Denotational Semantics is a unique course offered by the Computer Lab at UoC. It can be intimidating at first glance but at the same time bring you lots of fun and frustration at the same time. In this blog post we will look at three proof techniques in domain theory and investigate the connections between them.

In this blog we look at three techniques that can be used to prove the fixpoint of a function, namely, Tarski’s fixpoint theorem, lfp1 + lfp2 and Scott induction. We will show that they are equivalent to each other by working on an example.

## lfp1 + lfp2#

lfp1 and lfp2 are two properties of the least pre-fixed point of a function $$f: D\to D$$, where $$D$$ is a poset. We define a pre-fixed point of $$f$$ to satisfy the property that $$f(d)\sqcup d$$. And we denote the least pre-fixed point of $$f$$, if it exists, to be $$\mathit{fix}(f)$$ which is specified by two properties:

\begin{align} \mathrm{(lfp1)}\qquad & f(\mathit{fix}(f)) \sqsubseteq \mathit{fix}(f) \newline \mathrm{(lfp2)}\qquad & \forall d\in D. f(d)\sqsubseteq d\implies \mathit{fix}(f)\sqsubseteq d. \end{align}

lfp1 is saying that $$\mathit{fix}(f)$$ is a pre-fixed point of $$f$$, while lfp2 says that it is least such. These two properties start from the definition of the least pre-fixed point, i.e. it is 1. pre-fixed; 2. least.

## Tarski’s fixpoint#

Tarski’s fixpoint theorem states that for a function $$f:D\to D$$, where $$D$$ is a domain, then $$f$$ has a least pre-fixed point, given by:

$\mathit{fix}(f) = \bigsqcup_n f^n(\bot)$

Moreover, it is also a fixpoint of $$f$$, which means that $$f(\mathit{fix}(f)) = \mathit{fix}(f)$$, and because all fixpoints are also pre-fixed point, this makes it a least fixpoint 1.

Proving the Tarski’s fixed point theorem involves two parts: firstly, to prove that it is a pre-fixed point, i.e. lfp1, this involves induction on $$f(\bot)\sqsubseteq \bot$$ and using the monotonicity of $$f$$. And to show it is a least one, we can show that $$\mathit{fix}(f)$$ is below any other pre-fixed point using the transitive property of the poset and the property that $$\sqcup_n f^n(\bot) = \sqcup_{n+1} f^{n+1}(\bot)$$. We can use the same property to prove that $$\mathit{fix}(f)$$ is indeed a fixpoint in addition to being a pre-fixed point.

Intuitively, Tarski’s theorem is saying that if we have a $$\bot$$ element, and if we have a continuous function $$f$$, then we can always obtain the fixpoint of this function by repeatedly applying it to $$\bot$$ until we don’t see changes. We are guaranteed to arrive/terminate at the lub by the definition of a domain.

## Scott induction#

Scott induction is a technique to prove that a fixpoint has a certain property. It says that if $$f: D\to D$$ is a continuous function on a domain $$D$$. Then for any admissible subset $$S\subseteq D$$, then the following implication holds:

$$\begin{prooftree} \AxiomC{$$\forall d\in D.(d\in S \implies f(d)\in S)$$} \RightLabel{ $$S$$ admissible} \UnaryInfC{$$\mathit{fix}(f)\in S$$} \end{prooftree}$$

where admissibility is defined as every chain in $$S$$ has a lub and $$\bot\in S$$.

In other words, if we know that $$f$$ preserves the property of $$d\in S$$, then we know that $$f$$ preserves it to the lub, hence the least pre-fixed point $$\mathit{fix}(f) = \sqcup_nf^n(\bot)$$ which is obtained by repeatedly applying $$f$$ to the bottom element (by Tarski’s theorem) $$\bot \in S$$ must also be in $$S$$. The existence of the bottom element is implied by the fact that $$D$$ is a domain.

