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rudelson_selection_symmetrized_tensor_khintchine_dense

Proved

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

Symmetrized tensor non-commutative Khintchine bound (dense regime) = Steps 1 and 2 of the proof of Theorem 1 in Rudelson 1999 (J. Funct. Anal. 164). There is a constant Csym>0C_{sym}>0Csym​>0 such that, under the dense density hypothesis and the coordinate-radius bound ∥PT(eiej∗)∥F≤R\lVert P_T(e_ie_j^*)\rVert_F\le R∥PT​(ei​ej∗​)∥F​≤R, the expected tangent-sampling deviation satisfies EZ≤Csym (sR) EZ+1EZ\le C_{sym}\,(sR)\,\sqrt{EZ+1}EZ≤Csym​(sR)EZ+1​ with s=log⁡max⁡(n1,n2)/ps=\sqrt{\log\max(n_1,n_2)/p}s=logmax(n1​,n2​)/p​, p=m/(n1n2)p=m/(n_1n_2)p=m/(n1​n2​). The proof is symmetrization (δ−p)→ε(\delta-p)\to\varepsilon(δ−p)→ε (ghost copy) followed by the matrix Lust-Picquard / non-commutative Khintchine inequality applied to the rank-one tensor operators Xj=yj⊗yjX_j=y_j\otimes y_jXj​=yj​⊗yj​, yj=PT(eiej∗)y_j=P_T(e_ie_j^*)yj​=PT​(ei​ej∗​), using the spectral identity (yy∗)2=∥y∥2 yy∗(yy^*)^2=\lVert y\rVert^2\,yy^*(yy∗)2=∥y∥2yy∗ so that ∥(∑Xj2)1/2∥≤max⁡j∥yj∥ ∥∑yjyj∗∥1/2\lVert(\sum X_j^2)^{1/2}\rVert\le \max_j\lVert y_j\rVert\,\lVert\sum y_jy_j^*\rVert^{1/2}∥(∑Xj2​)1/2∥≤maxj​∥yj​∥∥∑yj​yj∗​∥1/2.

Preamble
import Definitions.Def_matrix_completion_tangent
open MatrixCompletion
open scoped Classical BigOperators
Formal statement
theorem rudelson_selection_symmetrized_tensor_khintchine_dense :
    ∃ Csym : ℝ, 0 < Csym ∧
      ∀ (β : ℝ), 2 < β →
      ∀ (n₁ n₂ r m : ℕ) (M : Matrix (Fin n₁) (Fin n₂) ℝ)
        (S : SVD M r) (R : ℝ),
        0 < n₁ → 0 < n₂ → 0 < r → m ≤ n₁ * n₂ →
        0 ≤ R →
        (m : ℝ) ≥ β * (↑(max n₁ n₂)) * (r : ℝ) * Real.log (↑(max n₁ n₂)) →
        (∀ i : Fin n₁, ∀ j : Fin n₂,
          frobeniusNorm (tangentProjection S (coordinateMatrix i j)) ≤ R) →
        bernoulliExpectation ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
            (fun Omega =>
              tangentSamplingDeviation Omega S
                ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) ≤
          Csym *
            (Real.sqrt
              (Real.log (↑(max n₁ n₂)) /
                ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) * R)
          * Real.sqrt
              (bernoulliExpectation ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
                (fun Omega =>
                  tangentSamplingDeviation Omega S
                    ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) + 1) := by sorry
Source
Rudelson, J. Funct. Anal. 164 (1999) 60-72, Theorem 1 proof Steps 1-2 (symmetrization p.3 + Lust-Picquard tensor lemma p.4); Lust-Picquard/Pisier non-commutative Khintchine; van Handel/Tropp matrix concentration. Candes-Recht 2009 arXiv:0805.4471 Section 4.2 eq(4.9) p.18.

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