Ackermann's function is μ-recursive

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In summary, the conversation discusses the proof that Ackermann's function is $\mu$-recursive. It is shown that the function can be represented by a single natural number at each step of its evaluation. This can be achieved through a scheme using primitive recursive functions. The conversation also introduces the trace function for the evaluation of Ackermann's function, which is shown to be primitive recursive. In order to prove this, several auxiliary predicates and functions are introduced. The conversation ends with the introduction of one final auxiliary function, $L$, which represents the length of a tuple.
  • #1
mathmari
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Hey! :eek:

In my book there is the following proof that Ackermann's function is $\mu$-recursive: We propose to show that Ackermann's funcition is $\mu$-recursive. The first part of the job is to devise a scheme whereby each step of an evaluation of Acerkmann's function can be represented by a single natural number. We will then show that the numbers associated with successive steps in an evaluation can be obtained from a primitive recursive function and that the result of the evaluation can be extracted by a suitably defined $\mu$-recursive function. We begin with the problem of representing evaluations numerically.

Ackermann's function is defined by three rules:

Rule 1: $A(0, y)=y+1$

Rule 2: $A(x+1, 0)=A(x, 1)$

Rule 3: $A(x+1, y+1)=A(x, A(x+1, y))$

Three facts are readily apparent from a study of Rules $1$ to $3$:

First, each step in the evaluation of $A(x, y)$ results either in a single natural number or else in a nested expression of the form $$A(w_1, A(w_2, A(w_3, \dots A(w_{k-1}, w_k) \dots )))$$

Second, exactly one of the three rules is applicable at any given step.

And third, no matter which rule applies, it must always be applied to the rightmost $A$ in the given expression-i.e., it must always be applied at the innermost level of the nest.

Perhaps less obvious than these observations is the fact that the evaluation procedure just outlined must always terminate after a finite number of steps.

Lemma 1. For each choice of natural numbers $x$ and $y$, the expression $A(x, y)$ can be reduced to a single natural number by means of a finite number of applications of Rules $1$, $2$, and $3$.

Let us now devise a method of representing the steps of an evaluation numerically. Remember that each step consists of an expression of the form $$A(w_1, A(w_2, \dots , A(w_{k-1}, w_k) \dots ))$$

Since the terms in such an expression are always associated to the right, most of the punctuation can be oitted without loss of information. In particular, the xression $A(w_1, A(w_2, \dots , A(w_{k-1}, w_k) \dots ))$ can b unambiguously represented by the $k$-tuple $$(w_1, \dots , w_{k-1}, w_k)$$

Thus each step in the evaluation of Ackermann's function can be described by a tuple of natural numbers.

We next use a Gödel-numbering scheme to reduce the description of each step in an evaluation to a single natural number. In particular, we choose to represent the tuple $(w_1, \dots , w_k)$ by the natural number $$2^k 3^{w_1} \cdots p_k^{w_k}$$

Note the length of the tuple is encoded as the exponent of two, while the components of the tuple are encoded as the exponents of the succeding primes. This provides a single way of telling how many components to "look for" in a given Gödel number.

This scheme allows us to describe the evaluation of Ackermann's function with a sequence of natural numbers. Such a numbering scheme is referred to as an arithmetization of the evaluation of Ackermann's function.

Note that the steps of the evaluation are numbered, starting with zero, and the zeroth step correpsonds to the expression $A(x, y)$ whose value is to be determined.

Wenow define the three-variable function $\psi$ so that $\psi(x, y, n)$ is theGödel number of the nth step in the evaluation of $A(x, y)$. In orrder to make $\psi$ a total function, we agree to "extend" each evaluation of Ackermann's function as follows. If the actual evaluation of $A(x, y)$ terminates at step $n_0$ and this step is assigned the Gödel number $z$, we simply set $\psi(x,y,n)=z$ for all $n \geq n_0$. The resulting function $\psi$ will be referred to as the trace function for the evaluation of $A$.

One next job is to sho wthta $\psi$ is a primitive recursive function. For this purpose it is conveninet to be able to view every natural number $z$ as being a Gödel number of some tuple. The simplest approach is to ignore any "extra" primes in the decomposition of $z$. Thus the natural number $$2^k3^{w_1}5^{w_2} \cdots p_k^{w_k}p_{k+1}^{w_{k+1}} \cdots p_m^{w_m}$$ will be treated as if it were the Gödel number of the $k$-tuple $(w_1, \dots , w_k)$.

