In the late 1920s, the mathematicians Gabriel Sudan and Wilhelm Ackermann, students of David Hilbert, were studying the foundations of computation. Both Sudan and Ackermann are credited4 with discovering total computable functions (termed simply "recursive" in some references) that are not primitive recursive. Sudan published the lesser-known Sudan function, then shortly afterwards and independently, in 1928, Ackermann published his function φ {\displaystyle \varphi } (from Greek, the letter phi). Ackermann's three-argument function, φ ( m , n , p ) {\displaystyle \varphi (m,n,p)} , is defined such that for p = 0 , 1 , 2 {\displaystyle p=0,1,2} , it reproduces the basic operations of addition, multiplication, and exponentiation as
φ ( m , n , 0 ) = m + n φ ( m , n , 1 ) = m × n φ ( m , n , 2 ) = m n {\displaystyle {\begin{aligned}\varphi (m,n,0)&=m+n\\\varphi (m,n,1)&=m\times n\\\varphi (m,n,2)&=m^{n}\end{aligned}}}
and for p > 2 {\displaystyle p>2} it extends these basic operations in a way that can be compared to the hyperoperations:
φ ( m , n , 3 ) = m [ 4 ] ( n + 1 ) φ ( m , n , p ) ⪆ m [ p + 1 ] ( n + 1 ) for p > 3 {\displaystyle {\begin{aligned}\varphi (m,n,3)&=m[4](n+1)\\\varphi (m,n,p)&\gtrapprox m[p+1](n+1)&&{\text{for }}p>3\end{aligned}}}
(Aside from its historic role as a total-computable-but-not-primitive-recursive function, Ackermann's original function is seen to extend the basic arithmetic operations beyond exponentiation, although not as seamlessly as do variants of Ackermann's function that are specifically designed for that purpose—such as Goodstein's hyperoperation sequence.)
In On the Infinite,5 David Hilbert hypothesized that the Ackermann function was not primitive recursive, but it was Ackermann, Hilbert's personal secretary and former student, who actually proved the hypothesis in his paper On Hilbert's Construction of the Real Numbers.67
Rózsa Péter8 and Raphael Robinson9 later developed a two-variable version of the Ackermann function that became preferred by almost all authors.
The generalized hyperoperation sequence, e.g. G ( m , a , b ) = a [ m ] b {\displaystyle G(m,a,b)=a[m]b} , is a version of the Ackermann function as well.10
In 1963 R.C. Buck based an intuitive two-variable 11 variant F {\displaystyle \operatorname {F} } on the hyperoperation sequence:1213
F ( m , n ) = 2 [ m ] n . {\displaystyle \operatorname {F} (m,n)=2[m]n.}
Compared to most other versions, Buck's function has no unessential offsets:
F ( 0 , n ) = 2 [ 0 ] n = n + 1 F ( 1 , n ) = 2 [ 1 ] n = 2 + n F ( 2 , n ) = 2 [ 2 ] n = 2 × n F ( 3 , n ) = 2 [ 3 ] n = 2 n F ( 4 , n ) = 2 [ 4 ] n = 2 2 2 . . . 2 ⋮ {\displaystyle {\begin{aligned}\operatorname {F} (0,n)&=2[0]n=n+1\\\operatorname {F} (1,n)&=2[1]n=2+n\\\operatorname {F} (2,n)&=2[2]n=2\times n\\\operatorname {F} (3,n)&=2[3]n=2^{n}\\\operatorname {F} (4,n)&=2[4]n=2^{2^{2^{{}^{.^{.^{{}_{.}2}}}}}}\\&\quad \vdots \end{aligned}}}
Many other versions of Ackermann function have been investigated.1415
Ackermann's original three-argument function φ ( m , n , p ) {\displaystyle \varphi (m,n,p)} is defined recursively as follows for nonnegative integers m , n , {\displaystyle m,n,} and p {\displaystyle p} :
φ ( m , n , 0 ) = m + n φ ( m , 0 , 1 ) = 0 φ ( m , 0 , 2 ) = 1 φ ( m , 0 , p ) = m for p > 2 φ ( m , n , p ) = φ ( m , φ ( m , n − 1 , p ) , p − 1 ) for n , p > 0 {\displaystyle {\begin{aligned}\varphi (m,n,0)&=m+n\\\varphi (m,0,1)&=0\\\varphi (m,0,2)&=1\\\varphi (m,0,p)&=m&&{\text{for }}p>2\\\varphi (m,n,p)&=\varphi (m,\varphi (m,n-1,p),p-1)&&{\text{for }}n,p>0\end{aligned}}}
Of the various two-argument versions, the one developed by Péter and Robinson (called "the" Ackermann function by most authors) is defined for nonnegative integers m {\displaystyle m} and n {\displaystyle n} as follows:
A ( 0 , n ) = n + 1 A ( m + 1 , 0 ) = A ( m , 1 ) A ( m + 1 , n + 1 ) = A ( m , A ( m + 1 , n ) ) {\displaystyle {\begin{array}{lcl}\operatorname {A} (0,n)&=&n+1\\\operatorname {A} (m+1,0)&=&\operatorname {A} (m,1)\\\operatorname {A} (m+1,n+1)&=&\operatorname {A} (m,\operatorname {A} (m+1,n))\end{array}}}
The Ackermann function has also been expressed in relation to the hyperoperation sequence:1617
A ( m , n ) = { n + 1 m = 0 2 [ m ] ( n + 3 ) − 3 m > 0 {\displaystyle A(m,n)={\begin{cases}n+1&m=0\\2[m](n+3)-3&m>0\\\end{cases}}}
or, written in Knuth's up-arrow notation (extended to integer indices ≥ − 2 {\displaystyle \geq -2} ):
A ( m , n ) = { n + 1 m = 0 2 ↑ m − 2 ( n + 3 ) − 3 m > 0 {\displaystyle A(m,n)={\begin{cases}n+1&m=0\\2\uparrow ^{m-2}(n+3)-3&m>0\\\end{cases}}}
or, equivalently, in terms of Buck's function F:18
A ( m , n ) = { n + 1 m = 0 F ( m , n + 3 ) − 3 m > 0 {\displaystyle A(m,n)={\begin{cases}n+1&m=0\\F(m,n+3)-3&m>0\\\end{cases}}}
Define f n {\displaystyle f^{n}} as the n-th iterate of f {\displaystyle f} :
f 0 ( x ) = x f n + 1 ( x ) = f ( f n ( x ) ) {\displaystyle {\begin{array}{rll}f^{0}(x)&=&x\\f^{n+1}(x)&=&f(f^{n}(x))\end{array}}}
Iteration is the process of composing a function with itself a certain number of times. Function composition is an associative operation, so f ( f n ( x ) ) = f n ( f ( x ) ) {\displaystyle f(f^{n}(x))=f^{n}(f(x))} .
Conceiving the Ackermann function as a sequence of unary functions, one can set A m ( n ) = A ( m , n ) {\displaystyle \operatorname {A} _{m}(n)=\operatorname {A} (m,n)} .
The function then becomes a sequence A 0 , A 1 , A 2 , . . . {\displaystyle \operatorname {A} _{0},\operatorname {A} _{1},\operatorname {A} _{2},...} of unary19 functions, defined from iteration:
A 0 ( n ) = n + 1 A m + 1 ( n ) = A m n + 1 ( 1 ) {\displaystyle {\begin{array}{lcl}\operatorname {A} _{0}(n)&=&n+1\\\operatorname {A} _{m+1}(n)&=&\operatorname {A} _{m}^{n+1}(1)\\\end{array}}}
The recursive definition of the Ackermann function can naturally be transposed to a term rewriting system (TRS).
The definition of the 2-ary Ackermann function leads to the obvious reduction rules2021
(r1) A ( 0 , n ) → S ( n ) (r2) A ( S ( m ) , 0 ) → A ( m , S ( 0 ) ) (r3) A ( S ( m ) , S ( n ) ) → A ( m , A ( S ( m ) , n ) ) {\displaystyle {\begin{array}{lll}{\text{(r1)}}&A(0,n)&\rightarrow &S(n)\\{\text{(r2)}}&A(S(m),0)&\rightarrow &A(m,S(0))\\{\text{(r3)}}&A(S(m),S(n))&\rightarrow &A(m,A(S(m),n))\end{array}}}
Example
Compute A ( 1 , 2 ) → ∗ 4 {\displaystyle A(1,2)\rightarrow _{*}4}
The reduction sequence is 22
To compute A ( m , n ) {\displaystyle \operatorname {A} (m,n)} one can use a stack, which initially contains the elements ⟨ m , n ⟩ {\displaystyle \langle m,n\rangle } .
Then repeatedly the two top elements are replaced according to the rules23
(r1) 0 , n → ( n + 1 ) (r2) ( m + 1 ) , 0 → m , 1 (r3) ( m + 1 ) , ( n + 1 ) → m , ( m + 1 ) , n {\displaystyle {\begin{array}{lllllllll}{\text{(r1)}}&0&,&n&\rightarrow &(n+1)\\{\text{(r2)}}&(m+1)&,&0&\rightarrow &m&,&1\\{\text{(r3)}}&(m+1)&,&(n+1)&\rightarrow &m&,&(m+1)&,&n\end{array}}}
Schematically, starting from ⟨ m , n ⟩ {\displaystyle \langle m,n\rangle } :
The pseudocode is published in Grossman & Zeitman (1988).
For example, on input ⟨ 2 , 1 ⟩ {\displaystyle \langle 2,1\rangle } ,
Remarks
Their own algorithm, inherently iterative, computes A ( m , n ) {\displaystyle \operatorname {A} (m,n)} within O ( m A ( m , n ) ) {\displaystyle {\mathcal {O}}(m\operatorname {A} (m,n))} time and within O ( m ) {\displaystyle {\mathcal {O}}(m)} space.
The definition of the iterated 1-ary Ackermann functions leads to different reduction rules
(r4) A ( S ( 0 ) , 0 , n ) → S ( n ) (r5) A ( S ( 0 ) , S ( m ) , n ) → A ( S ( n ) , m , S ( 0 ) ) (r6) A ( S ( S ( x ) ) , m , n ) → A ( S ( 0 ) , m , A ( S ( x ) , m , n ) ) {\displaystyle {\begin{array}{lll}{\text{(r4)}}&A(S(0),0,n)&\rightarrow &S(n)\\{\text{(r5)}}&A(S(0),S(m),n)&\rightarrow &A(S(n),m,S(0))\\{\text{(r6)}}&A(S(S(x)),m,n)&\rightarrow &A(S(0),m,A(S(x),m,n))\end{array}}}
As function composition is associative, instead of rule r6 one can define
(r7) A ( S ( S ( x ) ) , m , n ) → A ( S ( x ) , m , A ( S ( 0 ) , m , n ) ) {\displaystyle {\begin{array}{lll}{\text{(r7)}}&A(S(S(x)),m,n)&\rightarrow &A(S(x),m,A(S(0),m,n))\end{array}}}
Like in the previous section the computation of A m 1 ( n ) {\displaystyle \operatorname {A} _{m}^{1}(n)} can be implemented with a stack.
