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m-step Dobrushin certificate for a stationary probability

Statement

Let P be a finite column-stochastic matrix and let m≥1. Put q=δ(Pm), where δ is the zero-sum coefficient from Column-overlap formula for the zero-sum ℓ1 coefficient. If q<1, then P has a unique stationary probability vector π. Every column probability vector x satisfies

∥x−π∥1≤∥Pmx−x∥11−q≤m∥Px−x∥11−q.

The condition q<1 is a certified uniqueness and error condition; when a computed upper bound for q is at least one, this theorem supplies no accuracy certificate.

Facts & Assumptions

Given: A finite column-stochastic matrix P, an integer m≥1, and q=δ(Pm)<1.

[F1]

Finite column-stochastic matrices and probability vectors: P acts on column probability vectors, and the zero-sum space is H={z:1Tz=0} with ∥z∥1=∑i∣zi∣.

[F2]

Column-stochastic matrices preserve mass and contract ℓ1: P preserves coordinate sums and satisfies ∥Pz∥1≤∥z∥1 for every real vector z.

[F3]

Column-overlap formula for the zero-sum ℓ1 coefficient: ∥Pmz∥1≤q∥z∥1 for every z∈H.

[F4]

Invariant and stationary distribution for a Markov kernel: for the row kernel K=PT, a stationary law is an invariant probability row vector πK=π; transposing gives PπT=πT.

[F5]

Every transition matrix on a nonempty finite state space has a stationary distribution: every finite transition matrix on a nonempty finite state space has an invariant probability; the theorem gives existence only.

Proof

Given: P, m, and q as above, and a column probability vector x.

Proof technique: use contraction on zero-sum errors and telescope the one-step residual.

1.1F1F4F5given

The transpose K=PT is a finite row transition matrix by [F1]. By [F5] it has an invariant probability row vector, and [F4] transposes this to a stationary column probability π for P.

2.1F1F3step 1.1algebra

If π1,π2 are stationary column probabilities, then z=π1−π2 lies in H and Pmz=z. By [F3], ∥z∥1=∥Pmz∥1≤q∥z∥1; since q<1, this forces z=0. Thus π is unique.

2.2F1F3step 1.1algebra

Let e=x−π. Both vectors have coordinate sum one, so e∈H. Since Pmπ=π, reverse triangle inequality and [F3] give ∥Pmx−x∥1=∥Pme−e∥1≥∥e∥1−∥Pme∥1≥(1−q)∥e∥1. Dividing by 1−q>0 proves the first bound.

3.1F2step 2.2algebra

The telescoping identity Pmx−x=∑k=0m−1Pk(Px−x) follows by cancellation of adjacent terms. Applying [F2] repeatedly gives ∥Pk(Px−x)∥1≤∥Px−x∥1 for each k; the triangle inequality therefore yields ∥Pmx−x∥1≤m∥Px−x∥1.

4.1step 2.2step 3.1given∎

Combine steps 2.2 and 3.1. The proof does not require irreducibility or aperiodicity once the stated m-step contraction is certified; if q≥1, the division step is unavailable and no bound is asserted.

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