With this convention in mind, we now assign an auxiliary predicate and an auxiliary function to each of the rules defining Ackermann's function. For each of the values $i=1$, $i=2$, and $i=3$, lewt $Q_i$ denote the predicate such that:

$$Q_i(z) \Leftrightarrow z \text{ is a Gödel number of a tuple to which Ruile } i \text{ applies }$$

And let $h_i$ denote a function such that, if $Q_i(z)$ holds, then:

$$h_i(z) \text{ is the Gödel number of the tuple that results when Rule } i \text{ is applied to the tuple represented by the number } z$$

If $Q_i(z)$ does not hold-i.e., if $z$ does not represnet a tuple to which Rule $i$ applies, the value assumed by $h_i(z)$ is unimportant. (We will in fact choose these "default values" so as to ensure the primitive recursiveness of $h_i$.)

The predicates $Q_1, Q_2, Q_3$ and the functions $h1, h_2,h_3$ provide an easy way of specifying the trace function $\psi$. Since $\psi(x, y, 0)$ is just the Gödel number of the tuple $(x, y)$, we have: $$\psi(x, y, 0)=2^23^x5^y$$

And since $
\psi(x, y, n+1)$ can be obtained from $\psi(x, y, n)$ by determining whiich rule applies to the tuple represented by $\psi(x, y, n)$ and then computing the effect of applying that rule, we can write:

$$\psi(x, y, n+1)=\begin{cases} h_1(\psi(x, y, n)) & \text{ if } Q_1(\psi(x, y, n)) \\ h_2(\psi(x, y, n)) & \text{ if } Q_2(\psi(x, y, n)) \\ h_3(\psi(x, y, n)) & \text{ if } Q_3(\psi(x, y, n)) \\ \psi(x, y, n) & \text{ otherwise} \end{cases}$$

Thus to show that $\psi$ is primitive recursive, it is only necessary to show that the predicates $Q_1, Q_2, Q_3$ and the functions $h_1, h_2, h_3$ are primtive recursive.

To this end, we introduce one more auxiliary function $L$, whose values for argument $z$ is the length of the tuple represented by $z$. Since $$L(z)=E(0, z)$$ the function $L$ is certainly primitve recursive.

Now, the predicate $Q_1(z)$ is to hold iff $z$ represnets a tuple containing at least two components, of which the next-to-last is zero. Thus we can write $$Q_1(z) \Leftrightarrow (L(z)>1) \wedge (E(L(z)\overset{\cdot }{-}1, z)=0)$$

Similar reasoning yields the following definitions of $Q_2$ and $Q_3$, as the reader may verify.

$$Q_2(z) \Leftrightarrow (L(z)>1) \wedge (E(L(z)\overset{\cdot}{-}1, z)\neq 0) \wedge (E(L(z), z)=0) \\ Q_3(z) \Leftrightarrow (L(z)>1) \wedge (E(L(z)\overset{\cdot}{-}1, z)\neq 0) \wedge (E(L(z), z) \neq 0)$$

Thus the predicates $Q_1$, $Q_2$, and $Q_3$ are all primitive recursive.

The functions $h_1$, $h_2$. and $h_3$ are only slightly more difficult to deal with. First consider the case in which $z$ represents a tuple to which Rule $1$ applies. That is, suppose that the prime decomposition of $z$ has the form: $$z=2^k3^{w_1} \cdots p_{k-2}^{w_{k-2}}p_{k-1}^0p_k^{w_k} \cdots p_m^{w_m}$$

Inspection of Rule $1$ reveals that in ythis case the appropriate value of $h_1(z)$ is: $$2^{k-1}3^{w_1} \cdots p_{k-2}^{w_{k-2}}p_{k-1}^{w_{k+1}}$$

We are therefore led to define $h_1$ as follows:

$$h_1(z)=2^{L(z)\overset{\cdot}{-}1} \left [ \prod_{i=1}^{L(z)\overset{\cdot}{-}2}pn(i)^{E(i, z)}\right ] pn(L(z)\overset{\cdot}{-}1)^{E(L(z), z)+1}$$

The choice of this definition ensures that $h_1$ is primitive recursive and that $h_1(z)$ assumes the required values whenever $Q_1(z)$ holds. (Rememver that it does not matter what value $h_1(z)$ assumes when $Q_1(z)$ does not hold.)

Similar observations concerning Rules $2$ and $3$ lead to the following definitions of the functions $h_2$ and $h_3$.

$$h_2(z)= \left [ \prod_{i=1}^{L(z)\overset{\cdot}{-}2}pn(i)^{E(i, z)}\right ] pn(L(z)\overset{\cdot}{-}1)^{E(L(z)\overset{\cdot}{-}1, z)\overset{\cdot }{-}1}pn(L(z)) \\ h_3(z)=\dots $$

Thus the desired functions $h_1$, $h_2$, and $h_3$, like the predicates $Q_1$, $Q_2$, and $Q_3$, are primitive recursive. And this in urn implies that the trace function $\psi$ is primitive rcursive.