## Example question#

To better understand these theorems, we apply them to a example question:

### Problem statement#

Let $$f, g:D\to D$$ be continuous functions on domain $$D$$. Prove

$$\mathit{fix}(f\circ g) = f(\mathit{fix}(g\circ f))$$

by showing

1. $$\mathit{fix}(f\circ g) \sqsubseteq f(\mathit{fix}(g\circ f))$$
2. $$f(\mathit{fix}(g\circ f)) \sqsubseteq \mathit{fix}(f\circ g)$$

### Using lfp1 + lfp2#

1. Here is the proof for the first part

$$\begin{prooftree} \AxiomC{} \UnaryInfC{$$f(\mathit{fix}(g\circ f)) \sqsubseteq f(\mathit{fix}(g\circ f))$$} \RightLabel{ definition of $$\mathit{fix}(g\circ f)$$} \UnaryInfC{$$f\circ g(f(\mathit{fix}(g\circ f))) \sqsubseteq f(\mathit{fix}(g\circ f))$$} \RightLabel{ definition of $$\mathit{fix}(f\circ g)$$} \UnaryInfC{$$\mathit{fix}(f\circ g) \sqsubseteq f(\mathit{fix(g\circ f)})$$} \end{prooftree}$$

1. And the second part

$$\begin{prooftree} \AxiomC{} \UnaryInfC{$$g(\mathit{fix}(f\circ g)) \sqsubseteq g(\mathit{fix}(f\circ g))$$} \UnaryInfC{$$g\circ f(g(\mathit{fix}(f\circ g))) \sqsubseteq g(\mathit{fix}(f\circ g))$$} \RightLabel{ lfp2 of $$\mathit{fix}(g\circ f)$$} \UnaryInfC{$$\mathit{fix}(g\circ f) \sqsubseteq g(\mathit{fix}(f\circ g))$$} \RightLabel{ $$f$$ is monotine} \UnaryInfC{$$f(\mathit{fix}(g\circ f)) \sqsubseteq f\circ g(\mathit{fix}(f\circ g))$$} \RightLabel{ definition of $$\mathit{fix}(f\circ g)$$} \UnaryInfC{$$f(\mathit{fix}(g\circ f)) \sqsubseteq \mathit{fix}(f\circ g)$$} \end{prooftree}$$

These proofs can be read either way, but it is better to read them “upwards” to see how we go from a conclusion to an axiom, using different proof techniques along the way.

### Using Scott induction#

The hard part of Scott induction is usually to identify the set that is admissible and can be used to prove the property we want. For this question, the properties we want to prove are the less than relation, so we use this as our $$S$$

1. Define $$S = \{e\ |\ e\sqsubseteq f(\mathit{fix}(g\circ f)) \}$$ this is admissible because it is a downset, i.e. $$\downarrow f(\mathit{fix}(g\circ f))$$. Now we can apply Scott induction to prove $$\mathit{fix}(g\circ f)\in S$$:

$$\begin{prooftree} \AxiomC{$$e\sqsubseteq f(\mathit{fix}(g\circ f)) \iff e\in S$$} \UnaryInfC{$$g(e)\sqsubseteq g\circ f(\mathit{fix}(g\circ f))$$} \UnaryInfC{$$g(e)\sqsubseteq \mathit{fix}(g\circ f)$$} \UnaryInfC{$$f\circ g(e)\sqsubseteq f(\mathit{fix}(g\circ f))\iff f\circ g(e)\in S$$} \end{prooftree}$$

1. Define $$S = \{d\ |\ f(d)\sqsubseteq \mathit{fix}(f\circ g)\}$$, this set is the inverse image of the downset $$\downarrow \mathit{fix}(f\circ g)$$ under $$f$$, denoted as $$f^{-1}(\downarrow \mathit{fix}(f\circ g))$$, since we know that $$\downarrow \mathit{fix}(f\circ g))$$ is admissible, then so is its inverse image.

$$\begin{prooftree} \AxiomC{$$d\in S\iff f(d) \sqsubseteq \mathit{fix}(f\circ g)$$} \UnaryInfC{$$f(g\circ f(d))) \sqsubseteq f\circ g( \mathit{fix}(f\circ g))$$} \UnaryInfC{$$g\circ f(d)\in S\iff f(g\circ f(d))) \sqsubseteq \mathit{fix}(f\circ g)$$} \end{prooftree}$$

