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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.

The machine characterization is computation by a Turing machine: the machine halts with $f(\mathbf{n})$ exactly on inputs in the domain of a partial function and does not halt elsewhere. The syntactic characterization is the least class containing the initial functions of recursion theory and closed under composition of partial functions, primitive recursion and unbounded minimization. Such functions are the partial recursive functions, equivalently the partial computable functions.

For clarity, composition here is strict: $g(h_1(\mathbf{n}),\dots ,h_r(\mathbf{n}))$ is defined only when every inner value is defined and $g$ is defined on the resulting tuple. Likewise primitive recursion requires all preceding recursive values. The partial minimization $\mu y[g(\mathbf{n},y)=0]$ is defined when such a $y$ exists and all earlier values are defined and nonzero; an undefined earlier value blocks the search.

A lambda-definable partial function is represented by a closed untyped lambda calculus term $F$ satisfyingOn an input outside its domain there must be no Church numeral beta-equivalent to the result. A stronger representation can be arranged: undefined inputs have no head normal form.

Here are the components for proving that every partial recursive function is lambda-definable. Zero and projections are immediate; lambda definition of the successor function uses $\mathrm{Succ}=\lambda n.\lambda a.\lambda b.a(nab)$. Church Booleans encode a conditional by $bUV$, with $\mathrm{True}=\lambda u.\lambda v.u$ and $\mathrm{False}=\lambda u.\lambda v.v$. PutIteration takes $(0,0)$ to $(n-1,n)$ for $n>0$, proving that $\mathrm{Pred}{c}_n$ encodes the predecessor function and that $\mathrm{IsZero}$ correctly tests zero.

Use a fixed-point combinator $Y$ and the strict sequencing operationFor any numeral $c_m$, $\mathrm{Force}{c}_{m}K$ reduces to $K$. If the first argument has no head normal form, neither does the whole expression. Thus composition is implemented by nesting $\mathrm{Force}$ on every inner result before applying the outer representing term. This guard is important: an unguarded projection could discard an undefined inner computation.

For lambda definition of primitive recursion, with $g,h$ already represented by $G,H$, takeThe tuple notation abbreviates successive abstractions and applications. On numeral inputs, induction on $n$ proves the required recursion equations, including strict propagation of undefined preceding values. The beta reduction follows the selected conditional branch only.

For lambda definition of unbounded minimization, useThis searches in increasing order. An undefined value blocks the zero test; an infinite sequence of nonzero values continues forever; the first zero returns its numeral. Under normal-order beta reduction, both failure cases have no head normal form. Structural induction on partial-recursive declarations now supplies the stronger representation claimed above.

Conversely, beta reduction is an effective operation on finitely encoded lambda terms. Enumerate all finite reduction sequences from $F{\mathbf{c}}_n$, stopping when a result is a Church numeral in beta-normal form. If $F{\mathbf{c}}_n{\equiv }_{\beta }{c}_m$, the Church-Rosser theorem gives a reduction to that numeral. It also makes the output unique. The enumeration therefore computes exactly the represented partial function; by the equivalence of the first two characterizations it is partial recursive. Hence

The term $\mathrm{Force}{c}_{m}K$ beta-reduces to $K$ for every Church numeral $c_m$. If its first argument has no head normal form, the whole application has no head normal form. Nesting this operation forces each represented inner computation before applying an outer function, implementing composition of partial functions even when an outer projection would discard an argument. The same guard makes lambda definition of primitive recursion strict in the preceding recursive value.