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An arithmetic bounded formula uses only quantifiers bounded by arithmetic terms, such as $\exists u<t$ and $\forall u<t$, with the bound not containing the quantified variable. Existential bounded representation of a primitive recursive function places all unrestricted existential variables in a single outer block.

Search over the finite interval $0\le y\le b$ for the least witness to a primitive recursive predicate, with an explicitly chosen default if there is none. The result is a primitive recursive function, by scanning this finite interval using primitive recursion. This differs from unbounded minimization, which may fail to terminate.

The hyperoperation hierarchy successively iterates basic arithmetic operations. In the usual indexing, successor is followed by addition, multiplication, exponentiation and their higher iterates. A sequence beginning with addition shifts this index by one. Each fixed rank is a primitive recursive function; this does not imply that the evaluator taking the rank as an additional variable is primitive recursive.

For every partial computable function $f(\mathbf{x})$, there are total primitive recursive functions $C,U$ such thatHere $C$ is zero exactly on codes of valid halting computation histories for the chosen program and input, and $U$ extracts the final output. Coding finite configurations and finite histories makes checking every local step a bounded primitive recursive operation. If a computation halts, some history code passes and every passing code has the same output; if it does not halt, none passes. This is the usual normal-form version with the program index fixed; a universal predicate can also retain that index as an input.

If total functions $g$ and $h$ are represented by lambda terms $G,H$, defineandAfter $j$ iterations the Church pair holds $(c_j,c_{f(\mathbf{x},j)})$, proved by mathematical induction using the defining recursion equations. Consequently $R$ represents the total primitive recursive function $f$. This statement concerns total inputs and total recursion constituents; it is not an unguarded composition theorem for arbitrary partial functions.

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.

For a partial function $g(\mathbf{x},y)$, unbounded minimization definesas the least $y$ at which $g$ is zero, provided the preceding required computations are defined. If no such $y$ is found, $f(\mathbf{x})$ is undefined. Closing the primitive recursive functions under this operation gives the partial recursive functions.

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.

Ordinal arithmetic describes the order types of well-ordered constructions. Its operations are defined by transfinite recursion, continuous in their right argument at limits. For addition,For multiplication,and, for $\alpha >0$, exponentiation is defined byThe usual conventions give $0^0=1$ and $0^{\beta }=0$ for $\beta >0$. Ordinal addition concatenates orders, while ordinal multiplication takes a succession of copies of the left factor indexed by the right factor.

Ordinal addition and ordinal multiplication are associative but generally not commutative; exponentiation is not an associative operation. For example,The distributive identity is $\alpha (\beta +\gamma )=\alpha \beta +\alpha \gamma$; the reversed distributive law fails, since $(1+1)\omega =\omega$ but $1\omega +1\omega =\omega \cdot 2$. For positive bases, ${\alpha }^{\beta +\gamma }={\alpha }^{\beta }{\alpha }^{\gamma }$ follows from the recursive definitions. These are order-type identities, not an extension of commutative integer arithmetic.

Every nonzero ordinal has a unique Cantor normal formComparisons are lexicographic in the exponent/coefficient list. Adding a later ordinal can erase smaller final terms of the earlier one, explaining noncommutativity. Hessenberg natural sum instead aligns equal exponents and adds their coefficients, producing a commutative operation. Finite recursive expressions formed from zero, sums and powers of $\omega$ describe exactly the ordinals below epsilon zero, the least positive solution of ${\omega }^{\alpha }=\alpha$. With ${\omega }_0=1$ and ${\omega }_{m+1}={\omega }^{{\omega }_m}$,

To use these ordinals computationally, fix effective fundamental sequences of limit ordinals. Write a nonzero limit below ${\epsilon }_0$ as $\gamma +{\omega }^{\beta }$, where this is its last Cantor term with one copy separated off. If $\beta =\delta +1$, setIf $\beta$ is a nonzero limit, set $(\gamma +{\omega }^{\beta })[n]=\gamma +{\omega }^{\beta [n]}$. These sequences increase to their limit; for instance $\omega [n]=n+1$ and $({\omega }^{\omega })[n]={\omega }^{n+1}$. At the endpoint choose ${\epsilon }_0[n]={\omega }_{n+1}$. The shift by one is a convention; changing it changes exact finite values, so it must be specified.

