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fixed_matrix_centered_sampling_log_moment_bound

Proved

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

candes-rechtcentered-samplingconvex-optimizationlean4matrix-completionmoment-boundsprobability

Role. It is a centered-sampling fluctuation estimate, one of the reusable concentration interfaces used repeatedly by the Neumann-term bounds.

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.

Claim. Log-moment estimate behind Candes-Recht Theorem 6.3: for qqq comparable to βlog⁡n\beta \log nβlogn, the qqqth moment of the centered sampling fluctuation is bounded at the natural scale. This is the symmetrization plus noncommutative-Khintchine part of Section 6.1.

Lecture-note formulation:

Ep ⁣[∥p−1(PΩ−pI)X∥q]≤(Cβnlog⁡np∥X∥∞)q.\mathbb E_p\!\left[ \|p^{-1}(P_\Omega-pI)X\|^{q} \right] \le \left(C\sqrt{\frac{\beta n\log n}{p}}\|X\|_\infty\right)^q.Ep​[∥p−1(PΩ​−pI)X∥q]≤(Cpβnlogn​​∥X∥∞​)q.

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 2 subclaims: Bernoulli sampled row column energy controlled log moment bound; centered sampling log moment from row column energy.

Preamble
import Definitions.Def_matrix_completion_neumann
open MatrixCompletion
Formal statement
theorem fixed_matrix_centered_sampling_log_moment_bound :
    ∃ C : ℝ, 0 < C ∧
      ∀ (β : ℝ), 2 < β →
      ∀ (n₁ n₂ m : ℕ) (X : Matrix (Fin n₁) (Fin n₂) ℝ),
        0 < n₁ → 0 < n₂ → m ≤ n₁ * n₂ →
        (m : ℝ) ≥ β * (↑(max n₁ n₂)) *
          Real.log (↑(max n₁ n₂)) →
        ∃ q : ℕ, 1 ≤ q ∧
          (q : ℝ) ≥ β * Real.log (↑(max n₁ n₂)) ∧
          bernoulliExpectation ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
              (fun Omega =>
                spectralNorm
                  (centeredSamplingFluctuation Omega
                    ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) X) ^ q) ≤
            (C * Real.sqrt
              ((β * (↑(max n₁ n₂)) *
                  Real.log (↑(max n₁ n₂))) /
                ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) *
              entrySupNorm X) ^ q := 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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