Least fixed point
In order theory, a branch of mathematics, the least fixed point (lfp or LFP, sometimes also smallest fixed point) of a function from a partially ordered set to itself is the fixed point which is less than each other fixed point, according to the set's order. A function need not have a least fixed point, and cannot have more than one.
For example, with the usual order on the real numbers, the least fixed point of the real function f(x) = x² is x = 0 (since the only other fixed point is 1 and 0 < 1). In contrast, f(x) = x+1 has no fixed point at all, let alone a least one, and f(x)=x has infinitely many fixed points, but no least one.
Many fixed-point theorems yield algorithms for locating the least fixed point. Least fixed points often have desirable properties that arbitrary fixed points do not.
Immerman  and Vardi  independently showed the descriptive complexity result that the polynomial-time computable properties of linearly ordered structures are definable in FO(LFP), i.e. in first-order logic with a least fixed point operator. However, FO(LFP) is too weak to express all polynomial-time properties of unordered structures (for instance that a structure has even size).
Let G=(V,A) be a directed graph and v be a vertex. The set of nodes accessible from v can be defined as the set S which is the least fixed-point for the property: v belongs to S and if w belongs to S and there is an edge from w to x, then x belongs to S. The set of nodes which are co-accessible from v is defined by a similar least fix-point. On the one hand the strongly connected component of v is the intersection of those two least fixed-point.
Let G be a proper context-free grammar. The set E of symbols which produces the emptyword is defined as the least fixed-point which contains the symbols S such that , or such that where all symbols belongs to E.
Greatest fixed points
Greatest fixed points can also be determined, but they are less commonly used than least fixed points. However, in computer science they, analogously to the least fixed point, give rise to corecursion and codata.
- N. Immerman, Relational queries computable in polynomial time, Information and Control 68 (1–3) (1986) 86–104.
- Immerman, Neil (1982). "Relational Queries Computable in Polynomial Time". STOC '82: Proceedings of the fourteenth annual ACM symposium on Theory of computing. pp. 147–152. doi:10.1145/800070.802187. Revised version in Information and Control, 68 (1986), 86–104.
- Vardi, Moshe Y. (1982). "The Complexity of Relational Query Languages". STOC '82: Proceedings of the fourteenth annual ACM symposium on Theory of computing. pp. 137–146. doi:10.1145/800070.802186.