The fast-growing hierarchy now combines iteration at successors with diagonalization at limits:The superscript denotes repeated function composition, not exponentiation. ThusThe next level iterates an exponential-growth function $n+1$ times, yielding tower-like growth; further finite levels iterate the corresponding higher growth. Each fixed finite level is primitive recursive, and every unary primitive recursive function is eventually dominated by a sufficiently high finite level. The first limit diagonalizes over all of them: $F_{\omega }(n)=F_{n+1}(n)$ has Ackermannian growth and is not primitive recursive. Further limits give diagonals over already transfinite stretches of the hierarchy.

Transfinite induction proves totality for every fixed ordinal index in the chosen well-founded notation system: limit evaluation passes to a smaller index, while successor evaluation performs finitely many calls at the preceding index. For the standard fundamental sequences, $\alpha <\beta$ implies $F_{\alpha }(n)<F_{\beta }(n)$ for all sufficiently large $n$. This eventual dominance in a fast-growing hierarchy is not pointwise monotonicity in the ordinal index. For example,even though $3<\omega$.

The hierarchy also measures proof-theoretic strength. Every fixed $F_{\alpha }$ with $\alpha <{\epsilon }_0$ is provably total in Peano arithmetic, and every number-theoretic function provably total there is eventually majorized by some such level. The endpoint $F_{{\epsilon }_0}$ is a total recursive function in the usual mathematical metatheory but its totality is not provable in consistent PA. The classification is established in epub.ub.uni-muenchen.de/3843/1/3843.pdf . The distinction is between separately proving each fixed lower level and proving one uniform diagonal statement covering all lower levels; a theory cannot interchange those quantifiers without additional strength.

A related example is Goodstein's theorem. Changing the base in a hereditary base representation can increase the natural number dramatically, but replacing that base by $\omega$ gives an ordinal rank of a Goodstein term below ${\epsilon }_0$. Subtracting one strictly decreases this rank, so an infinite nonterminating process would give an impossible ordinal descent. This illustrates the central connection: ordinal descent proves termination while fast-growing hierarchies measure how long finite terminating processes can last. The size of intermediate natural numbers need not resemble the simplicity of their ordinal termination measure.

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 primitive recursive functions are the smallest collection containing zero functions, successor $S(n)=n+1$, and coordinate projections, and closed under composition and primitive recursion. The recursion scheme forms $F$ from already constructed functions $G,H$ byConstants follow by composing successor with zero. Define addition, multiplication and exponentiation successively byAt each stage the initial and step functions are compositions of primitive recursive functions already available. Induction on $n$ gives $A(m,n)=m+n$, $M(m,n)=mn$, and $E(m,n)=m^n$, with the recursive convention $0^0=1$. Composition with the second projection now gives

A partial computable function may return a natural-number output or diverge. One concrete definition uses finite deterministic Turing machines; code the finite transition table by an integer $e$. Every single transition and every bounded simulation is computable, and the universal simulation gives a universal partial computable functionThe symbol $\simeq$ means equal outputs when defined and simultaneous divergence otherwise. A total computable function is a partial computable function defined on every input. Different program codes may compute the same partial function; this distinction between syntax and computed behavior is crucial.

The equivalent recursive description starts with zero, successor and projections and closes under composition and primitive recursion, giving the primitive recursive functions. Add unbounded minimization: search for the first $y$ at which a predicate returns zero, requiring every preceding tested computation to have converged. This gives the partial recursive functions. To see the connection with machines, encode finite configurations and histories. Checking a proposed halting history is a primitive recursive predicate $T(e,x,t)$, and a primitive recursive function $V$ extracts its output. Thus a machine computation has the normal form $V(\mu tT(e,x,t))$. Conversely a machine can implement composition, primitive recursion and this successive search. This proves equivalence of the formal definitions. The Church–Turing thesis identifying them with informal effective calculability is a thesis, not an additional formal theorem used in the proof.

A computably enumerable set is one enumerated by a program, equivalently the domain of a partial computable function. For the forward implication wait until the given input is enumerated; for the reverse implication run all input computations by dovetailing, outputting those that halt. A set is decidable if its characteristic function is total computable. It is decidable exactly when both it and its complement are computably enumerable: dovetail their enumerators and stop when one lists the input.

