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Lemma 1.6 -- the period is constant on an irreducible chain

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MarkovMixing.period_eq_of_irreducible

by Shuze Chen · Aug 21, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-chainsmixing-timesprobability

For a stochastic irreducible matrix PPP on a finite state space, all states have the same period: gcd⁡{t≥1:Pt(x,x)>0}=gcd⁡{t≥1:Pt(y,y)>0}\gcd\{t\ge1:P^t(x,x)>0\}=\gcd\{t\ge1:P^t(y,y)>0\}gcd{t≥1:Pt(x,x)>0}=gcd{t≥1:Pt(y,y)>0} for all x,yx,yx,y. (The period is encoded as the largest common divisor of the return-time set, which equals the gcd when the return set is nonempty -- as irreducibility and stochasticity guarantee.) This makes the period of the chain well defined.

Preamble
import Definitions.Def_mm_basic
Formal statement
namespace MarkovMixing

/-- **Lemma 1.6** (LPW): for an irreducible chain, all states have the same
period, so the period of the chain is well defined. -/
theorem period_eq_of_irreducible {V : Type*} [Fintype V] [DecidableEq V]
    (P : Matrix V V ℝ) (hP : IsStochastic P) (hirr : Irreducible P) (x y : V) :
    period P x = period P y := by
  sorry

end MarkovMixing
Source
D. A. Levin, Y. Peres, E. L. Wilmer, Markov Chains and Mixing Times, AMS 2009, https://documents.epfl.ch/groups/i/ip/ipg/www/2013-2014/Random_Walks/markovmixing.pdf, Section 1.3, Lemma 1.6, p. 8
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What the Lean code literally says, in plain math · claude-fable-5

For any finite type VVV (with decidable equality; VVV may be empty, in which case the statement is vacuous since it takes two elements of VVV as arguments), any matrix PPP of real numbers indexed by V×VV \times VV×V that is stochastic — meaning Px,y≥0P_{x,y} \ge 0Px,y​≥0 for all x,y∈Vx, y \in Vx,y∈V and every row sums to one, ∑yPx,y=1\sum_{y} P_{x,y} = 1∑y​Px,y​=1 for all xxx — and that is irreducible in the following sense: for every pair x,y∈Vx, y \in Vx,y∈V there exists a natural number t≥0t \ge 0t≥0 with (Pt)x,y>0(P^t)_{x,y} > 0(Pt)x,y​>0 (note t=0t = 0t=0 is allowed, so the diagonal case x=yx = yx=y holds automatically because P0P^0P0 is the identity matrix), the theorem asserts that for all x,y∈Vx, y \in Vx,y∈V the period of PPP at xxx equals the period at yyy: perP(x)=perP(y)\mathrm{per}_P(x) = \mathrm{per}_P(y)perP​(x)=perP​(y). Here the period at a state xxx is defined via the return set RP(x)={ t∈N∣t≥1 and (Pt)x,x>0 }R_P(x) = \{\, t \in \mathbb{N} \mid t \ge 1 \text{ and } (P^t)_{x,x} > 0 \,\}RP​(x)={t∈N∣t≥1 and (Pt)x,x​>0}, as the supremum, taken in the natural numbers, of the set of common divisors of RP(x)R_P(x)RP​(x):

perP(x)=sup⁡{ d∈N∣d divides every t∈RP(x) }.\mathrm{per}_P(x) = \sup \{\, d \in \mathbb{N} \mid d \text{ divides every } t \in R_P(x) \,\}.perP​(x)=sup{d∈N∣d divides every t∈RP​(x)}.

When RP(x)R_P(x)RP​(x) is nonempty this supremum is the greatest common divisor of the return times (the set of common divisors is finite, and its largest element is the gcd, since 0∈RP(x)0 \in R_P(x)0∈RP​(x) is excluded by the condition t≥1t \ge 1t≥1). When RP(x)R_P(x)RP​(x) is empty (the state never returns to itself with positive probability at any time t≥1t \ge 1t≥1), every natural number is vacuously a common divisor, the set of common divisors is unbounded, and the supremum operator on N\mathbb{N}N assigns the junk value 000, so in that case perP(x)=0\mathrm{per}_P(x) = 0perP​(x)=0. The conclusion is exactly the equality of these two natural numbers for the two given states xxx and yyy, under the stochasticity and irreducibility hypotheses stated above.

Human review
  • Endorsed by Community (Bot) · Aug 21, 2026

  • Endorsed by Shuze Chen · Aug 21, 2026

    Confirmed by the mission captain (proposal self-audit).

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