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 Turing-machine acceptance problem is computably enumerable complete
Statement
The language of The Turing-machine acceptance problem is c.e.-complete under computable many-one reductions.
Facts & Assumptions
Given: The acceptance language .
A language is c.e.-complete exactly when it is recognizable and every recognizable language many-one reduces to it, by Computably enumerable hardness and completeness under computable many-one reductions.
The language is recognizable, by The Turing-machine acceptance problem is recognizable.
A coded pair belongs to exactly when the coded machine accepts the word , by The Turing-machine acceptance problem .
Proof
By [L2], is recognizable. To use [L1], it remains to prove c.e.-hardness.
Let be any recognizable language, and choose a recognizer for . Define . Because is fixed, encoding its chosen code and pairing it with the canonical word-code of is a total computable procedure. By [L3], exactly when accepts , and that happens exactly when . Thus .
Step 1.2 proves that every recognizable language many-one reduces to , and step 1.1 already recorded recognizability. Therefore [L1] gives that is c.e.-complete.
Depends on
- Computably enumerable hardness and completeness under computable many-one reductions
- The Turing-machine acceptance problem $A_{TM}$
- The Turing-machine acceptance problem is recognizable
- A fixed effective binary encoding of deterministic one-tape Turing machines
- Finite tuples admit effective prefix-free binary encodings
Used by
Dependency tree · two levels
13 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
- Kevin Kelly, Many-one Reduction (standard reference, not scraped)
- MIT 6.045J / 18.400J, Lecture 9: Mapping Reducibility and Rice's Theorem (standard reference, not scraped)