Initially the stack contains the three elements ⟨ 1 , m , n ⟩ {\displaystyle \langle 1,m,n\rangle } .
Then repeatedly the three top elements are replaced according to the rules26
(r4) 1 , 0 , n → ( n + 1 ) (r5) 1 , ( m + 1 ) , n → ( n + 1 ) , m , 1 (r6) ( x + 2 ) , m , n → 1 , m , ( x + 1 ) , m , n {\displaystyle {\begin{array}{lllllllll}{\text{(r4)}}&1&,0&,n&\rightarrow &(n+1)\\{\text{(r5)}}&1&,(m+1)&,n&\rightarrow &(n+1)&,m&,1\\{\text{(r6)}}&(x+2)&,m&,n&\rightarrow &1&,m&,(x+1)&,m&,n\\\end{array}}}
Schematically, starting from ⟨ 1 , m , n ⟩ {\displaystyle \langle 1,m,n\rangle } :
On input ⟨ 1 , 2 , 1 ⟩ {\displaystyle \langle 1,2,1\rangle } the successive stack configurations are
1 , 2 , 1 _ → r 5 2 , 1 , 1 _ → r 6 1 , 1 , 1 , 1 , 1 _ → r 5 1 , 1 , 2 , 0 , 1 _ → r 6 1 , 1 , 1 , 0 , 1 , 0 , 1 _ → r 4 1 , 1 , 1 , 0 , 2 _ → r 4 1 , 1 , 3 _ → r 5 4 , 0 , 1 _ → r 6 1 , 0 , 3 , 0 , 1 _ → r 6 1 , 0 , 1 , 0 , 2 , 0 , 1 _ → r 6 1 , 0 , 1 , 0 , 1 , 0 , 1 , 0 , 1 _ → r 4 1 , 0 , 1 , 0 , 1 , 0 , 2 _ → r 4 1 , 0 , 1 , 0 , 3 _ → r 4 1 , 0 , 4 _ → r 4 5 {\displaystyle {\begin{aligned}&{\underline {1,2,1}}\rightarrow _{r5}{\underline {2,1,1}}\rightarrow _{r6}1,1,{\underline {1,1,1}}\rightarrow _{r5}1,1,{\underline {2,0,1}}\rightarrow _{r6}1,1,1,0,{\underline {1,0,1}}\\&\rightarrow _{r4}1,1,{\underline {1,0,2}}\rightarrow _{r4}{\underline {1,1,3}}\rightarrow _{r5}{\underline {4,0,1}}\rightarrow _{r6}1,0,{\underline {3,0,1}}\rightarrow _{r6}1,0,1,0,{\underline {2,0,1}}\\&\rightarrow _{r6}1,0,1,0,1,0,{\underline {1,0,1}}\rightarrow _{r4}1,0,1,0,{\underline {1,0,2}}\rightarrow _{r4}1,0,{\underline {1,0,3}}\rightarrow _{r4}{\underline {1,0,4}}\rightarrow _{r4}5\end{aligned}}}
The corresponding equalities are
A 2 ( 1 ) = A 1 2 ( 1 ) = A 1 ( A 1 ( 1 ) ) = A 1 ( A 0 2 ( 1 ) ) = A 1 ( A 0 ( A 0 ( 1 ) ) ) = A 1 ( A 0 ( 2 ) ) = A 1 ( 3 ) = A 0 4 ( 1 ) = A 0 ( A 0 3 ( 1 ) ) = A 0 ( A 0 ( A 0 2 ( 1 ) ) ) = A 0 ( A 0 ( A 0 ( A 0 ( 1 ) ) ) ) = A 0 ( A 0 ( A 0 ( 2 ) ) ) = A 0 ( A 0 ( 3 ) ) = A 0 ( 4 ) = 5 {\displaystyle {\begin{aligned}&A_{2}(1)=A_{1}^{2}(1)=A_{1}(A_{1}(1))=A_{1}(A_{0}^{2}(1))=A_{1}(A_{0}(A_{0}(1)))\\&=A_{1}(A_{0}(2))=A_{1}(3)=A_{0}^{4}(1)=A_{0}(A_{0}^{3}(1))=A_{0}(A_{0}(A_{0}^{2}(1)))\\&=A_{0}(A_{0}(A_{0}(A_{0}(1))))=A_{0}(A_{0}(A_{0}(2)))=A_{0}(A_{0}(3))=A_{0}(4)=5\end{aligned}}}
When reduction rule r7 is used instead of rule r6, the replacements in the stack will follow
(r7) ( x + 2 ) , m , n → ( x + 1 ) , m , 1 , m , n {\displaystyle {\begin{array}{lllllllll}{\text{(r7)}}&(x+2)&,m&,n&\rightarrow &(x+1)&,m&,1&,m&,n\end{array}}}
The successive stack configurations will then be