### Using Tarski’s theorem#

The last method uses Tarski’s representation of $$\mathit{fix}(f)$$, and it is actually similar for both subquestions so we focus on the first one:

\begin{align} f(\mathit{fix}(g\circ f)) &= f(\bigsqcup_n (g\circ f)^n(\bot)) \\ &= f(\underbrace{g\circ f\circ g\circ f\cdots g\circ f(\bot)}_{n}) \\ &= f\circ g\cdots\circ f\circ g(f(\bot)) \\ &= \bigsqcup_n(f\circ g)^n(f(\bot)) \end{align}

And note that $$\mathit{fix}(f\circ g)=\bigsqcup_n(f\circ g)^n(\bot))\sqsubseteq \bigsqcup_n(f\circ g)^n(f(\bot))) = f(\mathit{fix}(g\circ f))$$ since the $$\bot \sqsubseteq f(\bot)$$ and $$(f\circ g)^n$$ is a continuous function.

The second subquestion just requires some massage of the form and we should get a similar form to the first one.

### Remarks#

The above three methods might look rather differnet, but in fact, they are related. I think it should always be possible, if we can prove something with one method, then we should be able to do it with other two methods. The lfp1+lfp2 method starts from the definition, and uses the properties of lfp. Tarski’s expansion actually “embeds” the two properties within one representation, i.e. $$\sqcup_n f^n(\bot)$$. Scott induction tries to prove that $$f$$ preserves some property. There is often something special about the property that makes our f preserve it, rather than something special about $$f$$. Often this property has something to do with $$f$$, since Scott induction’s premise is quite weak $$(d\in S\implies f(d)\in S)$$, but there is a lot of freedom in choosing what $$S$$ is.

## Aside: applications of denotational semantics#

I want to give a applications of an area that is as abstract as denotational semantics2, in case you were thinking that this is just some useless abstract math:

• Give semantics to while loops without recursion. The denotational semantics of a language PCF (programming computable function) is based the existence of fixpoint. Otherwise the while loop cannot be defined without using some kind of recursive definition. If we want to exclude recursion as a “built-in”/first-class element of our semantics, then the fixpoint semantics becomes essential to us.

• Proving contextual equivalence. Contextual equivalence of two programs can be thought of as two black boxes with pluggable holes in them. If they are contextually equivalent, then we cannot distinguish them by plugging in different values into the hole and observe the behaviour of the program (crucially, behaviour here includes both output non-terminating behaviour, i.e. the program steps into something that is not a value and cannot step further). One of the central results of this course is that if two terms have the same denotation, then they are contextually equivalent, i.e.

$$[\![M]\!] = [\![N]\!] \implies M \cong_{\mathrm{ctx}} N$$

The converse is not true, though, due to the parallel or function.

• Compiler optimisation. Domain theory is also useful in compiler optimisation where, for example, we want to apply some data flow analysis and want to show that our algorithm terminates. Or if we want to do strictness analysis, an algorithm that helps transfrom CBN to CBV, we need to use Tarski’s construction to help us solve some of the equations.

• CRDTs (conflict-free replicated data types). These are some special replicated data types that guarantees convergence. Some of these data structures use something called a join semilattice, where every two states in it has a lub, to resolve conflicts.

## Acknowledgements#

This blog post is by no means my original idea, hence I want to acknowledge the inspiration from the excellent courses denotational semantics, optimising compilers. And also this and this exam question.

## Appendix#

If you are a Mandarin speaker, then here are some jokes on denotational semantics compiled for you, enjoy :)

### 女生说话像 Denotational Semantics 的正确方式 #

1. It actually took me a while to realise this. To elaborate on this, it is like 0 is the smallest positive real number, and since 0 is an integer and all integers are real numbers, 0 is also the smallest positive integer. ↩︎

2. There is actually something more abstract called Category theory :), which is so-called abstract nonsense. ↩︎