ledoux_talagrand_finite_bernoulli_coordinate_process_log_tail
ProvedThis is the finite Bernoulli-coordinate specialization of the textbook Talagrand--Ledoux concentration theorem for bounded centered empirical-process suprema.
Let be sampled in the independent Bernoulli model with
For each element of a finite nonempty index class, let be a real coefficient array and define the centered coordinate sum
Set
Assume the uniform envelope bound
with , and the Bernoulli variance proxy bound
Then there is a universal numerical constant such that for every ,
This node is intended as the textbook terminal analytic input for the Candès--Recht Appendix 9.1 Talagrand step. The absolute supremum is kept because this is the standard non-symmetric empirical-process form; the symmetric Candès--Recht specialization is obtained by the elementary identity when the coefficient class is closed under .
Source location: Ledoux, The Concentration of Measure Phenomenon, Section 7, Corollary 7.8. The equivalent local lecture-note statement is Ledoux, Section 3.4, Theorem 3.6 plus the following paragraph stating that the same bound controls up to numerical constants. Candès--Romberg, PDF p. 11, Theorem 3.2, cites this Ledoux corollary before applying it to Bernoulli coordinate sampling; Candès--Recht Appendix 9.1, PDF p. 46, invokes the symmetric form as Theorem 9.1.
import Definitions.Def_matrix_completion_bernoulli import Mathlib.Analysis.SpecialFunctions.Log.Basic open MatrixCompletion open scoped Classical BigOperators
theorem ledoux_talagrand_finite_bernoulli_coordinate_process_log_tail :
∃ K : ℝ, 0 < K ∧
∀ (n₁ n₂ m : ℕ) (ι : Type) [Fintype ι] [Nonempty ι]
(coeff : ι → Fin n₁ → Fin n₂ → ℝ) (B sigmaSq t : ℝ),
0 < n₁ → 0 < n₂ → m ≤ n₁ * n₂ →
0 < B → 0 ≤ sigmaSq → 0 ≤ t →
(∀ a : ι, ∀ i : Fin n₁, ∀ j : Fin n₂,
|coeff a i j| ≤ B) →
(∀ a : ι,
∑ i : Fin n₁, ∑ j : Fin n₂,
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) *
(1 - ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) *
(coeff a i j) ^ 2 ≤ sigmaSq) →
let p : ℝ := (m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))
let process : ι → Finset (Fin n₁ × Fin n₂) → ℝ :=
fun a Omega =>
∑ i : Fin n₁, ∑ j : Fin n₂,
(((if (i, j) ∈ Omega then (1 : ℝ) else 0) - p) *
coeff a i j)
let Z : Finset (Fin n₁ × Fin n₂) → ℝ :=
fun Omega =>
Finset.univ.sup' Finset.univ_nonempty (fun a : ι => process a Omega)
let Zbar : Finset (Fin n₁ × Fin n₂) → ℝ :=
fun Omega =>
Finset.univ.sup' Finset.univ_nonempty
(fun a : ι => |process a Omega|)
bernoulliEventProb p
(fun Omega => |Z Omega - bernoulliExpectation p Z| ≤ t) ≥
1 -
3 * Real.exp
(-(t / (K * B)) *
Real.log
(1 + (B * t) /
(sigmaSq + B * bernoulliExpectation p Zbar))) := by
sorry