Lemma 1, p. 12 — ‖PJ‖_D ≤ ‖J‖_D for every J ∈ L₂(S, D)
ProvedTDApprox.Conv.lemma_1invariant-distributionmarkov-chainnonexpansivep2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1weighted-norm
Let be the transition matrix of a Markov chain on a countable state space , and suppose that the chain has a unique invariant distribution with for every (Assumption 1(a)). Write , so that , and .
For every , the vector is well defined (each series converges absolutely), lies in , and
So is a nonexpansion of the steady-state weighted norm. Every contraction estimate of the paper (Lemmas 2–5) rests on this inequality.
Formalization Note. is the integral of against the row . The well-definedness of and its membership in are added as conjuncts; the page uses both implicitly when it writes .
Preamble
import Mathlib import Definitions.Def_TDApprox_Conv_Model open MeasureTheory ProbabilityTheory Filter Topology Finset Matrix
Formal statement
namespace TDApprox.Conv
/-- **Lemma 1** (Tsitsiklis & Van Roy, LIDS-P-2322 (1996), p. 12). Under Assumption 1(a), for any
`J ∈ L₂(S, D)` we have `‖PJ‖_D ≤ ‖J‖_D`, where `(PJ)(i) = Σ_j p_ij J(j) = ∫ J d(P i)`.
The conclusion also records that `PJ` is well defined (`J` is integrable under every row `P i`)
and lies in `L₂(S, D)`; both are implicit in the page's `‖PJ‖_D`. -/
theorem lemma_1 {S : Type*} [MeasurableSpace S] [DiscreteMeasurableSpace S] [Countable S]
(P : Kernel S S) [IsMarkovKernel P] (π : Measure S) [IsProbabilityMeasure π]
(h1a : Assumption1a P π) (J : S → ℝ) (hJ : MemL2D π J) :
(∀ i, Integrable J (P i)) ∧ MemL2D π (Pop P J) ∧ normD π (Pop P J) ≤ normD π J := by sorry
end TDApprox.Conv
Source
Tsitsiklis & Van Roy, LIDS-P-2322 (1996), Lemma 1, p. 12
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.