1 , 2 , 1 _ → r 5 2 , 1 , 1 _ → r 7 1 , 1 , 1 , 1 , 1 _ → r 5 1 , 1 , 2 , 0 , 1 _ → r 7 1 , 1 , 1 , 0 , 1 , 0 , 1 _ → r 4 1 , 1 , 1 , 0 , 2 _ → r 4 1 , 1 , 3 _ → r 5 4 , 0 , 1 _ → r 7 3 , 0 , 1 , 0 , 1 _ → r 4 3 , 0 , 2 _ → r 7 2 , 0 , 1 , 0 , 2 _ → r 4 2 , 0 , 3 _ → r 7 1 , 0 , 1 , 0 , 3 _ → r 4 1 , 0 , 4 _ → r 4 5 {\displaystyle {\begin{aligned}&{\underline {1,2,1}}\rightarrow _{r5}{\underline {2,1,1}}\rightarrow _{r7}1,1,{\underline {1,1,1}}\rightarrow _{r5}1,1,{\underline {2,0,1}}\rightarrow _{r7}1,1,1,0,{\underline {1,0,1}}\\&\rightarrow _{r4}1,1,{\underline {1,0,2}}\rightarrow _{r4}{\underline {1,1,3}}\rightarrow _{r5}{\underline {4,0,1}}\rightarrow _{r7}3,0,{\underline {1,0,1}}\rightarrow _{r4}{\underline {3,0,2}}\\&\rightarrow _{r7}2,0,{\underline {1,0,2}}\rightarrow _{r4}{\underline {2,0,3}}\rightarrow _{r7}1,0,{\underline {1,0,3}}\rightarrow _{r4}{\underline {1,0,4}}\rightarrow _{r4}5\end{aligned}}}
A 2 ( 1 ) = A 1 2 ( 1 ) = A 1 ( A 1 ( 1 ) ) = A 1 ( A 0 2 ( 1 ) ) = A 1 ( A 0 ( A 0 ( 1 ) ) ) = A 1 ( A 0 ( 2 ) ) = A 1 ( 3 ) = A 0 4 ( 1 ) = A 0 3 ( A 0 ( 1 ) ) = A 0 3 ( 2 ) = A 0 2 ( A 0 ( 2 ) ) = A 0 2 ( 3 ) = A 0 ( A 0 ( 3 ) ) = A 0 ( 4 ) = 5 {\displaystyle {\begin{aligned}&A_{2}(1)=A_{1}^{2}(1)=A_{1}(A_{1}(1))=A_{1}(A_{0}^{2}(1))=A_{1}(A_{0}(A_{0}(1)))\\&=A_{1}(A_{0}(2))=A_{1}(3)=A_{0}^{4}(1)=A_{0}^{3}(A_{0}(1))=A_{0}^{3}(2)\\&=A_{0}^{2}(A_{0}(2))=A_{0}^{2}(3)=A_{0}(A_{0}(3))=A_{0}(4)=5\end{aligned}}}
As Sundblad (1971) — or Porto & Matos (1980) — showed explicitly, the Ackermann function can be expressed in terms of the hyperoperation sequence:
or, after removal of the constant 2 from the parameter list, in terms of Buck's function
Buck's function F ( m , n ) = 2 [ m ] n {\displaystyle \operatorname {F} (m,n)=2[m]n} ,28 a variant of Ackermann function by itself, can be computed with the following reduction rules:
(b1) F ( S ( 0 ) , 0 , n ) → S ( n ) (b2) F ( S ( 0 ) , S ( 0 ) , 0 ) → S ( S ( 0 ) ) (b3) F ( S ( 0 ) , S ( S ( 0 ) ) , 0 ) → 0 (b4) F ( S ( 0 ) , S ( S ( S ( m ) ) ) , 0 ) → S ( 0 ) (b5) F ( S ( 0 ) , S ( m ) , S ( n ) ) → F ( S ( n ) , m , F ( S ( 0 ) , S ( m ) , 0 ) ) (b6) F ( S ( S ( x ) ) , m , n ) → F ( S ( 0 ) , m , F ( S ( x ) , m , n ) ) {\displaystyle {\begin{array}{lll}{\text{(b1)}}&F(S(0),0,n)&\rightarrow &S(n)\\{\text{(b2)}}&F(S(0),S(0),0)&\rightarrow &S(S(0))\\{\text{(b3)}}&F(S(0),S(S(0)),0)&\rightarrow &0\\{\text{(b4)}}&F(S(0),S(S(S(m))),0)&\rightarrow &S(0)\\{\text{(b5)}}&F(S(0),S(m),S(n))&\rightarrow &F(S(n),m,F(S(0),S(m),0))\\{\text{(b6)}}&F(S(S(x)),m,n)&\rightarrow &F(S(0),m,F(S(x),m,n))\end{array}}} Instead of rule b6 one can define the rule
(b7) F ( S ( S ( x ) ) , m , n ) → F ( S ( x ) , m , F ( S ( 0 ) , m , n ) ) {\displaystyle {\begin{array}{lll}{\text{(b7)}}&F(S(S(x)),m,n)&\rightarrow &F(S(x),m,F(S(0),m,n))\end{array}}} To compute the Ackermann function it suffices to add three reduction rules
(r8) A ( 0 , n ) → S ( n ) (r9) A ( S ( m ) , n ) → P ( F ( S ( 0 ) , S ( m ) , S ( S ( S ( n ) ) ) ) ) (r10) P ( S ( S ( S ( m ) ) ) ) → m {\displaystyle {\begin{array}{lll}{\text{(r8)}}&A(0,n)&\rightarrow &S(n)\\{\text{(r9)}}&A(S(m),n)&\rightarrow &P(F(S(0),S(m),S(S(S(n)))))\\{\text{(r10)}}&P(S(S(S(m))))&\rightarrow &m\\\end{array}}}
These rules take care of the base case A(0,n), the alignment (n+3) and the fudge (-3).
