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Recursion
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A visual form of recursion known as the Droste effect. The woman in this image holds an object that contains a smaller image of her holding an identical object, which in turn contains a smaller image of herself holding an identical object, and so forth. 1904 Droste cocoa tin, designed by Jan Misset.

Recursion occurs when the definition of a concept or process depends on a simpler or previous version of itself.[1] Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in mathematics and computer science, where a function being defined is applied within its own definition. While this apparently defines an infinite number of instances (function values), it is often done in such a way that no infinite loop or infinite chain of references can occur.

A process that exhibits recursion is recursive. Video feedback displays recursive images, as does an infinity mirror.

Formal definitions

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Ouroboros, an ancient symbol depicting a serpent or dragon eating its own tail

In mathematics and computer science, a class of objects or methods exhibits recursive behavior when it can be defined by two properties:

  • A simple base case (or cases) — a terminating scenario that does not use recursion to produce an answer
  • A recursive step — a set of rules that reduces all successive cases toward the base case.

For example, the following is a recursive definition of a person's ancestor. One's ancestor is either:

  • One's parent (base case), or
  • One's parent's ancestor (recursive step).

The Fibonacci sequence is another classic example of recursion:

Fib(0) = 0 as base case 1,
Fib(1) = 1 as base case 2,
For all integers n > 1, Fib(n) = Fib(n − 1) + Fib(n − 2).

Many mathematical axioms are based upon recursive rules. For example, the formal definition of the natural numbers by the Peano axioms can be described as: "Zero is a natural number, and each natural number has a successor, which is also a natural number."[2] By this base case and recursive rule, one can generate the set of all natural numbers.

Other recursively defined mathematical objects include factorials, functions (e.g., recurrence relations), sets (e.g., Cantor ternary set), and fractals.

There are various more tongue-in-cheek definitions of recursion; see recursive humor.

Informal definition

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Sourdough starter being stirred into flour to produce sourdough: the recipe calls for some sourdough left over from the last time the same recipe was made.

Recursion is the process a procedure goes through when one of the steps of the procedure involves invoking the procedure itself. A procedure that goes through recursion is said to be 'recursive'.[3]

To understand recursion, one must recognize the distinction between a procedure and the running of a procedure. A procedure is a set of steps based on a set of rules, while the running of a procedure involves actually following the rules and performing the steps.

Recursion is related to, but not the same as, a reference within the specification of a procedure to the execution of some other procedure.

When a procedure is thus defined, this immediately creates the possibility of an endless loop; recursion can only be properly used in a definition if the step in question is skipped in certain cases so that the procedure can complete.

Even if it is properly defined, a recursive procedure is not easy for humans to perform, as it requires distinguishing the new from the old, partially executed invocation of the procedure; this requires some administration as to how far various simultaneous instances of the procedures have progressed. For this reason, recursive definitions are very rare in everyday situations.

In language

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Linguist Noam Chomsky, among many others, has argued that the lack of an upper bound on the number of grammatical sentences in a language, and the lack of an upper bound on grammatical sentence length (beyond practical constraints such as the time available to utter one), can be explained as the consequence of recursion in natural language.[4][5]

This can be understood in terms of a recursive definition of a syntactic category, such as a sentence. A sentence can have a structure in which what follows the verb is another sentence: Dorothy thinks witches are dangerous, in which the sentence witches are dangerous occurs in the larger one. So a sentence can be defined recursively (very roughly) as something with a structure that includes a noun phrase, a verb, and optionally another sentence. This is really just a special case of the mathematical definition of recursion.[clarification needed]

This provides a way of understanding the creativity of language—the unbounded number of grammatical sentences—because it immediately predicts that sentences can be of arbitrary length: Dorothy thinks that Toto suspects that Tin Man said that.... There are many structures apart from sentences that can be defined recursively, and therefore many ways in which a sentence can embed instances of one category inside another.[6] Over the years, languages in general have proved amenable to this kind of analysis.

