Alphabeta Math
ExampleConstruction: Literature-sourcedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Uniform ergodicity gives no uniform practical mixing time

Statement

For every 0<η<1/2, the two-state kernel Pη=(1−ηηη1−η) is uniformly geometrically ergodic, yet the family has no common finite practical mixing time as η↓0.

Facts & Assumptions

Given: The parameter range and transition matrix in the Statement.

[F1]

Aperiodicity for a general-state Markov kernel: positive self-loops rule out a cyclic decomposition.

[F2]

Total variation distance for probability laws: total variation is the supremum of event-probability differences.

[F3]

Whole-space minorization gives uniform geometric ergodicity: a whole-space minorization yields a uniform geometric bound.

[F4]

Phi-irreducibility for a general-state Markov kernel: each positive counting-measure set is accessible from every state.

Verification

Given: The matrix Pη and facts [F1]–[F3].

1.1F1F4givenalgebra

The invariant law is π=(1/2,1/2) by symmetry, and each state has positive holding probability 1−η. Every entry is positive, so it is irreducible by [F4], and each state has positive holding probability, so it is aperiodic. Moreover every row dominates 2η π, because its two entries are at least η. This is a whole-space minorization with m=1, ϵ=2η.

2.1F3step 1.1

The theorem [F3] gives sup⁡x∥Pηn(x,⋅)−π∥TV≤(1−2η)n, so each fixed chain is uniformly geometrically ergodic.

3.1F2step 1.1algebra∎

The nonconstant eigenvector (1,−1) has eigenvalue 1−2η. Direct multiplication, or induction, gives ∥Pηn(0,⋅)−π∥TV=12(1−2η)n. For every fixed n this tends to 1/2 as η↓0. Hence no finite n makes the distance smaller than (for example) 1/4 uniformly over the family. Each chain is uniformly ergodic, but the mixing time depends on η.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

12 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources