Least fixed point

From Wikipedia, the free encyclopedia
  (Redirected from Greatest fixpoint)
Jump to navigation Jump to search
The function f(x) = x2 − 4 has two fixed points, shown as the intersection with the blue line; its least one is at 1/2 − 17/2.

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 order of the poset. A function need not have a least fixed point, but if it does then the least fixed point is unique.

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 points at all, so has no least one, and f(x) = x has infinitely many fixed points, but has no least one.


Let be a directed graph and be a vertex. The set of vertices accessible from can be defined as the least fixed-point of the function , defined as The set of vertices which are co-accessible from is defined by a similar least fix-point. The strongly connected component of is the intersection of those two least fixed-points.

Let be a context-free grammar. The set of symbols which produces the empty string can be obtained as the least fixed-point of the function , defined as , where denotes the power set of .


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.

Denotational semantics[edit]

Partial order on

In computer science, the denotational semantics approach uses least fixed points to obtain from a given program text a corresponding mathematical function, called its semantics. To this end, an artificial mathematical object, , is introduced, denoting the exceptional value "undefined". Given e.g. the program datatype int, its mathematical counterpart is defined as it is made a partially ordered set by defining for each and letting any two different members be uncomparable w.r.t. , see picture.

The semantics of a program definition int f(int n){...} is some mathematical function If the program definition f does not terminate for some input n, this can be expressed mathematically as The set of all mathematical functions is made partially ordered by defining if, for each the relation holds, that is, if is less defined or equal to For example, the semantics of the expression x+x/x is less defined than that of x+1, since the former, but not the latter, maps to and they agree otherwise.

Given some program text f, its mathematical counterpart is obtained as least fixed point of some mapping from functions to functions that can be obtained by "translating" f. For example, the C definition

int fact(int n) { if (n == 0) then return 1; else return n * fact(n-1); }

is translated to a mapping

defined as

The mapping is defined in a non-recursive way, although fact was defined recursively. Under certain restrictions (see Kleene fixed-point theorem), which are met in the example, necessarily has a least fixed point, , that is for all .[1] It is possible to show that

A larger fixed point of is e.g. the function defined by

however, this function does not correctly reflect the behavior of the above program text for negative e.g. the call fact(-1) will not terminate at all, let alone return 0. Only the least fixed point, can reasonably be used as a mathematical program semantic.

Descriptive complexity[edit]

Immerman[2][3] and Vardi[4] 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).

Greatest fixed points[edit]

Greatest fixed points can also be determined. In the case of the real numbers the definition is symmetric with the least fixed point, but in computer science they are less commonly used than least fixed points. Specifically, the posets found in domain theory usually do not have a greatest element, hence there may be multiple, mutually incomparable maximal fixed points, and the greatest fixed point may not exist. To address this issue, the optimal fixed point has been defined as the most-defined fixed point compatible with all other fixed points. The optimal fixed point always exists, and is the greatest fixed point if the greatest fixed point exists. The optimal fixed point allows formal study of recursive and corecursive functions that do not converge with the least fixed point.[5] Unfortunately the optimal fixed point of an effective recursive definition may be a non-computable function[6] so it is primarily of theoretical interest.

See also[edit]


  1. ^ C.A. Gunter; D.S. Scott (1990). "Semantic Domains". In Jan van Leeuwen (ed.). Formal Models and Semantics. Handbook of Theoretical Computer Science. Vol. B. Elsevier. pp. 633–674. ISBN 0-444-88074-7. Here: pp. 636–638
  2. ^ N. Immerman, Relational queries computable in polynomial time, Information and Control 68 (1–3) (1986) 86–104.
  3. ^ 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.
  4. ^ 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.
  5. ^ Charguéraud, Arthur (2010). "The Optimal Fixed Point Combinator" (PDF). Interactive Theorem Proving. 6172: 195–210. doi:10.1007/978-3-642-14052-5_15. Retrieved 30 October 2021.
  6. ^ Shamir, Adi (October 1976). The fixedpoints of recursive definitions (Ph.D. thesis). Weizmann Institute of Science. OCLC 884951223. Here: Example 12.1, pp. 12.2–3