The generally accepted idea that recursion is an essential property of human language has been challenged by Daniel Everett on the basis of his claims about the Pirahã language. Andrew Nevins, David Pesetsky and Cilene Rodrigues are among many who have argued against this.[7] Literary self-reference can in any case be argued to be different in kind from mathematical or logical recursion.[8]

Recursion plays a crucial role not only in syntax, but also in natural language semantics. The word and, for example, can be construed as a function that can apply to sentence meanings to create new sentences, and likewise for noun phrase meanings, verb phrase meanings, and others. It can also apply to intransitive verbs, transitive verbs, or ditransitive verbs. In order to provide a single denotation for it that is suitably flexible, and is typically defined so that it can take any of these different types of meanings as arguments. This can be done by defining it for a simple case in which it combines sentences, and then defining the other cases recursively in terms of the simple one.[9]

A recursive grammar is a formal grammar that contains recursive production rules.[10]

Recursive humor

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Recursion is sometimes used humorously in computer science, programming, philosophy, or mathematics textbooks, generally by giving a circular definition or self-reference, in which the putative recursive step does not get closer to a base case, but instead leads to an infinite regress. It is not unusual for such books to include a joke entry in their glossary along the lines of:

Recursion, see Recursion.[11]

A variation is found on page 269 in the index of some editions of Brian Kernighan and Dennis Ritchie's book The C Programming Language; the index entry recursively references itself ("recursion 86, 139, 141, 182, 202, 269"). Early versions of this joke can be found in Let's talk Lisp by Laurent Siklóssy (published by Prentice Hall PTR on December 1, 1975, with a copyright date of 1976) and in Software Tools by Kernighan and Plauger (published by Addison-Wesley Professional on January 11, 1976). The joke also appears in The UNIX Programming Environment by Kernighan and Pike. It did not appear in the first edition of The C Programming Language. The joke is part of the functional programming folklore and was already widespread in the functional programming community before the publication of the aforementioned books.[12][13]

A plaque commemorates the Toronto Recursive History Project of Toronto's Recursive History.

Another joke is that "To understand recursion, you must understand recursion."[11] In the English-language version of the Google web search engine, when a search for "recursion" is made, the site suggests "Did you mean: recursion."[14] An alternative form is the following, from Andrew Plotkin: "If you already know what recursion is, just remember the answer. Otherwise, find someone who is standing closer to Douglas Hofstadter than you are; then ask him or her what recursion is."

Recursive acronyms are other examples of recursive humor. PHP, for example, stands for "PHP Hypertext Preprocessor", WINE stands for "WINE Is Not an Emulator", GNU stands for "GNU's not Unix", and SPARQL denotes the "SPARQL Protocol and RDF Query Language".

In mathematics

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The Sierpiński triangle—a confined recursion of triangles that form a fractal

Recursively defined sets

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Example: the natural numbers

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The canonical example of a recursively defined set is given by the natural numbers:

0 is in
if n is in , then n + 1 is in
The set of natural numbers is the smallest set satisfying the previous two properties.

In mathematical logic, the Peano axioms (or Peano postulates or Dedekind–Peano axioms), are axioms for the natural numbers presented in the 19th century by the German mathematician Richard Dedekind and by the Italian mathematician Giuseppe Peano. The Peano Axioms define the natural numbers referring to a recursive successor function and addition and multiplication as recursive functions.

Example: Proof procedure

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Another interesting example is the set of all "provable" propositions in an axiomatic system that are defined in terms of a proof procedure which is inductively (or recursively) defined as follows:

  • If a proposition is an axiom, it is a provable proposition.
  • If a proposition can be derived from true reachable propositions by means of inference rules, it is a provable proposition.
  • The set of provable propositions is the smallest set of propositions satisfying these conditions.

Finite subdivision rules

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Finite subdivision rules are a geometric form of recursion, which can be used to create fractal-like images. A subdivision rule starts with a collection of polygons labelled by finitely many labels, and then each polygon is subdivided into smaller labelled polygons in a way that depends only on the labels of the original polygon. This process can be iterated. The standard `middle thirds' technique for creating the Cantor set is a subdivision rule, as is barycentric subdivision.

Functional recursion

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A function may be recursively defined in terms of itself. A familiar example is the Fibonacci number sequence: F(n) = F(n − 1) + F(n − 2). For such a definition to be useful, it must be reducible to non-recursively defined values: in this case F(0) = 0 and F(1) = 1.

Proofs involving recursive definitions

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Applying the standard technique of proof by cases to recursively defined sets or functions, as in the preceding sections, yields structural induction — a powerful generalization of mathematical induction widely used to derive proofs in mathematical logic and computer science.

Recursive optimization

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Dynamic programming is an approach to optimization that restates a multiperiod or multistep optimization problem in recursive form. The key result in dynamic programming is the Bellman equation, which writes the value of the optimization problem at an earlier time (or earlier step) in terms of its value at a later time (or later step).

The recursion theorem

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In set theory, this is a theorem guaranteeing that recursively defined functions exist. Given a set X, an element a of X and a function f: XX, the theorem states that there is a unique function (where denotes the set of natural numbers including zero) such that

for any natural number n.

Dedekind was the first to pose the problem of unique definition of set-theoretical functions on by recursion, and gave a sketch of an argument in the 1888 essay "Was sind und was sollen die Zahlen?" [15]

Proof of uniqueness

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Take two functions and such that:

where a is an element of X.

It can be proved by mathematical induction that F(n) = G(n) for all natural numbers n:

Base Case: F(0) = a = G(0) so the equality holds for n = 0.
Inductive Step: Suppose F(k) = G(k) for some . Then F(k + 1) = f(F(k)) = f(G(k)) = G(k + 1).
Hence F(k) = G(k) implies F(k + 1) = G(k + 1).

By induction, F(n) = G(n) for all .

In computer science

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A common method of simplification is to divide a problem into subproblems of the same type. As a computer programming technique, this is called divide and conquer and is key to the design of many important algorithms. Divide and conquer serves as a top-down approach to problem solving, where problems are solved by solving smaller and smaller instances. A contrary approach is dynamic programming. This approach serves as a bottom-up approach, where problems are solved by solving larger and larger instances, until the desired size is reached.

A classic example of recursion is the definition of the factorial function, given here in Python code:

def factorial(n):
    if n > 0:
        return n * factorial(n - 1)
    else:
        return 1

The function calls itself recursively on a smaller version of the input (n - 1) and multiplies the result of the recursive call by n, until reaching the base case, analogously to the mathematical definition of factorial.

Recursion in computer programming is exemplified when a function is defined in terms of simpler, often smaller versions of itself. The solution to the problem is then devised by combining the solutions obtained from the simpler versions of the problem. One example application of recursion is in parsers for programming languages. The great advantage of recursion is that an infinite set of possible sentences, designs or other data can be defined, parsed or produced by a finite computer program.

Recurrence relations are equations which define one or more sequences recursively. Some specific kinds of recurrence relation can be "solved" to obtain a non-recursive definition (e.g., a closed-form expression).

Use of recursion in an algorithm has both advantages and disadvantages. The main advantage is usually the simplicity of instructions. The main disadvantage is that the memory usage of recursive algorithms may grow very quickly, rendering them impractical for larger instances.

In biology

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Shapes that seem to have been created by recursive processes sometimes appear in plants and animals, such as in branching structures in which one large part branches out into two or more similar smaller parts. One example is Romanesco broccoli.[16]

In the social sciences

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Authors use the concept of recursivity to foreground the situation in which specifically social scientists find themselves when producing knowledge about the world they are always already part of.[17][18] According to Audrey Alejandro, “as social scientists, the recursivity of our condition deals with the fact that we are both subjects (as discourses are the medium through which we analyse) and objects of the academic discourses we produce (as we are social agents belonging to the world we analyse).”[19] From this basis, she identifies in recursivity a fundamental challenge in the production of emancipatory knowledge which calls for the exercise of reflexive efforts:

we are socialised into discourses and dispositions produced by the socio-political order we aim to challenge, a socio-political order that we may, therefore, reproduce unconsciously while aiming to do the contrary. The recursivity of our situation as scholars – and, more precisely, the fact that the dispositional tools we use to produce knowledge about the world are themselves produced by this world – both evinces the vital necessity of implementing reflexivity in practice and poses the main challenge in doing so.

— Audrey Alejandro, Alejandro (2021)

In business

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Recursion is sometimes referred to in management science as the process of iterating through levels of abstraction in large business entities.[20] A common example is the recursive nature of management hierarchies, ranging from line management to senior management via middle management. It also encompasses the larger issue of capital structure in corporate governance.[21]

In art

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Recursive dolls: the original set of Matryoshka dolls by Zvyozdochkin and Malyutin, 1892
Front face of Giotto's Stefaneschi Triptych, 1320, recursively contains an image of itself (held up by the kneeling figure in the central panel).

