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Take $\mathbb{N}=\{0,1,2,\dots \}$ and fix a standard effective enumeration $({\phi }_e)$ of the unary partial computable functions by program texts. WriteThe diagonal halting set $K$ is not a computable set: a supposed decision procedure would give a program that halts on input $e$ exactly when $e\notin K$, contradicting its own index. Each $K_s$ is finite and uniformly decidable by bounded simulation of a computation, and $K_s\subseteq K_{s+1}$ with union $K$.

For $a<b<c$, define the computable colouringThis is a two-part decidable set partition on three-element subsets: sort the three inputs and perform the two finite simulations. Here $[0,a)=\{0,\dots ,a-1\}$. The halting-stage colouring of triples cannot have an infinite homogeneous set for a colouring of colour $1$. Indeed, fix its least element $a$. The finitely many bits of $K_s\cap [0,a)$ eventually stabilize. Choose two later elements $b<c$ of the putative homogeneous set for a colouring beyond that stabilization stage; the corresponding triple has colour $0$.

Now suppose $H$ is an infinite homogeneous set for a colouring of colour $0$. Its membership oracle computes $K$: to decide whether $n\in K$, search for $a,b\in H$ with $n<a<b$, and return whether $n\in K_b$. For every $c\in H$ above $b$, homogeneity gives $K_c\cap [0,a)=K_b\cap [0,a)$. Since $H$ is unbounded, taking such $c$ arbitrarily large proves that this common finite set is $K\cap [0,a)$. ThusIf $H$ were a computable set, the displayed Turing reduction would decide $K$, a contradiction. The displayed decidable two-colouring has no infinite decidable homogeneous set.

For the second construction, let $2^{<\omega }$ be the finite binary strings, with coordinates starting at $0$, and define the computable binary treeMembership is decided by finitely many tests using bounded simulation of a computation. It is a binary tree of finite strings: if a string satisfies the tests, each prefix satisfies its shorter and fewer tests. The infinite paths through a binary tree are exactlyThis set is nonempty: at a coordinate whose diagonal computation halts with a binary value choose the other value, and choose either bit elsewhere. This is an existence argument, not a proposed computable decision procedure for convergence. No member is a total computable function. If $f={\phi }_d$ were a total binary-valued function, the condition at $d$ would say $f(d)\ne {\phi }_d(d)=f(d)$. Thus each member is a binary diagonally noncomputable function.

Moreover, $[T]$ has no isolated point. There are arbitrarily large indices of programs that never halt: for example, distinct program texts with successively more unused instructions followed by an infinite loop provide infinitely many such indices in the usual syntactic program coding. At every one of these coordinates the bit is unrestricted. Given any $f\in [T]$ and any prefix length $n$, change its bit at one such index $e\ge n$ and leave all other bits unchanged. The resulting different path still belongs to $[T]$ and has the same length-$n$ prefix. Since a path space is closed in Cantor space, $T$ is decidable, $[T]$ is nonempty and perfect, and no infinite path is computable.

There is a definition issue with the adjective “perfect”. The preceding conclusion uses perfect path space, allowing finite dead ends in the decidable presentation. If “perfect tree” instead requires every node to have two incompatible extensions in the tree, the requested combination is impossible. Such a nonempty tree has no terminal nodes. Starting at the empty string, test the two immediate successors and choose the first that belongs to the tree; one always exists. This constructs a total computable function whose successive prefixes remain in the tree. Thus computable pruned binary trees have computable paths. Our $T$ necessarily has dead ends; its subtree consisting only of prefixes of infinite paths is perfect in the pruned sense but is not decidable. The construction therefore supplies the intended perfect closed path space and also resolves the stronger, inconsistent interpretation.

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.

For subsets $A,B\subseteq \mathbb{N}$, a many-one reduction $A{\le }_{m}B$ is a total computable function $f:\mathbb{N}\to \mathbb{N}$ such thatA Turing reduction $A{\le }_{T}B$ is an oracle machine that, using membership queries to $B$, halts on every input and computes the indicator function of $A$. It may make several adaptive queries, and need not preserve the yes/no answer to a single query. A many-one reduction yields a Turing reduction by computing $f(x)$ and querying $B$ once. Two sets have the same Turing degree when each is Turing reducible to the other. Write $0^{\prime }$ for the degree of the diagonal halting set $K$.

