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.
Statement
Let Then
Facts & Assumptions
Given: The classes , , and .
A language in has a polynomial-time verifier with polynomially bounded certificates, by The class NP via polynomial-time verifiers.
The class notation DSPACE records deterministic polynomial-space deciders, and DTIME records deterministic time-bounded deciders, by The classes DTIME, NTIME, DSPACE, and NSPACE.
Worst-case space counts visited tape cells and worst-case time counts executed steps, by Worst-case time and space complexity of a machine.
EXP is the union of the classes , by The classes EXP and NEXP.
A configuration records a machine state, head positions, and finite tape data, and tape contents have finite support, by Configurations of a deterministic one-tape Turing machine and Right-infinite tapes as finite-support functions.
Proof
To prove , let . By [L1], choose a verifier for with runtime bounded by a polynomial and certificate length bounded by a polynomial . A deterministic decider for can enumerate all certificates of length at most in lexicographic order, run the verifier on , and accept as soon as one certificate succeeds. It stores only the input , the current certificate , and the verifier workspace, so the total space used is polynomial in . Hence by [L2] and [L3].
To prove , let . Then by definition there exists and a deterministic machine deciding while using at most tape cells on inputs of length , for some constant . Fix such an input length . Because of [L5], a configuration of is determined by one of finitely many states, the head positions on the finitely many tapes, and the contents of at most visited cells. Therefore the total number of possible configurations is at most for a constant depending only on the fixed number of tapes.
Since is deterministic and halts on every input, it cannot visit the same configuration twice on one accepting or rejecting run: a repeated configuration would force an infinite loop from that point onward. So the running time on inputs of length is bounded by the number of reachable configurations from step 1.2, hence by . Enlarging the exponent if necessary, there exists with for all sufficiently large . Therefore by [L2] and [L4].
Steps 1.1 and 2.1 prove .
Depends on
Used by
Dependency tree · two levels
16 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
- Michael Sipser, MIT 18.404J Theory of Computation, Lecture 17: Space Complexity, PSPACE, Savitch's Theorem (standard reference, not scraped)
- Sanjeev Arora and Boaz Barak, Computational Complexity: A Modern Approach (standard reference, not scraped)