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 verifier and nondeterministic definitions of NP agree
Statement
For a language , the following are equivalent:
- in the verifier sense of The class NP via polynomial-time verifiers.
- There exists such that .
Facts & Assumptions
Given: A language .
means that has a polynomial-time verifier with polynomially bounded certificates, by The class NP via polynomial-time verifiers.
means that some nondeterministic Turing machine accepts exactly the words of and every branch halts within steps, by The classes DTIME, NTIME, DSPACE, and NSPACE.
A machine that fully reads every input of length needs at least linear time, by Any machine that fully reads every input of length n needs at least linear time.
Proof
Assume item 1. By [L1], choose a verifier relation and a polynomial certificate bound . Let be a deterministic polynomial-time decider for the paired language . Build a nondeterministic machine that, on input , first guesses a binary word of length at most and then runs on , accepting exactly when accepts. Because every branch chooses at most guessed bits and then performs one polynomial-time deterministic verification, every branch halts within polynomial time. Since exactly when some certificate makes , the machine accepts exactly the words of . Thus item 2 holds.
Assume item 2. By [L2], fix a nondeterministic machine and an exponent such that every branch on inputs of length halts within steps for some constant . Let be the maximum number of available nondeterministic choices in any one local configuration of , and choose an integer with . An accepting branch is then determined by a finite sequence of at most choice numbers from . Encode that sequence as the binary word where each is the -bit binary expansion of . The initial unary header recovers , and then the decoder reads exactly consecutive blocks of length , so this branch description is effectively decodable.
Define a deterministic verifier for as follows. On input , first decode by reading its unary header and then the declared number of -bit choice blocks; reject malformed encodings. Next simulate on while forcing those choices in order, reject if the certificate requests a nonexistent choice or ends before acceptance, and accept exactly when the forced run ends in an accepting state after consuming the full encoded branch description. Step 1.2 guarantees that yields some accepting-branch certificate, and any accepting certificate describes a genuine accepting branch. The certificate length is polynomial in because it has one unary header bit per simulated choice and each choice uses the fixed block length , with at most choices. The verifier runtime is therefore polynomial in because it only decodes and replays that many steps of . Therefore item 1 holds by [L1].
Steps 1.1 and 2.1 prove both directions, so the verifier and nondeterministic definitions of NP agree. The lower bound in [L3] is compatible with these polynomial bounds: reading the whole input and certificate is already only linear in their lengths.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
8 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
- Sanjeev Arora and Boaz Barak, Computational Complexity: A Modern Approach (standard reference, not scraped)
- Stephen A. Cook, The Complexity of Theorem-Proving Procedures (standard reference, not scraped)