Theorems in computational complexity theory | Articles containing proofs | Structural complexity theory

Space hierarchy theorem

In computational complexity theory, the space hierarchy theorems are separation results that show that both deterministic and nondeterministic machines can solve more problems in (asymptotically) more space, subject to certain conditions. For example, a deterministic Turing machine can solve more decision problems in space n log n than in space n. The somewhat weaker analogous theorems for time are the time hierarchy theorems. The foundation for the hierarchy theorems lies in the intuition thatwith either more time or more space comes the ability to compute morefunctions (or decide more languages). The hierarchy theorems are usedto demonstrate that the time and space complexity classes form ahierarchy where classes with tighter bounds contain fewer languagesthan those with more relaxed bounds. Here we define and prove thespace hierarchy theorem. The space hierarchy theorems rely on the concept of space-constructible functions. The deterministic and nondeterministic space hierarchy theorems state that for all space-constructible functions f(n), , where SPACE stands for either DSPACE or NSPACE, and o refers to the little o notation. (Wikipedia).

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Related pages

DSPACE | Depth-first search | NSPACE | NL (complexity) | EXPSPACE | Computational complexity theory | Computable function | Unary language | Decision problem | Immerman–Szelepcsényi theorem | Time hierarchy theorem | Savitch's theorem | PSPACE