We are now ready to show that Ackermann's function is $\mu$-recursive. Fr this purpose we define the two-variable function $\eta$ so that $\eta(x, y)$ is te number of steps needed to evaluate $A(x, y)$. Evidently $\eta(x, y)$ is the smallest value of $n$ for which $\psi(x, y, n)$ represents a single natural number. Thus: $$\eta (x, y) =\mu n(L(\psi(x, y, n))=1]$$ Since $L$ and $\psi$ aare primitive recursive, it follows that $\eta$ is a $\mu$-recursive function. Next note that the outcome of the evaluation of $A(x, y)$ is just the value of the natural number represented by $\psi(x, y, \eta(x, y))$. We may therefore write $$A(x, y)=E(1, \psi(x, y, \eta (x, y)))$$ Since $E$ and $\psi$ are primitive recursive and $\eta$is $\mu$-recursive, we have the finally established:

Theorem:

Ackermann's function is $\mu$-recursive.


Could you explain to the idea of the proof?? I haven't understood it... Why do we need the functions $h_i$, $\psi$, an dso on to show that Ackermann's function is $\mu$-recursive??
 
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  • #2
Wow. That's a serious effort invested in typing a question. Perhaps in the future you can scan the relevant portion of your book and post it.

Are you sure you are not allowed to use Church-Turing thesis that every intuitively computable function is mu-recursive? That would make things much easier. If this is not allowed, I'll look at the proof later.
 
  • #3
Evgeny.Makarov said:
Are you sure you are not allowed to use Church-Turing thesis that every intuitively computable function is mu-recursive? That would make things much easier.

How does the proof look like when we are allowed to use Church-Turing thesis that every intuitively computable function is mu-recursive?
 
  • #4
You describe an algorithm, which is the equations for computing $A(m,n)$ recursively. It may be necessary to explain termination and determinism: for each values of $m,n$, only one equation fits when considered as a rewriting rule from left to right. Then you say that every algorithm can be represented as a mu-recursive function by Church-Turing thesis. The thesis says that all main formalizations of the concept of algorithm, being equivalent (this is a theorem), describe all intuitively computable functions.
 
  • #5
Evgeny.Makarov said:
You describe an algorithm, which is the equations for computing $A(m,n)$ recursively. It may be necessary to explain termination and determinism: for each values of $m,n$, only one equation fits when considered as a rewriting rule from left to right. Then you say that every algorithm can be represented as a mu-recursive function by Church-Turing thesis. The thesis says that all main formalizations of the concept of algorithm, being equivalent (this is a theorem), describe all intuitively computable functions.

What do you mean by "describe an algorithm" ?

Do we have to write a code?

Or do we maybe have to say something as at the beginning of the proof of the post #1 ?

Ackermann's function is defined by three rules:

Rule 1: $A(0, y)=y+1$

Rule 2: $A(x+1, 0)=A(x, 1)$

Rule 3: $A(x+1, y+1)=A(x, A(x+1, y))$

Three facts are readily apparent from a study of Rules $1$ to $3$:

First, each step in the evaluation of $A(x, y)$ results either in a single natural number or else in a nested expression of the form $$A(w_1, A(w_2, A(w_3, \dots A(w_{k-1}, w_k) \dots )))$$

Second, exactly one of the three rules is applicable at any given step.

And third, no matter which rule applies, it must always be applied to the rightmost $A$ in the given expression-i.e., it must always be applied at the innermost level of the nest.

Perhaps less obvious than these observations is the fact that the evaluation procedure just outlined must always terminate after a finite number of steps.

Lemma 1. For each choice of natural numbers $x$ and $y$, the expression $A(x, y)$ can be reduced to a single natural number by means of a finite number of applications of Rules $1$, $2$, and $3$.
 
  • #6
mathmari said:
Or do we maybe have to say something as at the beginning of the proof of the post #1 ?
Yes, that's what I meant.
 
  • #7
mathmari said:
Could you explain to the idea of the proof?? I haven't understood it... Why do we need the functions $h_i$, $\psi$, an so on to show that Ackermann's function is $\mu$-recursive??
The proof, in fact, is not hard and is written very well. Could you say which book it comes from? The main idea is to make a primitive recursive function $\psi(x,y,n)$ that returns the code of a tuple $(w_1,\dots,w_k)$ such that after $n$ applications of rewriting rules that define the Ackermann's function $A(m,n)$ is rewritten to $A(w_1, A(w_2, \dots , A(w_{k-1}, w_k) \dots ))$.
 
