Alphabeta Math
RemarkRemark: AI-adaptedProof: Not applicableSession-authored (Fable 5 assisted)judge pass (deepseek-v4-pro + gpt-5.6-terra)audited 2026-08-26
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.

Bounded degree against bounded density: the two statements of Rödl's theorem, and which one is stronger

The maximum-degree form is stronger than the density form. If every vertex of X has at most ϵX neighbours, then the self-density is at most ϵ by A c-sparse set has self-density at most c, and a c-dense set has self-density at least 1c1/X. The converse fails: small average degree does not control exceptional vertices, and a large star is the basic witness.

What this page proves is the stronger form Rödl: for every H and every ϵ(0,12) there is δ>0 such that every nonempty H-free graph has an ϵ-restricted vertex set of size at least δV(G), then derives the density form The edge-density form of Rödl's theorem: every nonempty H-free graph has a linearly large set of self-density at most ϵ or at least 1ϵ, and finally shows by The edge-density form of Rödl's theorem implies the maximum-degree form, with ϵ and δ each shrunk by a constant factor that the weaker statement implies the stronger one after shrinking the constants by a fixed factor.

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Dependency tree · two levels

25 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.

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