The Matryoshka doll is a physical artistic example of the recursive concept.[22]

Recursion has been used in paintings since Giotto's Stefaneschi Triptych, made in 1320. Its central panel contains the kneeling figure of Cardinal Stefaneschi, holding up the triptych itself as an offering.[23][24] This practice is more generally known as the Droste effect, an example of the Mise en abyme technique.

M. C. Escher's Print Gallery (1956) is a print which depicts a distorted city containing a gallery which recursively contains the picture, and so ad infinitum.[25]

In culture

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The film Inception has colloquialized the appending of the suffix -ception to a noun to jokingly indicate the recursion of something.[26]

See also

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References

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Bibliography

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Recursion is a fundamental concept in and where a function, , or definition refers to itself to solve a problem by breaking it down into simpler instances of the same problem, typically terminating at a base case to prevent . This self-referential approach enables the of complex structures and computations that exhibit . In mathematics, recursion is used to define sets, sequences, and functions inductively, such as the natural numbers—where is a natural number and if nn is a , then n+1n+1 is also—or the function, defined as [0](/page/0)!=1[0](/page/0)! = 1 and (n+1)!=(n+1)n!(n+1)! = (n+1) \cdot n! for n[0](/page/0)n \geq [0](/page/0). The theory of recursive functions, a cornerstone of , classifies functions on s as primitive recursive (built from basic functions via composition and primitive recursion) or general recursive (including minimization), encompassing all effectively computable functions as per Church's thesis. These functions, formalized in the 1930s by , , and , underpin the foundations of logic and prove key results like . In , recursion manifests in algorithms that call themselves to process data structures like trees or graphs, as seen in or quicksort's divide-and-conquer paradigm. Classic examples include computing the —where F(0)=0F(0) = 0, F(1)=1F(1) = 1, and F(n)=F(n1)+F(n2)F(n) = F(n-1) + F(n-2) for n>1n > 1—and solving the Towers of puzzle, which requires 2n12^n - 1 moves for nn disks via recursive subproblem decomposition. While powerful for elegant, concise code, recursion demands a base case and progress toward it to avoid ; tail-recursive variants can be optimized into iterative loops for efficiency.

Definitions

Informal definition

Recursion is a fundamental in problem-solving and , characterized by a process that breaks down a complex task into simpler, self-similar subtasks until reaching a straightforward base case that can be directly resolved. This self-referential approach allows for elegant solutions to problems exhibiting inherent structure, such as hierarchical or fractal patterns, without explicitly outlining every step in advance. Unlike linear methods, recursion relies on the idea that understanding the whole emerges from grasping progressively smaller parts of the same kind. A classic analogy for recursion is the Russian nesting doll, or Matryoshka, where each doll contains a slightly smaller version of itself inside, continuing until the tiniest doll is reached, which requires no further opening. This illustrates how a recursive process "unpacks" a problem layer by layer, handling each level by deferring to the next smaller instance, until the innermost, simplest case halts the progression. Similarly, consulting a to define a word often involves looking up terms that lead to further definitions, indirectly circling back through related concepts but eventually resolving through foundational entries, demonstrating recursion's indirect self-reference in everyday use. The notion of recursion traces back to ancient symbols of self-reference, such as the —a serpent devouring its own tail—depicted in Egyptian iconography around 1600 BCE and later adopted in Greek and alchemical traditions to signify eternal cycles and unity. In modern interpretations, this image evokes recursive processes through its equation of a function with its self-application, highlighting recursion's roots in intuitive ideas of perpetuity and closure long before formal . While recursion emphasizes self-similarity and decomposition into identical subproblems, it complements , which achieves repetition through explicit loops that execute a fixed block of instructions multiple times without self-calls. Both techniques enable repeated , but recursion naturally suits problems with nested structures, whereas iteration excels in sequential, non-hierarchical tasks, often making them interchangeable in practice with appropriate transformations.

