Appendix A, p. 966 — an ergodic chain has a unique, positive stationary vector and is Cesàro-summable to
ProvedJewellMRP.InfiniteStep.stationary_cesaro_limitcesaro-meanmarkov-chainp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1stationary-distribution
Let be an irreducible row-stochastic matrix on a nonempty finite state set . Then there is a probability vector with such that
- every entry of is strictly positive,
- is the only stationary probability vector of , and
- the Cesàro means of the powers of converge entrywise to the matrix whose rows all equal :
This is the Markov-chain fact on which Appendix A rests: it identifies the limiting per-step reward and makes the matrix used in the fundamental matrix well defined.
Formalization Note The paper says " converges or is Cesàro-summable"; ordinary convergence fails for periodic chains, so the statement is the Cesàro limit, which holds for every irreducible chain.
Preamble
import Mathlib import Definitions.Def_JewellMRP_InfiniteStep_Model open Matrix Filter Topology
Formal statement
namespace JewellMRP.InfiniteStep
theorem stationary_cesaro_limit {S : Type*} [Fintype S] [DecidableEq S] [Nonempty S]
(P : Matrix S S ℝ) (hP : IsErgodic P) :
∃ π : S → ℝ, IsStationary P π ∧ (∀ i, 0 < π i) ∧
(∀ π' : S → ℝ, IsStationary P π' → π' = π) ∧
Tendsto (fun n : ℕ => (n : ℝ)⁻¹ • ∑ k ∈ Finset.range n, P ^ k) atTop
(𝓝 (limitMatrix π)) := by sorry
end JewellMRP.InfiniteStep
Source
Jewell, Markov-Renewal Programming. II: Infinite Return Models, Example, Oper. Res. 11 (1963), p. 966, Appendix A (after Eq. (A 1)), citing Kemeny and Snell [13]
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.