Alphabeta Math
LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generated
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.

The maximal ergodic inequality on a probability space

Statement

Let T preserve a probability measure P, and let f be integrable, real-valued and measurable. Put Skf=∑j=0k−1f∘Tj, MN=max⁡(0,S1f,…,SNf) and EN={MN>0} for N≥1. Then ∫ENf dP≥0, and also ∫Ef dP≥0 for E={sup⁡k≥1Skf>0}.

Facts & Assumptions

[F1]

Measure-preserving transformations and systems: Let (X,A,μ) be a measure space. A measurable self-map T:X→X is measure preserving if μ(T−1E)=μ(E) for every E∈A. The quadruple (X,A,μ,T) is a measure-preserving system; it is a probability system if μ(X)=1. Here T−1E={x:T(x)∈E} denotes an inverse image, whether or not T is invertible. Neither completeness nor finiteness is implicit. The measure-space and measurable-map conventions are def-measure-space and def-measurable-function-between-measurable-spaces.

[F2]

Arithmetic and lattice operations preserve measurability whenever they are defined: Let (X,A) be a measurable space and let f,g:X→R‾ be measurable. Then:

  1. cf is measurable for every real scalar c;
  2. max⁡(f,g), min⁡(f,g), ∣f∣, f+, and f− are measurable;
  3. if f+g is pointwise defined, then f+g is measurable;
  4. with the total function-product convention declared in that theorem, the pointwise product fg is measurable.
[F3]

Integral invariance under measure-preserving maps: If T preserves μ and f:X→[0,∞] is measurable, then ∫f∘T dμ=∫f dμ, allowing infinity. If f is integrable real or complex valued, f∘T is integrable and the same equality holds. Conversely, for a measurable self-map, equality for every measurable indicator implies measure preservation.

[F4]

The Lebesgue integral is linear on L1(μ): The class L1(μ) is a complex vector space, and the Lebesgue integral is complex-linear on it: ∫(αf+βg) dμ=α∫f dμ+β∫g dμ(α,β∈C, f,g∈L1(μ)).

[F5]

Dominated convergence: Let f and (fn) be measurable complex-valued functions such that fn→f almost everywhere and ∣fn∣≤g almost everywhere for a single nonnegative measurable function g with ∫g dμ<+∞. Then f∈L1(μ), ∫∣fn−f∣ dμ⟶0, and hence ∫fn dμ⟶∫f dμ.

Proof

Given: The objects, hypotheses and definitions in the statement. Its conclusions are to be established below.

1.1F1F2F3

The measurable self-map in F1 and F2 make all finite sums and maxima measurable. Also 0≤MN≤∑j<N∣f∣∘Tj, whose integral is N∫∣f∣ dP<∞ by F3. Thus M_N and its composition with T are integrable.

1.2givenalgebra

For 1≤k≤N, Skf=f+(Sk−1f)∘T≤f+MN∘T, with S0f=0. On E_N take a maximizing k to get f≥MN−MN∘T. On the complement M_N=0 and −MN∘T≤0. Hence everywhere f1EN≥MN−MN∘T.

2.1F3F4step 1.2step 1.1

Integrate the inequality in step 1.2. Integrability is supplied by step 1.1; F4 and F3 give ∫ENf dP≥∫MN dP−∫MN∘T dP=0.

3.1F5step 2.1∎

The sets E_N increase to E. Since ∣f1EN∣≤∣f∣ and f1EN→f1E pointwise, F5 takes step 2.1 to ∫Ef dP≥0.

Depends on

Used by

Dependency tree · two levels

30 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