Formal definitions

In and , recursion is formally defined through mechanisms that ensure well-definedness via fixed points or inductive closures, providing a foundation for constructing functions and sets without circularity. These definitions rely on prerequisites such as partial orders and closure operators to guarantee and . One standard formalization expresses recursion via fixed points of a functional derived from base operations. A function f:XYf: X \to Y is recursively defined if it satisfies the f(x)=g(x,f(h(x)))f(x) = g(x, f(h(x))) for all xXx \in X, where g:X×YYg: X \times Y \to Y and h:XXh: X \to X are given base functions, and ff is the least fixed point of the associated monotone functional F(f)=λx.g(x,f(h(x)))F(f) = \lambda x. g(x, f(h(x))) in a suitable of functions. This framework captures general recursive definitions by solving the implicit f=F(f)f = F(f), ensuring the solution is the minimal element satisfying the recursion scheme under a order. For sets, an inductive definition constructs a recursively defined set SES \subseteq E as the smallest closed under specified operations. Formally, given a base set BEB \subseteq E and a collection KK of finitary operations Φ:Ea(Φ)E\Phi: E^{a(\Phi)} \to E for each ΦK\Phi \in K, SS is the of all subsets YEY \subseteq E such that BYB \subseteq Y and for every ΦK\Phi \in K and x1,,xa(Φ)Yx_1, \dots, x_{a(\Phi)} \in Y, Φ(x1,,xa(Φ))Y\Phi(x_1, \dots, x_{a(\Phi)}) \in Y. This yields the least fixed point of the closure operator generated by BB and KK, often realized through transfinite iteration up to the first ordinal where stabilization occurs. To prove properties of such recursive structures, Noetherian induction serves as a key formal tool, leveraging well-founded orders to establish claims by minimal counterexample elimination. On a poset (E,<)(E, <) that is Noetherian (every nonempty subset has a minimal element), a property PP holds for all δE\delta \in E if, for every δ\delta, whenever P(γ)P(\gamma) holds for all γ<δ\gamma < \delta, then P(δ)P(\delta) holds; recursive structures induce such orders via construction depth or subterm relations, enabling proofs of invariance or termination.

Mathematics

Recursively defined sets

In set theory, particularly within the framework of Zermelo-Fraenkel axioms, recursively defined sets are constructed through a combination of base cases and closure rules, yielding the smallest set that satisfies these conditions. A base case specifies initial elements, while closure rules dictate how new elements are generated from existing ones, often via operations like union or power set. For instance, an inductive set is defined as one containing the empty set \emptyset as a base and closed under the successor function S(x)=x{x}S(x) = x \cup \{x\}, ensuring the set includes all elements obtainable by finite applications of these rules. This mechanism, formalized in the , guarantees the existence of such infinite sets without requiring prior enumeration. These recursive constructions play a crucial role in formalizing infinite structures, such as ordinals and trees, by enabling transfinite extensions beyond finite iterations. Ordinals, for example, are built recursively as transitive sets well-ordered by membership, where each ordinal α\alpha consists precisely of all ordinals less than α\alpha, starting from 0=0 = \emptyset and closing under successors and limits. Similarly, the von Neumann hierarchy VαV_\alpha is defined by transfinite recursion: V0=V_0 = \emptyset, Vα+1=P(Vα)V_{\alpha+1} = \mathcal{P}(V_\alpha) (the power set), and for limit ordinals λ\lambda, Vλ=β<λVβV_\lambda = \bigcup_{\beta < \lambda} V_\beta. This hierarchical buildup stratifies the universe of sets by rank, allowing the representation of complex infinite objects like well-ordered trees without invoking unrestricted comprehension. Unlike explicit definitions, which rely on direct enumeration or property-based comprehension for finite or simply describable sets, recursive definitions circumvent the need for complete listing, which is infeasible for uncountable or highly complex structures. Explicit approaches falter when sets grow transfinitely or involve operations without closed-form expressions, whereas recursion leverages well-foundedness to iteratively generate elements, ensuring consistency within axiomatic systems like ZFC. This distinction is essential for handling infinities, as recursive closure provides a controlled, paradox-free pathway to define sets that explicit methods cannot practically achieve.