Compute A ( 2 , 1 ) → ∗ 5 {\displaystyle A(2,1)\rightarrow _{*}5}
The matching equalities are
A ( 2 , 1 ) + 3 = F ( 2 , 4 ) = ⋯ = F 6 ( 0 , 2 ) = F ( 0 , F 5 ( 0 , 2 ) ) = F ( 0 , F ( 0 , F 4 ( 0 , 2 ) ) ) = F ( 0 , F ( 0 , F ( 0 , F 3 ( 0 , 2 ) ) ) ) = F ( 0 , F ( 0 , F ( 0 , F ( 0 , F 2 ( 0 , 2 ) ) ) ) ) = F ( 0 , F ( 0 , F ( 0 , F ( 0 , F ( 0 , F ( 0 , 2 ) ) ) ) ) ) = F ( 0 , F ( 0 , F ( 0 , F ( 0 , F ( 0 , 3 ) ) ) ) ) = F ( 0 , F ( 0 , F ( 0 , F ( 0 , 4 ) ) ) ) = F ( 0 , F ( 0 , F ( 0 , 5 ) ) ) = F ( 0 , F ( 0 , 6 ) ) = F ( 0 , 7 ) = 8 {\displaystyle {\begin{aligned}&A(2,1)+3=F(2,4)=\dots =F^{6}(0,2)=F(0,F^{5}(0,2))=F(0,F(0,F^{4}(0,2)))\\&=F(0,F(0,F(0,F^{3}(0,2))))=F(0,F(0,F(0,F(0,F^{2}(0,2)))))=F(0,F(0,F(0,F(0,F(0,F(0,2))))))\\&=F(0,F(0,F(0,F(0,F(0,3)))))=F(0,F(0,F(0,F(0,4))))=F(0,F(0,F(0,5)))=F(0,F(0,6))=F(0,7)=8\end{aligned}}}
A ( 2 , 1 ) + 3 = F ( 2 , 4 ) = ⋯ = F 6 ( 0 , 2 ) = F 5 ( 0 , F ( 0 , 2 ) ) = F 5 ( 0 , 3 ) = F 4 ( 0 , F ( 0 , 3 ) ) = F 4 ( 0 , 4 ) = F 3 ( 0 , F ( 0 , 4 ) ) = F 3 ( 0 , 5 ) = F 2 ( 0 , F ( 0 , 5 ) ) = F 2 ( 0 , 6 ) = F ( 0 , F ( 0 , 6 ) ) = F ( 0 , 7 ) = 8 {\displaystyle {\begin{aligned}&A(2,1)+3=F(2,4)=\dots =F^{6}(0,2)=F^{5}(0,F(0,2))=F^{5}(0,3)=F^{4}(0,F(0,3))=F^{4}(0,4)\\&=F^{3}(0,F(0,4))=F^{3}(0,5)=F^{2}(0,F(0,5))=F^{2}(0,6)=F(0,F(0,6))=F(0,7)=8\end{aligned}}} Remarks
To demonstrate how the computation of A ( 4 , 3 ) {\displaystyle A(4,3)} results in many steps and in a large number:32
A ( 4 , 3 ) → A ( 3 , A ( 4 , 2 ) ) → A ( 3 , A ( 3 , A ( 4 , 1 ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 4 , 0 ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 3 , 1 ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 3 , 0 ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 2 , 1 ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 1 , A ( 2 , 0 ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 1 , A ( 1 , 1 ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 1 , A ( 0 , A ( 1 , 0 ) ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 1 , A ( 0 , A ( 0 , 1 ) ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 1 , A ( 0 , 2 ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 1 , 3 ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , A ( 1 , 2 ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , A ( 0 , A ( 1 , 1 ) ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , A ( 0 , A ( 0 , A ( 1 , 0 ) ) ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , A ( 0 , A ( 0 , A ( 0 , 1 ) ) ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , A ( 0 , A ( 0 , 2 ) ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , A ( 0 , 3 ) ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , A ( 0 , 4 ) ) ) ) ) → A ( 3 , A ( 3 , A ( 3 , A ( 2 , 5 ) ) ) ) ⋮ → A ( 3 , A ( 3 , A ( 3 , 13 ) ) ) ⋮ → A ( 3 , A ( 3 , 65533 ) ) ⋮ → A ( 3 , 2 65536 − 3 ) ⋮ → 2 2 65536 − 3. {\displaystyle {\begin{aligned}A(4,3)&\rightarrow A(3,A(4,2))\\&\rightarrow A(3,A(3,A(4,1)))\\&\rightarrow A(3,A(3,A(3,A(4,0))))\\&\rightarrow A(3,A(3,A(3,A(3,1))))\\&\rightarrow A(3,A(3,A(3,A(2,A(3,0)))))\\&\rightarrow A(3,A(3,A(3,A(2,A(2,1)))))\\&\rightarrow A(3,A(3,A(3,A(2,A(1,A(2,0))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(1,A(1,1))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(1,A(0,A(1,0)))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(1,A(0,A(0,1)))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(1,A(0,2))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(1,3)))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,A(1,2))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,A(0,A(1,1)))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,A(0,A(0,A(1,0))))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,A(0,A(0,A(0,1))))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,A(0,A(0,2)))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,A(0,3))))))\\&\rightarrow A(3,A(3,A(3,A(2,A(0,4)))))\\&\rightarrow A(3,A(3,A(3,A(2,5))))\\&\qquad \vdots \\&\rightarrow A(3,A(3,A(3,13)))\\&\qquad \vdots \\&\rightarrow A(3,A(3,65533))\\&\qquad \vdots \\&\rightarrow A(3,2^{65536}-3)\\&\qquad \vdots \\&\rightarrow 2^{2^{65536}}-3.\\\end{aligned}}}
Computing the Ackermann function can be restated in terms of an infinite table. First, place the natural numbers along the top row. To determine a number in the table, take the number immediately to the left. Then use that number to look up the required number in the column given by that number and one row up. If there is no number to its left, simply look at the column headed "1" in the previous row. Here is a small upper-left portion of the table:
The numbers here which are only expressed with recursive exponentiation or Knuth arrows are very large and would take up too much space to notate in plain decimal digits.
