ledoux_talagrand_finite_bool_product_linear_process_good_event_log_tail

Proved

by Minghui · Jun 30, 2026 · Mathlib c5ea003 (Lean v4.30.0)

appendix-91bernoulli-samplingboolean-product-measurecandes-rechtcandes-rombergempirical-processfinite-product-spaceledouxsource-backedtalagrand

Generic finite Boolean product-measure Ledoux--Talagrand concentration theorem in good-event form.

Primary source: Ledoux, The Concentration of Measure Phenomenon, Section 7, Corollary 7.8. Mission source connection: Candes--Romberg, Sparsity and incoherence in compressive sampling, PDF p. 11, Section 3, Theorem 3.2, equation (3.9), states the equivalent bad-event form, and Candes--Recht, Exact Matrix Completion via Convex Optimization, Appendix 9.1, PDF p. 46, Theorem 9.1 and equations (9.1)--(9.2), cites this Talagrand--Ledoux product-space concentration input for the matrix-completion tangent-sampling proof.

Mathematical statement and notation: let $\kappa$ be a finite coordinate type and let $\omega:\kappa\to\{0,1\}$ be sampled from the independent product Bernoulli probability model

\mu_p=\prod_{x\in\kappa}\mathrm{Bernoulli}(p),\qquad 0\le p\le1,

represented in Lean as p : NNReal with proof hp : p \le 1. For a finite nonempty coefficient class $c_a(x)$ indexed by $a\in\iota$ , define

S_a(\omega)=\sum_{x\in\kappa}(\omega_x-p)c_a(x),\qquad Z(\omega)=\max_a S_a(\omega),\qquad \bar Z(\omega)=\max_a |S_a(\omega)|.

Assume the envelope bound

|c_a(x)|\le B

for all $a,x$ , with $B>0$ , and the variance proxy bound

\sum_{x\in\kappa}p(1-p)c_a(x)^2\le\sigma^2

for every $a$ . Then there is a universal constant $K>0$ such that for every $t\ge0$ ,

\mathbb P_{\mu_p}\{|Z-\mathbb E_{\mu_p}Z|\le t\} \ge 1-3\exp\!\left(-{t\over KB}\log\left(1+{Bt\over\sigma^2+B\mathbb E_{\mu_p}\bar Z}\right)\right).

Here $\kappa,p,B,\sigma^2,t,Z,\bar Z$ and the product Bernoulli probability model are the main quantities. The matrix dimensions $n_1,n_2$ , powerset sample set $\Omega$ , fixed-cardinality successProb, and coherence parameters $\mu_0,\mu_1$ do not appear in this external concentration theorem; they appear only after specializing $\kappa=[n_1]\times[n_2]$ in downstream matrix-completion nodes.

Formalization note: this is a direct source theorem / source-derived finite Boolean product-measure formulation of the Ledoux--Talagrand concentration input. It is not a formal bridge and not a Lean-only arithmetic lemma. The parent candes_romberg_talagrand_finite_bool_product_linear_process_bad_event_log_tail is a formal complement bridge from this good-event theorem to the Candes--Romberg bad-event theorem.

Preamble

import Definitions.Def_matrix_completion_bernoulli_measure
import Mathlib.Analysis.SpecialFunctions.Log.Basic
import Mathlib.MeasureTheory.Integral.Pi
import Mathlib.Probability.ProbabilityMassFunction.Integrals

open MatrixCompletion
open MeasureTheory
open scoped Classical BigOperators

Formal statement

theorem ledoux_talagrand_finite_bool_product_linear_process_good_event_log_tail :
    ∃ K : ℝ, 0 < K ∧
      ∀ (κ : Type) [Fintype κ] (p : NNReal) (hp : p ≤ 1)
        (ι : Type) [Fintype ι] [Nonempty ι]
        (coeff : ι → κ → ℝ) (B sigmaSq t : ℝ),
        0 < B → 0 ≤ sigmaSq → 0 ≤ t →
        (∀ a : ι, ∀ x : κ, |coeff a x| ≤ B) →
        (∀ a : ι,
          ∑ x : κ, (p : ℝ) * (1 - (p : ℝ)) * (coeff a x) ^ 2 ≤ sigmaSq) →
        let boolProcess : ι → (κ → Bool) → ℝ :=
          fun a ω =>
            ∑ x : κ, ((cond (ω x) (1 : ℝ) 0 - (p : ℝ)) * coeff a x)
        let boolZ : (κ → Bool) → ℝ :=
          fun ω => Finset.univ.sup' Finset.univ_nonempty (fun a : ι => boolProcess a ω)
        let boolZbar : (κ → Bool) → ℝ :=
          fun ω => Finset.univ.sup' Finset.univ_nonempty (fun a : ι => |boolProcess a ω|)
        (Measure.pi (fun _ : κ => (PMF.bernoulli p hp).toMeasure)).real
            {ω | |boolZ ω -
                  (∫ ω, boolZ ω
                    ∂(Measure.pi (fun _ : κ => (PMF.bernoulli p hp).toMeasure)))| ≤ t} ≥
          1 -
            3 * Real.exp
              (-(t / (K * B)) *
                Real.log
                  (1 + (B * t) /
                    (sigmaSq + B *
                      (∫ ω, boolZbar ω
                        ∂(Measure.pi (fun _ : κ => (PMF.bernoulli p hp).toMeasure))))))
        := by
  sorry

Source

Ledoux, *The Concentration of Measure Phenomenon*, Section 7, Corollary 7.8; Candes--Romberg, *Sparsity and incoherence in compressive sampling*, PDF p. 11, Section 3, Theorem 3.2, equation (3.9); cited by Candes--Recht, *Exact Matrix Completion via Convex Optimization*, Appendix 9.1, PDF p. 46, Theorem 9.1 and equations (9.1)--(9.2).

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