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Recursive function theory

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(Adding link to Dr Matt Fairtlough's lecture notes, as they contain theoretical texts, implementations written in Haskell, and further useful links. Using also his term ,,initial functions'')
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== Introduction ==
 
== Introduction ==
   
[http://planetmath.org/encyclopedia/RecursiveFunction.html PlanetMath article]
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* [http://planetmath.org/encyclopedia/RecursiveFunction.html PlanetMath article]
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* Dr Matt Fairtlough's [http://www.dcs.shef.ac.uk/~matt/teaching/04/com2030/lectures/tomlect13.pdf Introduction to recursive function theory] among his [http://www.dcs.shef.ac.uk/~matt/teaching/04/com2030/ lecture notes]
   
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== Designed languages ==
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* Dr Matt Fairtlough's [http://www.dcs.shef.ac.uk/~matt/teaching/04/com2030/lectures/tomlect16.pdf Minimal Programming Language (MIN)] is not exactly a recursive function theory language, but it is based on natural numbers, too and its equivalent power with partal recursive functions is shown in its description.
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== Implementations ==
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[http://www.dcs.shef.ac.uk/~matt/teaching/04/com2030/lectures/Recursion.hs In Haskell], among other implementations (e.g. written in Java) in Dr Matt Fairtlough's [http://www.dcs.shef.ac.uk/~matt/teaching/04/com2030/index.html lecture notes] (see the bottom of the page).
 
== Plans towards a programming language ==
 
== Plans towards a programming language ==
   
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:<math>\begin{matrix}\left\lfloor n + 1\right\rfloor = \underbrace{\mathbb N\times\dots\times\mathbb N}\to\mathbb N\\\;\;\;\;\;\;\;\;n+1\end{matrix}</math>
 
:<math>\begin{matrix}\left\lfloor n + 1\right\rfloor = \underbrace{\mathbb N\times\dots\times\mathbb N}\to\mathbb N\\\;\;\;\;\;\;\;\;n+1\end{matrix}</math>
   
=== Base functions ===
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=== Initial functions ===
   
 
==== Constant ====
 
==== Constant ====

Revision as of 13:20, 30 April 2006

Contents

1 Introduction

2 Designed languages

  • Dr Matt Fairtlough's Minimal Programming Language (MIN) is not exactly a recursive function theory language, but it is based on natural numbers, too and its equivalent power with partal recursive functions is shown in its description.

3 Implementations

In Haskell, among other implementations (e.g. written in Java) in Dr Matt Fairtlough's lecture notes (see the bottom of the page).

4 Plans towards a programming language

Well-known concepts are taken from [Mon:MatLog], but several new notations (only notations, not concepts) are introduced to reflect all concepts described in [Mon:MatLog], and some simplification are made (by allowing zero-arity generalizations). These are plans to achive formalizations that can allow us in the future to incarnate the main concepts of recursive function theory in a programming language.

5 Primitive recursive functions

5.1 Type system

\left\lfloor0\right\rfloor = \mathbb N
\begin{matrix}\left\lfloor n + 1\right\rfloor = \underbrace{\mathbb N\times\dots\times\mathbb N}\to\mathbb N\\\;\;\;\;\;\;\;\;n+1\end{matrix}

5.2 Initial functions

5.2.1 Constant

\mathbf 0 : \left\lfloor0\right\rfloor
\mathbf 0 = 0

This allows us to deal with a concept of zero in recursive function theory. In the literature (in [Mon:MathLog], too) this aim is achieved in another way: a

\mathbf{zero} : \left\lfloor1\right\rfloor
\mathbf{zero}\;x = 0

is defined instead. Is this approach superfluously overcomplicated? Can we avoid it and use the more simple and indirect looking

\mathbf 0 : \left\lfloor0\right\rfloor
\mathbf 0 = 0

approach?

Are these approaches equivalent? Is the latter (more simple looking) one as powerful as the former one? Could we define a \mathbf{zero} using the

\mathbf 0 : \left\lfloor0\right\rfloor
\mathbf 0 = 0

approach? Let us try:

\mathbf{zero} = \underline\mathbf K^0_1 \mathbf0

(see the definition of \underline\mathbf K^m_n somewhat below). This looks like working, but raises new questions: what about generalizing operations (here: composition) to deal with zero-arity cases in an appropriate way? E.g.

\underline\mathbf\dot K^0_n c\left\langle\right\rangle
\underline\mathbf K^0_n c

where c : \left\lfloor0\right\rfloor, n \in \mathbb N can be regarded as n-ary functions throwing all their n arguments away and returning c.

Does it take a generalization to allow such cases, or can they be inferred? A practical approach to solve such questions: let us write a Haskell program which implements (at least partially) recursive function theory. Then we can see clearly which things have to be defined and things which are consequences. I think the \underline\mathbf K^0_n c construct is a rather straighforward thing.

Why all this can be important: it may be exactly \underline\mathbf K^0_n c that saves us from defining the concept of zero in recursive function theory as

\mathbf{zero} : \left\lfloor1\right\rfloor
\mathbf{zero}\;x = 0

-- it may be superfluous: if we need functions that throw away (some or all) of their arguments and return a constant, then we can combine them from \underline\mathbf K^m_n, \mathbf s and \mathbf 0, if we allow concepts like \underline\mathbf K^0_m.

