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Every nonempty computably enumerable set $X\subseteq \mathbb{N}$ is the range of a primitive recursive function $f:\mathbb{N}\to \mathbb{N}$. Choose $x_0\in X$, decode $n=⟨x,t⟩$ using a primitive recursive pairing function, and output $x$ if the recognizing machine halts within $t$ steps, or $x_0$ otherwise. The bounded halting predicate makes this function primitive recursive, and every member of $X$ occurs for a sufficiently large bound. The empty set is excluded because such a function is total.

A pairing function is a computable function that gives a bijection from the Cartesian product ${\mathbb{N}}_0^2$ to ${\mathbb{N}}_0$, with two computable functions recovering its coordinates. Here ${\mathbb{N}}_0=\{0,1,2,\dots \}$ consists of the natural numbers, including zero. If $p$ is the pairing function and $L,R$ its decoding functions, the requirements areEvery ordered pair has exactly one code, every code represents an ordered pair, and decoding always terminates. For a bijection given by a total computable function, computability of the inverse function also follows by enumerating all ordered pairs until the one with code $z$ appears. A primitive recursive pairing function makes the stronger requirement that both encoding and coordinate decoding are primitive recursive.

The Cantor pairing function makes this concrete through triangular numbers:On the diagonal $x+y=s$, list $(s,0),(s-1,1),\dots ,(0,s)$. There are $s+1$ ordered pairs there, and $T(s)$ ordered pairs on earlier diagonals. Their codes therefore occupy precisely the consecutive natural numbers from $T(s)$ through $T(s+1)-1$. These intervals touch without overlapping and cover all natural numbers; within each interval the offset $y$ determines the ordered pair. This proves both injectivity and surjectivity, hence the bijection. It also explains why the infinite grid is a countably infinite set: every ordered pair is reached after finitely many steps. A row-by-row listing would never finish its first infinite row. Authored treatments give the same diagonal construction with the coordinates exchanged.^[ref]

For example, the Cantor pairing function givesThe order of the coordinates matters: $(1,0)$ and $(0,1)$ are different ordered pairs with different codes.

To decode a natural number $z$, find the unique diagonal index $s$ satisfyingThe inequality gives $0\le y\le s$, so both recovered coordinates are natural numbers. At $z=18$, $T(5)=15\le 18<T(6)=21$, hence $s=5$, $y=18-15=3$, and $x=5-3=2$. At $z=0$, $s=0$ and both coordinates are zero. Solving the quadratic inequality gives the equivalent floor function expressionThis is exact as a statement about the real numbers and their square roots. An implementation using floating-point arithmetic can round across a diagonal boundary, especially for large $z$. Use an exact integer square root instead: with $q=\lfloor \sqrt{8z+1}\rfloor$ computed using integer arithmetic, $s=\lfloor (q-1)/2\rfloor$. Alternatively, search for the diagonal using exact integer comparisons. The primitive recursive pairing function proof below uses bounded minimization and needs no approximate square root.

A pairing function is a reversible encoding: unlike a hash function mapping arbitrarily many inputs into a fixed finite range, it loses no coordinates. This works because the mathematical natural numbers have no upper bound. A machine word with $b$ binary digits has $2^b$ possible values, while two such words have $2^{2b}$ possible ordered pairs; a bijection into one word of the same width is impossible. Evaluating the Cantor pairing function in a fixed-width integer type can overflow, including in the intermediate product $(x+y)(x+y+1)$. Exact implementation requires sufficiently large integer storage or an explicit restricted input range. One mathematical code need not occupy fewer binary digits than its two inputs.

Iterating a pairing function gives a reversible encoding of tuples of any fixed positive length. With the left-nesting convention, the Cantor pairing function encodes $(2,3,4)$ asDecode the outer ordered pair first: $T(22)\le 257<T(23)=276$ gives $(18,4)$. Decode its first coordinate $18$ to recover $(2,3)$, giving the nested ordered pair $((2,3),4)$ and thus the original tuple $(2,3,4)$.

