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.
ZPP equals RP intersection coRP
Statement
Facts & Assumptions
Given: the randomized classes RP, coRP, and ZPP.
RP, coRP, and ZPP are defined by one-sided error, complementary one-sided error, and zero-error expected polynomial time respectively (The classes RP, coRP, ZPP, BPP, and PP).
If is a nonnegative random variable and , then (Markov's inequality on a finite probability space).
Proof
Suppose and let be a zero-error expected-time machine for with . Run for exactly steps and accept iff it has accepted by then. Because never errs, this truncated machine never accepts a no-instance. By [L2], , so on yes-instances it accepts with probability at least . Thus . By the same truncation with accept and reject swapped, also .
Conversely, suppose . Choose an RP machine for and a coRP machine for . On input , perform independent rounds; in each round run both machines with fresh randomness. If accepts, accept. If rejects, reject. Otherwise repeat. By [L1], on a yes-instance the first event occurs with probability at least and the second never occurs, while on a no-instance the second occurs with probability at least and the first never occurs. So the algorithm is zero-error and the expected number of rounds is at most .
Each round of step 1.2 uses polynomial time, so the expected runtime is polynomial. Hence , proving the reverse inclusion. Therefore .
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
5 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)
- Eric Blais, Models of Computation, 14. Randomized Computation (standard reference, not scraped)