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conditional_khintchine_scale_moment_from_row_column_energy_moment

Proved

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

candes-rechtconvex-optimizationkhintchinelean4matrix-completionmoment-boundsprobabilityscale-absorption

Role. It is part of the symmetrization and matrix-moment machinery behind the spectral norm concentration estimates.

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 sampled row/column nodes, Ni(Ω)N_i(\Omega)Ni​(Ω) counts observed entries in row iii, Nj(Ω)N^j(\Omega)Nj(Ω) counts observed entries in column jjj, and the corresponding energies sum Xij2X_{ij}^2Xij2​ over sampled entries. These estimates feed the noncommutative Khintchine and spectral-norm concentration bounds.

Claim. Convert the Bernoulli moment of the conditional Khintchine scale into the displayed (qn/p) ∥X∥∞\sqrt(q n/p)\,\lVert X\rVert_{\infty}(​qn/p)∥X∥∞​ bound, using the row/column energy moment estimate from Lemma 6.2.

Lecture-note formulation:

Ep ⁣[max⁡{Erowmax⁡,Ecolmax⁡}q]≤(Cpn∥X∥∞2)q⟹Ep ⁣[(q p−1max⁡{Erowmax⁡,Ecolmax⁡})q]≤(C′qnp∥X∥∞)q.\begin{gathered} \mathbb E_p\!\left[\max\{E_{\mathrm{row}}^{\max},E_{\mathrm{col}}^{\max}\}^q\right] \le (Cpn\|X\|_\infty^2)^q\\ \Longrightarrow\quad \mathbb E_p\!\left[ \left(\sqrt q\,p^{-1}\sqrt{\max\{E_{\mathrm{row}}^{\max},E_{\mathrm{col}}^{\max}\}}\right)^q \right] \le \left(C'\sqrt{\frac{qn}{p}}\|X\|_\infty\right)^q . \end{gathered}Ep​[max{Erowmax​,Ecolmax​}q]≤(Cpn∥X∥∞2​)q⟹Ep​[(q​p−1max{Erowmax​,Ecolmax​}​)q]≤(C′pqn​​∥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 1 subclaim: sampled energy scale moment bound from row column energy moment.

Preamble
import Definitions.Def_matrix_completion_rademacher
open MatrixCompletion
Formal statement
theorem conditional_khintchine_scale_moment_from_row_column_energy_moment
    (Cenergy Ckh : ℝ) :
    0 < Cenergy →
    0 < Ckh →
    ∃ Crad : ℝ, 0 < Crad ∧
      ∀ (β : ℝ), 2 < β →
      ∀ (n₁ n₂ m q : 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)) →
        1 ≤ q →
        (q : ℝ) ≥ β * Real.log (↑(max n₁ n₂)) →
        (q : ℝ) ≤ 2 * (β * Real.log (↑(max n₁ n₂))) →
        (q : ℝ) ≤
          ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) *
            (↑(max n₁ n₂)) →
        bernoulliExpectation ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (fun Omega =>
              (max (sampledRowEnergyMax Omega X)
                (sampledColumnEnergyMax Omega X)) ^ q) ≤
          (Cenergy * ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) *
            (↑(max n₁ n₂)) * entrySupNorm X ^ 2) ^ q →
        bernoulliExpectation ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (fun Omega =>
              (Ckh * Real.sqrt (q : ℝ) *
                (((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))⁻¹) *
                Real.sqrt
                  (max (sampledRowEnergyMax Omega X)
                    (sampledColumnEnergyMax Omega X))) ^ q) ≤
          (Crad * Real.sqrt
            (((q : ℝ) * (↑(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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