The S-m-n theorem supplies effective specialization of parameters. Given a code for a program on $(a,x)$, compile the constant $a$ into a wrapper that writes it and then calls that program. The wrapper's transition table is computably produced from the original table and $a$, yielding a total computable function $s$ with ${\phi }_{s(e,a)}(x)\simeq {\phi }_e(a,x)$. This elementary code-construction proof is all the specialization machinery needed below.

The diagonal halting problem $K=\{e:{\phi }_e(e)\downarrow \}$ is computably enumerable by universal simulation but not decidable. If a total decider $h$ existed, the program that halts on $x$ precisely when $h(x)=0$ would have an index $d$. Evaluating it at $d$ gives $d\in K$ exactly when $d\notin K$, a contradiction.

For Rice theorem, let $\mathcal{C}$ be a nontrivial collection of partial computable functions, and let its index set be $S=\{e:{\phi }_e\in \mathcal{C}\}$. Membership must depend only on the computed function, not on its code. Let $\perp$ be the nowhere-defined function. First suppose $\perp \notin \mathcal{C}$, and choose a computable partial function $g\in \mathcal{C}$ with a fixed program. Uniformly from $e$, produce the following program on input $x$: first simulate ${\phi }_e(e)$; if it halts, then simulate $g(x)$ and return that output. Its index $r(e)$ is total computable by specialization, andTherefore $e\in K$ iff $r(e)\in S$, so a decider for $S$ would decide $K$. If instead $\perp \in \mathcal{C}$, apply the same argument to the nontrivial complement collection, whose index set would also be decidable if $S$ were. We concludeFor computably enumerable languages, use the same waiting program before beginning a fixed enumeration, taking the empty language as the bottom object. Properties such as having finite domain, computing a total function or recognizing a particular nontrivial language are therefore undecidable. Purely syntactic properties such as the parity of the code are excluded: they are not extensional. Rice's conclusion is undecidability, not that every such index set must fail to be computably enumerable.

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.

A function in intension is a finite description of a computation, such as a program or a formal construction expression. A function in extension is the resulting input-output map, including its domain if it is partial. Different descriptions may have the same extension: the programs that return $x$ directly and that compute $x+0$ describe the same function. Finite descriptions have Gödel numbers; equality of the resulting partial computable functions is a separate semantic question.

The primitive recursive functions are the smallest class of numerical functions containing the zero functions, the successor function and all projection functions, and closed under function composition in recursion theory and primitive recursion. More explicitly, from $g:{\mathbb{N}}^k\to \mathbb{N}$ and $h:{\mathbb{N}}^{k+2}\to \mathbb{N}$ the latter operation formsFor function composition in recursion theory, an $m$-ary $g$ and $k$-ary $h_1,\dots ,h_m$ give $g(h_1(\mathbf{x}),\dots ,h_m(\mathbf{x}))$. Zero functions of all allowed arities can be included, with nullary zero included if arity $0$ is part of the coding convention. Every resulting function is total: the initial functions are total, function composition in recursion theory preserves totality, and mathematical induction on $n$ verifies totality of primitive recursion. Applying structural induction for primitive recursive functions then covers all construction expressions.

Use a fixed effective Gödel numbering of finite tagged construction trees with decidable decoding. A node is tagged as zero, successor, projection, composition, or recursion. The primitive recursive syntax and arity checking algorithm first checks that the input decodes as a finite tree, then works upwards from its leaves. A projection tag $(k,j)$ is legal exactly when $1\le j\le k$, with output arity $k$. A composition node declares its output arity $k$ and is legal when its outer child has arity $m$, there are exactly $m$ inner children, and each has arity $k$; its arity is $k$. The declared arity also handles the case $m=0$. A recursion node is legal when its base child has arity $k$ and its step child has arity $k+2$; its arity is $k+1$. Zero and successor tags have their declared arities. Ill-formed nodes are rejected. The process terminates because there are only finitely many nodes. Thus, for each fixed $i$,This is a syntactic assertion about functions in intension. It does not assert that arbitrary machine indices computing primitive recursive functions form a decidable set. That extensional property is nontrivial, hence undecidable by Rice theorem. For an explicit reduction, given a program $e$ construct a program that, on every input, waits for ${\phi }_e(e)$ to halt and then returns $0$. Its extension is a primitive recursive function if $e\in K$, and otherwise is nowhere defined and so is not a primitive recursive function. A decision procedure for this latter semantic set would decide the diagonal halting set.

