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The Cook--Levin construction can be made parsimonious
Statement
For every fixed polynomial-time nondeterministic machine , one can compute in polynomial time a formula together with an ordered list of all its declared variables, whose satisfying assignments are in bijection with the accepting computation paths of on . Consequently is a parsimonious reduction from the accepting-path count of to .
Facts & Assumptions
Given: a fixed nondeterministic polynomial-time machine .
A bounded tableau records one computation branch, including the unique state-tagged head position in each row, by A bounded computation tableau for a nondeterministic Turing-machine run.
The standard local tableau constraints are satisfiable exactly when an accepting bounded tableau exists, by The Cook-Levin formula is satisfiable if and only if an accepting bounded tableau exists.
The Cook--Levin formula is computable in polynomial time for fixed , by The Cook-Levin reduction map is computable in polynomial time.
NumberSAT counts assignments to the complete ordered declared-variable list, including unused variables, by NumberSAT.
A fixed nondeterministic multitape machine has a finite transition table and finitely many tape heads (Multitape and nondeterministic Turing machine variants).
A parsimonious reduction preserves the exact numerical count, by Parsimonious reductions between counting functions.
Proof
Replace by a fixed one-tape simulator . Encode the finitely many simulated tapes as delimiter-separated blocks with marked head cells. To simulate one step, make a deterministic sweep to collect the finitely many scanned symbols, choose exactly one transition of nondeterministically, record its transition tag in a distinct intermediate state, and make deterministic sweeps to update marked cells and shift blocks if a head enters a new blank cell. The finite transition table from [F1] makes all sweep-control states fixed. After simulated steps on an input of length , at most cells are represented, so each sweep costs and the full simulation costs ; the tag step adds constant overhead. Even choices with the same eventual successor yield different tagged tableau rows. Erasing deterministic sweeps and reading tags is a bijection between accepting paths. Choose a polynomial bounding the running time and every visited tape position on all bounded branches, then deterministically pad every halted branch to time . This supplies [L1]--[L3].
Use one-hot tableau variables for the symbol (including the tagged state) in every time-cell position. The initial-row, exactly-one-symbol, local-transition, and accepting constraints use only those variables. Take their conjunction as without introducing auxiliary variables. Let list every cell variable once, in lexicographic order of time, cell, and a fixed order on the simulator alphabet. Output the encoded pair , which is well formed for NumberSAT.
An accepting path fixes every cell of its padded tableau and therefore one truth value for every tableau variable. Every declared variable is such a cell variable, so none contributes a free choice. Conversely, any satisfying assignment gives exactly one legal accepting tableau by [L2], and the retained choice tags recover exactly one accepting path of , hence exactly one accepting path of by step 1.1. These maps are inverse, so
The tableau has polynomial dimensions, its local constraints have polynomial total size, and [L3] supplies the polynomial-time construction. Enumerating the polynomially many declared variable names in the specified order also takes polynomial time. Together with the exact equality in step 3.1, [L4] proves parsimony.
Depends on
- Parsimonious reductions between counting functions
- NumberSAT
- A bounded computation tableau for a nondeterministic Turing-machine run
- Multitape and nondeterministic Turing machine variants
- The Cook-Levin formula is satisfiable if and only if an accepting bounded tableau exists
- The Cook-Levin reduction map is computable in polynomial time
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
- Arora and Barak, Computational Complexity: A Modern Approach (standard reference, not scraped)
- Lance Fortnow, Counting Complexity (standard reference, not scraped)