PSPACE-complete

In computational complexity theory, a decision problem is PSPACE-complete if it can be solved using an amount of memory that is polynomial in the input length (polynomial space) and if every other problem that can be solved in polynomial space can be transformed to it in polynomial time. The problems that are PSPACE-complete can be thought of as the hardest problems in PSPACE, because a solution to any one such problem could easily be used to solve any other problem in PSPACE.

The PSPACE-complete problems are widely suspected to be outside the more famous complexity classes P and NP, but that is not known. It is known that they lie outside of the class NC (a class of problems with highly efficient parallel algorithms), because problems in NC can be solved in an amount of space polynomial in the logarithm of the input size, and the class of problems solvable in such a small amount of space is strictly contained in PSPACE by the space hierarchy theorem.

Below are descriptions of a few PSPACE-complete problems. More examples can be found at the list of PSPACE-complete problems.

Given a regular expression R, determining whether it generates every string over its alphabet is PSPACE-complete.

A related result is that the class of languages recognizable with zero error by automata with two-way infinite random tape equals nondeterministic linear space. This holds for both two-way and multipass one-way access to input. Testing whether an automaton (with two-way infinite random tape) accepts a word with zero error is NSPACE(O(kn)) complete, where n is the input size and k is the number of states.

The first known PSPACE-complete problem was the word problem for deterministic context-sensitive grammars. In the word problem for context-sensitive grammars, one is given a set of grammatical transformations which can increase, but cannot decrease, the length of a sentence, and wishes to determine if a given sentence could be produced by these transformations. The technical condition of "determinism" (implying roughly that each transformation makes it obvious that it was used) ensures that this process can be solved in polynomial space, and Kuroda (1964) showed that every (possibly non-deterministic) program computable in linear space could be converted into the parsing of a context-sensitive grammar, in a way which preserves determinism. In 1970, Savitch's theorem showed that PSPACE is closed under nondeterminism, implying that even non-deterministic context-sensitive grammars are in PSPACE.

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