The tuple length and nesting convention must be known. The same code $257$ represents $(18,4)$ as a two-entry tuple and $(2,3,4)$ as a three-entry tuple; nesting alone does not make variable-length tuples self-describing. To encode variable-length tuples, supply their length or another explicit termination convention as part of the encoding. This simple change of representation is useful in computability theory and Gödel numbering: a function of several natural numbers can be studied through a single numerical input, while all coordinates remain recoverable. The striking fact is that a whole infinite grid can be serialized without omitting a point.

A historical reference for comparing sets through bijections is Georg Cantor's paper Ein Beitrag zur Mannigfaltigkeitslehre, published in 1878 in volume 84 of Journal für die reine und angewandte Mathematik, pages 242–258.^[ref]

The arrow in the original PDF permits partial functions. A partial computable function ${\mathbb{N}}^k\rightharpoonup \mathbb{N}$ is computed by a finite program which halts with the specified value on every input in its domain and diverges on inputs outside the domain. A total computable function is the special case with full domain. Coding finite tuples by a primitive recursive pairing function identifies the different finite arities effectively, without identifying total and partial computability.

One precise machine model is a Turing machine. Its finite transition rules and configurations admit effective numerical coding. Bounded simulation is primitive recursive, and unbounded execution gives the partial result. Equivalently, start from zero, successor and projections and close under composition, primitive recursion and unbounded minimization. The Kleene normal form theorem makes the equivalence concrete:where $T$ is a primitive recursive predicate checking a coded halting computation and $U$ reads its output. If no computation exists, the minimum and hence the value are undefined. Checking a finite history is effective because every consecutive configuration must obey one of the finitely many transition rules.

The universal partial computable function evaluates the program indexed by $e$ on an input. The S-m-n theorem says that fixing finitely many input parameters can be done by a total computable operation on program Gödel numbers: a new program stores those parameters and calls the original universal evaluator. These facts explain both universal simulation and uniform reductions. A set is computably enumerable when it is the domain of a partial computable function, equivalently the range of an effective enumeration. It is recursive when its characteristic function is total computable. A set is recursive precisely when it and its complement are computably enumerable: dovetail the two enumerations to decide each input.

The diagonal halting problem $K=\{e:{\phi }_e(e)\downarrow \}$ is computably enumerable but not recursive. A total decider would yield a program which, on $e$, diverges if the decider says $e\in K$ and halts otherwise. Applying this program to its own index gives a contradiction. There is likewise no total computable universal listing of all total computable functions: the diagonal function $n\mapsto U(n,n)+1$ would be total and absent from the listing.

Here is a full proof of the Rice theorem. Let $\mathcal{P}$ be a nonempty proper class of partial computable functions whose property depends only on the function, not its program. First suppose the everywhere-undefined function $\perp$ is not in $\mathcal{P}$, and choose a program for a function $g\in \mathcal{P}$. Given an index $e$, construct uniformly a program which on $x$ first waits for ${\phi }_e(e)$ to halt and, only then, simulates $g(x)$. The S-m-n theorem gives a total computable index map $h$. If $e\notin K$, then ${\phi }_{h(e)}=\perp$; if $e\in K$, then ${\phi }_{h(e)}=g$. ThusA decision procedure for the property would decide $K$, impossible. If $\perp \in \mathcal{P}$, apply the same argument to its nontrivial complement. Every nontrivial extensional property of partial computable functions has an undecidable index set. Totality, having finite domain, and taking a particular value somewhere are examples. A syntactic property such as program length is not covered; nor does the theorem say that no individual program's behaviour can be established.

Self-reference can also be made effective. For a total computable index transformation $f$, let $d(x)$ be an index for the program which first computes ${\phi }_x(x)$ and then runs the program with that resulting index on its own input. The S-m-n theorem makes $d$ total computable. Choose an index $e$ for the total function $x\mapsto f(d(x))$, and put $n=d(e)$. ThenThis proves the recursion theorem: every effective transformation of programs has a fixed point at the level of the function computed, though not necessarily at the level of its numerical code.

Finally, consider the Jockusch triple coloring computing the halting set. Fix computable finite approximations $K_0\subseteq K_1\subseteq \cdots$ with union $K$. For $x<y<z$, defineThis is a total recursive two-coloring because both stages are finite computations. Suppose $H$ is infinite and homogeneous. Its color cannot be one: fix $x\in H$, wait until every enumeration below $x$ has stabilized, and choose later $y<z$ from $H$ beyond that stage. Their color is zero.

If $H$ has color zero, it computes $K$. On input $n$, use $H$ to find $x>n$ and $y>x$ in $H$. For arbitrarily large $z\in H$ with $z>y$, homogeneity gives $K_y\cap x=K_z\cap x$. Taking stages sufficiently large to reach the final finite approximation gives $K_y\cap x=K\cap x$. Consequently membership of $n$ in $K$ is decided by the finite stage $K_y$. A recursive $H$ would therefore make $K$ recursive, a contradiction. There is a recursive partition of triples with no infinite recursive homogeneous set. This is the nontrivial finite-color meaning of the partition of an infinite domain; if “infinite partition” meant infinitely many colors, the coloring by the least member would already give a trivial example. Ramsey's theorem still guarantees infinite homogeneous sets for the displayed two-coloring, but every such set is computationally nontrivial.

The printed arrow is a partial-function arrow. Thus the central objects are partial computable functions $f:{\mathbb{N}}^k\rightharpoonup \mathbb{N}$, with total computable functions as the special case in which every input has an output. Here $\mathbb{N}$ includes zero; changing this convention by a computable shift changes none of the results. A program computing $f$ must halt with output $f(\mathbf{x})$ exactly on its domain, and run forever elsewhere. A finite program is a function in intension, whereas its possibly partial input-output map is a function in extension; many programs have the same extension.

A precise machine model is a register machine with finitely many instructions to increment a register, decrement it conditionally, jump, and halt. Registers hold natural numbers. A Turing machine instead uses a finite transition table, a finite alphabet and an unbounded tape. These models simulate one another: encode the finite nonblank tape, state and head position as numbers for the register simulation; conversely represent each register by a finite tape block and implement its increment and decrement by finite scans. Tuple inputs can be encoded by a primitive recursive pairing function. The equivalence of these formal models is a mathematical result. Their identification with every informal effective procedure is the Church–Turing thesis, rather than an additional mathematical theorem about an already formal notion.

There is also an algebraic description. Begin with zero, successor and projections, close under composition of partial functions and primitive recursion, and then under unbounded minimization. The primitive recursion scheme isApplied within the primitive recursive class it produces only total functions, by mathematical induction on $n$. Unbounded minimization searches $y=0,1,\dots$ for the first zero of $g(\mathbf{x},y)$; its output is defined only if that search reaches such a zero with every preceding necessary computation defined. Composition is likewise strict about the intermediate values it requires. These schemes compile into machine programs using finite subroutines, loops and an unbounded search loop.

For the converse, encode a machine's finite configurations and finite computation histories using Gödel numbering. Testing that a history starts with the specified input, that every adjacent pair follows the transition table, and that its final configuration halts is primitive recursive: all the tests are bounded by the finite code. Extracting the final output is primitive recursive too. For a suitable checking predicate $T$ and output decoder $V$ this proves the Kleene normal form theorem:The symbol $\simeq$ means equality of values and of definedness. A halting computation has a passing history code and every passing history has its actual output; a nonhalting computation has none. This proves that the partial recursive and machine-computable functions coincide, including their domains of divergence.

Finite programs can be effectively listed. Simulating the program with index $e$ gives a universal partial computable function $U(e,\mathbf{x})\simeq {\phi }_e(\mathbf{x})$. Fixing some inputs is effective syntactically: prepend instructions writing those constants and then call the original program. This proves the S-m-n theorem in this machine presentation. In particular, reductions that construct a program from numerical parameters really do produce its index effectively.