  • #8
Evgeny.Makarov said:
Could you say which book it comes from?

It is from the book: Introduction to Computability, HENNIE
 
  • #9
mathmari said:
Ackermann's function is defined by three rules:

Rule 1: $A(0, y)=y+1$

Rule 2: $A(x+1, 0)=A(x, 1)$

Rule 3: $A(x+1, y+1)=A(x, A(x+1, y))$

Three facts are readily apparent from a study of Rules $1$ to $3$:

First, each step in the evaluation of $A(x, y)$ results either in a single natural number or else in a nested expression of the form $$A(w_1, A(w_2, A(w_3, \dots A(w_{k-1}, w_k) \dots )))$$

Second, exactly one of the three rules is applicable at any given step.

And third, no matter which rule applies, it must always be applied to the rightmost $A$ in the given expression-i.e., it must always be applied at the innermost level of the nest.

Perhaps less obvious than these observations is the fact that the evaluation procedure just outlined must always terminate after a finite number of steps.

Lemma 1. For each choice of natural numbers $x$ and $y$, the expression $A(x, y)$ can be reduced to a single natural number by means of a finite number of applications of Rules $1$, $2$, and $3$.
Is this the complete proof that Ackermann's function is intuitively computable?
 
  • #10
mathmari said:
Is this the complete proof that Ackermann's function is intuitively computable?
Does it convince you? Then yes; otherwise, no. In fact, it is difficult to call something a complete proof when it contains the phrase, "Perhaps less obvious… is the fact that the evaluation procedure… must always terminate after a finite number of steps" without any additional explanation.
 
  • #11
Evgeny.Makarov said:
In fact, it is difficult to call something a complete proof when it contains the phrase, "Perhaps less obvious… is the fact that the evaluation procedure… must always terminate after a finite number of steps" without any additional explanation.

This phrase describes the idea of the Lemma that follows, or not? In my book there is also the proof of that Lemma.

Is it OK to write this justification that Ackermann's function is intuitively computable for the presentation or should I write a more formal proof, maybe using induction or something else?
 
  • #12
mathmari said:
This phrase describes the idea of the Lemma that follows, or not?
Yes.

mathmari said:
In my book there is also the proof of that Lemma.

Is it OK to write this justification that Ackermann's function is intuitively computable for the presentation or should I write a more formal proof, maybe using induction or something else?
I am not sure what you mean by "this justification": what comes before or after the proof. Some idea of the reason for termination would be good; I already wrote this in one of the threads.

In general, I am reluctant to continue the discussion of the presentation, as opposed to mathematical issues, which are fine. What makes a good presentation is not a mathematical question. It may be best to discuss it with your professor. My two advices are: be sure you understand everything you say (and be prepared to explain something that is mentioned but is not fully included in the presentation), and don't make it overly technical for the sake of the audience. Of course, the latter advice depends on your goals: it may be a safer way to get a good grade by making it technical, reading it in a monotone voice, putting your audience to sleep and avoiding questions in this way. But if you really want to teach something, then you should try to imagine how a person in the audience feels. They have to grasp the content of each slide in a minute or less and cannot go back. Usually erring on the side of simplicity is better.
 

1. What is Ackermann's function?

Ackermann's function is a mathematical function named after mathematician Wilhelm Ackermann. It is a total, computable function that maps non-negative integers to non-negative integers. It is defined recursively and grows extremely quickly as the input values increase.

2. How is Ackermann's function μ-recursive?

A function is μ-recursive if it can be computed using a μ-recursive scheme, which is a set of basic operations and rules for combining them. Ackermann's function can be defined using a μ-recursive scheme, making it a μ-recursive function.

3. What is the significance of Ackermann's function being μ-recursive?

Ackermann's function being μ-recursive shows that it is a computable function, meaning it can be calculated by a computer. This has significant implications in computer science and theory of computation, as it demonstrates the existence of functions that are computable but not primitive recursive.

4. How does Ackermann's function differ from other recursive functions?

Ackermann's function differs from other recursive functions in its extremely fast growth rate. While most recursive functions have a polynomial time complexity, Ackermann's function has an exponential time complexity, making it much more difficult to compute for larger input values.

5. What are some practical applications of Ackermann's function being μ-recursive?

Although Ackermann's function has primarily been studied for its theoretical implications, it has found some practical applications in computer science. It has been used to analyze the efficiency of algorithms and data structures, and has also been applied in the design of programming languages and compilers.

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