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rudelson_selection_expected_tangent_deviation_from_coordinate_radius_bound_dense_proviso

Proved

by Harry_Xu · Jun 23, 2026 · Mathlib 0df444a (Lean v4.33.1)

concentrationmatrix-completionprobabilityrudelson

Corrected Rudelson selection estimate (expected tangent-sampling deviation) WITH the Candès–Recht Theorem 4.2 “right-hand side ≤1\le 1≤1” proviso. There is an absolute constant Csel>0C_{sel}>0Csel​>0 such that for every β>2\beta>2β>2, all sizes with m≤n1n2m\le n_1n_2m≤n1​n2​, every reduced SVD SSS of MMM, and every radius R≥0R\ge 0R≥0: if the sampling is dense, m≥β (max⁡n) r log⁡(max⁡n)m\ge\beta\,(\max n)\,r\,\log(\max n)m≥β(maxn)rlog(maxn), if the desymmetrization parameter is bounded, log⁡(max⁡n)/p⋅R≤1\sqrt{\log(\max n)/p}\cdot R\le 1log(maxn)/p​⋅R≤1 (with p=m/(n1n2)p=m/(n_1n_2)p=m/(n1​n2​)), and if every tangent-coordinate projection has Frobenius norm at most RRR, then the expected tangent-sampling deviation EΩ p−1∥PTPΩPT−pPT∥\mathbb{E}_\Omega\,p^{-1}\|P_TP_\Omega P_T-pP_T\|EΩ​p−1∥PT​PΩ​PT​−pPT​∥ is at most Csel log⁡(max⁡n)/p RC_{sel}\,\sqrt{\log(\max n)/p}\,RCsel​log(maxn)/p​R. The added proviso log⁡/p⋅R≤1\sqrt{\log/p}\cdot R\le 1log/p​⋅R≤1 is exactly Candès–Recht 2009, Theorem 4.2 part 1 (eq. (4.9), p.18): “provided the right-hand side is smaller than 1.” It comes from the self-bounding desymmetrization EZ≤A+AEZ\mathbb{E}Z\le A+A\sqrt{\mathbb{E}Z}EZ≤A+AEZ​ with A=log⁡/p RA=\sqrt{\log/p}\,RA=log/p​R, which yields the linear bound only when A≤1A\le 1A≤1; without it the stated linear bound can fail (the true bound is ∼A2\sim A^2∼A2 when A≥1A\ge 1A≥1). This corrects the radius_dense node which omitted the proviso (R a free parameter).

Preamble
import Definitions.Def_matrix_completion_tangent
open MatrixCompletion
Formal statement
theorem rudelson_selection_expected_tangent_deviation_from_coordinate_radius_bound_dense_proviso :
    ∃ Csel : ℝ, 0 < Csel ∧
      ∀ (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)) →
        Real.sqrt
            (Real.log (↑(max n₁ n₂)) /
              ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) * R ≤ 1 →
        (∀ 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)))) ≤
          Csel *
            Real.sqrt
              (Real.log (↑(max n₁ n₂)) /
                ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) *
            R := by sorry
Source
Candès–Recht 2009, arXiv:0805.4471, Theorem 4.2 part 1, eq. (4.9), p.18 (“provided the RHS is smaller than 1”); self-bounding desymmetrization of the Rudelson–Candès–Tao expectation bound.

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