Examples of recursive definitions

One prominent example of a recursive definition in mathematics is the construction of the natural numbers N\mathbb{N}, which forms the foundation of arithmetic. This set is defined as the smallest collection containing the base element 0 and closed under the successor function ss, where s(n)=n+1s(n) = n + 1 for any nNn \in \mathbb{N}. Formally, N={0}{s(n)nN}\mathbb{N} = \{0\} \cup \{s(n) \mid n \in \mathbb{N}\}, ensuring that every natural number is either 0 or obtained by successive applications of ss to prior elements. This recursive characterization aligns with the , providing a rigorous basis for defining operations like addition and multiplication on N\mathbb{N}. In formal logic, the set of provable theorems within a deductive system, such as propositional logic, is recursively defined to capture the notion of derivability. The base cases consist of a fixed set of axioms, such as the Hilbert-style axioms for implication and negation: (1) A(BA)A \to (B \to A), (2) [A(BC)][(AB)(AC)][A \to (B \to C)] \to [(A \to B) \to (A \to C)], (3) ¬A(AB)\neg A \to (A \to B), and (4) (¬AA)A(\neg A \to A) \to A. The recursive step applies the inference rule of modus ponens: if AA and ABA \to B are theorems, then BB is a theorem. Thus, the set of theorems is the smallest collection containing all axioms and closed under modus ponens, generating all derivable formulas from the base through finite iterations. A classic illustration of recursion in sequences is the Fibonacci sequence, which arises in various combinatorial contexts. It is defined with initial conditions F(0)=0F(0) = 0 and F(1)=1F(1) = 1, and the recursive rule F(n)=F(n1)+F(n2)F(n) = F(n-1) + F(n-2) for all integers n>1n > 1. This generates the sequence 0, 1, 1, 2, 3, 5, 8, 13, and so on, where each term depends on the two preceding ones, demonstrating how recursive definitions can produce infinite structures from finite base cases.

Functional recursion

In mathematics, functional recursion involves defining functions on domains such as the natural numbers through recursive equations that reference the function itself at simpler arguments, enabling the construction of complex mappings from basic operations. This approach contrasts with iterative methods by building computations via self-referential calls, often formalized through specific schemas that ensure well-definedness on well-founded structures like the naturals. Primitive recursive functions represent a foundational class of such functions, formalized by in 1931 as part of his work on formal systems, though the concept drew from earlier recursive definitions by Dedekind and Skolem. They are generated from three initial functions—the constant zero function Z(x1,,xk)=0Z(x_1, \dots, x_k) = 0, the S(x)=x+1S(x) = x + 1, and the projection functions πik(x1,,xk)=xi\pi_i^k(x_1, \dots, x_k) = x_i for 1ik1 \leq i \leq k—and closed under two operations: composition and primitive recursion. Composition allows combining existing primitive recursive functions; if g(y1,,ym)g(y_1, \dots, y_m) and f1(x),,fm(x)f_1(\mathbf{x}), \dots, f_m(\mathbf{x}) are primitive recursive, then h(x)=g(f1(x),,fm(x))h(\mathbf{x}) = g(f_1(\mathbf{x}), \dots, f_m(\mathbf{x})) is as well. The primitive recursion schema defines a new function f(x,0)=g(x)f(\mathbf{x}, 0) = g(\mathbf{x}) and f(x,y+1)=h(x,y,f(x,y))f(\mathbf{x}, y+1) = h(\mathbf{x}, y, f(\mathbf{x}, y)), where gg and hh are previously defined primitive recursive functions and x\mathbf{x} denotes parameters. Rózsa Péter in 1932 further analyzed their properties, coining the term "primitive recursive" and proving closure under additional operations like bounded minimization. A example is the function add(x,y)\mathrm{add}(x, y), defined by add(x,0)=x\mathrm{add}(x, 0) = x and add(x,y+1)=S(add(x,y))\mathrm{add}(x, y+1) = S(\mathrm{add}(x, y)), which applies the primitive recursion schema with g(x)=xg(x) = x and h(x,y,z)=S(z)h(x, y, z) = S(z). This builds from successor applications, illustrating how arithmetic operations emerge from recursive composition without unbounded search. Functional recursion can be categorized as structural or generative based on how recursive calls relate to the input. Structural recursion follows the inductive structure of the domain, such as recursing on the predecessors of natural numbers or subtrees in a , ensuring each call processes a proper substructure until a base case. In contrast, generative recursion produces new arguments for recursive calls that may not directly mirror the input's , often generating sequences or values through iterative deepening, as seen in searches or nested definitions. This distinction highlights the flexibility of recursive schemas beyond simple induction, though generative forms risk non-termination if not carefully bounded. The exemplifies a total recursive function outside the primitive recursive class, introduced by in 1928 to demonstrate functions computable yet beyond primitive schemas. Defined as A(0,n)=n+1A(0, n) = n + 1, A(m+1,0)=A(m,1)A(m + 1, 0) = A(m, 1), and A(m+1,n+1)=A(m,A(m+1,n))A(m + 1, n + 1) = A(m, A(m + 1, n)) for natural numbers m,nm, n, it employs nested recursion where the inner call's result serves as the outer argument, yielding hyper-exponential growth that dominates all primitive recursive functions. Péter in 1935 provided an equivalent formulation confirming its non-primitive recursive nature, as no primitive schema can capture its double-exponential escalation without violating closure bounds.