We prove the Kleene–Post incomparability theorem by an oracle-assisted finite-extension construction. Fix an effective list $({\Phi }_e)$ of all Turing functionals. We shall build increasing finite binary strings ${\alpha }_s,{\beta }_s$ whose unions are the indicator functions of $A,B$. A finite oracle computation ${\Phi }_e^{\tau }(n)\downarrow =v$ means that the computation halts with output $v$ and every oracle query is strictly below $|\tau |$. Its answers are supplied by $\tau$. By the finite oracle use property, every infinite oracle extending $\tau$ preserves this computation.

Here is the decision available from $K$. Given $e,n$ and a finite string $\beta$, considerThis predicate is semidecidable: enumerate all finite extensions $\tau$ of $\beta$ and dovetail their finite oracle simulations, stopping at the first halting binary output. There is a computably produced program which, on its own index as well as on every other input, performs this search and halts exactly if it succeeds. Membership of its code in $K$ therefore decides $E$. In a yes case, running the search finds an actual witness $(\tau ,v)$. The decision oracle is essential in a no case; merely waiting would not produce a terminating construction.

Start with empty strings. At stage $s$ first ensureChoose $n=|{\alpha }_s|$, which is not yet assigned in $A$. Ask whether $E(s,n,{\beta }_s)$ holds. If yes, find a witness $(\tau ,v)$, extend the current $B$ string to $\tau$, and append the bit $1-v$ to the current $A$ string. If no, leave the current $B$ string unchanged and append $0$ to the current $A$ string. The yes case permanently forces disagreement at $n$, since later strings extend both commitments. In the no case no final oracle extending the current $B$ string can give a binary output at $n$: any such convergent computation would use finitely many bits and hence supply a forbidden finite witness. Thus it cannot be the indicator function of $A$ either, regardless of whether it diverges or returns a nonbinary value.

Next, using the strings just obtained, ensureby the same procedure with the roles reversed. Choose $m$ equal to the current length of the $B$ string, ask the analogous extension question about the current $A$ string, and, if it has witness output $v$, extend $A$ to that witness and append $1-v$ to $B$. Otherwise append $0$ to $B$. Finally pad both strings with zeros if necessary so that each length is at least $s+1$, and call the resulting strings ${\alpha }_{s+1},{\beta }_{s+1}$. Every operation is computable using the membership oracle $K$ and terminates. Earlier disagreements remain protected because no already fixed bit is ever changed.

Set $1_A={\bigcup }_s{\alpha }_s$ and $1_B={\bigcup }_s{\beta }_s$. The length condition makes these total binary functions. To compute either bit $n$ with oracle $K$, run the construction through stage $n$, when both strings have length at least $n+1$, and read that bit. Consequently $A,B{\le }_{T}K$. Every Turing functional has been defeated in both directions, soNeither set is computable, since a computable set is Turing reducible to every oracle. Neither can have degree $0^{\prime }$: if $K{\le }_{T}A$, then $B{\le }_{T}K{\le }_{T}A$, contrary to incomparability; the other case is symmetric. ThereforeThis proves the requested theorem. The argument does not claim $A$ or $B$ is computably enumerable; it constructs sets computable in $K$. Obtaining incomparable computably enumerable degrees is the stronger Friedberg–Muchnik theorem, which is not needed for the printed request.

If $M$ halts on every input, simulate $M$ on input $n$ while counting its steps, and output that count when it halts. The resulting function $t_M(n)$ is a total computable function. Consequently yes: one may take the exact running time,This construction does not promise a simple closed expression or a bound of any particular complexity class; it uses the promised totality of this particular machine.

For a machine computing a partial computable function, its exact halting-time function is still a partial computable function, with the same domain. A finite bound cannot cover a genuinely infinite computation. Even restricting attention to inputs on which $M$ halts, a total computable function bounding all halting times need not exist. In fact the precise characterization is the computable bound on halting time criterion:For the forward implication, compute $f(n)$ and simulate $M(n)$ for that many steps. If it has not halted, the proposed bound ensures that it never will. This decides the domain. Conversely, first decide whether $n$ is in the domain; return zero outside it and the simulated halting time inside it. This defines a total computable function satisfying the bound. The time needed to compute the bound itself is irrelevant to this argument.

