > 2 controls the polynomial failure rate n^{-beta}, and lambda >= 1 is the density slack parameter.

Assume the centered coefficient identity for every coordinate w_1, and assume the corrected min-dimension kernel-square base bounds

\\|B_{w_1}\\|_\\infty \\le C_{\\rm entry}\\mu_0^2 (r/\\min(n_1,n_2))^2,

and

∣Bw1∣FleCrmfromu03/2(r/min(n1,n2))3/2.\\|B_{w_1}\\|_F \\le C_{\\rm fro}\\mu_0^{3/2} (r/\\min(n_1,n_2))^{3/2}.∣Bw1​​∣F​leCrmfro​mu03/2​(r/min(n1​,n2​))3/2.

This bridge deliberately requires both the usual density floor

mgelambda,mu04/3,n,r4/3,betalognm \\ge \\lambda\\,\\mu_0^{4/3}\\,n\\,r^{4/3}\\,\\beta\\log nmgelambda,mu04/3​,n,r4/3,betalogn

and the strengthened shifted density floor

mgelambda,mu04/3,n,r4/3,(beta+2)logn.m \\ge \\lambda\\,\\mu_0^{4/3}\\,n\\,r^{4/3}\\,(\\beta+2)\\log n.mgelambda,mu04/3​,n,r4/3,(beta+2)logn.

The strengthened beta+2 hypothesis is essential for this formal route: the pointwise tail is applied with parameter beta+2, producing per-coordinate failure of order n^{-(beta+2)}, and the shifted uniformization theorem then pays the n_1 n_2 <= n^2 union-bound loss to obtain a uniform n^{-beta} bound.

Under these hypotheses, there exist positive universal constants Ccoef and ccoef, depending only on the supplied base-bound constants, such that

\\mathbb P_p\\{\\|Q(\\Omega_2)\\|_\\infty \\le C_{\\rm coef}\\lambda^{-1}\\}\\ge 1-c_{\\rm coef}n^{-\\beta},

where Q(Omega_2) is the centered conditional coefficient matrix quadraticMiddleIndexDistinctCenteredCoefficientMatrix Omega2 S p, and the Lean event is QuadraticMiddleIndexDistinctCenteredCoefficientBound Omega2 S p (Ccoef * lambda^{-1}). This node should not be cited as removing the beta+2 sample-density requirement; it is exactly the formal bridge that records that strengthened hypothesis.

Preamble
import Definitions.Def_matrix_completion_neumann
open MatrixCompletion
Formal statement
theorem quadratic_neumann_middle_index_distinct_centered_coefficients_from_kernel_square_base_bounds_min_dim_shifted
    (Centry Cfro : ℝ) :
    0 < Centry → 0 < Cfro →
    ∃ Ccoef ccoef : ℝ, 0 < Ccoef ∧ 0 < ccoef ∧
      ∀ (β lam : ℝ), 2 < β → 1 ≤ lam →
      ∀ (n₁ n₂ r m : ℕ) (M : Matrix (Fin n₁) (Fin n₂) ℝ)
        (μ₀ μ₁ : ℝ) (S : SVD M r),
        0 < n₁ → 0 < n₂ → 0 < r → m ≤ n₁ * n₂ →
        1 ≤ μ₀ → 1 ≤ μ₁ →
        A0 S μ₀ → A1 S μ₁ →
        (m : ℝ) ≥
          lam * Real.rpow μ₀ ((4 : ℝ) / 3) *
            (↑(max n₁ n₂)) * Real.rpow (r : ℝ) ((4 : ℝ) / 3) *
              (β * Real.log (↑(max n₁ n₂))) →
        (m : ℝ) ≥
          lam * Real.rpow μ₀ ((4 : ℝ) / 3) *
            (↑(max n₁ n₂)) * Real.rpow (r : ℝ) ((4 : ℝ) / 3) *
              ((β + 2) * Real.log (↑(max n₁ n₂))) →
        (∀ w1 : Fin n₁ × Fin n₂,
          ∀ Omega2 : Finset (Fin n₁ × Fin n₂),
          quadraticMiddleIndexDistinctCenteredCoefficientMatrix Omega2 S
              ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) w1.1 w1.2 =
            signMatrix S w1.1 w1.2 *
              matrixEntrySum
                (centeredSamplingFluctuation Omega2
                  ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
                  (quadraticMiddleIndexDistinctKernelSquareBaseMatrix S w1))) →
        (∀ w1 : Fin n₁ × Fin n₂,
          entrySupNorm
              (quadraticMiddleIndexDistinctKernelSquareBaseMatrix S w1) ≤
            Centry * μ₀ ^ 2 *
              (((r : ℝ) / (↑(min n₁ n₂))) ^ 2)) →
        (∀ w1 : Fin n₁ × Fin n₂,
          frobeniusNorm
              (quadraticMiddleIndexDistinctKernelSquareBaseMatrix S w1) ≤
            Cfro * Real.rpow μ₀ ((3 : ℝ) / 2) *
              Real.rpow ((r : ℝ) / (↑(min n₁ n₂))) ((3 : ℝ) / 2)) →
        bernoulliEventProb ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (fun Omega2 =>
              QuadraticMiddleIndexDistinctCenteredCoefficientBound Omega2 S
                ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
                (Ccoef * Real.rpow lam (-1))) ≥
          1 - ccoef * Real.rpow (↑(max n₁ n₂)) (-β) := by
  sorry
Source
Candes, Emmanuel, and Benjamin Recht. Exact Matrix Completion via Convex Optimization. arXiv:0805.4471 / Foundations of Computational Mathematics 9 (2009), 717--772. Exact locations: Section 6.3, Lemma 4.6 proof, PDF p. 30 equation (6.20), PDF pp. 32--33 third-term estimate; Section 6.2, PDF p. 29 after equation (6.17). PDF: https://pages.cs.wisc.edu/~brecht/papers/08.Candes.Recht.MatrixCompletion.pdf

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