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linear_neumann_diagonal_centered_threshold_from_centered_sampling_bound

Proved

by Shuze Chen · Jun 14, 2026 · Mathlib c5ea003 (Lean v4.30.0)

candes-rechtcentered-samplingconvex-optimizationlean4linear-termsmatrix-completionneumann-seriesprobabilityscale-absorption

Role. It belongs to the golfing/Neumann-series certificate branch, where the certificate is decomposed into linear and quadratic sampling terms.

Problem and notation. Exact matrix completion asks when an unknown low-rank real matrix can be recovered from a random subset of its entries. Here M∈Rn1×n2M\in\mathbb R^{n_1\times n_2}M∈Rn1​×n2​ has rank rrr, mmm entries are observed, and n=max⁡(n1,n2)n=\max(n_1,n_2)n=max(n1​,n2​). Recovery means nuclear-norm minimization: minimize ∥X∥∗\|X\|_*∥X∥∗​ among matrices XXX agreeing with MMM on the observed entries. Probability notation. successProb⁡(m,M)\operatorname{successProb}(m,M)successProb(m,M) is the fixed-cardinality success probability: Ω\OmegaΩ is chosen uniformly among all subsets of n1n2n_1n_2n1​n2​ entries with ∣Ω∣=m|\Omega|=m∣Ω∣=m, and the event is that the convex program uniquely returns MMM. In Bernoulli nodes, Pp(E)\mathbb P_p(E)Pp​(E) or bernoulliEventProb⁡(p,E)\operatorname{bernoulliEventProb}(p,E)bernoulliEventProb(p,E) means each entry is sampled independently with probability ppp, usually p=m/(n1n2)p=m/(n_1n_2)p=m/(n1​n2​). Coherence notation. The object SSS records SVD/singular-vector data for MMM. The hypotheses A0(S,μ0)A0(S,\mu_0)A0(S,μ0​) and A1(S,μ1)A1(S,\mu_1)A1(S,μ1​) are the Candes-Recht incoherence assumptions: μ0\mu_0μ0​ measures how spread out the singular vector spaces are, and μ1\mu_1μ1​ measures the largest entry of the sign matrix UV⊤UV^\topUV⊤. The parameter β>2\beta>2β>2 controls polynomial failure probabilities such as n−βn^{-\beta}n−β. For certificate nodes, TTT is the tangent space at MMM, PTP_TPT​ and PT⊥P_{T^\perp}PT⊥​ are the tangent and normal projections, and PΩP_\OmegaPΩ​ keeps only observed entries. The Neumann-series estimates control the dual certificate used to prove uniqueness of nuclear-norm recovery.

Claim. Deterministic threshold absorption for the centered diagonal linear Neumann contribution. The fixed-matrix fluctuation representation, the base entry-sup estimate, and a centered sampling spectral event imply the final λ−1\lambda^{-1}λ−1 threshold after absorbing the scalar prefactor and sample-size scale.

Lecture-note formulation:

∥p−1(PΩ−pI)B∥≤A(B)⟹∥Ldiag∥≤C λ−1after substituting the coefficient matrix B.\|p^{-1}(P_\Omega-pI)B\|\le A(B) \quad\Longrightarrow\quad \|L_{\mathrm{diag}}\|\le C\,\lambda^{-1} \quad\text{after substituting the coefficient matrix }B.∥p−1(PΩ​−pI)B∥≤A(B)⟹∥Ldiag​∥≤Cλ−1after substituting the coefficient matrix B.

The constants in this node are universal existential constants; the theorem asserts that some positive constants with these roles exist.

Decomposition status. A corresponding proof sketch reduces this node to smaller mathematical subclaims. The checked reduction uses 1 subclaim: prefactored centered sampling linear Neumann threshold from base entry scale.

Preamble
import Definitions.Def_matrix_completion_neumann
open MatrixCompletion
Formal statement
theorem linear_neumann_diagonal_centered_threshold_from_centered_sampling_bound
    (Cfixed Cbase : ℝ) :
    0 < Cfixed → 0 < Cbase →
    ∃ Cthreshold : ℝ, 0 < Cthreshold ∧
      ∀ (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 * μ₁ * max (Real.sqrt μ₀) μ₁ *
            (↑(max n₁ n₂)) * (r : ℝ) *
              (β * Real.log (↑(max n₁ n₂))) →
        ∀ Omega : Finset (Fin n₁ × Fin n₂),
        linearNeumannDiagonalCenteredContribution Omega S
            ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) =
          (((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))⁻¹ *
              (1 - 2 * ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))))) •
            centeredSamplingFluctuation Omega
              ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
              (linearNeumannDiagonalBaseMatrix S) →
        entrySupNorm (linearNeumannDiagonalBaseMatrix S) ≤
          Cbase * μ₁ *
            Real.sqrt ((r : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) *
              (μ₀ * (r : ℝ) / (↑(max n₁ n₂))) →
        CenteredSamplingSpectralBound Omega
            ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (linearNeumannDiagonalBaseMatrix S)
            (Cfixed * Real.sqrt
              ((β * (↑(max n₁ n₂)) *
                  Real.log (↑(max n₁ n₂))) /
                ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) *
              entrySupNorm (linearNeumannDiagonalBaseMatrix S)) →
        spectralNorm
            (linearNeumannDiagonalCenteredContribution Omega S
              ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) ≤
          Cthreshold * Real.rpow lam (-1) := by
  sorry
Source
Candes, Emmanuel, and Benjamin Recht. "Exact matrix completion via convex optimization." Communications of the ACM 55.6 (2012): 111-119.

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