Despite the large values occurring in this early section of the table, some even larger numbers have been defined, such as Graham's number, which cannot be written with any small number of Knuth arrows. This number is constructed with a technique similar to applying the Ackermann function to itself recursively.
This is a repeat of the above table, but with the values replaced by the relevant expression from the function definition to show the pattern clearly:
The Ackermann function grows faster than any primitive recursive function and therefore is not itself primitive recursive. Proof sketch: primitive recursive function defined using up to k recursions must grow slower than f k + 1 ( n ) {\displaystyle f_{k+1}(n)} , the (k+1)-th function in the fast-growing hierarchy, but the Ackermann function grows at least as fast as f ω ( n ) {\displaystyle f_{\omega }(n)} .
Specifically, one shows that, for every primitive recursive function f ( x 1 , … , x n ) {\displaystyle f(x_{1},\ldots ,x_{n})} , there exists a non-negative integer t {\displaystyle t} , such that for all non-negative integers x 1 , … , x n {\displaystyle x_{1},\ldots ,x_{n}} , f ( x 1 , … , x n ) < A ( t , max i x i ) . {\displaystyle f(x_{1},\ldots ,x_{n})<A(t,\max _{i}x_{i}).} Once this is established, it follows that A {\displaystyle A} itself is not primitive recursive, since otherwise putting x 1 = x 2 = t {\displaystyle x_{1}=x_{2}=t} would lead to the contradiction A ( t , t ) < A ( t , t ) . {\displaystyle A(t,t)<A(t,t).}
The proof proceeds as follows: define the class A {\displaystyle {\mathcal {A}}} of all functions that grow slower than the Ackermann function
A = { f | ∃ t ∀ x 1 ⋯ ∀ x n : f ( x 1 , … , x n ) < A ( t , max i x i ) } {\displaystyle {\mathcal {A}}=\left\{f\,{\bigg |}\,\exists t\ \forall x_{1}\cdots \forall x_{n}:\ f(x_{1},\ldots ,x_{n})<A(t,\max _{i}x_{i})\right\}}
and show that A {\displaystyle {\mathcal {A}}} contains all primitive recursive functions. The latter is achieved by showing that A {\displaystyle {\mathcal {A}}} contains the constant functions, the successor function, the projection functions and that it is closed under the operations of function composition and primitive recursion.
Since the function f(n) = A(n, n) considered above grows very rapidly, its inverse function, f−1, grows very slowly. This inverse Ackermann function f−1 is usually denoted by α. In fact, α(n) is less than 5 for any practical input size n, since A(4, 4) is on the order of 2 2 2 2 16 {\displaystyle 2^{2^{2^{2^{16}}}}} .
This inverse appears in the time complexity of some algorithms, such as the disjoint-set data structure and Chazelle's algorithm for minimum spanning trees. Sometimes Ackermann's original function or other variations are used in these settings, but they all grow at similarly high rates. In particular, some modified functions simplify the expression by eliminating the −3 and similar terms.
A two-parameter variation of the inverse Ackermann function can be defined as follows, where ⌊ x ⌋ {\displaystyle \lfloor x\rfloor } is the floor function:
α ( m , n ) = min { i ≥ 1 : A ( i , ⌊ m / n ⌋ ) ≥ log 2 n } . {\displaystyle \alpha (m,n)=\min\{i\geq 1:A(i,\lfloor m/n\rfloor )\geq \log _{2}n\}.}
This function arises in more precise analyses of the algorithms mentioned above, and gives a more refined time bound. In the disjoint-set data structure, m represents the number of operations while n represents the number of elements; in the minimum spanning tree algorithm, m represents the number of edges while n represents the number of vertices. Several slightly different definitions of α(m, n) exist; for example, log2 n is sometimes replaced by n, and the floor function is sometimes replaced by a ceiling.
