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Every partial computable function is represented on Church numerals by a closed untyped lambda calculus term. First represent all primitive recursive functions using zero, successor, projections, composition and lambda definition of primitive recursion by pair iteration. Apply the Kleene normal form theorem and search successive history codes with a fixed-point combinator. Test the represented total predicate using a Church numeral zero test; return the decoded output only on the true branch. If no history passes, normal-order head reduction continues the search indefinitely and the term has no head normal form, so cannot be in beta equivalence with a Church numeral. This avoids the incorrect assumption that a lazy outer function must evaluate a divergent argument in an unguarded composition.

Use Church numeralsExplicit lambda terms for the successor and zero test areDirect beta reduction gives $\mathsf{Succ}{c}_n{\equiv }_{\beta }{c}_{n+1}$, and the Church numeral zero test returns $\mathsf{T}$ exactly at zero. For the predecessor, use the Church pair operationsand setAfter $j$ iterations this pair holds $(c_j,c_{\max (j-1,0)})$: the initial pair gives the case $j=0$, and one step moves the old first coordinate into the second and increments the first. This proves the predecessor equation, including $\mathsf{Pred}{c}_0{\equiv }_{\beta }{c}_0$.

A total function is a lambda-definable function when a closed lambda term sends each tuple of numerals to the numeral of its value. Suppose $G,H$ represent total $g,h$, andFor lambda definition of primitive recursion by pair iteration, putOn numeral inputs, induction gives the pair $(c_j,c_{r(\mathbf{x},j)})$ after $j$ iterations. The base case is the equation for $G$, and the induction step is exactly the equation for $H$. Hence $R$ represents $r$, proving closure of total lambda-definable functions under primitive recursion.

For unbounded minimization, the careful closure statement is partial: if total $f(\mathbf{x},n)$ is lambda-definable, thenis a lambda-definable partial function, defined when a zero exists and otherwise undefined. It need not be total. With $F$ representing $f$, use a fixed-point combinator to defineEach test terminates because $f$ is total. Normal-order beta reduction chooses the return branch at the first zero and does not evaluate the discarded branch. If there is no zero, the required head computation passes through tests for all $n$, never exposing a numeral; standardization theorem for beta reduction rules out a hidden numeral reduct. For partial $f$, the usual mu operator is defined only if all earlier tests terminate and the successful test is zero. This stronger partial closure requires representatives that diverge on undefined inputs, or an explicit strict evaluator; the weaker condition of producing no numeral on undefined inputs alone must not be used as a test for convergence.

The zero function, successor, and projections have immediate numeral representations; function composition in recursion theory is implemented by substitution on total inputs. Thus every primitive recursive function is lambda-definable. The Kleene normal form theorem writes every partial recursive function as a total primitive-recursive decoding after a single minimization of a total primitive-recursive computation-history predicate. The search above consequently represents every partial recursive function, diverging when no successful history exists. Conversely a machine can effectively enumerate the beta reductions of a fixed representing lambda term on numeral inputs until it finds a Church numeral; distinct numerals cannot be beta-equivalent by the Church-Rosser theorem. It computes exactly the represented partial function. Therefore lambda-definable partial functions are precisely partial computable functions, and the total ones are precisely total computable functions.

This also gives the full partial minimization closure without assuming that an arbitrary weak representative diverges on every undefined input. Run its effective numeral evaluator successively at $n=0,1,2,\dots$; stop at the first zero, and run forever if an earlier evaluation fails to terminate or no zero is found. This is exactly the sequential-domain convention for the mu operator. The resulting partial computable function has a lambda representation by the construction just proved. Thus partial lambda-definable functions are closed under partial minimization; a minimized function belongs to the total family only when the required successful search exists for every input.

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.

Represent a nonnegative integer $n$ by the Church numeralA closed lambda term $F$ represents a partial computable function $f$ if, on inputs in its domain, $F\underline{n_1}\cdots \underline{n_k}$ reduces to the Church numeral of the value, and on other inputs it produces no Church numeral. We use untyped lambda calculus and normal-order beta reduction.

First every primitive recursive function has such a representation on all inputs. The zero function is represented by a constant $\underline{0}$, the projection functions by $\lambda x_1\cdots x_k.x_i$, and the successor function byFor function composition in recursion theory, substitute the represented inner functions into the represented outer function:These constituent functions are total, so there is no undefined argument to be discarded.

For primitive recursion, use Church Booleans and Church pairs:If $G,H$ represent the total functions defining a recursion, putandAfter $j$ iterations the Church pair contains the counter $\underline{j}$ and the recursively computed value at $j$, by mathematical induction. Thus this lambda definition of primitive recursion by pair iteration represents the recursion. A structural induction for primitive recursive functions now represents every primitive recursive function.

