Blum axioms

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by 147.229.196.135 (talk) at 14:29, 9 July 2020 (→‎Notes: Removed incorrect and misleading statements: "There is an algorithm which, on input (M, x, n) decides if Φ(M, x) = n" -- such algorithm cannot exist, for it would solve the halting problem. "If M exceeds n steps, it can halt and reject, so there is no need to determine if M halts on x." -- the author of this statement is quite clearly not familiar with Blum axioms.). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

In computational complexity theory the Blum axioms or Blum complexity axioms are axioms that specify desirable properties of complexity measures on the set of computable functions. The axioms were first defined by Manuel Blum in 1967.[1]

Importantly, Blum's speedup theorem and the Gap theorem hold for any complexity measure satisfying these axioms. The most well-known measures satisfying these axioms are those of time (i.e., running time) and space (i.e., memory usage).

Definitions

A Blum complexity measure is a pair with a Gödel numbering of the partial computable functions and a computable function

which satisfies the following Blum axioms. We write for the i-th partial computable function under the Gödel numbering , and for the partial computable function .

  • the domains of and are identical.
  • the set is recursive.

Examples

  • is a complexity measure, if is either the time or the memory (or some suitable combination thereof) required for the computation coded by i.
  • is not a complexity measure, since it fails the second axiom.

Complexity classes

For a total computable function complexity classes of computable functions can be defined as

is the set of all computable functions with a complexity less than . is the set of all boolean-valued functions with a complexity less than . If we consider those functions as indicator functions on sets, can be thought of as a complexity class of sets.

References

  1. ^ Blum, Manuel (1967). "A Machine-Independent Theory of the Complexity of Recursive Functions" (PDF). Journal of the ACM. 14 (2): 322–336. doi:10.1145/321386.321395.