Other studies might define an inverse function of one where m is set to a constant, such that the inverse applies to a particular row.33
The inverse of the Ackermann function is primitive recursive, since it is graph primitive recursive, and it is upper bounded by a primitive recursive function.34
The Ackermann function appears in the time complexity of some algorithms,35 such as vector addition systems36 and Petri net reachability, thus showing they are computationally infeasible for large instances.37
The inverse of the Ackermann function appears in some time complexity results. For instance, the disjoint-set data structure takes amortized time per operation proportional to the inverse Ackermann function,38 and cannot be made faster within the cell-probe model of computational complexity.39
Certain problems in discrete geometry related to Davenport–Schinzel sequences have complexity bounds in which the inverse Ackermann function α ( n ) {\displaystyle \alpha (n)} appears. For instance, for n {\displaystyle n} line segments in the plane, the unbounded face of the arrangement of the segments has complexity O ( n α ( n ) ) {\displaystyle O(n\alpha (n))} , and some systems of n {\displaystyle n} line segments have an unbounded face of complexity Ω ( n α ( n ) ) {\displaystyle \Omega (n\alpha (n))} .40
The Ackermann function, due to its definition in terms of extremely deep recursion, can be used as a benchmark of a compiler's ability to optimize recursion. The first published use of Ackermann's function in this way was in 1970 by Dragoș Vaida41 and, almost simultaneously, in 1971, by Yngve Sundblad.42
Sundblad's seminal paper was taken up by Brian Wichmann (co-author of the Whetstone benchmark) in a trilogy of papers written between 1975 and 1982.434445
Monin & Hinchey 2003, p. 61. - Monin, Jean-Francois; Hinchey, M. G. (2003). Understanding Formal Methods. Springer. p. 61. ISBN 9781852332471. https://books.google.com/books?id=rUudIPZD-B0C&pg=PA61 ↩
Ackermann 1928. - Ackermann, Wilhelm (1928). "Zum Hilbertschen Aufbau der reellen Zahlen" [On the Hilbertian construction of the real numbers]. Mathematische Annalen (in German). 99: 118–133. doi:10.1007/BF01459088. S2CID 123431274. http://gdz.sub.uni-goettingen.de/en/dms/loader/img/?PPN=PPN235181684_0099&DMDID=DMDLOG_0009 ↩
"Decimal expansion of A(4,2)". kosara.net. 27 August 2000. Archived from the original on 20 January 2010. https://web.archive.org/web/20100120134707/http://kosara.net/thoughts/ackermann42.html ↩
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Hilbert 1926, p. 185. - Hilbert, David (1926). "Über das Unendliche" [On the infinite]. Mathematische Annalen (in German). 95: 161–190. doi:10.1007/BF01206605. S2CID 121888793. https://doi.org/10.1007%2FBF01206605 ↩
van Heijenoort 1977. - van Heijenoort, Jean (1977) [reprinted with corrections, first published in 1967]. From Frege to Gödel: A Source Book in Mathematical Logic, 1879–1931. Harvard University Press. ↩
Péter 1935. - Péter, Rózsa (1935). "Konstruktion nichtrekursiver Funktionen" [Construction of non-recursive functions]. Mathematische Annalen (in German). 111: 42–60. doi:10.1007/BF01472200. S2CID 121107217. https://doi.org/10.1007%2FBF01472200 ↩
Robinson 1948. - Robinson, Raphael Mitchel (1948). "Recursion and Double Recursion". Bulletin of the American Mathematical Society. 54 (10): 987–93. doi:10.1090/S0002-9904-1948-09121-2. http://projecteuclid.org/DPubS?verb=Display&version=1.0&service=UI&handle=euclid.bams/1183512393&page=record ↩
Ritchie 1965, p. 1028. - Ritchie, Robert Wells (November 1965). "Classes of recursive functions based on Ackermann's function". Pacific Journal of Mathematics. 15 (3): 1027–1044. doi:10.2140/pjm.1965.15.1027. https://msp.org/pjm/1965/15-3/p25.xhtml ↩
with parameter order reversed ↩
Buck 1963. - Buck, R. C. (1963). "Mathematical Induction and Recursive Definitions". American Mathematical Monthly. 70 (2): 128–135. doi:10.2307/2312881. JSTOR 2312881. https://doi.org/10.2307%2F2312881 ↩
Meeussen & Zantema 1992, p. 6. - Meeussen, V. C. S.; Zantema, H. (1992). Derivation lengths in term rewriting from interpretations in the naturals (PDF) (Report). University of Utrecht Department of Computer Science. ISSN 0924-3275. Archived (PDF) from the original on 9 October 2022. https://research.tue.nl/files/4245011/398270.pdf ↩
Munafo 1999a. - Munafo, Robert (1999a). "Versions of Ackermann's Function". Large Numbers at MROB. Retrieved 6 November 2021. http://www.mrob.com/pub/math/ln-2deep.html#ack ↩
Ritchie 1965. - Ritchie, Robert Wells (November 1965). "Classes of recursive functions based on Ackermann's function". Pacific Journal of Mathematics. 15 (3): 1027–1044. doi:10.2140/pjm.1965.15.1027. https://msp.org/pjm/1965/15-3/p25.xhtml ↩