Enumerate the decidable syntax set $D_1$ in increasing code order as $p_0,p_1,\dots$. There are infinitely many such descriptions; iterating successor after a unary zero expression already supplies infinitely many. Let $F_p$ be the extension of a valid construction and defineTo compute $U$, find $p_n$ by the syntax test and interpret its finite construction tree. Evaluate function composition in recursion theory by evaluating the children, and evaluate primitive recursion by the prescribed finite loop of length equal to its final input. Termination follows from the totality argument above, so $U$ and $d$ are total computable functions.

If $d$ were a unary primitive recursive function, some construction $p_j$ would have extension $d$. Then the diagonal argument giveswhich is impossible. The displayed $d$ is total and computable but not primitive recursive. Repetitions of extensions in the enumeration cause no problem: every possible extension is represented, which is all the diagonal argument needs. The same argument shows that the two-variable interpreter $U$ cannot itself be primitive recursive.

In the simply typed lambda calculus, types are generated from atomic types by the arrow constructor $A\to B$. A typed lambda term is a lambda term equipped with a derivation of a judgement $\Gamma ⊢t:A$, where the context assigns types to its free variables. The basic typing rules areThe Implicational Curry-Howard correspondence reads atomic types as propositions and $A\to B$ as logical implication. A typed variable is an assumption, abstraction is the implication introduction rule discharging that assumption, and application is the implication elimination rule. Consequently a typed lambda term is a proof in implicational Intuitionistic propositional logic, and a natural deduction proof recursively supplies such a term. For example, $\lambda x.x:A\to A$ expresses implication reflexivity. Products, sums, and the empty type extend the correspondence to conjunction, disjunction, and falsity; pure arrow types give precisely the implicational fragment. Beta reduction removes an introduction followed immediately by its elimination, matching proof simplification.

A Church numeral iswhere $f^{n}x$ means $n$ repetitions of application. At base type $A$, it has type $C_A=(A\to A)\to A\to A$. The following Church numeral arithmetic terms use left-associated application:Their concise conclusions areFor successor, the initial $f$ adds one application. For addition, the inner $nfx$ supplies $n$ applications and $mf$ supplies $m$ more. For multiplication, the repeated operation $nf$ applies $f$ $n$ times, and $m$ repetitions give $mn$. For exponentiation, $\overline{n}$ iterates the operation $\overline{m}$ on $f$: each iteration replaces a function $g$ by its $m$-fold iterate, yielding $f^{m^n}$. Zero iterations give $f$, so this encoding takes $m^0=1$, including $0^0=1$. The explicit outer $f,x$ abstractions make the result the standard Church numeral even at exponent zero, without needing eta equivalence.

Successor, addition, and multiplication operate on the same $C_A$. The displayed exponentiation term has type $C_A\to C_{A\to A}\to C_A$: its exponent iterates an operation on functions. Thus the numeral syntax admits several type instances; one should not force every occurrence into one fixed monomorphic Church type.

The Y combinator is the fixed-point combinatorFor $X_g=(\lambda x.g(xx))(\lambda x.g(xx))$, beta reduction gives $Yg{\to }_{\beta }{X}_g{\to }_{\beta }g{X}_g$. Since $X_g{\equiv }_{\beta }Yg$, we obtain $Yg{\equiv }_{\beta }g(Yg)$. This allows a recursive program body to receive a representation of itself.

Here the computation is in the untyped lambda calculus. The self-application $xx$ would require a simple type satisfying $A=A\to B$, impossible for finite simple types. Accordingly $Y$ is not a typed lambda term of the simply typed system. The Strong normalization theorem for simply typed lambda calculus also rules out a universal divergent fixed-point operator there.

