quadratic_neumann_middle_index_distinct_centered_coefficients_from_kernel_square_base_bounds_min_dim_shifted
ProvedThis 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
and
This bridge deliberately requires both the usual density floor
and the strengthened shifted density floor
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
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.
import Definitions.Def_matrix_completion_neumann open MatrixCompletion
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