Mean ergodic convergence from near-diagonal subadditivity
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Abstract
We prove a mean ergodic theorem whose subadditivity hypothesis is required only for nearly equal block lengths. A positive unital integral-preserving operator on a probability space need not be ergodic or invertible. Our main mechanism is conditional: convergence of the invariant projections of uniformly integrable normalized inputs, together with both ordered inequalities at length differences at most two, forces convergence in L^1. Combining this mechanism with an existing scalar shrinking-corridor theorem gives invariant L^p convergence, for every finite p≥1, when the relative corridor width has divergent square integral and eventually admits differences two. Nonnegative inputs require only individual L^p membership. For signed inputs, uniform integrability of the normalized negative-part pth powers is necessary and sufficient. The scalar width threshold and its converse are inherited; the contribution is their Markov mean-ergodic lift. Under the same scalar-admissible width and norm criterion, signed integrable inputs also satisfy lim sup g_n/n=F almost surely, with a possibly nonconstant invariant limit. The upper control is uniform over macroscopic operator powers and lengths. An all-legal adjacent-halving tree has a vanishing occupation gradient; a classical maximal-inequality argument admits integrable rewards directly. No uniform boundedness assumption is needed.
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Research details
- Subjects
- Mathematics · Functional Analysis · Dynamical Systems
- Keywords
- Functional Analysis, Dynamical Systems, mathematical research
- Manuscript date
- 2026-10-24
- Reader reuse
- No additional permission granted
Scope
A conditional Markov mean-ergodic lift of the inherited scalar near-diagonal width criterion, with the stated signed-input norm condition and almost-sure/macroscopic upper conclusions.
Disclosures
AI-assisted tools supported research exploration, literature review, manuscript preparation and internal verification. The author retains responsibility for the work. Internal AI-assisted and computational checks are distinct from external human peer review.
Reproduction
12-page manuscript and 9 source-package members. Use build.py and Tectonic to compile main.tex and upper.tex. Python 3.8+ and its standard library run python verify.py, also under -O, to compare exact adjacent-halving/tree diagnostics with halving_checks.json; --write regenerates that finite reference. The asymptotic and almost-sure arguments are analytic.