5.2.2 Successor function

\mathbf s : \left\lfloor1\right\rfloor
\mathbf s = \lambda x . x + 1

5.2.3 Projection functions

For all 0\leq i<m:

\mathbf U^m_i : \left\lfloor m\right\rfloor
\mathbf U^m_i x_0\dots x_i \dots x_{m-1} = x_i

5.3 Operations

5.3.1 Composition

\underline\mathbf\dot K^m_n : \left\lfloor m\right\rfloor \times \left\lfloor n\right\rfloor^m \to \left\lfloor n\right\rfloor
\underline\mathbf\dot K^m_n f \left\langle g_0,\dots, g_{m-1}\right\rangle x_0 \dots x_{n-1} = f \left(g_0 x_0 \dots x_{n-1}\right) \dots \left(g_{m-1} x_0 \dots x_{n-1}\right)

This resembles to the \mathbf\Phi^n_m combinator of Combinatory logic (as described in [HasFeyCr:CombLog1, 171]). If we prefer avoiding the notion of the nested tuple, and use a more homogenous style (somewhat resembling to currying):

\begin{matrix}\underline\mathbf K^m_n : \left\lfloor m\right\rfloor \times \underbrace{\left\lfloor n\right\rfloor\times\dots\times\left\lfloor n\right\rfloor} \to \left\lfloor n\right\rfloor\\\;\;\;\;\;\;\;\;\;\;m\end{matrix}

Let underbrace not mislead us -- it does not mean any bracing.

\underline\mathbf K^m_n f g_0\dots g_{m-1} x_0 \dots x_{n-1} = f \left(g_0 x_0 \dots x_{n-1}\right) \dots \left(g_{m-1} x_0 \dots x_{n-1}\right)

remembering us to

\underline\mathbf K^m_n f g_0\dots g_{m-1} x_0 \dots x_{n-1} = \mathbf \Phi^n_m f g_0 \dots g_{m-1} x_0 \dots x_{n-1}

5.3.2 Primitive recursion

\underline\mathbf R^m : \left\lfloor m\right\rfloor \times \left\lfloor m+2\right\rfloor \to \left\lfloor m+1\right\rfloor
\underline\mathbf R^m f h = g\;\mathrm{where}
g x_0 \dots x_{m-1} 0 = f x_0 \dots x_{m-1}
g x_0 \dots x_{m-1} \left(\mathbf s y\right) = h x_0 \dots x_{m-1} y \left(g x_0\dots x_{m-1} y\right)

The last equation resembles to the \mathbf S_n combinator of Combinatory logic (as described in [HasFeyCr:CombLog1, 169]):

g x_0 \dots x_{m-1} \left(\mathbf s y\right) = \mathbf S_{m+1} h g x_0 \dots x_{m-1} y

6 General recursive functions

Everything seen above, and the new concepts:

6.1 Type system

 \widehat{\,m\,} = \left\{ f : \left\lfloor m+1\right\rfloor\;\vert\;f \mathrm{\ is\ special}\right\}

See the definition of being special [Mon:MathLog, 45]. This property ensures, that minimalization does not lead us out of the world of total functions. Its definition is the rather straightforward formalization of this expectation.

\mathbf{special}^m f \equiv \forall x_0, \dots, x_{m-1} \in \mathbb N\;\;\exists y \in \mathbb N\;\;f x_0 \dots x_{m-1} y = 0

It resembles to the concept of inverse -- more exactly, to the existence part.


6.2 Operations

6.2.1 Minimalization

\underline\mu^m : \widehat m \to \left\lfloor m\right\rfloor
\underline\mu^m f x_0 \dots x_{m-1} = \min \left\{y\in\mathbb N\;\vert\;f x_0 \dots x_{m-1} y = 0\right\}

Minimalization does not lead us out of the word of total functions, if we use it only for special functions -- the property of being special is defined exactly for this purpose [Mon:MatLog, 45]. As we can see, minimalization is a concept resembling somehow to the concept of inverse.

Existence of the required minimum value of the set -- a sufficient and satisfactory condition for this is that the set is never empty. And this is equivalent to the statement

\forall x_0, \dots, x_{m-1} \in \mathbb N\;\;\exists y \in \mathbb N\;\;f x_0 \dots x_{m-1} y = 0

7 Partial recursive functions

Everything seen above, but new constructs are provided, too.

7.1 Type system

\begin{matrix}\left\lceil n + 1\right\rceil = \underbrace{\mathbb N\times\dots\times\mathbb N}\supset\!\to\mathbb N\\\;\;\;\;\;\;n+1\end{matrix}

Question: is there any sense to define \left\lceil0\right\rceil in another way than simply \left\lceil0\right\rceil = \left\lfloor0\right\rfloor = \mathbb N? Partial constant? Is

\left\lceil0\right\rceil = \mathbb N

or

\left\lceil0\right\rceil = \mathbb N\;\cup\;\left\{\bot\right\}?

7.2 Operations

\overline\mathbf\dot K^m_n : \left\lceil m\right\rceil \times \left\lceil n\right\rceil^m \to \left\lceil n\right\rceil
\overline\mathbf R^m : \left\lceil m\right\rceil \times \left\lceil m+2\right\rceil \to \left\lceil m+1\right\rceil
\overline\mu^m : \left\lceil m+1\right\rceil \to \left\lceil m\right\rceil

Their definitions are straightforward extension of the corresponding total function based definitions.

Remark: these operations take partial functions as arguments, but they are total operations themselves in the sense that they always yield a result -- at worst an empty function (as an ultimate partial function).

8 Bibliography

[HasFeyCr:CombLog1]
Curry, Haskell B; Feys, Robert; Craig, William: Combinatory Logic. Volume I. North-Holland Publishing Company, Amsterdam, 1958.
[Mon:MathLog]
Monk, J. Donald: Mathematical Logic. Springer-Verlag, New York * Heidelberg * Berlin, 1976.