A universal halting recognizer has undecidable domain by the halting problem, so it has no such total bound. On the other hand, a machine that immediately halts on even inputs and loops on odd inputs computes a nontotal partial computable function but has a constant bound on its halting times. Thus nontotality alone does not decide whether a total bound exists.

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.

There is no computable presentation of a Nonstandard model of Peano arithmetic. We prove Tennenbaum theorem by obtaining a computable set separating two recursively inseparable sets.

Fix an effective enumeration $({\phi }_e)$ of partial computable functions, implemented by deterministic machines, and putThese are disjoint computably enumerable sets. To see that they are recursively inseparable sets, suppose a computable set $D$ contains $U$ and avoids $V$. Its indicator function has some index $k$. If ${\phi }_k(k)=0$, then $k\in U\subseteq D$, contradicting that output. If ${\phi }_k(k)=1$, then $k\in V$, contradicting $D\cap V=\varnothing$. Thus neither output is possible.

Let $M\models \mathrm{PA}$ be a Nonstandard model of Peano arithmetic. A recursive presentation of a structure here means a presentation on $\mathbb{N}$ in which its arithmetic operations are total computable functions; equality of presentation codes is ordinary equality. Write $\bar{n}$ for the element represented by the standard numeral $n$. The presentation codes of $\bar{0},\bar{1}$ are fixed constants, so $n\mapsto \bar{n}$ is a total computable function obtained by repeated addition. Presentation codes and the arithmetic values they name must be kept distinct.

Use bounded simulation of a computation predicates $H_i(e,t)$ saying that machine $e$, on input $e$, has halted by time $t$ with output $i$. We can choose these as primitive recursive predicates represented in Peano arithmetic. Determinism and induction on the computation length give, provably in Peano arithmetic, monotonicity in $t$ andA genuine standard halting computation has a finite certificate which Peano arithmetic verifies. Consequently, if $e\in U$ or $e\in V$, the corresponding $H_i(\bar{e},\bar{t})$ holds in $M$ for some standard $t$.

Choose a nonstandard element $c$ of $M$. It exceeds every standard numeral. Let $p_e$ be the $e$th prime number, starting with $p_0=2$. Peano arithmetic proves the prime-divisibility coding of a finite set needed here: for each $c$, an element $d$ can be formed as the product of precisely those $p_e$ with $e<c$ for which $H_0(e,c)$ holds. Formally,This is an internally finite product, not a claim that its externally observed index set is finite. Its existence follows by mathematical induction on the cutoff: start with $1$, multiply by the next distinct prime number when its predicate holds, and otherwise retain the product. Unique prime factorization ensures that earlier divisibility decisions are preserved. The standard prime enumeration and these finite-product constructions are provably total in Peano arithmetic.

Define the external subset $D=\{e\in \mathbb{N}:M\models p_{\bar{e}}\mid d\}$. If $e\in U$, its standard halting time is below $c$, and monotonicity gives $H_0(\bar{e},c)$, so $e\in D$. If $e\in V$, its output-one certificate and the provable incompatibility above exclude $H_0(\bar{e},c)$, so $e\notin D$. Hence $D$ separates $U$ and $V$.

Finally $D$ is a computable set. Given standard $e$, compute the ordinary integer $p_e$ and its numeral in the presentation. Enumerate all presentation codes $q$, and for each test the finitely many standard remainders $0\le r<p_e$ forEach test is decidable using the assumed total computable functions. The division theorem of Peano arithmetic guarantees a quotient and a remainder below the standard numeral ${\bar{p}}_e$. Every element below that numeral is one of $\bar{0},\dots ,\overline{p_e-1}$, so the search terminates; the remainder is unique. Return yes exactly when $r=0$. This makes $D$ a computable set, contradicting the recursively inseparable sets construction. Therefore

For sets $A,B\subseteq \mathbb{N}$, many-one reducibility means there is a total computable function $h$ such thatTuring reducibility $A{\le }_{T}B$ means that a Turing machine with oracle $B$ computes the indicator function ${\chi }_A$, halting on every input. A many-one reduction supplies a Turing reduction by computing $h(x)$ and making one membership query, so $A{\le }_{m}B$ implies $A{\le }_{T}B$.

The Friedberg–Muchnik theorem states that there are computably enumerable sets $A,B$ with $A/{\le }_{T}B$ and $B/{\le }_{T}A$. We prove this by a finite-injury priority construction. Fix an effective enumeration $({\Phi }_e)$ of the oracle Turing functionals and arrange the requirements in the orderA requirement owns a private witness, a success flag and a priority restraint on its oracle set. All witnesses ever chosen are distinct. An $R_e$ witness $x$ is reserved outside $A$, and only $R_e$ may enumerate that number into $A$; an $S_e$ witness is treated symmetrically. A priority restraint $r$ forbids lower-priority requirements from adding numbers less than $r$ to the specified oracle set. Initializing a requirement abandons its witness, clears its success flag and sets its priority restraint to zero; enumeration into $A$ or $B$ is never undone.

At stage $s$ consider the first $s+1$ requirements and serve the highest-priority one requiring attention. An unsuccessful requirement requires attention if it has no witness, or its current witness has a stage-$s$ oracle computation with output zero, found within $s$ steps. If there is no such requirement, do nothing. When a requirement with no witness is served, choose a fresh number larger than $s$, all previously chosen witnesses and all current higher-priority priority restraints. When an $R_e$ with a witness $x$ and observed computation ${\Phi }_e^{B_s}(x)=0$ is served, enumerate $x$ into $A$, mark it successful, and restrain $B$ below the use of that computation. The use is one more than the largest queried oracle position, or zero if there were no queries. An $S_e$ acts in exactly the same way with $A,B$ interchanged. After either kind of service, initialize every currently assigned lower-priority requirement; the remaining lower priorities are already in their initial state.

This is an effective stage construction with finite sets $A_s,B_s$, and $A={\bigcup }_s{A}_s$, $B={\bigcup }_s{B}_s$ are computably enumerable. Every enumeration respects all higher-priority priority restraints: its private witness was selected above them, and any later service to a higher-priority requirement would have initialized it before it could act. We do not need to restrain a computation whose output is nonzero; a persistent nonzero output already disagrees with an unenumerated witness.

We verify the finite injury assertion by induction along the priority order. After the last service to requirements above a fixed requirement $Q$, the requirement $Q$ is never initialized again. It can be served at most twice thereafter, once to choose its final witness and once to diagonalize. Whenever it needs attention it is eventually served, since the higher-priority requirements have ceased acting and each later stage considers a longer initial segment. Thus $Q$ has a final witness and makes only finitely many actions; this proves the induction and justifies the assumed last service above it.

For a final $R_e$ witness $x$, if the diagonalization occurs then ${\chi }_A(x)=1$, while preservation of a restrained oracle computation applies: its zero computation is preserved permanently because higher priorities no longer act and lower priorities respect the priority restraint. Thus ${\Phi }_e^B(x)=0\ne {\chi }_A(x)$. If it never occurs, then ${\chi }_A(x)=0$. Were ${\Phi }_e^B(x)=0$, that convergent computation would use finitely many oracle answers. Because $B_s$ increases to $B$, those finitely many answers eventually stabilize, and a sufficiently late stage would discover the zero computation and cause diagonalization. This contradicts its never occurring. So in this case ${\Phi }_e^B(x)$ is undefined or nonzero and again disagrees with ${\chi }_A$. The identical argument verifies every $S_e$.

All the requirements hold, proving the theorem. Both sets are noncomputable: a computable set is Turing reducible to every oracle. Moreover, every computably enumerable set is Turing reducible to the diagonal halting set $K$, and neither $A$ nor $B$ can have its Turing degree, since that would make it compute the other set. HenceThis also gives many-one incomparability and answers the existence of intermediate computably enumerable Turing degrees.

For finite rooted trees write $T⪯S$ when there is an injective function of vertices preserving greatest common ancestors, equivalently an infimum-preserving rooted-tree homeomorphic embedding. No linear ordering of siblings is imposed. Kruskal's tree theorem says that every infinite sequence has an earlier tree embedding into a later tree: this embedding relation is a well-quasi-ordering.

The finite form uses a parameter $k$ to restrict the growth of the trees. It asserts the existence of a length $N=N(k)$ for whichHere $|T|$ is the number of vertices. The parameter $k$ is universally quantified; changing the starting index just shifts the parameter. This is Friedman's finite form of Kruskal's theorem.

Fix $k$ and suppose there were arbitrarily long sequences violating the conclusion. Form a tree whose nodes are their finite prefixes, including the empty prefix. Choose one representative of each finite rooted-tree isomorphism type. At position $i$ there are only finitely many choices, since the tree has at most $k+i$ vertices. Hence this finite bad-sequence tree is finitely branching. It has nodes at arbitrarily large heights by the supposed failure of the finite form. König infinity lemma supplies an infinite branch. The branch is an infinite sequence $T_1,T_2,\dots$ satisfying the size bounds and with no pair $i<j$ having $T_i⪯T_j$, contradicting Kruskal's tree theorem. Therefore the finite length exists for every $k$.

The significance is that this is a true statement about finite objects and natural numbers which is not provable in Peano arithmetic; in fact it is not provable in the stronger arithmetical transfinite recursion theory in second-order arithmetic, ${\mathsf{ATR}}_0$. It is a natural combinatorial instance of incompleteness. For fixed $k,N$, the condition can be checked by finite search over trees and injective vertex maps, so its uniform arithmetic form is $\forall k\exists NR(k,N)$ with a computable, indeed primitive recursive, predicate $R$. Thus its least sufficient length is a total computable function, but its totality cannot be proved in those theories. Each fixed numerical instance can nevertheless be proved in Peano arithmetic by verifying some particular finite witness. The obstruction concerns the uniform statement for all parameters, rather than the decidability of any one finite search.

An immune set is an infinite set containing no infinite computably enumerable subset. For binary strings, use an effective bijection with natural numbers when discussing enumeration. Fix an optimal description machine $U$ and define the plain Kolmogorov complexityAn incompressible string has no description shorter than itself, that is, $C_U(\sigma )\ge |\sigma |$. The choice of machine is fixed throughout the proof.

Let $I$ be the set of incompressible strings. There are $2^n$ strings of length $n$, but only $1+2+\cdots +2^{n-1}=2^n-1$ programs of length less than $n$. Each halting program describes at most one string, so at least one string of each length belongs to $I$. Consequently $I$ is infinite.

Suppose an infinite computably enumerable set $E$ were contained in $I$. Define a total computable function $q(n)$ by running an enumeration of $E$ until a string of length at least $n$ appears, and outputting the first such string. This search always terminates because there are only finitely many binary strings of bounded length. A fixed program can reconstruct $q(n)$ from a self-delimiting binary code of $n$. For example, if the binary expansion of $n$ has length $\ell$, encode it using $\ell$ copies of one, a zero separator, and its $\ell$ binary digits. This givesfor a constant $c$ depending on the enumeration algorithm and the optimal description machine, not on $n$. For sufficiently large $n$ this is less than $n\le |q(n)|$, contradicting $q(n)\in I$. Thus the set of incompressible strings is an immune set. This proves immunity of incompressible strings. The argument uses the ability to describe a selected long string by the short threshold specifying how it was selected.

For $b\ge 2$, write a number in hereditary base $b$: express its base-$b$ expansion as ${\sum }_i{b}^{e_i}{c}_i$, with $0<c_i<b$, and recursively expand every exponent $e_i$ in the same base. Let $B_{b,c}(m)$, for $c>b$, be the number obtained by replacing every occurrence of the base $b$ in this hereditary expression by $c$. Coefficients and the symbols for addition and exponentiation are retained.

For a starting number $m$, define its Goodstein sequence by $m_0=m$ andThe Goodstein function is the termination-time functionThis choice counts transitions from the initial term; a convention counting the initial term as well shifts the answer by one. For example the sequence starting at $3$ is $3,3,3,2,1,0$, so $\mathcal{G}(3)=5$. Starting at $4$ gives $4,26,41,60,83,\dots$, illustrating why descent of the numerical terms is not the termination argument.

Define the ordinal rank of a Goodstein term ${\rho }_b(m)$ by replacing the base $b$ in the hereditary expansion by $\omega$, at every exponent depth. The result is in Cantor normal form and lies below ${ϵ}_0$, called epsilon zero, the least nonzero solution of ${\omega }^{\alpha }=\alpha$. Finite hereditary expressions have finite nesting depth, so their ranks are bounded by a sufficiently high finite tower of powers of $\omega$.

For a fixed base, ${\rho }_b$ is strictly increasing. To see this, compare two ordinary base-$b$ expansions at their largest differing exponent. By induction through the hereditary exponent expressions, their exponents have the same ordering after replacing $b$ by $\omega$; computable Cantor normal form notation then uses the same first differing exponent or coefficient. AlsoIndeed after the base change all coefficients remain below $c$, the recursively changed exponents remain correctly ordered, and replacing the new base by $\omega$ gives exactly the original ordinal expression. These facts can be proved together by structural induction for primitive recursive functions on the hereditary expression.

Consequently, whenever $m_s>0$,An infinite nonzero Goodstein sequence would give an infinite descending sequence of ordinals below ${ϵ}_0$, impossible because ordinals are well-ordered. Thus the Goodstein function is total. Its steps are effective finite operations, so simulating the sequence until zero also proves that it is a total computable function.

The same ordinal notation system gives the requested decidable well-order. Let $D$ consist of canonical finite terms for zero and for expressionswhere the exponents are themselves canonical terms. Membership in $D$ is decidable by recursively checking the syntax, positive coefficients and decreasing exponents. Comparison is decidable by recursive computable Cantor normal form notation, first of exponents, then coefficients, and then the remaining summands. All recursive calls inspect proper subterms, so the algorithm terminates.

Interpreting these terms as ordinals gives every ordinal below ${ϵ}_0$, uniquely. One way to connect this directly to hereditary base representations is to choose, for a given finite ordinal term, a base larger than every coefficient anywhere in that term. Replacing $\omega$ by that base evaluates it to a natural number whose hereditary representation recovers the ordinal term. Thus all bases together supply the complete notation system; a single fixed base is not enough.

Choose an effective injective coding of finite terms by natural numbers, and enumerate the valid codes in increasing numerical order. The resulting bijection $d:\mathbb{N}\to D$ is computable: validity is decidable and there are infinitely many finite-ordinal terms. Its inverse is computable by counting valid codes below a given code. DefineThis is a decidable relation on all of $\mathbb{N}$. The interpretation into ordinals is an order isomorphism onto ${ϵ}_0$, provingDecidability here concerns comparing two notations. Well-foundedness is supplied by their ordinal interpretation, the same mathematical descent principle used to prove Goodstein's theorem.

Rice theorem: every nontrivial extensional property of programs computing partial computable functions has an undecidable index set. More precisely, take an acceptable effective enumeration $({\phi }_e)$ of the unary partial computable functions. If $\mathcal{P}$ contains some but not all of these functions, then“Extensional” means that equality of partial functions, including their domains, preserves membership; no syntactic restriction on program codes is being considered.

We use two standard facts, stated explicitly. The diagonal halting set $K=\{e:{\phi }_e(e)\downarrow \}$ is undecidable. For the associated acceptable enumerations ${\phi }_e^{(r)}$ of $r$-ary partial computable functions, with ${\phi }_e={\phi }_e^{(1)}$, the S-m-n theorem says that for every $m,n\ge 1$ there is a total computable function $s_n^m$ such thatwhere $\simeq$ means equality of defined values and equality of undefinedness. Thus fixed parameters in a program can be compiled into its index effectively.

Let $\perp$ be the nowhere-defined partial computable function. Choose a partial computable function $h$ whose membership in $\mathcal{P}$ is opposite to that of $\perp$; this is possible by nontriviality. For each $e$, construct the programThis is a uniform partial computation. Take an index $d$ for the binary partial computable function $(e,y)\mapsto {\psi }_e(y)$. The S-m-n theorem gives the total computable function $q(e)=s_1^1(d,e)$ with ${\phi }_{q(e)}={\psi }_e$. Crucially,Even when $h(y)$ itself is undefined, the equality of partial functions remains correct. If $\perp \notin \mathcal{P}$, this gives a many-one reduction of $K$ to $I_{\mathcal{P}}$; if $\perp \in \mathcal{P}$, it gives a many-one reduction of $K$ to the complement of $I_{\mathcal{P}}$. A decision procedure for $I_{\mathcal{P}}$ would therefore decide $K$ in either case, a contradiction.

The equivalent language formulation says that every nontrivial property of computably enumerable languages depending only on the recognized language has an undecidable index set. Apply the function formulation to the property of a partial computable function that its domain has the specified language property: every computably enumerable language occurs as such a domain.

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 many-one reduction $A{\le }_{m}B$ is a total computable function $f:\mathbb{N}\to \mathbb{N}$ such thatA Turing reduction $A{\le }_{T}B$ is an oracle machine which, with membership oracle $B$, halts on every input and computes the indicator function ${\chi }_A$. It may make finitely many adaptive queries on each input. A many-one reduction gives a Turing reduction by a single query, but Turing reducibility allows more general procedures.

The Friedberg–Muchnik theorem states that there are computably enumerable sets $A,B$ with incomparable Turing degrees:Here is a finite-injury priority construction proving it. Effectively enumerate all Turing functionals $({\Phi }_e)$. Arrange the requirements in the priority orderEquality would require the Turing functional to be total and give the correct bit at every input; one disagreement or one undefined input defeats it.

Start with $A_0=B_0=\varnothing$ and enumerate elements without ever removing them. A strategy has a private witness, a flag recording whether it has acted, and a restraint on its oracle. Every new witness is larger than every previously assigned witness and every current higher-priority restraint. Retired witnesses are never reused. The only possible enumeration of a witness is by its owner, so an unacted witness remains outside its target set.

The strategy for $R_e$ chooses a witness $x$ and waits for a stage at which the bounded simulation givesOn seeing this, put $x$ into $B$, declare the strategy acted, and impose restraint $u$ on $A$, where $u$ is the oracle use: one more than the largest queried number, or zero if there were no queries. Initialize every lower-priority strategy, discarding its current witness, acted flag and restraint. An acted strategy does nothing further until initialized. The strategy for $S_e$ is identical with $A,B$ interchanged. Waiting specifically for zero is enough: if the eventual output is nonzero while the witness stays out, it already disagrees with the target indicator function.

For a completely effective schedule, at stage $s$ visit the first $s+1$ strategies in priority order. Assign an unassigned witness when visited. Simulate each unacted strategy's computation for at most $s$ steps with the current finite oracle. If some strategy can act, perform the first such action and end the stage; otherwise end after all visits. All steps are finite and algorithmic, so the limiting $A,B$ are computably enumerable sets.

Every lower-priority enumeration respects higher-priority restraints. When its witness was chosen it lay above these restraints. Any subsequent action of a higher-priority strategy would have initialized it, so its current witness remains safe to enumerate. The initialization of all lower strategies also removes their obsolete restraints before they can obstruct a legitimate higher-priority action.

We verify finite injury by mathematical induction on priority. The first strategy is never injured and acts at most once. If all predecessors of a strategy act finitely often, there is a last stage when a higher-priority action initializes it. It is subsequently visited, chooses its permanent witness, and acts at most once. Thus it too acts finitely often and all its lower-priority strategies suffer only finitely many injuries. The scheduling cannot starve a fixed strategy: after the finitely many higher-priority actions, all sufficiently large stages visit it.

Consider the final uninjured $R_e$ with witness $x$. If it acts, then $x\in B$ permanently, and every later lower-priority enumeration into $A$ respects its restraint. No later higher-priority action occurs. The preserved computation is thereforeIf it never acts, $x\notin B$. Were ${\Phi }_e^A={\chi }_B$, the final computation on $x$ would halt with zero and have finite oracle use. The finitely many oracle bits it queries eventually stabilize, and a sufficiently late bounded stage simulation would see exactly this zero computation. The strategy would then act, a contradiction. Hence $R_e$ holds in this case too. The same reasoning proves every $S_e$.

All possible Turing reductions in both directions are excluded. This proves the Friedberg–Muchnik theorem; in particular both constructed Turing degrees are noncomputable, since a computable set is Turing reducible to every oracle.

For subsets $A,B\subseteq \mathbb{N}$, a many-one reduction $A{\le }_{m}B$ is a total computable function $h$ with $n\in A⟺h(n)\in B$. A Turing reduction $A{\le }_{T}B$ is an oracle machine with oracle $B$ computing the total indicator function ${\chi }_A$. It may make several adaptive oracle queries. A many-one reduction gives a Turing reduction using one query; the definitions impose no enumerability assumption on $A,B$.

The Friedberg–Muchnik theorem asserts that there exist computably enumerable sets $A,B$ withWe give the finite-injury priority construction. Fix an effective list ${\Phi }_e$ of Turing functionals and imposein priority order $R_0,S_0,R_1,S_1,\dots$. Each strategy has an unassigned, waiting or protected state; a private witness $x$ when assigned; and a restraint on the oracle set when protected. All witnesses, even abandoned ones, are permanently recorded and never reused. A strategy for $R_e$ is the only strategy ever allowed to enumerate its witness into $A$; the analogous statement holds for $S_e$ and $B$.

Start with finite sets $A_0=B_0=\varnothing$. At stage $s$, inspect the first $s+1$ strategies. An unassigned strategy needs attention. A waiting $R_e$ strategy needs attention if the computation ${\Phi }_e^{B_s}(x)$ converges within $s$ steps; waiting $S_e$ is symmetric. Act on the highest-priority strategy needing attention, if any. On assignment choose a fresh witness greater than $s$, every earlier witness and every higher-priority restraint, and enter the waiting state. On a convergence with output $v$ and oracle use $u$, act as follows: if $v=0$, enumerate the witness into its own target set; if $v\ne 0$, leave the witness out. Enter the protected state and restrain the oracle below $u$. The current characteristic value at the witness is now different from $v$, including outputs other than zero or one. Whenever a strategy acts, initialize every lower-priority strategy, discarding its current witness and restraint but retaining the permanent record of used witnesses. If none needs attention, change nothing.

Every enumeration respects higher-priority restraints: its witness was chosen beyond them after its last initialization, and a later higher-priority action would have initialized it. Each stage is effective, finite and monotone in $A_s,B_s$, so $A={\bigcup }_s{A}_s$ and $B={\bigcup }_s{B}_s$ are computably enumerable. Lower-priority restraints may be violated by a higher-priority action, exactly the allowed injuries.

Induct on priority to verify finite injury and satisfaction. Once all higher-priority strategies have made their last actions, the current strategy receives its final assignment and can act at most once more, on a convergence. If convergence is observed it is then protected permanently; otherwise it waits without further action. In either case every strategy acts only finitely often. After the final initialization of $R_e$, if it acts on a convergence, no higher strategy subsequently changes its assumptions and every lower strategy respects its restraint. The observed oracle computation therefore remains valid for the final $B$, while the permanently private witness retains the opposite characteristic value in $A$.

If it waits forever, then ${\Phi }_e^B(x)$ cannot converge. A convergent final computation uses only finitely many oracle bits; those bits in the increasing enumeration $B_s$ eventually equal their final values. For a sufficiently large stage the same finite computation would have been observed, and after the higher strategies have stopped it would receive attention, contradicting perpetual waiting. Thus $R_e$ is satisfied either by disagreement or by divergence. The same proof satisfies every $S_e$. This is private-witness diagonalization for incomparable enumerable sets, with the finite-injury verification supplying the required permanent disagreements.

A computable set reduces to every oracle, so incomparability makes both sets noncomputable. Every computably enumerable set reduces to the halting problem; if either of these sets had the halting degree, the other would reduce to it. Thus their c.e. degrees are also incomplete, giving the intermediate degrees sought by the Post problem.

A many-one reduction $A{\le }_{m}B$ is a total computable function $f:\mathbb{N}\to \mathbb{N}$ with $x\in A⟺f(x)\in B$. If $B$ is recursively enumerable, compute $f(x)$ and run a semidecision procedure for $B$ on that value. It halts exactly when $x\in A$, so $A$ is recursively enumerable. Totality of $f$ is essential to this argument.