To obtain a lambda representation of partial computable functions, encode machine configurations as finite data using Church numerals, Church pairs, and Church Booleans. Initial configuration, one transition, the halting test, and output extraction are primitive recursive functions, represented by lambda terms through the initial functions, composition, and lambda definition of primitive recursion by pair iteration. If these representations are $\mathsf{Init}$, $\mathsf{Next}$, $\mathsf{Halt}$, and $\mathsf{Out}$, defineUse normal-order beta reduction so the Church Boolean halting test selects one branch without evaluating the unused recursive branch. A halting computation follows finitely many transitions and returns the Church numeral of the output. A nonhalting computation forces successive tests forever and has no head normal form; by normal-order normalization theorem, it cannot be beta equivalent to a Church numeral. Thus every partial computable function is represented, with divergence preserved; every total computable function is the halting special case. This explains the role of $Y$ in computational universality while keeping the typed proof interpretation precise.

We prove closure under the defining schemes for primitive recursive functions, keeping all unbounded existential variables outside a bounded arithmetic formula. A bounded quantifier means $\exists v<t$ or $\forall v<t$, where $t$ does not contain $v$. All formulas are interpreted in the standard natural numbers.

First we need a finite sequence code using only the permitted language of ordered rings. Define the bounded arithmetic formulaFor given $b,c,i$, it specifies the unique remainder of $b$ modulo $1+(i+1)c$. In particular the quotient and remainder variables are bounded by arithmetic terms; division is not a new language symbol.

Every finite list $a_0,\dots ,a_n$ can be coded by some pair $b,c$. Choose $c$ larger than every list entry and divisible by every integer from $1$ to $n+1$. The moduli $m_i=1+(i+1)c$ are pairwise coprime. Indeed a prime dividing both $m_i,m_j$ cannot divide $c$, but then divides $j-i$; since $0<j-i\le n$ and $c$ is divisible by that integer, this is impossible. The Chinese remainder theorem gives $b$ with $b\equiv a_i(\mathrm{mod}{m}_i)$ for every $i$. Since $a_i<c<m_i$, each $B(b,c,i,a_i)$ holds. This is Gödel beta-function sequence coding. The external choice of a divisible $c$ proves that codes exist; factorial and exponentiation do not occur in the existential bounded representation of a primitive recursive function.

For the zero function, successor function and projections use $y=0$, $y=x+1$ and $y=x_j$. These formulas need no auxiliary variables. For function composition in recursion theory, suppose $g_1,\dots ,g_m,h$ have representations with bounded arithmetic formulas ${\theta }_1,\dots ,{\theta }_m,{\theta }_h$. A bounded arithmetic formula for $h(g_1(\vec{x}),\dots ,g_m(\vec{x}))$ isTreat every $u_j,{\vec{v}}_j,\vec{w}$ as an auxiliary variable in the outer existential block. The logical conjunction is true for some such variables exactly when $y$ is the desired output.

Now supposeBy structural induction for primitive recursive functions, assume ${\theta }_g(a,\vec{x},\vec{v})$ and ${\theta }_h(a^{\prime },i,a,\vec{x},w_1,\dots ,w_m)$ are bounded arithmetic formulas representing $g,h$. We code the values $f(0,\vec{x}),\dots ,f(n,\vec{x})$ with $b,c$. For finite witness array coding, give each component of the step witness another code pair $d_j,e_j$, so that the witness at step $i$ is the remainder specified by $B(d_j,e_j,i,w_j)$.

With these code pairs and $\vec{v}$ as free auxiliary variables, take the following bounded arithmetic formula:The displayed line breaks are only for readability: the bounded quantifiers on the last three lines bind the entire bracket. If $m=0$, their empty block and logical conjunction are omitted. Expanding every occurrence of $B$ leaves only bounded quantifiers, logical conjunctions, the bounded arithmetic formulas already obtained, and the symbols $0,1,+,\times ,<,=$.

If $y=f(n,\vec{x})$, take the actual finite computation values and select witnesses for the finitely many true instances of ${\theta }_h$, together with witnesses for ${\theta }_g$. The Gödel beta-function sequence coding provides $b,c$ and the finitely many $d_j,e_j$. These make $\varphi$ true. Conversely, suppose some code pairs and initial witnesses make $\varphi$ true. The unique remainders of $b$ give values $a_0,\dots ,a_n$. The initial clause makes $a_0=g(\vec{x})$. Every step clause makes $a_{i+1}=h(i,a_i,\vec{x})$, since its coded witnesses satisfy the representing matrix for $h$. Induction on $i$ gives $a_i=f(i,\vec{x})$, and the final remainder clause gives $y=a_n$. This argument includes $n=0$, when the step condition is empty.

