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Polynomial-time approximate counting and uniform sampling of contingency tables with arbitrary margins

FPRAS manuscript: one randomized algorithm counts nonnegative integer matrices with prescribed row sums, column sums and entry bounds bijb_{ij} (zeros allowed) within relative error ε\varepsilon with probability 1−δ1-\delta, using unbiased bits and a polynomial bound in the input length, ε−1\varepsilon^{-1} and log⁡δ−1\log\delta^{-1} on every execution. Sampling manuscript: for ordinary tables, an almost-uniform sampler (TV error 2−k2^{-k}) in worst-case polynomial bit time and an exactly uniform sampler in expected polynomial bit time in m,n,log⁡(N+1)m,n,\log(N+1). Exponents are deliberately large; the results are complexity classifications, not practical algorithms, and the exact sampler is polynomial only in expectation.

Exact FrontierDelta

Prior state unknown→proved

Scope and record

Occurred: Sep 24, 2026

Delta type: SOURCE CLAIM

Assumptions: VibeMathed verification: lean-checked. Publication: announcement. AI contribution: ai-discovered. VibeMathed editorial classifications, scores, notes, relations, and dataset structure are CC BY 4.0. Source statements and linked content retain their own rights.

Canonical aliases: Polynomial-time approximate counting and uniform sampling of contingency tables with arbitrary margins · Counting and sampling contingency tables

Confidence: Not scored

Registry verification: lean checked · announcement · candidate

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Attribution

VibeMathed
registry · event recorded by

Unreleased internal OpenAI model
model · ai model contributor · OpenAI

Artifacts and verifiers

Lean: OAI/Combinatorics/ContingencyTables/UnconditionalMain.lean

lean artifact · pending

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Compute record

No linked compute attempts recorded.

Lineage and corrections

VibeMathed record: Polynomial-time approximate counting and uniform sampling of contingency tables with arbitrary margins evidence for this event

This event attributed to Unreleased internal OpenAI model

Lean: OAI/Combinatorics/ContingencyTables/UnconditionalMain.lean evidence for this event

Exact uniform sampling of contingency tables with arbitrary margins (companion) evidence for this event

Polynomial-time approximate counting and uniform sampling of contingency tables with arbitrary margins evidence for this event

FPRAS manuscript: one randomized algorithm counts nonnegative integer matrices with prescribed row sums, column sums and entry bounds bijb_{ij} (zeros allowed) within relative error ε\varepsilon with probability 1−δ1-\delta, using unbiased bits and a polynomial bound in the input length, ε−1\varepsilon^{-1} and log⁡δ−1\log\delta^{-1} on every execution. Sampling manuscript: for ordinary tables, an almost-uniform sampler (TV error 2−k2^{-k}) in worst-case polynomial bit time and an exactly uniform sampler in expected polynomial bit time in m,n,log⁡(N+1)m,n,\log(N+1). Exponents are deliberately large; the results are complexity classifications, not practical algorithms, and the exact sampler is polynomial only in expectation. parent of this event

Cryan, Dyer, Randall, Approximately counting integral flows and cell-bounded contingency tables (SICOMP 2010) evidence for this event

Polynomial-time approximate counting and uniform sampling of contingency tables with arbitrary margins parent of this event

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