The primitive recursive functions are a proper subclass of the total computable functions. Enumerate all valid unary primitive recursive descriptions, interpreting invalid descriptions as zero. Evaluating any fixed valid description terminates by structural induction. Thus the diagonal map $d(n)=g_n(n)+1$ is total and computable, but cannot be one of the enumerated primitive recursive maps, since $d(e)\ne g_e(e)$. This is a total computable diagonal over primitive recursive syntax. The evaluator can terminate on every particular description without itself being primitive recursive.

In contrast, one cannot effectively enumerate exactly the programs for all total computable functions. If such an infinite enumeration were available, wait for its $n$th program and run it on $n$, then add one. Totality makes this another total computable function, while its diagonal disagrees with every listed function. Equivalently, there is no total computable universal evaluator listing precisely all total computable maps.

The halting problem is undecidable. If $H(e,x)$ decided whether program $e$ halts on $x$, construct a program $D$ which halts on input $e$ exactly when $H(e,e)$ says that it does not halt. Taking $e$ to be $D$'s own index gives a contradiction. The halting relation is nevertheless computably enumerable, since finite halting histories can be searched for.

More generally, a set is computably enumerable exactly when it is the domain of a partial computable function. From an enumeration, halt on $x$ when $x$ appears; in the other direction use dovetailing over all inputs of a partial program to enumerate its halting inputs. A set is a computable set exactly when both it and its complement are computably enumerable: dovetail the two searches and return the answer from the one that succeeds. Neither a semidecision procedure nor an unbounded search supplies a uniform negative answer.

Finally, the Rice theorem explains the scope of this obstruction. Let $P$ be a nontrivial property of the extension of a partial computable program. Choose a partial computable $g$ whose $P$-status differs from that of the nowhere-defined function. Given a program $e$ and input $x$, effectively form the program which, on $z$, first waits for $e(x)$ to halt and then computes $g(z)$. If $e(x)$ never halts its extension is nowhere defined; otherwise its extension is $g$. A decision procedure for $P$ would therefore decide the halting problem. Effective descriptions allow universal simulation and positive verification, but not a decision procedure for every semantic property or for termination.

For (1)(a), a primitive recursive function is obtained by a finite construction from the zero function, successor function and projection functions, using function composition in recursion theory and primitive recursion. The recursion rule, with parameters $\mathbf{x}$, isHere $g,h$ have already been constructed. All these functions are total on nonnegative integers: the initial functions are total, composition preserves totality, and ordinary mathematical induction proves totality of each recursion.

For (1)(b), use a shifted hyperoperation sequence, beginning with addition. Defineand, for each fixed $r\ge 0$, defineThe first three members are therefore addition, multiplication and exponentiation:The exponentiation convention includes $a^0=1$, hence $0^0=1$. The next member iterates exponentiation, with $H_3(a,0)=1$; there is no need for an exceptional domain restriction at $a=0$.

For (1)(c), $H_0$ is a primitive recursive function by its displayed recursion. If $H_r$ is a primitive recursive function, thenare primitive recursive functions, using constants, projection functions and function composition in recursion theory. Primitive recursion applied to $g_r,h_r$ gives $H_{r+1}$. Mathematical induction on the fixed rank $r$ proves every member of the sequence is primitive recursive. This does not assert that the joint three-variable evaluator $(r,a,b)\mapsto H_r(a,b)$ is a primitive recursive function.

For (2), as written, the answer is no: the empty set $X=\varnothing$ is semidecidable, whereas a total computable function from $\mathbb{N}$ to $\mathbb{N}$ always has a nonempty range. This is the only obstruction. To prove the stronger useful statement, suppose $X$ is a nonempty computably enumerable set and choose $x_0\in X$. Fix a Turing machine which halts exactly on the inputs in $X$. Its bounded halting predicateis primitive recursive: encode configurations arithmetically, iterate its total single-step operation $t$ times by primitive recursion, and check the halt state. A halted configuration is kept fixed. This is a bounded simulation, not unbounded waiting.

