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.
Every -uniform hypergraph with fewer than edges is -colourable
Statement
Let . Every finite -uniform hypergraph with fewer than edges admits a vertex two-colouring with no monochromatic edge.
Facts & Assumptions
Given: A finite -uniform hypergraph with and .
An edge in a -uniform hypergraph has exactly vertices (-uniform hypergraphs and complete balanced -partite -graphs ).
Independent coordinate events in a finite product space have product probability (Product weights normalize, and coordinate events are mutually independent).
A sum of indicators counts the corresponding events, each indicator has expectation equal to its event probability, and expectation is linear without independence (Indicators turn event probabilities, intersections, and finite counts into expectations and products, Expectation is linear for every finite family of random variables, without any independence hypothesis).
A nonnegative integer-valued variable with expectation less than vanishes at some outcome (The first-moment method for avoiding or forcing a finite count of bad events).
Proof
Colour every vertex independently and uniformly red or blue. For a fixed edge, its colours are all red or all blue with probability .
Let count monochromatic edges. Then .
By [L4], some colouring has and is proper. If , the edge hypothesis forces , and the same proof applies.
Depends on
- $r$-uniform hypergraphs and complete balanced $r$-partite $r$-graphs $K^{(r)}_{s,\ldots,s}$
- Product weights normalize, and coordinate events are mutually independent
- Indicators turn event probabilities, intersections, and finite counts into expectations and products
- Expectation is linear for every finite family of random variables, without any independence hypothesis
- The first-moment method for avoiding or forcing a finite count of bad events
Used by
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 54 results over 16 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- J. Matousek and J. Vondrak, The Probabilistic Method, Theorem 2.2.4 (standard reference, not scraped)