Sundblad 1971. - Sundblad, Yngve (March 1971). "The Ackermann function. A theoretical, computational, and formula manipulative study". BIT Numerical Mathematics. 11 (1): 107–119. doi:10.1007/BF01935330. S2CID 123416408. https://doi.org/10.1007%2FBF01935330 ↩
Porto & Matos 1980. - Porto, António; Matos, Armando B. (1 September 1980). "Ackermann and the superpowers" (PDF). ACM SIGACT News. 12 (3): 90–95. doi:10.1145/1008861.1008872. S2CID 29780652. Archived (PDF) from the original on 9 October 2022. https://www.dcc.fc.up.pt/~acm/ack.pdf ↩
'curried' /wiki/Currying ↩
Grossman & Zeitman 1988. - Grossman, Jerrold W.; Zeitman, R. Suzanne (May 1988). "An inherently iterative computation of ackermann's function". Theoretical Computer Science. 57 (2–3): 327–330. doi:10.1016/0304-3975(88)90046-1. https://doi.org/10.1016%2F0304-3975%2888%2990046-1 ↩
Paulson 2021. - Paulson, Lawrence C. (2021). "Ackermann's Function in Iterative Form: A Proof Assistant Experiment". Retrieved 19 October 2021. https://www.researchgate.net/publication/351063906 ↩
In each step the underlined redex is rewritten. ↩
here: leftmost-innermost strategy! ↩
For better readabilityS(0) is notated as 1,S(S(0)) is notated as 2,S(S(S(0))) is notated as 3,etc... ↩
Cohen 1987, p. 56, Proposition 3.16 (see in proof). - Cohen, Daniel E. (January 1987). Computability and logic. Halsted Press. ISBN 9780745800349. ↩
The maximum depth of recursion refers to the number of levels of activation of a procedure which exist during the deepest call of the procedure. Cornelius & Kirby (1975) - Cornelius, B. J.; Kirby, G. H. (1975). "Depth of recursion and the Ackermann function". BIT Numerical Mathematics. 15 (2): 144–150. doi:10.1007/BF01932687. S2CID 120532578. https://doi.org/10.1007%2FBF01932687 ↩
LOOP n+1 TIMES DO F ↩
Pettie 2002. - Pettie, S. (2002). "An inverse-Ackermann style lower bound for the online minimum spanning tree verification problem". The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings. pp. 155–163. doi:10.1109/SFCS.2002.1181892. ISBN 0-7695-1822-2. S2CID 8636108. https://doi.org/10.1109%2FSFCS.2002.1181892 ↩
Matos 2014. - Matos, Armando B (7 May 2014). "The inverse of the Ackermann function is primitive recursive" (PDF). Archived (PDF) from the original on 9 October 2022. http://www.dcc.fc.up.pt/~acm/PRinv.pdf ↩
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Czerwiński & Orlikowski 2022. - Czerwiński, Wojciech; Orlikowski, Łukasz (7 February 2022). Reachability in Vector Addition Systems is Ackermann-complete. Proceedings of the 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science. arXiv:2104.13866. doi:10.1109/FOCS52979.2021.00120. https://ieeexplore.ieee.org/document/9719806 ↩
Leroux 2022. - Leroux, Jérôme (7 February 2022). The Reachability Problem for Petri Nets is Not Primitive Recursive. Proceedings of the 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science. arXiv:2104.12695. doi:10.1109/FOCS52979.2021.00121. https://ieeexplore.ieee.org/document/9719763 ↩
Tarjan 1975. - Tarjan, Robert Endre (1975). "Efficiency of a Good But Not Linear Set Union Algorithm". Journal of the ACM. 22 (2): 215–225. doi:10.1145/321879.321884. hdl:1813/5942. S2CID 11105749. https://doi.org/10.1145%2F321879.321884 ↩
Fredman & Saks 1989. - Fredman, M.; Saks, M. (May 1989). "The cell probe complexity of dynamic data structures". Proceedings of the twenty-first annual ACM symposium on Theory of computing – STOC '89. pp. 345–354. doi:10.1145/73007.73040. ISBN 0897913078. S2CID 13470414. https://doi.org/10.1145%2F73007.73040 ↩
Wiernik & Sharir 1988. - Wiernik, Ady; Sharir, Micha (1988). "Planar realizations of nonlinear Davenport–Schinzel sequences by segments". Discrete & Computational Geometry. 3 (1): 15–47. doi:10.1007/BF02187894. MR 0918177. https://doi.org/10.1007%2FBF02187894 ↩
Vaida 1970. - Vaida, Dragoș (1970). "Compiler Validation for an Algol-like Language". Bulletin Mathématique de la Société des Sciences Mathématiques de la République Socialiste de Roumanie. Nouvelle série. 14 (62) (4): 487–502. JSTOR 43679758. https://www.jstor.org/stable/43679758 ↩
Wichmann 1976. - Wichmann, Brian A. (March 1976). "Ackermann's function: A study in the efficiency of calling procedures". BIT Numerical Mathematics. 16: 103–110. CiteSeerX 10.1.1.108.4125. doi:10.1007/BF01940783. S2CID 16993343. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.4125 ↩
Wichmann 1977. - Wichmann, Brian A. (July 1977). "How to call procedures, or second thoughts on Ackermann's function". BIT Numerical Mathematics. 16 (3): 103–110. doi:10.1002/spe.4380070303. S2CID 206507320. https://doi.org/10.1002%2Fspe.4380070303 ↩
Wichmann 1982. - Wichmann, Brian A. (July 1982). "Latest results from the procedure calling test, Ackermann's function" (PDF). Archived (PDF) from the original on 9 October 2022. http://history.dcs.ed.ac.uk/archive/docs/Imp_Benchmarks/acklt.pdf ↩