Fix a primitive recursive pairing function $⟨x,t⟩$ with primitive recursive inverse coordinate functions. For example, the Cantor pairing function has inverses obtained by bounded minimization. DefineThe two coordinate functions, the bounded predicate, and the finite case distinction are primitive recursive functions, so $f$ is a primitive recursive function. Its values always lie in $X$. Conversely, if $x\in X$, its computation halts within some finite $t$, and $f(⟨x,t⟩)=x$. ThusThe range need not be a computable set; deciding whether a value occurs anywhere in this total enumeration is an unbounded search.

A primitive recursive pairing function is a pairing function whose encoding function and both coordinate functions of its inverse function are primitive recursive functions. Thus the bijection can be used in either direction inside a construction by primitive recursion and function composition in recursion theory. A decoder that is merely a computable function would not establish this stronger closure property. Sutner's definition of a primitive recursive pairing system explicitly includes the decoding functions; his more general systems need only be injective, whereas a pairing function here is a bijection.^[ref]

The Cantor pairing function is a primitive recursive pairing function. For its encoding, construct the triangular numbers directly by primitive recursion:Since addition is primitive recursive, so are $T$ and $\pi$ by primitive recursion and function composition in recursion theory. This establishes the claim without treating division by two as an unexplained primitive operation.

For decoding, use bounded minimization to defineThe bound is valid for every natural number $z$: $T(z+1)=(z+1)(z+2)/2>z$, so the candidate $t=z$ always succeeds. The search includes zero; when $z=0$, it immediately returns $s(0)=0$. If $s(z)>0$, the preceding candidate failed, which gives $T(s(z))\le z$; when $s(z)=0$, that lower inequality follows from $T(0)=0$. Thus this search finds exactly the diagonal in the Cantor pairing function inverse, withThe comparison is primitive recursive, and bounded minimization of a primitive recursive predicate is primitive recursive. Therefore $s$ is a primitive recursive function. Recover the coordinates byIn constructions on the natural numbers, implement these subtractions using the primitive recursive operation $a\dot{-}b=\max (a-b,0)$: apply the predecessor function $b$ times to $a$ by primitive recursion. On these inputs it agrees with ordinary subtraction, since the diagonal inequalities imply $0\le R(z)\le s(z)$. Consequently $L$ and $R$ are primitive recursive functions, and $\pi (L(z),R(z))=z$. Zimmermann proves the relevant closure under bounded minimization and the primitive recursive character of the Cantor encoding and coordinate decoding; his convention adds the first coordinate, so its coordinates are exchanged relative to $\pi$ here.^[ref]

For a worked bounded minimization, take $z=18$. Candidates $t=0,1,2,3,4$ give $T(t+1)=1,3,6,10,15$, none exceeding $18$. Candidate $t=5$ gives $T(6)=21>18$, so $s(18)=5$, $R(18)=3$, and $L(18)=2$. The search bound was $18$; it terminates much earlier in this example. The bound proves membership in the primitive recursive functions; it is not a claim that scanning all candidates is the fastest implementation.

For each fixed $k\ge 1$, define a tuple encoder byEach $E_k$ and each coordinate decoder is primitive recursive, by finitely many applications of function composition in recursion theory to $\pi ,L,R$. For the three-entry tuple encoded as $257$, applying $R$ returns $4$, applying $L$ returns $18$, and then $L(18)=2$, $R(18)=3$. The tuple length remains part of the agreed convention, as explained for a general pairing function.

This makes primitive recursive pairing functions useful for encoding several evolving values as one state in primitive recursion. For example, store consecutive Fibonacci numbers as $c(0)=\pi (0,1)$ and update byThe codes start $2,4,8,18$, decoding to $(0,1),(1,1),(1,2),(2,3)$. Every step uses primitive recursive encoding, decoding and addition; a single numerical state therefore carries the two values needed for the next step. This is also the reason a suitable Gödel numbering should permit sufficiently simple decoding: representation should preserve the class of functions being studied.