\[ \newcommand{\tr}{\Rightarrow} \newcommand{\trs}{\tr^{\!\ast}} \newcommand{\rlnm}[1]{\mathsf{(#1)}} \newcommand{\rred}[1]{\xrightarrow{#1}} \newcommand{\rreds}[1]{\mathrel{\xrightarrow{#1}\!\!^*}} \newcommand{\cl}{\mathsf{Cl}} \newcommand{\pow}{\mathcal{P}} \newcommand{\matches}{\mathrel{\mathsf{matches}}} \newcommand{\kw}[1]{\mathsf{#1}} \]

Reductions

Establishing that the Halting Problem was undecidable was a laborious task. It required an explicit proof using a diagonal method, and lots of careful reasoning.

We can sometimes prove that a predicate is undecidable by another method: we show that if it were decidable, then $\textsf{HALT}$ would be decidable as well. This is a proof by reduction.

Definition

Let $U, V \subseteq \mathbb{N}$ be predicates, and let $f : \mathbb{N} \to \mathbb{N}$. The function $f$ is a many-one reduction from $U$ to $V$ just if

  1. it is computable, and
  2. it is also the case that
\[n \in U\ ⟺ f(n) \in V\]

We may sometimes write $f : U ≲ V$ to mean that $f$ is a reduction from $U$ to $V$.

Intuition

A many-one reduction $f : U ≲ V$ reduces the problem of deciding $U$ to the problem of deciding $V$.

Suppose I have a number $n$, and I want to determine whether $n \in U$. Instead of doing that, I can compute $f(n)$. this is always defined because $f$ is total, and it’s always possible to compute because $f$ is a computable function. Then, I can ask whether $f(n) \in V$ instead. Because the definition of $f$ involves a logical equivalence, I can deduce the following facts:

  • if $f(n) \in V$, then I can tell that $n \in U$
  • if $f(n) \not\in V$, then I can tell that $n \not\in U$

Thus: instead of deciding whether $n \in U$, I can compute $f(n)$ and ask whether $f(n) \in V$ instead.

This proves the

Lemma. If $f : U ≲ V$ and $V$ is decidable, then so is $U$.

This explains the notation $U ≲ V$. If I can decide $V$, then I can decide $U$. In other words, $U$ is an easier, “smaller” problem.

From this I can infer the contrapositive of this statement:

Lemma. If $f : U ≲ V$ and $U$ is undecidable, then so is $V$.

Thus, if $U$ is difficult, then the harder, “bigger” problem $V$ is also difficult.

The strategy is now clear: if for a predicate $V \subseteq \mathbb{N}$ I can find a reduction $f : \textsf{HALT} ≲ V$, then I have shown that $V$ is more difficult than $\textsf{HALT}$. In other words: if I could ‘solve’ $V$, then I could also ‘solve’ $\textsf{HALT}$. But the latter cannot happen, so

Example

neither can the former.

For example, we can show the undecidability of the set

\[\textsf{ALL} = \{ \ulcorner S \urcorner \mid \forall n \in \mathbb{N}.\ ⟦ S ⟧_\texttt{x}(n) \downarrow \}\]

of (the Gödel numbers of) programs which never go into an infinite loop.

To construct the reduction $g : \textsf{HALT} ≲ \textsf{ALL}$, recall the notion of a code transformation. Define a function

\[g : \textbf{Stmt} \times \mathbb{N} \to \textbf{Stmt}\]

where $g(S, n)$ is a While program $G_{S, n}$ that performs the following steps: on input $m$,

  1. Ignore the input.
  2. Simulate $S$ on input $n$ (i.e. run the interpreter/universal function, giving it both $S$ and $n$, which are now hardcoded).
  3. If that simulation ever finishes, set x <- 0 to output $0$.

$g$ is a function that takes some source code ($S$) and a number ($n$) as input, and returns another program as output. The program $g(S, n)$ it outputs is a program that first simulates the running of $S$ on $n$, and - if and when that finishes - simply returns 0 as its final result. We know that we can write such a program by adapting the code of the universal function.

The reduction we seek is the reflection $\tilde{g} : \mathbb{N} \to \mathbb{N}$ of $g$. Hence, $\tilde{g}$ is a function maps the natural number/encoding $\langle \ulcorner S \urcorner, n \rangle$ to the natural number/encoding $\ulcorner G_{S, n} \urcorner$.

Is this $\tilde{g}$ computable? It is indeed! It is not a stretch of the imagination to think of a bash or Python script that assembles the source code $G_{S, n}$ from the source code $S$ and the number $n$.

Finally, $\tilde{g}$ is a reduction $\textsf{HALT} ≲ \textsf{ALL}$ because it is the case that

\[\langle \ulcorner S \urcorner, n \rangle \in \textsf{HALT} ⟺ \ulcorner G_{S, n} \urcorner \in \textsf{ALL}\]

Indeed, the program $G_{S, n}$ always simulates the running of $S$ on input $n$ before stopping with its own input as output. Thus,

  • If $\langle \ulcorner S \urcorner, n \rangle \in \textsf{HALT}$, then $S$ halts on input $n$. Then, the program $G_{S, n}$ always halts, returning $0$. Consequently, the program $G_{S, n}$ is an expensive and roundabout way of computing the identity function. The identity function is always defined on all inputs, so $\ulcorner G_{S, n} \urcorner \in \textsf{ALL}$.
  • If $\langle \ulcorner S \urcorner, n \rangle \not\in \textsf{HALT}$, then $S$ does not halt on input $n$. Then, the program $G_{S, n}$ also never halts: the simulation of $S$ on $n$ runs forever! Hence, $G_{S, n}$ computes a function that is undefined for any input. Hence $\ulcorner G_{S, n} \urcorner \not\in \textsf{ALL}$.

These two points prove the aforementioned equivalence.

Thus $\textsf{HALT}$ is “easier” than $\textsf{ALL}$. Hence $\textsf{ALL}$ is not decidable.

Intuition

Showing the reduction above required some heavy symbols and some non-trivial reasoning. Here is an intuitive explanation of what is going on:

  1. Suppose that someone challenges you to solve the halting problem: they provide a particular $S$ and $n$ (encoded as natural numbers), and ask you if they are an instance of the halting problem (i.e. if $\langle \ulcorner S \urcorner, n \rangle \in \textsf{HALT}$.)

  2. Given those two pieces of data (encoded as numbers), we were able to write a program $G_{S, n}$ whose encoding $\ulcorner G_{S, n} \urcorner$ was in $\textsf{ALL}$ exactly when $\langle \ulcorner S \urcorner, n \rangle$ was in $\mathsf{HALT}$. This program $G_{S, n}$ had $S$ and $n$ hard-coded into its source code (in the data section of the binary, as a constant, etc.) Morever, writing down this program $G_{S, n}$ was quite mechanical, and could be done by a computer.

  3. This shows that every question about halting can be transformed to a question about $\textsf{ALL}$. Moreover, this transformation did not require thinking or ingenuity, and could be done automatically by a computer program (“metaprogramming”, i.e. writing programs that input and output other programs).

  4. Hence, if we could solve $\textsf{ALL}$, we could solve $\textsf{HALT}$.

  5. We cannot solve $\textsf{HALT}$.

  6. Therefore we cannot solve $\textsf{ALL}$ either.