Proofs involving recursive definitions

Proofs involving recursive definitions require specialized induction techniques to establish properties of objects defined recursively, ensuring that the proofs align with the recursive structure to guarantee completeness and termination. These methods extend the principle of , adapting it to the inductive nature of the definitions themselves, and are essential for verifying correctness in mathematical structures like sequences, sets, and functions. Structural induction is a proof technique used to demonstrate that a property holds for all elements of a recursively defined set, such as lists or trees, by exploiting the inductive clauses in the definition. The proof consists of a base case, verifying the property for the simplest elements (e.g., the empty list or single-node tree), and an inductive step, assuming the property holds for substructures and showing it extends to the larger structure formed by combining them. For instance, to prove that every binary tree with nn nodes has exactly n1n-1 edges, the base case confirms it for a single node (0 edges), and the inductive step assumes it for the left and right subtrees, then adds the connecting edge to verify the total. This method directly mirrors the recursive construction, ensuring the property propagates through all possible structures. Complete induction, also known as strong induction, is particularly suited for proving properties of recursively defined sequences, where the value at step nn depends on all previous values. In this approach, the base cases are established for the initial terms, and the inductive step assumes the property holds for all k<nk < n to prove it for nn. A classic example is verifying the recursive definition of the , where 0!=10! = 1 and n!=n×(n1)!n! = n \times (n-1)! for n1n \geq 1; to prove n!=1×2××nn! = 1 \times 2 \times \cdots \times n, the base case holds for n=0n=0 and n=1n=1, and assuming the product formula for all k<nk < n allows multiplication by nn to confirm it for nn. This method is powerful for sequences with dependencies spanning multiple prior terms, such as the . Well-founded relations provide a general framework for ensuring termination in recursive proofs, particularly when dealing with arbitrary partial orders rather than just natural numbers. A relation RR on a set is well-founded if every nonempty subset has a minimal element with respect to RR, equivalently, there are no infinite descending chains x0Rx1Rx2Rx_0 R x_1 R x_2 R \cdots. In proofs, this principle allows induction over the relation: to show a property P(x)P(x) holds for all xx, verify P(x)P(x) whenever P(y)P(y) holds for all yy such that yRxy R x, starting from minimal elements. This guarantees termination for recursive definitions by preventing infinite regressions, as seen in where well-foundedness of the membership relation ensures recursive constructions halt. The concept originates from , where it underpins transfinite recursion without cycles.

The recursion theorem

The recursion theorem, also known as Kleene's fixed-point theorem, is a cornerstone of computability theory that guarantees the existence of self-referential indices for partial recursive functions. Formally, for any total recursive function f:NNf: \mathbb{N} \to \mathbb{N}, there exists an index eNe \in \mathbb{N} such that φe=φf(e)\varphi_e = \varphi_{f(e)}, where φi\varphi_i denotes the ii-th partial recursive function in some standard enumeration of all partial recursive functions. This result, first established by Stephen Kleene, enables the formal construction of recursive definitions that refer to their own computational descriptions, facilitating proofs of undecidability and incompleteness in arithmetic. The proof of the recursion theorem relies on the s-m-n theorem (parametrization theorem), which states that for any recursive g(k,x,y)g(k, \vec{x}, \vec{y})
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