Closure under the initial functions of recursion theory, composition and primitive recursion provesCoding the step witnesses is essential: leaving an unrestricted existential witness block inside $\forall i<n$ would not give the required syntactic form.

We use untyped lambda calculus and normal-order beta reduction. The Church numeral for $n$ is $\overline{n}=\lambda s.\lambda z.s^{n}z$. A lambda term represents a partial function if on numeral inputs it has the correct numeral normal form exactly when the function is defined. We use a lambda simulation of a Turing machine; this also ensures that undefined computations do not accidentally produce an output through an ignored argument.

First all total primitive recursive functions are lambda-definable. The initial functions of recursion theory are represented byComposition is obtained by substitution of representing terms. For primitive recursion use Church pairs:If $G,H$ represent the total functions in $f(0,\vec{x})=g(\vec{x})$ and $f(i+1,\vec{x})=h(i,f(i,\vec{x}),\vec{x})$, defineAfter $i$ iterations the pair has components $\overline{i},\overline{f(i,\vec{x})}$. Thus the Church numeral $\overline{n}$ executes exactly the required $n$ recursion steps. Since these functions are total on numeral inputs, substitution for composition causes no undefined-argument issue.

Now fix a deterministic Turing machine computing the given partial computable function. Encode a configuration by its finite control state and two natural numbers for the tape portions on the two sides of the head, allowing blank bits beyond the finite nonblank tape. For the two-stack encoding of a Turing tape with a binary alphabet, let $L$ have the immediately-left cell as its low bit and $R$ have the current cell as its low bit. Reading and removing a bit use remainder and quotient by two. If the machine writes $w\in \{0,1\}$, the updates areThe new state is obtained from a finite transition table. These operations, the initial configuration, the halting test, and output decoding are total primitive recursive functions on configuration codes. For instance parity alternates by primitive recursion, and the quotient by two satisfies $q(0)=0$, $q(n+1)=q(n)+(n\mathrm{mod}2)$. A fixed finite alphabet can be handled by the same stack construction in a larger base. Choose a standard delimited input and output convention, for example unary words in a finite tape alphabet with a distinct blank symbol. Initialization is primitive recursive. On a halting configuration the output is a finite word; decoding can be implemented by a bounded scan of the encoded tape, with a default output for malformed words. This is total primitive recursive and introduces no additional unbounded search. Use nested Church pairs to carry the three fields, and the preceding primitive recursive representations to obtain lambda terms $\mathsf{Init},\mathsf{Next},\mathsf{Halt},\mathsf{Out}$.

Represent Church Booleans by $\mathsf{True}=\lambda ab.a$ and $\mathsf{False}=\lambda ab.b$. A Church numeral zero test is $\mathsf{IsZero}=\lambda n.n(\lambda z.\mathsf{False})\mathsf{True}$. Use a numerical test equal to zero on halting configurations and one otherwise; applying this zero test gives the required Boolean halting test. With the fixed-point combinatorputNormal-order reduction evaluates the halting test on the current configuration. If it is true, it selects the output branch without evaluating the recursive branch. Otherwise it advances the configuration and repeats. Induction on the number of machine steps shows that a computation halting with output $y$ makes $F\overline{x_1}\cdots \overline{x_k}$ reduce to $\overline{y}$.

If the machine never halts, the normal-order beta reduction repeatedly takes the recursive branch. Each individual configuration transition and test terminates, but there is always another required transition; hence the reduction never reaches a normal form. The normal-order normalization theorem says that a lambda term with a beta-normal form is normalized by normal-order beta reduction. Therefore in the nonhalting case there is no numeral normal form at all. We have provedConfluence of beta reduction ensures uniqueness of the resulting numeral. This handles genuinely partial computations. Simply composing terms for partial subcomputations would need extra care, because lambda reduction can discard an unevaluated argument.

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.

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: