% Chapter Template
\chapter{Regular Expressions and POSIX Lexing} % Main chapter title
\label{Inj} % In chapter 2 \ref{Chapter2} we will introduce the concepts
%and notations we
% used for describing the lexing algorithm by Sulzmann and Lu,
%and then give the algorithm and its variant and discuss
%why more aggressive simplifications are needed.
In this chapter, we define the basic notions
for regular languages and regular expressions.
This is essentially a description in ``English''
of our formalisation in Isabelle/HOL.
We also give the definition of what $\POSIX$ lexing means,
followed by a lexing algorithm by Sulzmanna and Lu\parencite{Sulzmann2014}
that produces the output conforming
to the $\POSIX$ standard.
\footnote{In what follows
we choose to use the Isabelle-style notation
for function applications, where
the parameters of a function are not enclosed
inside a pair of parentheses (e.g. $f \;x \;y$
instead of $f(x,\;y)$). This is mainly
to make the text visually more concise.}
\section{Basic Concepts}
Usually, formal language theory starts with an alphabet
denoting a set of characters.
Here we just use the datatype of characters from Isabelle,
which roughly corresponds to the ASCII characters.
In what follows, we shall leave the information about the alphabet
implicit.
Then using the usual bracket notation for lists,
we can define strings made up of characters:
\begin{center}
\begin{tabular}{lcl}
$\textit{s}$ & $\dn$ & $[] \; |\; c :: s$
\end{tabular}
\end{center}
where $c$ is a variable ranging over characters.
Strings can be concatenated to form longer strings in the same
way as we concatenate two lists, which we shall write as $s_1 @ s_2$.
We omit the precise
recursive definition here.
We overload this concatenation operator for two sets of strings:
\begin{center}
\begin{tabular}{lcl}
$A @ B $ & $\dn$ & $\{s_A @ s_B \mid s_A \in A \land s_B \in B \}$\\
\end{tabular}
\end{center}
We also call the above \emph{language concatenation}.
The power of a language is defined recursively, using the
concatenation operator $@$:
\begin{center}
\begin{tabular}{lcl}
$A^0 $ & $\dn$ & $\{ [] \}$\\
$A^{n+1}$ & $\dn$ & $A @ A^n$
\end{tabular}
\end{center}
The union of all powers of a language
can be used to define the Kleene star operator:
\begin{center}
\begin{tabular}{lcl}
$A*$ & $\dn$ & $\bigcup_{i \geq 0} A^i$ \\
\end{tabular}
\end{center}
\noindent
However, to obtain a more convenient induction principle
in Isabelle/HOL,
we instead define the Kleene star
as an inductive set:
\begin{center}
\begin{mathpar}
\inferrule{\mbox{}}{[] \in A*\\}
\inferrule{s_1 \in A \;\; s_2 \in A*}{s_1 @ s_2 \in A*}
\end{mathpar}
\end{center}
\noindent
We also define an operation of "chopping off" a character from
a language, which we call $\Der$, meaning \emph{Derivative} (for a language):
\begin{center}
\begin{tabular}{lcl}
$\textit{Der} \;c \;A$ & $\dn$ & $\{ s \mid c :: s \in A \}$\\
\end{tabular}
\end{center}
\noindent
This can be generalised to ``chopping off'' a string
from all strings within a set $A$,
namely:
\begin{center}
\begin{tabular}{lcl}
$\textit{Ders} \;s \;A$ & $\dn$ & $\{ s' \mid s@s' \in A \}$\\
\end{tabular}
\end{center}
\noindent
which is essentially the left quotient $A \backslash L$ of $A$ against
the singleton language with $L = \{s\}$
in formal language theory.
However, for the purposes here, the $\textit{Ders}$ definition with
a single string is sufficient.
The reason for defining derivatives
is that it provides a different approach
to test membership of a string in
a set of strings.
For example, to test whether the string
$bar$ is contained in the set $\{foo, bar, brak\}$, one takes derivative of the set with
respect to the string $bar$:
\begin{center}
\begin{tabular}{lclll}
$S = \{foo, bar, brak\}$ & $ \stackrel{\backslash b}{\rightarrow }$ &
$\{ar, rak\}$ &
$\stackrel{\backslash a}{\rightarrow}$ &
\\
$\{r \}$ & $\stackrel{\backslash r}{\rightarrow}$ & $\{[]\}$ &
$\stackrel{[] \in S \backslash bar}{\longrightarrow}$ & $bar \in S$\\
\end{tabular}
\end{center}
\noindent
and in the end test whether the set
has the empty string \footnote{ we use the infix notation $A\backslash c$
instead of $\Der \; c \; A$ for brevity, as it is clear we are operating
on languages rather than regular expressions }.
In general, if we have a language $S_{start}$,
then we can test whether $s$ is in $S_{start}$
by testing whether $[] \in S \backslash s$.
With the sequencing, Kleene star, and $\textit{Der}$ operator on languages,
we have a few properties of how the language derivative can be defined using
sub-languages.
For example, for the sequence operator, we have
something similar to the ``chain rule'' of the calculus derivative:
\begin{lemma}
\[
\Der \; c \; (A @ B) =
\begin{cases}
((\Der \; c \; A) \, @ \, B ) \cup (\Der \; c\; B) , & \text{if} \; [] \in A \\
(\Der \; c \; A) \, @ \, B, & \text{otherwise}
\end{cases}
\]
\end{lemma}
\noindent
This lemma states that if $A$ contains the empty string, $\Der$ can "pierce" through it
and get to $B$.
The language derivative for $A*$ can be described using the language derivative
of $A$:
\begin{lemma}
$\textit{Der} \;c \;(A*) = (\textit{Der}\; c A) @ (A*)$\\
\end{lemma}
\begin{proof}
There are two inclusions to prove:
\begin{itemize}
\item{$\subseteq$}:\\
The set
\[ \{s \mid c :: s \in A*\} \]
is enclosed in the set
\[ \{s_1 @ s_2 \mid s_1 \, s_2.\; s_1 \in \{s \mid c :: s \in A\} \land s_2 \in A* \} \]
because whenever you have a string starting with a character
in the language of a Kleene star $A*$,
then that character together with some sub-string
immediately after it will form the first iteration,
and the rest of the string will
be still in $A*$.
\item{$\supseteq$}:\\
Note that
\[ \Der \; c \; (A*) = \Der \; c \; (\{ [] \} \cup (A @ A*) ) \]
holds.
Also the following holds:
\[ \Der \; c \; (\{ [] \} \cup (A @ A*) ) = \Der\; c \; (A @ A*) \]
where the $\textit{RHS}$ can be rewritten
as \[ (\Der \; c\; A) @ A* \cup (\Der \; c \; (A*)) \]
which of course contains $\Der \; c \; A @ A*$.
\end{itemize}
\end{proof}
\noindent
Before we define the $\textit{Der}$ and $\textit{Ders}$ counterpart
for regular languages, we need to first give definitions for regular expressions.
\subsection{Regular Expressions and Their Meaning}
The \emph{basic regular expressions} are defined inductively
by the following grammar:
\[ r ::= \ZERO \mid \ONE
\mid c
\mid r_1 \cdot r_2
\mid r_1 + r_2
\mid r^*
\]
\noindent
We call them basic because we will introduce
additional constructors in later chapters such as negation
and bounded repetitions.
We use $\ZERO$ for the regular expression that
matches no string, and $\ONE$ for the regular
expression that matches only the empty string\footnote{
some authors
also use $\phi$ and $\epsilon$ for $\ZERO$ and $\ONE$
but we prefer our notation}.
The sequence regular expression is written $r_1\cdot r_2$
and sometimes we omit the dot if it is clear which
regular expression is meant; the alternative
is written $r_1 + r_2$.
The \emph{language} or meaning of
a regular expression is defined recursively as
a set of strings:
%TODO: FILL in the other defs
\begin{center}
\begin{tabular}{lcl}
$L \; \ZERO$ & $\dn$ & $\phi$\\
$L \; \ONE$ & $\dn$ & $\{[]\}$\\
$L \; c$ & $\dn$ & $\{[c]\}$\\
$L \; r_1 + r_2$ & $\dn$ & $ L \; r_1 \cup L \; r_2$\\
$L \; r_1 \cdot r_2$ & $\dn$ & $ L \; r_1 @ L \; r_2$\\
$L \; r^*$ & $\dn$ & $ (L\;r)*$
\end{tabular}
\end{center}
\noindent
Now with language derivatives of a language and regular expressions and
their language interpretations in place, we are ready to define derivatives on regular expressions.
\subsection{Brzozowski Derivatives and a Regular Expression Matcher}
%Recall, the language derivative acts on a set of strings
%and essentially chops off a particular character from
%all strings in that set, Brzozowski defined a derivative operation on regular expressions
%so that after derivative $L(r\backslash c)$
%will look as if it was obtained by doing a language derivative on $L(r)$:
Recall that the language derivative acts on a
language (set of strings).
One can decide whether a string $s$ belongs
to a language $S$ by taking derivative with respect to
that string and then checking whether the empty
string is in the derivative:
\begin{center}
\parskip \baselineskip
\def\myupbracefill#1{\rotatebox{90}{\stretchto{\{}{#1}}}
\def\rlwd{.5pt}
\newcommand\notate[3]{%
\unskip\def\useanchorwidth{T}%
\setbox0=\hbox{#1}%
\def\stackalignment{c}\stackunder[-6pt]{%
\def\stackalignment{c}\stackunder[-1.5pt]{%
\stackunder[-2pt]{\strut #1}{\myupbracefill{\wd0}}}{%
\rule{\rlwd}{#2\baselineskip}}}{%
\strut\kern7pt$\hookrightarrow$\rlap{ \footnotesize#3}}\ignorespaces%
}
\Longstack{
\notate{$\{ \ldots ,\;$
\notate{s}{1}{$(c_1 :: s_1)$}
$, \; \ldots \}$
}{1}{$S_{start}$}
}
\Longstack{
$\stackrel{\backslash c_1}{\longrightarrow}$
}
\Longstack{
$\{ \ldots,\;$ \notate{$s_1$}{1}{$(c_2::s_2)$}
$,\; \ldots \}$
}
\Longstack{
$\stackrel{\backslash c_2}{\longrightarrow}$
}
\Longstack{
$\{ \ldots,\; s_2
,\; \ldots \}$
}
\Longstack{
$ \xdashrightarrow{\backslash c_3\ldots\ldots} $
}
\Longstack{
\notate{$\{\ldots, [], \ldots\}$}{1}{$S_{end} =
S_{start}\backslash s$}
}
\end{center}
\begin{center}
$s \in S_{start} \iff [] \in S_{end}$
\end{center}
\noindent
Brzozowski noticed that this operation
can be ``mirrored'' on regular expressions which
he calls the derivative of a regular expression $r$
with respect to a character $c$, written
$r \backslash c$. This infix operator
takes an original regular expression $r$ as input
and a character as a right operand and
outputs a result, which is a new regular expression.
The derivative operation on regular expression
is defined such that the language of the derivative result
coincides with the language of the original
regular expression being taken
derivative with respect to the same character:
\begin{property}
\[
L \; (r \backslash c) = \Der \; c \; (L \; r)
\]
\end{property}
\noindent
Pictorially, this looks as follows:
\parskip \baselineskip
\def\myupbracefill#1{\rotatebox{90}{\stretchto{\{}{#1}}}
\def\rlwd{.5pt}
\newcommand\notate[3]{%
\unskip\def\useanchorwidth{T}%
\setbox0=\hbox{#1}%
\def\stackalignment{c}\stackunder[-6pt]{%
\def\stackalignment{c}\stackunder[-1.5pt]{%
\stackunder[-2pt]{\strut #1}{\myupbracefill{\wd0}}}{%
\rule{\rlwd}{#2\baselineskip}}}{%
\strut\kern8pt$\hookrightarrow$\rlap{ \footnotesize#3}}\ignorespaces%
}
\Longstack{
\notate{$r$}{1}{$L \; r = \{\ldots, \;c::s_1,
\;\ldots\}$}
}
\Longstack{
$\stackrel{\backslash c}{\xrightarrow{\hspace*{8cm}}}$
}
\Longstack{
\notate{$r\backslash c$}{1}{$L \; (r\backslash c)=
\{\ldots,\;s_1,\;\ldots\}$}
}
\\
The derivatives on regular expression can again be
generalised to a string.
One could compute $r\backslash s$ and test membership of $s$
in $L \; r$ by checking
whether the empty string is in the language of
$r\backslash s$.
\Longstack{
\notate{$r_{start}$}{4}{
\Longstack{$L \; r_{start} = \{\ldots, \;$
\notate{$s$}{1}{$c_1::s_1$}
$, \ldots\} $
}
}
}
\Longstack{
$\stackrel{\backslash c_1}{ \xrightarrow{\hspace*{1.8cm}} }$
}
\Longstack{
\notate{$r_1$}{3}{
$r_1 = r_{start}\backslash c_1$,
$L \; r_1 = $
\Longstack{
$\{ \ldots,\;$ \notate{$s_1$}{1}{$c_2::s_2$}
$,\; \ldots \}$
}
}
}
\Longstack{
$\stackrel{\backslash c_2}{\xrightarrow{\hspace*{1.8cm}}}$
}
\Longstack{
$r_2$
}
\Longstack{
$ \xdashrightarrow{\hspace*{0.3cm} \backslash c_3 \ldots \ldots \ldots \hspace*{0.3cm}} $
}
\Longstack{
\notate{$r_{end}$}{1}{
$L \; r_{end} = \{\ldots, \; [], \ldots\}$}
}
\begin{property}
$s \in L \; r_{start} \iff [] \in L \; r_{end}$
\end{property}
\noindent
Now we give the recursive definition of derivative on
regular expressions, so that it satisfies the properties above.
The derivative function, written $r\backslash c$,
takes a regular expression $r$ and character $c$, and
returns a new regular expression representing
the original regular expression's language $L \; r$
being taken the language derivative with respect to $c$.
\begin{center}
\begin{tabular}{lcl}
$\ZERO \backslash c$ & $\dn$ & $\ZERO$\\
$\ONE \backslash c$ & $\dn$ & $\ZERO$\\
$d \backslash c$ & $\dn$ &
$\mathit{if} \;c = d\;\mathit{then}\;\ONE\;\mathit{else}\;\ZERO$\\
$(r_1 + r_2)\backslash c$ & $\dn$ & $r_1 \backslash c \,+\, r_2 \backslash c$\\
$(r_1 \cdot r_2)\backslash c$ & $\dn$ & $\mathit{if} \, [] \in L(r_1)$\\
& & $\mathit{then}\;(r_1\backslash c) \cdot r_2 \,+\, r_2\backslash c$\\
& & $\mathit{else}\;(r_1\backslash c) \cdot r_2$\\
$(r^*)\backslash c$ & $\dn$ & $(r\backslash c) \cdot r^*$\\
\end{tabular}
\end{center}
\noindent
The most involved cases are the sequence case
and the star case.
The sequence case says that if the first regular expression
contains an empty string, then the second component of the sequence
needs to be considered, as its derivative will contribute to the
result of this derivative:
\begin{center}
\begin{tabular}{lcl}
$(r_1 \cdot r_2 ) \backslash c$ & $\dn$ &
$\textit{if}\;\,([] \in L(r_1))\;
\textit{then} \; r_1 \backslash c \cdot r_2 + r_2 \backslash c$ \\
& & $\textit{else} \; (r_1 \backslash c) \cdot r_2$
\end{tabular}
\end{center}
\noindent
Notice how this closely resembles
the language derivative operation $\Der$:
\begin{center}
\begin{tabular}{lcl}
$\Der \; c \; (A @ B)$ & $\dn$ &
$ \textit{if} \;\, [] \in A \;
\textit{then} \;\, ((\Der \; c \; A) @ B ) \cup
\Der \; c\; B$\\
& & $\textit{else}\; (\Der \; c \; A) @ B$\\
\end{tabular}
\end{center}
\noindent
The derivative of the star regular expression $r^*$
unwraps one iteration of $r$, turns it into $r\backslash c$,
and attaches the original $r^*$
after $r\backslash c$, so that
we can further unfold it as many times as needed:
\[
(r^*) \backslash c \dn (r \backslash c)\cdot r^*.
\]
Again,
the structure is the same as the language derivative of Kleene star:
\[
\textit{Der} \;c \;(A*) \dn (\textit{Der}\; c A) @ (A*)
\]
In the above definition of $(r_1\cdot r_2) \backslash c$,
the $\textit{if}$ clause's
boolean condition
$[] \in L(r_1)$ needs to be
somehow recursively computed.
We call such a function that checks
whether the empty string $[]$ is
in the language of a regular expression $\nullable$:
\begin{center}
\begin{tabular}{lcl}
$\nullable(\ZERO)$ & $\dn$ & $\mathit{false}$ \\
$\nullable(\ONE)$ & $\dn$ & $\mathit{true}$ \\
$\nullable(c)$ & $\dn$ & $\mathit{false}$ \\
$\nullable(r_1 + r_2)$ & $\dn$ & $\nullable(r_1) \vee \nullable(r_2)$ \\
$\nullable(r_1\cdot r_2)$ & $\dn$ & $\nullable(r_1) \wedge \nullable(r_2)$ \\
$\nullable(r^*)$ & $\dn$ & $\mathit{true}$ \\
\end{tabular}
\end{center}
\noindent
The $\ZERO$ regular expression
does not contain any string and
therefore is not \emph{nullable}.
$\ONE$ is \emph{nullable}
by definition.
The character regular expression $c$
corresponds to the singleton set $\{c\}$,
and therefore does not contain the empty string.
The alternative regular expression is nullable
if at least one of its children is nullable.
The sequence regular expression
would require both children to have the empty string
to compose an empty string, and the Kleene star
is always nullable because it naturally
contains the empty string.
\noindent
We have the following correspondence between
derivatives on regular expressions and
derivatives on a set of strings:
\begin{lemma}\label{derDer}
\begin{itemize}
\item
$\textit{Der} \; c \; L(r) = L (r\backslash c)$
\item
$c\!::\!s \in L(r)$ \textit{iff} $s \in L(r\backslash c)$.
\end{itemize}
\end{lemma}
\begin{proof}
By induction on $r$.
\end{proof}
\noindent
The main property of the derivative operation
(that enables us to reason about the correctness of
derivative-based matching)
is
\noindent
We can generalise the derivative operation shown above for single characters
to strings as follows:
\begin{center}
\begin{tabular}{lcl}
$r \backslash_s (c\!::\!s) $ & $\dn$ & $(r \backslash c) \backslash_s s$ \\
$r \backslash_s [\,] $ & $\dn$ & $r$
\end{tabular}
\end{center}
\noindent
When there is no ambiguity, we will
omit the subscript and use $\backslash$ instead
of $\backslash_s$ to denote
string derivatives for brevity.
Brzozowski's regular-expression matching algorithm can then be described as:
\begin{definition}
$\textit{match}\;s\;r \;\dn\; \nullable \; (r\backslash s)$
\end{definition}
\noindent
Assuming the string is given as a sequence of characters, say $c_0c_1..c_n$,
this algorithm presented graphically is as follows:
\begin{equation}\label{graph:successive_ders}
\begin{tikzcd}
r_0 \arrow[r, "\backslash c_0"] & r_1 \arrow[r, "\backslash c_1"] &
r_2 \arrow[r, dashed] & r_n \arrow[r,"\textit{nullable}?"] &
\;\textrm{true}/\textrm{false}
\end{tikzcd}
\end{equation}
\noindent
It can be
relatively easily shown that this matcher is correct:
\begin{lemma}
$\textit{match} \; s\; r = \textit{true} \; \textit{iff} \; s \in L(r)$
\end{lemma}
\begin{proof}
By induction on $s$ using property of derivatives:
lemma \ref{derDer}.
\end{proof}
\noindent
\begin{center}
\begin{figure}
\begin{tikzpicture}
\begin{axis}[
xlabel={$n$},
ylabel={time in secs},
ymode = log,
legend entries={Naive Matcher},
legend pos=north west,
legend cell align=left]
\addplot[red,mark=*, mark options={fill=white}] table {NaiveMatcher.data};
\end{axis}
\end{tikzpicture}
\caption{Matching the regular expression $(a^*)^*b$ against strings of the form
$\protect\underbrace{aa\ldots a}_\text{n \textit{a}s}
$ using Brzozowski's original algorithm}\label{NaiveMatcher}
\end{figure}
\end{center}
\noindent
If we implement the above algorithm naively, however,
the algorithm can be excruciatingly slow, as shown in
\ref{NaiveMatcher}.
Note that both axes are in logarithmic scale.
Around two dozens characters
would already explode the matcher on the regular expression
$(a^*)^*b$.
Too improve this situation, we need to introduce simplification
rewrite rules for the intermediate results,
such as $r + r \rightarrow r$,
and make sure those rules do not change the
language of the regular expression.
One simpled-minded simplification function
that achieves these requirements is given below:
\begin{center}
\begin{tabular}{lcl}
$\simp \; r_1 \cdot r_2 $ & $ \dn$ &
$(\simp \; r_1, \simp \; r_2) \; \textit{match}$\\
& & $\quad \case \; (\ZERO, \_) \Rightarrow \ZERO$\\
& & $\quad \case \; (\_, \ZERO) \Rightarrow \ZERO$\\
& & $\quad \case \; (\ONE, r_2') \Rightarrow r_2'$\\
& & $\quad \case \; (r_1', \ONE) \Rightarrow r_1'$\\
& & $\quad \case \; (r_1', r_2') \Rightarrow r_1'\cdot r_2'$\\
$\simp \; r_1 + r_2$ & $\dn$ & $(\simp \; r_1, \simp \; r_2) \textit{match}$\\
& & $\quad \; \case \; (\ZERO, r_2') \Rightarrow r_2'$\\
& & $\quad \; \case \; (r_1', \ZERO) \Rightarrow r_1'$\\
& & $\quad \; \case \; (r_1', r_2') \Rightarrow r_1' + r_2'$\\
$\simp \; r$ & $\dn$ & $r\quad\quad (otherwise)$
\end{tabular}
\end{center}
If we repeatedly apply this simplification
function during the matching algorithm,
we have a matcher with simplification:
\begin{center}
\begin{tabular}{lcl}
$\derssimp \; [] \; r$ & $\dn$ & $r$\\
$\derssimp \; c :: cs \; r$ & $\dn$ & $\derssimp \; cs \; (\simp \; (r \backslash c))$\\
$\textit{matcher}_{simp}\; s \; r $ & $\dn$ & $\nullable \; (\derssimp \; s\;r)$
\end{tabular}
\end{center}
\begin{figure}
\begin{tikzpicture}
\begin{axis}[
xlabel={$n$},
ylabel={time in secs},
ymode = log,
xmode = log,
grid = both,
legend entries={Matcher With Simp},
legend pos=north west,
legend cell align=left]
\addplot[red,mark=*, mark options={fill=white}] table {BetterMatcher.data};
\end{axis}
\end{tikzpicture}
\caption{$(a^*)^*b$
against
$\protect\underbrace{aa\ldots a}_\text{n \textit{a}s}$ Using $\textit{matcher}_{simp}$}\label{BetterMatcher}
\end{figure}
\noindent
The running time of $\textit{ders}\_\textit{simp}$
on the same example of Figure \ref{NaiveMatcher}
is now ``tame'' in terms of the length of inputs,
as shown in Figure \ref{BetterMatcher}.
So far the story is use Brzozowski derivatives,
simplifying where possible and at the end test
whether the empty string is recognised by the final derivative.
But what if we want to
do lexing instead of just getting a true/false answer?
Sulzmanna and Lu \cite{Sulzmann2014} first came up with a nice and
elegant (arguably as beautiful as the definition of the original derivative) solution for this.
\section{Values and the Lexing Algorithm by Sulzmann and Lu}
In this section, we present a two-phase regular expression lexing
algorithm.
The first phase takes successive derivatives with
respect to the input string,
and the second phase does the reverse, \emph{injecting} back
characters, in the meantime constructing a lexing result.
We will introduce the injection phase in detail slightly
later, but as a preliminary we have to first define
the datatype for lexing results,
called \emph{value} or
sometimes also \emph{lexical value}. Values and regular
expressions correspond to each other as illustrated in the following
table:
\begin{center}
\begin{tabular}{c@{\hspace{20mm}}c}
\begin{tabular}{@{}rrl@{}}
\multicolumn{3}{@{}l}{\textbf{Regular Expressions}}\medskip\\
$r$ & $::=$ & $\ZERO$\\
& $\mid$ & $\ONE$ \\
& $\mid$ & $c$ \\
& $\mid$ & $r_1 \cdot r_2$\\
& $\mid$ & $r_1 + r_2$ \\
\\
& $\mid$ & $r^*$ \\
\end{tabular}
&
\begin{tabular}{@{\hspace{0mm}}rrl@{}}
\multicolumn{3}{@{}l}{\textbf{Values}}\medskip\\
$v$ & $::=$ & \\
& & $\Empty$ \\
& $\mid$ & $\Char \; c$ \\
& $\mid$ & $\Seq\,v_1\, v_2$\\
& $\mid$ & $\Left \;v$ \\
& $\mid$ & $\Right\;v$ \\
& $\mid$ & $\Stars\,[v_1,\ldots\,v_n]$ \\
\end{tabular}
\end{tabular}
\end{center}
\noindent
A value has an underlying string, which
can be calculated by the ``flatten" function $|\_|$:
\begin{center}
\begin{tabular}{lcl}
$|\Empty|$ & $\dn$ & $[]$\\
$|\Char \; c|$ & $ \dn$ & $ [c]$\\
$|\Seq \; v_1, \;v_2|$ & $ \dn$ & $ v_1| @ |v_2|$\\
$|\Left \; v|$ & $ \dn$ & $ |v|$\\
$|\Right \; v|$ & $ \dn$ & $ |v|$\\
$|\Stars \; []|$ & $\dn$ & $[]$\\
$|\Stars \; v::vs|$ & $\dn$ & $ |v| @ |\Stars(vs)|$
\end{tabular}
\end{center}
Sulzmann and Lu used a binary predicate, written $\vdash v:r $,
to indicate that a value $v$ could be generated from a lexing algorithm
with input $r$. They call it the value inhabitation relation.
\begin{mathpar}
\inferrule{\mbox{}}{\vdash \Char \; c : \mathbf{c}} \hspace{2em}
\inferrule{\mbox{}}{\vdash \Empty : \ONE} \hspace{2em}
\inferrule{\vdash v_1 : r_1 \;\; \vdash v_2 : r_2 }{\vdash \Seq \; v_1,\; v_2 : (r_1 \cdot r_2)}
\inferrule{\vdash v_1 : r_1}{\vdash \Left \; v_1 : r_1+r_2}
\inferrule{\vdash v_2 : r_2}{\vdash \Right \; v_2:r_1 + r_2}
\inferrule{\forall v \in vs. \vdash v:r \land |v| \neq []}{\vdash \Stars \; vs : r^*}
\end{mathpar}
\noindent
The condition $|v| \neq []$ in the premise of star's rule
is to make sure that for a given pair of regular
expression $r$ and string $s$, the number of values
satisfying $|v| = s$ and $\vdash v:r$ is finite.
Given the same string and regular expression, there can be
multiple values for it. For example, both
$\vdash \Seq(\Left \; ab)(\Right \; c):(ab+a)(bc+c)$ and
$\vdash \Seq(\Right\; a)(\Left \; bc ):(ab+a)(bc+c)$ hold
and the values both flatten to $abc$.
Lexers therefore have to disambiguate and choose only
one of the values to output. $\POSIX$ is one of the
disambiguation strategies that is widely adopted.
Ausaf and Urban\parencite{AusafDyckhoffUrban2016}
formalised the property
as a ternary relation.
The $\POSIX$ value $v$ for a regular expression
$r$ and string $s$, denoted as $(s, r) \rightarrow v$, can be specified
in the following set of rules\footnote{The names of the rules are used
as they were originally given in \cite{AusafDyckhoffUrban2016}}:
\noindent
\begin{figure}
\begin{mathpar}
\inferrule[P1]{\mbox{}}{([], \ONE) \rightarrow \Empty}
\inferrule[PC]{\mbox{}}{([c], c) \rightarrow \Char \; c}
\inferrule[P+L]{(s,r_1)\rightarrow v_1}{(s, r_1+r_2)\rightarrow \Left \; v_1}
\inferrule[P+R]{(s,r_2)\rightarrow v_2\\ s \notin L \; r_1}{(s, r_1+r_2)\rightarrow \Right \; v_2}
\inferrule[PS]{(s_1, v_1) \rightarrow r_1 \\ (s_2, v_2)\rightarrow r_2\\
\nexists s_3 \; s_4. s_3 \neq [] \land s_3 @ s_4 = s_2 \land
s_1@ s_3 \in L \; r_1 \land s_4 \in L \; r_2}{(s_1 @ s_2, r_1\cdot r_2) \rightarrow
\Seq \; v_1 \; v_2}
\inferrule[P{[]}]{\mbox{}}{([], r^*) \rightarrow \Stars([])}
\inferrule[P*]{(s_1, v) \rightarrow v \\ (s_2, r^*) \rightarrow \Stars \; vs \\
|v| \neq []\\ \nexists s_3 \; s_4. s_3 \neq [] \land s_3@s_4 = s_2 \land
s_1@s_3 \in L \; r \land s_4 \in L \; r^*}{(s_1@s_2, r^*)\rightarrow \Stars \;
(v::vs)}
\end{mathpar}
\caption{POSIX Lexing Rules}
\end{figure}
\noindent
\begin{figure}
\begin{tikzpicture}[]
\node [minimum width = 6cm, rectangle split, rectangle split horizontal,
rectangle split parts=2, rectangle split part fill={red!30,blue!20}, style={draw, rounded corners, inner sep=10pt}]
(node1)
{$r_{token1}$
\nodepart{two} $\;\;\; \quad r_{token2}\;\;\;\quad$ };
%\node [left = 6.0cm of node1] (start1) {hi};
\node [left = 0.2cm of node1] (middle) {$v.s.$};
\node [minimum width = 6cm, left = 0.2cm of middle, rectangle split, rectangle split horizontal,
rectangle split parts=2, rectangle split part fill={red!30,blue!20}, style={draw, rounded corners, inner sep=10pt}]
(node2)
{$\quad\;\;\;r_{token1}\quad\;\;\;$
\nodepart{two} $r_{token2}$ };
\node [below = 0.1cm of node2] (text1) {\checkmark preferred by POSIX};
\node [above = 1.5cm of middle, minimum width = 6cm,
rectangle, style={draw, rounded corners, inner sep=10pt}]
(topNode) {$s$};
\path[->,draw]
(topNode) edge node {split $A$} (node2)
(topNode) edge node {split $B$} (node1)
;
\end{tikzpicture}
\caption{Maximum munch example: $s$ matches $r_{token1} \cdot r_{token2}$}\label{munch}
\end{figure}
The above $\POSIX$ rules follows the intuition described below:
\begin{itemize}
\item (Left Priority)\\
Match the leftmost regular expression when multiple options of matching
are available.
\item (Maximum munch)\\
Always match a subpart as much as possible before proceeding
to the next token.
For example, when the string $s$ matches
$r_{token1}\cdot r_{token2}$, and we have two ways $s$ can be split:
Then the split that matches a longer string for the first token
$r_{token1}$ is preferred by this maximum munch rule (See
\ref{munch} for an illustration).
\noindent
\end{itemize}
\noindent
These disambiguation strategies can be
quite practical.
For instance, when lexing a code snippet
\[
\textit{iffoo} = 3
\]
using the regular expression (with
keyword and identifier having their
usualy definitions on any formal
language textbook, for instance
keyword is a nonempty string starting with letters
followed by alphanumeric characters or underscores):
\[
\textit{keyword} + \textit{identifier},
\]
we want $\textit{iffoo}$ to be recognized
as an identifier rather than a keyword (if)
followed by
an identifier (foo).
POSIX lexing achieves this.
We know that a $\POSIX$ value is also a normal underlying
value:
\begin{lemma}
$(r, s) \rightarrow v \implies \vdash v: r$
\end{lemma}
\noindent
The good property about a $\POSIX$ value is that
given the same regular expression $r$ and string $s$,
one can always uniquely determine the $\POSIX$ value for it:
\begin{lemma}
$\textit{if} \,(s, r) \rightarrow v_1 \land (s, r) \rightarrow v_2\quad \textit{then} \; v_1 = v_2$
\end{lemma}
\begin{proof}
By induction on $s$, $r$ and $v_1$. The inductive cases
are all the POSIX rules.
Probably the most cumbersome cases are
the sequence and star with non-empty iterations.
We shall give the details for proving the sequence case here.
When we have
\[
(s_1, r_1) \rightarrow v_1 \;\, and \;\,
(s_2, r_2) \rightarrow v_2 \;\, and \;\,\\
\nexists s_3 \; s_4. s_3 \neq [] \land s_3 @ s_4 = s_2 \land
s_1@ s_3 \in L \; r_1 \land s_4 \in L \; r_2
\]
we know that the last condition
excludes the possibility of a
string $s_1'$ longer than $s_1$ such that
\[
(s_1', r_1) \rightarrow v_1' \;\;
and\;\; (s_2', r_2) \rightarrow v_2'\;\; and \;\;s_1' @s_2' = s
\]
hold.
A shorter string $s_1''$ with $s_2''$ satisfying
\[
(s_1'', r_1) \rightarrow v_1''
\;\;and\;\; (s_2'', r_2) \rightarrow v_2'' \;\;and \;\;s_1'' @s_2'' = s
\]
cannot possibly form a $\POSIX$ value either, because
by definition there is a candidate
with longer initial string
$s_1$. Therefore, we know that the POSIX
value $\Seq \; a \; b$ for $r_1 \cdot r_2$ matching
$s$ must have the
property that
\[
|a| = s_1 \;\; and \;\; |b| = s_2.
\]
The goal is to prove that $a = v_1 $ and $b = v_2$.
If we have some other POSIX values $v_{10}$ and $v_{20}$ such that
$(s_1, r_1) \rightarrow v_{10}$ and $(s_2, r_2) \rightarrow v_{20}$ hold,
then by induction hypothesis $v_{10} = v_1$ and $v_{20}= v_2$,
which means this "other" $\POSIX$ value $\Seq(v_{10}, v_{20})$
is the same as $\Seq(v_1, v_2)$.
\end{proof}
\noindent
Now we know what a $\POSIX$ value is and why it is unique;
the problem is generating
such a value in a lexing algorithm using derivatives.
\subsection{Sulzmann and Lu's Injection-based Lexing Algorithm}
Sulzmann and Lu extended Brzozowski's
derivative-based matching
to a lexing algorithm by a second pass
after the initial phase of successive derivatives.
This second phase generates a POSIX value
if the regular expression matches the string.
Two functions are involved: $\inj$ and $\mkeps$.
The first one used is $\mkeps$, which constructs a POSIX value from the last
derivative $r_n$:
\begin{ceqn}
\begin{equation}\label{graph:mkeps}
\begin{tikzcd}
r_0 \arrow[r, "\backslash c_0"] & r_1 \arrow[r, "\backslash c_1"] & r_2 \arrow[r, dashed, "\ldots"] & r_n \arrow[d, "mkeps" description] \\
& & & v_n
\end{tikzcd}
\end{equation}
\end{ceqn}
\noindent
In the above diagram, again we assume that
the input string $s$ is made of $n$ characters
$c_0c_1 \ldots c_{n-1}$, and the input regular expression $r$
is given label $0$ and after each character $c_i$ is taken off
by the derivative operation the resulting derivative regular
expressioin is $r_{i+1}$.The last derivative operation
$\backslash c_{n-1}$ gives back $r_n$, which is transformed into
a value $v_n$ by $\mkeps$.
$v_n$ tells us how an empty string is matched by the (nullable)
regular expression $r_n$, in a $\POSIX$ way.
The definition of $\mkeps$ is
\begin{center}
\begin{tabular}{lcl}
$\mkeps \; \ONE$ & $\dn$ & $\Empty$ \\
$\mkeps \; (r_{1}+r_{2})$ & $\dn$
& $\textit{if}\; (\nullable \; r_{1}) \;\,
\textit{then}\;\, \Left \; (\mkeps \; r_{1})$\\
& & $\phantom{if}\; \textit{else}\;\, \Right \;(\mkeps \; r_{2})$\\
$\mkeps \; (r_1 \cdot r_2)$ & $\dn$ & $\Seq\;(\mkeps\;r_1)\;(\mkeps \; r_2)$\\
$\mkeps \; r^* $ & $\dn$ & $\Stars\;[]$
\end{tabular}
\end{center}
\noindent
We favour the left child $r_1$ of $r_1 + r_2$
to match an empty string if there is a choice.
When there is a star for us to match the empty string,
we give the $\Stars$ constructor an empty list, meaning
no iteration is taken.
The result of a call to $\mkeps$ on a $\nullable$ $r$ would
be a $\POSIX$ value corresponding to $r$:
\begin{lemma}\label{mePosix}
$\nullable\; r \implies (r, []) \rightarrow (\mkeps\; v)$
\end{lemma}
\begin{proof}
By induction on the shape of $r$.
\end{proof}
\noindent
After the $\mkeps$-call, we inject back the characters one by one
in reverse order as they were chopped off in the derivative phase.
The fucntion for this is called $\inj$. $\inj$ and $\backslash$
are not exactly reverse operations of one another, as $\inj$
operates on values instead of regular
expressions.
In the diagram below, $v_i$ stands for the (POSIX) value
for how the regular expression
$r_i$ matches the string $s_i$ consisting of the last $n-i$ characters
of $s$ (i.e. $s_i = c_i \ldots c_{n-1}$ ) from the previous lexical value $v_{i+1}$.
After injecting back $n$ characters, we get the lexical value for how $r_0$
matches $s$.
\begin{ceqn}
\begin{equation}\label{graph:inj}
\begin{tikzcd}
r_0 \arrow[r, dashed] \arrow[d]& r_i \arrow[r, "\backslash c_i"] \arrow[d] & r_{i+1} \arrow[r, dashed] \arrow[d] & r_n \arrow[d, "mkeps" description] \\
v_0 \arrow[u] & v_i \arrow[l, dashed] & v_{i+1} \arrow[l,"inj_{r_i} c_i"] & v_n \arrow[l, dashed]
\end{tikzcd}
\end{equation}
\end{ceqn}
\noindent
$\textit{inj}$ takes three arguments: a regular
expression ${r_{i}}$, before the character is chopped off,
a character ${c_{i}}$, the character we want to inject back and
the third argument $v_{i+1}$ the value we want to inject into.
The result of an application
$\inj \; r_i \; c_i \; v_{i+1}$ is a new value $v_i$ such that
\[
(s_i, r_i) \rightarrow v_i
\]
holds.
The definition of $\textit{inj}$ is as follows:
\begin{center}
\begin{tabular}{l@{\hspace{1mm}}c@{\hspace{5mm}}l}
$\textit{inj}\;(c)\;c\,Empty$ & $\dn$ & $\Char\,c$\\
$\textit{inj}\;(r_1 + r_2)\;c\; (\Left\; v)$ & $\dn$ & $\Left \; (\textit{inj}\; r_1 \; c\,v)$\\
$\textit{inj}\;(r_1 + r_2)\,c\; (\Right\;v)$ & $\dn$ & $\Right \; (\textit{inj}\;r_2\;c \; v)$\\
$\textit{inj}\;(r_1 \cdot r_2)\; c\;(\Seq \; v_1 \; v_2)$ & $\dn$ &
$\Seq \; (\textit{inj}\;r_1\;c\;v_1) \; v_2$\\
$\textit{inj}\;(r_1 \cdot r_2)\; c\;(\Left \; (\Seq \; v_1\;v_2) )$ &
$\dn$ & $\Seq \; (\textit{inj}\,r_1\,c\,v_1)\; v_2$\\
$\textit{inj}\;(r_1 \cdot r_2)\; c\; (\Right\; v)$ & $\dn$ & $\Seq\; (\textit{mkeps}\; r_1) \; (\textit{inj} \; r_2\;c\;v)$\\
$\textit{inj}\;(r^*)\; c \; (\Seq \; v\; (\Stars\;vs))$ & $\dn$ & $\Stars\;\,((\textit{inj}\;r\;c\;v)\,::\,vs)$\\
\end{tabular}
\end{center}
\noindent
The function does a recursion on
the shape of regular
expression $r_i$ and value $v_{i+1}$.
Intuitively, each clause analyses
how $r_i$ could have transformed when being
derived by $c$, identifying which subpart
of $v_{i+1}$ has the ``hole''
to inject the character back into.
Once the character is
injected back to that sub-value;
$\inj$ assembles all things together
to form a new value.
For instance, the last clause is an
injection into a sequence value $v_{i+1}$
whose second child
value is a star, and the shape of the
regular expression $r_i$ before injection
is a star.
We therefore know
the derivative
starts on a star and ends as a sequence:
\[
(r^*) \backslash c \longrightarrow r\backslash c \cdot r^*
\]
during which an iteration of the star
had just been unfolded, giving the below
value inhabitation relation:
\[
\vdash \Seq \; v \; (\Stars \; vs) : (r\backslash c) \cdot r^*.
\]
The value list $vs$ corresponds to
matched star iterations,
and the ``hole'' lies in $v$ because
\[
\vdash v: r\backslash c.
\]
Finally,
$\inj \; r \;c \; v$ is prepended
to the previous list of iterations, and then
wrapped under the $\Stars$
constructor, giving us $\Stars \; ((\inj \; r \; c \; v) ::vs)$.
Recall that lemma
\ref{mePosix} tells us
$\mkeps$ always selects the POSIX matching among
multiple values that flatten to the empty string.
Now $\inj$ preserves the POSIXness, provided
the value before injection is POSIX:
\begin{lemma}\label{injPosix}
If
\[
(r \backslash c, s) \rightarrow v
\]
then
\[
(r, c :: s) \rightarrow (\inj r \; c\; v).
\]
\end{lemma}
\begin{proof}
By induction on $r$.
The involved cases are sequence and star.
When $r = a \cdot b$, there could be
three cases for the value $v$ satisfying $\vdash v:a\backslash c$.
We give the reasoning why $\inj \; r \; c \; v$ is POSIX in each
case.
\begin{itemize}
\item
$v = \Seq \; v_a \; v_b$.\\
The ``not nullable'' clause of the $\inj$ function is taken:
\begin{center}
\begin{tabular}{lcl}
$\inj \; r \; c \; v$ & $=$ & $ \inj \;\; (a \cdot b) \;\; c \;\; (\Seq \; v_a \; v_b) $\\
& $=$ & $\Seq \; (\inj \;a \; c \; v_a) \; v_b$
\end{tabular}
\end{center}
We know that there exists a unique pair of
$s_a$ and $s_b$ satisfaying
$(a \backslash c, s_a) \rightarrow v_a$,
$(b , s_b) \rightarrow v_b$, and
$\nexists s_3 \; s_4. s_3 \neq [] \land s_a @ s_3 \in
L \; (a\backslash c) \land
s_4 \in L \; b$.
The last condition gives us
$\nexists s_3 \; s_4. s_3 \neq [] \land (c :: s_a )@ s_3 \in
L \; a \land
s_4 \in L \; b$.
By induction hypothesis, $(a, c::s_a) \rightarrow \inj \; a \; c \; v_a $ holds,
and this gives us
\[
(a\cdot b, (c::s_a)@s_b) \rightarrow \Seq \; (\inj \; a\;c \;v_a) \; v_b.
\]
\item
$v = \Left \; (\Seq \; v_a \; v_b)$\\
The argument is almost identical to the above case,
except that a different clause of $\inj$ is taken:
\begin{center}
\begin{tabular}{lcl}
$\inj \; r \; c \; v$ & $=$ & $ \inj \;\; (a \cdot b) \;\; c \;\; (\Left \; (\Seq \; v_a \; v_b)) $\\
& $=$ & $\Seq \; (\inj \;a \; c \; v_a) \; v_b$
\end{tabular}
\end{center}
With a similar reasoning,
\[
(a\cdot b, (c::s_a)@s_b) \rightarrow \Seq \; (\inj \; a\;c \;v_a) \; v_b.
\]
again holds.
\item
$v = \Right \; v_b$\\
Again the injection result would be
\begin{center}
\begin{tabular}{lcl}
$\inj \; r \; c \; v$ & $=$ & $ \inj \;\; (a \cdot b) \;\; c \;\; \Right \; (v_b) $\\
& $=$ & $\Seq \; (\mkeps \; a) \; (\inj \;b \; c\; v_b)$
\end{tabular}
\end{center}
We know that $a$ must be nullable,
allowing us to call $\mkeps$ and get
\[
(a, []) \rightarrow \mkeps \; a.
\]
Also by inductive hypothesis
\[
(b, c::s) \rightarrow \inj\; b \; c \; v_b
\]
holds.
In addition, as
$\Right \;v_b$ instead of $\Left \ldots$ is
the POSIX value for $v$, it must be the case
that $s \notin L \;( (a\backslash c)\cdot b)$.
This tells us that
\[
\nexists s_3 \; s_4.
s_3 @s_4 = s \land s_3 \in L \; (a\backslash c)
\land s_4 \in L \; b
\]
which translates to
\[
\nexists s_3 \; s_4. \; s_3 \neq [] \land
s_3 @s_4 = c::s \land s_3 \in L \; a
\land s_4 \in L \; b.
\]
(Which basically says there cannot be a longer
initial split for $s$ other than the empty string.)
Therefore we have $\Seq \; (\mkeps \; a) \;(\inj \;b \; c\; v_b)$
as the POSIX value for $a\cdot b$.
\end{itemize}
The star case can be proven similarly.
\end{proof}
\noindent
Putting all the functions $\inj$, $\mkeps$, $\backslash$ together
by following the procedure outlined in the diagram \ref{graph:inj},
and taking into consideration the possibility of a non-match,
a lexer can be built with the following recursive definition:
\begin{center}
\begin{tabular}{lcl}
$\lexer \; r \; [] $ & $=$ & $\textit{if} \; (\nullable \; r)\; \textit{then}\; \Some(\mkeps \; r) \; \textit{else} \; \None$\\
$\lexer \; r \;c::s$ & $=$ & $\textit{case}\; (\lexer \; (r\backslash c) \; s) \;\textit{of}\; $\\
& & $\quad \phantom{\mid}\; \None \implies \None$\\
& & $\quad \mid \Some(v) \implies \Some(\inj \; r\; c\; v)$
\end{tabular}
\end{center}
\noindent
The central property of the $\lexer$ is that it gives the correct result by
$\POSIX$ standards:
\begin{theorem}\label{lexerCorrectness}
The $\lexer$ based on derivatives and injections is correct:
\begin{center}
\begin{tabular}{lcl}
$\lexer \; r \; s = \Some(v)$ & $ \Longleftrightarrow$ & $ (r, \; s) \rightarrow v$\\
$\lexer \;r \; s = \None $ & $\Longleftrightarrow$ & $ \neg(\exists v. (r, s) \rightarrow v)$
\end{tabular}
\end{center}
\end{theorem}
\begin{proof}
By induction on $s$. $r$ is allowed to be an arbitrary regular expression.
The $[]$ case is proven by lemma \ref{mePosix}, and the inductive case
by lemma \ref{injPosix}.
\end{proof}
\noindent
As we did earlier in this chapter on the matcher, one can
introduce simplification on the regular expression.
However, now one needs to do a backward phase and make sure
the values align with the regular expressions.
Therefore one has to
be careful not to break the correctness, as the injection
function heavily relies on the structure of the regular expressions and values
being correct and matching each other.
It can be achieved by recording some extra rectification functions
during the derivatives step, and applying these rectifications in
each run during the injection phase.
With extra care
one can show that POSIXness will not be affected---although it is much harder
to establish.
Some initial results in this regard have been
obtained in \cite{AusafDyckhoffUrban2016}.
However, with all the simplification rules allowed
in an injection-based lexer, one could still end up in
trouble, when cases that require more involved and aggressive
simplifications arise.
\section{A Case Requring More Aggressive Simplifications}
For example, when starting with the regular
expression $(a^* \cdot a^*)^*$ and building just over
a dozen successive derivatives
w.r.t.~the character $a$, one obtains a derivative regular expression
with millions of nodes (when viewed as a tree)
even with simplification, which is not much better compared
with the naive version without any simplifications:
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
xlabel={$n$},
ylabel={size},
legend entries={Simple-Minded Simp, Naive Matcher},
legend pos=north west,
legend cell align=left]
\addplot[red,mark=*, mark options={fill=white}] table {BetterWaterloo.data};
\addplot[blue,mark=*, mark options={fill=white}] table {BetterWaterloo1.data};
\end{axis}
\end{tikzpicture}
\caption{Size of $(a^*\cdot a^*)^*$ against $\protect\underbrace{aa\ldots a}_\text{n \textit{a}s}$}
\end{figure}\label{fig:BetterWaterloo}
That is because Sulzmann and Lu's
injection-based lexing algorithm keeps a lot of
"useless" values that will not be used.
These different ways of matching will grow exponentially with the string length.
Take
\[
r= (a^*\cdot a^*)^* \quad and \quad
s=\underbrace{aa\ldots a}_\text{n \textit{a}s}
\]
as an example.
This is a highly ambiguous regular expression, with
many ways to split up the string into multiple segments for
different star iteratioins,
and for each segment
multiple ways of splitting between
the two $a^*$ sub-expressions.
When $n$ is equal to $1$, there are two lexical values for
the match:
\[
\Stars \; [\Seq \; (\Stars \; [\Char \; a])\; (\Stars \; [])] \quad (value 1)
\]
and
\[
\Stars \; [\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a])] \quad (value 2)
\]
The derivative of $\derssimp \;s \; r$ is
\[
(a^*a^* + a^*)\cdot(a^*a^*)^*.
\]
The $a^*a^*$ and $a^*$ in the first child of the above sequence
correspond to value 1 and value 2, respectively.
When $n=2$, the number goes up to 7:
\[
\Stars \; [\Seq \; (\Stars \; [\Char \; a, \Char \; a])\; (\Stars \; [])]
\]
,
\[
\Stars \; [\Seq \; (\Stars \; [\Char \; a])\; (\Stars \; [\Char \; a])]
\]
,
\[
\Stars \; [\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a, \Char \; a])]
\]
,
\[
\Stars \; [\Seq \; (\Stars \; [\Char \; a])\; (\Stars \; []), \Seq \; (\Stars \; [\Char\;a])\; (\Stars\; []) ]
\]
,
\[
\Stars \; [\Seq \; (\Stars \; [\Char \; a])\; (\Stars \; []),
\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a])
]
\]
,
\[
\Stars \; [
\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a]),
\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a])
]
\]
and
\[
\Stars \; [
\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a]),
\Seq \; (\Stars \; [\Char \; a])\; (\Stars \; [])
]
\]
And $\derssimp \; aa \; (a^*a^*)^*$ would be
\[
((a^*a^* + a^*)+a^*)\cdot(a^*a^*)^* +
(a^*a^* + a^*)\cdot(a^*a^*)^*.
\]
which removes two out of the seven terms corresponding to the
seven distinct lexical values.
It is not surprising that there are exponentially many
distinct lexical values that cannot be eliminated by
the simple-minded simplification of $\derssimp$.
A lexer without a good enough strategy to
deduplicate will naturally
have an exponential runtime on ambiguous regular expressions.
On the other hand, the
$\POSIX$ value for $r= (a^*\cdot a^*)^*$ and
$s=\underbrace{aa\ldots a}_\text{n \textit{a}s}$ is
\[
\Stars\,
[\Seq \; (\Stars\,[\underbrace{\Char(a),\ldots,\Char(a)}_\text{n iterations}]), \Stars\,[]]
\]
and at any moment the subterms in a regular expression
that will result in a POSIX value is only
a minority among the many other terms,
and one can remove ones that are absolutely not possible to
be POSIX.
In the above example,
\begin{equation}\label{eqn:growth2}
((a^*a^* + \underbrace{a^*}_\text{A})+\underbrace{a^*}_\text{duplicate of A})\cdot(a^*a^*)^* +
\underbrace{(a^*a^* + a^*)\cdot(a^*a^*)^*}_\text{further simp removes this}.
\end{equation}
can be further simplified by
removing the underlined term first,
which would open up possibilities
of further simplification that removes the
underbraced part.
The result would be
\[
(\underbrace{a^*a^*}_\text{term 1} + \underbrace{a^*}_\text{term 2})\cdot(a^*a^*)^*.
\]
with corresponding values
\begin{center}
\begin{tabular}{lr}
$\Stars \; [\Seq \; (\Stars \; [\Char \; a, \Char \; a])\; (\Stars \; [])]$ & $(\text{term 1})$\\
$\Stars \; [\Seq \; (\Stars \; [\Char \; a])\; (\Stars \; [\Char \; a])] $ & $(\text{term 2})$
\end{tabular}
\end{center}
Other terms with an underlying value such as
\[
\Stars \; [\Seq \; (\Stars \; [])\; (\Stars \; [\Char \; a, \Char \; a])]
\]
is simply too hopeless to contribute a POSIX lexical value,
and is therefore thrown away.
Ausaf and Dyckhoff and Urban \cite{AusafDyckhoffUrban2016}
have come up with some simplification steps, however those steps
are not yet sufficiently strong so that they achieve the above effects.
And even with these relatively mild simplifications the proof
is already quite a bit complicated than the theorem \ref{lexerCorrectness}.
One would prove something like:
\[
\textit{If}\; (\textit{snd} \; (\textit{simp} \; r\backslash c), s) \rightarrow v \;\;
\textit{then}\;\; (r, c::s) \rightarrow
\inj\;\, r\, \;c \;\, ((\textit{fst} \; (\textit{simp} \; r \backslash c))\; v)
\]
instead of the simple lemma \ref{injPosix}, where now $\textit{simp}$
not only has to return a simplified regular expression,
but also what specific simplifications
has been done as a function on values
showing how one can transform the value
underlying the simplified regular expression
to the unsimplified one.
We therefore choose a slightly different approach to
get better simplifications, which uses
some augmented data structures compared to
plain regular expressions.
We call them \emph{annotated}
regular expressions.
With annotated regular expressions,
we can avoid creating the intermediate values $v_1,\ldots v_n$ and a
second phase altogether.
In the meantime, we can also ensure that simplifications
are easily handled without breaking the correctness of the algorithm.
We introduce this new datatype and the
corresponding algorithm in the next chapter.