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quadratic_neumann_first_index_distinct_contribution_small_with_lambda

Proved

by Shuze Chen · Jun 14, 2026 · Mathlib 0df444a (Lean v4.33.1)

candes-rechtconvex-optimizationlean4matrix-completionneumann-seriesprobabilityquadratic-terms

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. The ω1≠ω2=ω3\omega_{1} \ne \omega_{2} = \omega_{3}ω1​=ω2​=ω3​ contribution in Lemma 4.6 is small. This packages the second term estimate after applying Lemma 6.7 and the deterministic auxiliary matrix bound (Lemma 6.8).

Lecture-note formulation:

m≥C λμ04/3nr4/3βlog⁡n⟹Pp ⁣(∥Q1≠2=3(Ω)∥≤C′ λ−3/2)≥1−c′n−β.\begin{gathered} m\ge C\,\lambda\mu_0^{4/3}n r^{4/3}\beta\log n\\ \Longrightarrow\quad \mathbb P_p\!\left(\|Q_{1\ne2=3}(\Omega)\|\le C'\,\lambda^{-3/2}\right) \ge 1-c'n^{-\beta}. \end{gathered}m≥Cλμ04/3​nr4/3βlogn⟹Pp​(∥Q1=2=3​(Ω)∥≤C′λ−3/2)≥1−c′n−β.​

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 6 subclaims: quadratic Neumann first index distinct centered contribution small with lambda; quadratic Neumann first index distinct mean contribution small with lambda; quadratic Neumann first index distinct bound from centered and mean bounds; Bernoulli event intersection probability from lower bounds; Bernoulli event probability mono; sample ratio between zero and one.

Preamble
import Definitions.Def_matrix_completion_neumann
open MatrixCompletion
Formal statement
theorem quadratic_neumann_first_index_distinct_contribution_small_with_lambda :
    ∃ C c : ℝ, 0 < C ∧ 0 < c ∧
      ∀ (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))) →
        bernoulliEventProb ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (fun Omega =>
              spectralNorm
                (quadraticNeumannFirstIndexDistinctContribution Omega S
                  ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) ≤
                C * Real.rpow lam (-((3 : ℝ) / 2))) ≥
          1 - c * Real.rpow (↑(max n₁ n₂)) (-β) := 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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