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bernoulli_least_squares_certificate_normal_bound_under_general_sample_bound

Proved

by Shuze Chen · Jun 13, 2026 · Mathlib 0df444a (Lean v4.33.1)

bernoulli-samplingcandes-rechtconvex-optimizationdual-certificatelean4matrix-completionprobability

Role. It belongs to the dual-certificate branch, controlling the certificate that proves uniqueness of nuclear-norm recovery.

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. Under the general Candes-Recht sample lower bound, every least-squares certificate has normal component of spectral norm < 1 with high probability. This is the theorem-regime form of the Neumann-series estimates from Section 4.3.

Lecture-note formulation:

m≥Cmax⁡{μ12,μ0μ1,μ0n1/4}nrβlog⁡n⟹Pp ⁣(∥p−1PTPΩPT−PT∥T→T≤12)≥1−cn−β.m\ge C\max\{\mu_1^2,\sqrt{\mu_0}\mu_1,\mu_0n^{1/4}\}nr\beta\log n \Longrightarrow \mathbb P_p\!\left(\|p^{-1}P_TP_\Omega P_T-P_T\|_{T\to T}\le \frac12\right) \ge 1-cn^{-\beta}.m≥Cmax{μ12​,μ0​​μ1​,μ0​n1/4}nrβlogn⟹Pp​(∥p−1PT​PΩ​PT​−PT​∥T→T​≤21​)≥1−cn−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: sampled sign matrix Neumann term small under general sample bound; linear Neumann correction small under general sample bound; quadratic Neumann correction small under general sample bound; Neumann certificate remainder small under general sample bound; least squares certificate normal bound from Neumann term bounds; sample ratio between zero and one.

Preamble
import Definitions.Def_matrix_completion_tangent
open MatrixCompletion
Formal statement
theorem bernoulli_least_squares_certificate_normal_bound_under_general_sample_bound :
    ∃ C c : ℝ, 0 < C ∧ 0 < c ∧
      ∀ C' : ℝ, C ≤ C' →
      ∀ (β : ℝ), 2 < β →
      ∀ (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) ≥
          C' * max (max (μ₁ ^ 2) (Real.sqrt μ₀ * μ₁))
                  (μ₀ * Real.rpow (↑(max n₁ n₂)) ((1 : ℝ) / 4))
            * (↑(max n₁ n₂)) * (r : ℝ) * (β * Real.log (↑(max n₁ n₂))) →
        bernoulliEventProb ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (fun Omega =>
              ∀ Y : Matrix (Fin n₁) (Fin n₂) ℝ,
                LeastSquaresDualCertificate Omega S Y →
                spectralNorm (normalProjection S Y) < 1) ≥
          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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