To pass to arbitrary partial computable functions, use the Kleene normal form theorem in the following form: for every $f:{\mathbb{N}}^k\rightharpoonup \mathbb{N}$, there are total primitive recursive functions $C,U$ such thatwith $f$ undefined precisely when no such $t$ exists. The predicate checks codes of valid finite halting histories for the chosen program; $U$ extracts their common output. This expresses unbounded minimization of a total predicate, so no earlier undefined test can interfere with the search.

Let $\hat{C},\hat{U}$ be the represented total functions. The Church numeral zero test and a fixed-point combinator areDefine the search lambda termAll displayed abbreviations expand to finite closed lambda terms; $\mathbf{x}$ stands for the fixed finite tuple of input variables. Because $YH{\equiv }_{\beta }H(YH)$, normal-order beta reduction tests $t=0,1,2,\dots$. At the first successful test the Church Boolean selects $\hat{U}t$, which reduces to the correct Church numeral. The unused recursive branch is discarded.

If no test succeeds, every finite search step returns to another head search, never exposing a numeral. The term has no head normal form and hence cannot be beta equivalent to a Church numeral. This last point is why the output operation occurs inside the successful branch: simply applying a possibly argument-discarding outer function to a divergent search would not justify undefinedness. Therefore every computable function is representable, including partial computable functions with their correct undefined inputs:

Use untyped lambda calculus and normal-order beta reduction. Write $c_n=\lambda fx.f^{n}x$ for the Church numeral of $n$. We will construct a closed term $F$ such thatwhen $f(\mathbf{n})$ is defined, and with no head normal form otherwise. The latter condition implies that the result is not in beta equivalence with any Church numeral, so both definedness and the output are represented.

First represent all total primitive recursive functions. The zero function is $\lambda \mathbf{x}.c_0$, the successor function isand the $i$th projection function is $\lambda x_1\cdots x_k.x_i$. Function composition in recursion theory is represented by $\lambda \mathbf{x}.G(H_1\mathbf{x})\cdots (H_r\mathbf{x})$. This composition claim is used here for total functions, whose inner terms all reduce to numerals.

For primitive recursion use Church pairs and iteration. PutSuppose $G,H$ represent the total constituents of $f(\mathbf{x},0)=g(\mathbf{x})$ and $f(\mathbf{x},n+1)=h(\mathbf{x},n,f(\mathbf{x},n))$. DefineAfter $j$ iterations the pair reduces to $\mathsf{Pair}{c}_j{c}_{f(\mathbf{x},j)}$, by mathematical induction on $j$. Its second projection is the required result. This lambda definition of primitive recursion by pair iteration and structural induction on the primitive recursive construction give terms for all primitive recursive functions.

For an arbitrary partial computable function use the Kleene normal form theorem with its program index fixed. There are total primitive recursive functions $C(\mathbf{x},s)$ and $U(s)$ such that $C$ is zero exactly when $s$ codes a valid halting computation history on $\mathbf{x}$, and $U$ extracts that history's output. Checking finite coded configurations and each transition is primitive recursive. Determinism ensures that every accepted history on an input has the same output. HenceIf the input computation never halts, no history is accepted. Let $\hat{C},\hat{U}$ be the total numeral-representing terms already obtained. The Church numeral zero test is $\mathsf{Zero}=\lambda n.n(\lambda z.\mathsf{F})\mathsf{T}$, returning $\mathsf{T}$ exactly on zero. A Church Boolean applied to two arguments selects the appropriate branch without evaluating the other.

Take the fixed-point combinator $Y=\lambda h.(\lambda z.h(zz))(\lambda z.h(zz))$. For the input tuple defineExpanding the displayed abbreviations gives a genuine finite combinator. At a numeral code $c_j$, the total predicate computation terminates. If it returns a positive numeral, the false Church Boolean selects the next search code. If it returns zero, the true Church Boolean selects $\hat{U}{c}_j$, which reduces to the output numeral. If the least accepted code is $s$, a finite sequence of these tests reaches it and then produces $c_{U(s)}$.

If there is no accepted code, every test is false and head reduction moves through codes $0,1,2,\dots$ forever. At no finite point can a head variable or an outer numeral abstraction be produced: the applied search term must first unfold the fixed point and evaluate the next total test, and the decoder branch is always discarded. Thus it has no head normal form. The standard head-normalization property, or the Church-Rosser theorem together with normalization of normal-order reduction, rules out beta equivalence to a numeral. In particular, we have avoided a lazy-composition pitfall: an outer function that discards an argument need not force a divergent inner computation. The decoder is reached only after the guarded search succeeds. This proves lambda representation of partial computable functions: