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rademacher_sampled_matrix_schatten_moment_khintchine_q_ge_two_variance_scale

Proved

by Shuze Chen · Jun 23, 2026 · Mathlib c5ea003 (Lean v4.30.0)

candes-rechtmatrix-completionnoncommutative-khintchinerademachersample-complexityschatten

Source-faithful q≥2q\ge 2q≥2 Khintchine-to-variance branch.

Fix a sampled entry set Ω⊆[n1]×[n2]\Omega\subseteq [n_1]\times[n_2]Ω⊆[n1​]×[n2​], a deterministic matrix X∈Rn1×n2X\in\mathbb R^{n_1\times n_2}X∈Rn1​×n2​, and p=m/(n1n2)p=m/(n_1n_2)p=m/(n1​n2​). Let

Sε=p−1∑(i,j)∈ΩεijXijeiej⊤S_\varepsilon=p^{-1}\sum_{(i,j)\in\Omega}\varepsilon_{ij}X_{ij}e_ie_j^\topSε​=p−1(i,j)∈Ω∑​εij​Xij​ei​ej⊤​

be the Rademacher-symmetrized sampled coordinate matrix.

This theorem asserts that there is a universal constant Ckh>0C_{\mathrm{kh}}>0Ckh​>0 such that, for every larger constant C′≥CkhC'\ge C_{\mathrm{kh}}C′≥Ckh​, every β>2\beta>2β>2, and every integer q≥2q\ge2q≥2 satisfying q≥βlog⁡(max⁡(n1,n2))q\ge \beta\log(\max(n_1,n_2))q≥βlog(max(n1​,n2​)),

Eε ∥Sε∥Sqq≤(C′q varianceScale⁡(Ω,p,X))q.\mathbb E_\varepsilon\,\lVert S_\varepsilon\rVert_{S_q}^{q} \le \bigl(C'\sqrt q\,\operatorname{varianceScale}(\Omega,p,X)\bigr)^q.Eε​∥Sε​∥Sq​q​≤(C′q​varianceScale(Ω,p,X))q.

This is the Candes-Recht Section 6.1, Lemma 6.1 noncommutative Khintchine estimate in the range where the source theorem actually applies, together with the diagonal Gram-to-variance comparison. The monotone-constant formulation is intentional: downstream reductions may enlarge C′C'C′ when combining this branch with a separate boundary case.

Preamble
import Definitions.Def_matrix_completion_gram_schatten
open MatrixCompletion
Formal statement
theorem rademacher_sampled_matrix_schatten_moment_khintchine_q_ge_two_variance_scale :
    ∃ Ckh : ℝ, 0 < Ckh ∧
      ∀ C' : ℝ, Ckh ≤ C' →
      ∀ (β : ℝ), 2 < β →
      ∀ (n₁ n₂ m q : ℕ)
        (Omega : Finset (Fin n₁ × Fin n₂))
        (X : Matrix (Fin n₁) (Fin n₂) ℝ),
        2 ≤ q →
        (q : ℝ) ≥ β * Real.log (↑(max n₁ n₂)) →
        rademacherExpectation
            (fun eps =>
              schattenNorm (q : ℝ)
                (rademacherSampledMatrix Omega eps
                  ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) X) ^ q) ≤
          (C' * Real.sqrt (q : ℝ) *
            rademacherSampledVarianceScale Omega
              ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) 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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