Lemma 4.3 (ii) — discrete Bellman–Gronwall inequality, one-step form
ProvedBorkarMeynODE.Tapering.gronwall_discrete_stepgronwallinequalityp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1
Let , and be nonnegative real sequences such that
Then for all ,
This version is applied to conditional second moments in the proof of Lemma 4.5. The factor (rather than ) makes it sharper than the textbook discrete Gronwall bound.
Preamble
import Mathlib
Formal statement
namespace BorkarMeynODE.Tapering
/-- **Lemma 4.3 (ii)** (Borkar–Meyn 2000, p. 461), a discrete Bellman–Gronwall lemma.
If `α, A, γ` are nonnegative sequences with `A(n+1) ≤ (1 + α(n)) A(n) + γ(n)` for all
`n ≥ 0`, then for all `n ≥ 1`
`A(n+1) ≤ exp(∑_{k=1}^{n} α(k)) ((1 + α(0)) A(0) + β(n))`, where `β(n) = ∑_{k=0}^{n} γ(k)`. -/
theorem gronwall_discrete_step (A α γ : ℕ → ℝ)
(hA : ∀ n, 0 ≤ A n) (hα : ∀ n, 0 ≤ α n) (hγ : ∀ n, 0 ≤ γ n)
(hrec : ∀ n : ℕ, A (n + 1) ≤ (1 + α n) * A n + γ n) :
∀ n : ℕ, 1 ≤ n →
A (n + 1) ≤ Real.exp (∑ k ∈ Finset.Icc 1 n, α k) *
((1 + α 0) * A 0 + ∑ k ∈ Finset.range (n + 1), γ k) := by sorry
end BorkarMeynODE.Tapering
Source
Borkar and Meyn, The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning, SIAM J. Control Optim. 38(2) (2000), p. 461, Lemma 4.3 (ii)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.