% Chapter Template

\chapter{A Better Bound and Other Extensions} % Main chapter title

\label{Cubic} %In Chapter 5\ref{Chapter5} we discuss stronger simplifications to improve the finite bound

%in Chapter 4 to a polynomial one, and demonstrate how one can extend the

%algorithm to include constructs such as bounded repetitions and negations.

\lstset{style=myScalastyle}

This chapter is a ``miscellaneous''

chapter which records various

extensions to our $\blexersimp$'s formalisations.\\

Firstly we present further improvements

made to our lexer algorithm $\blexersimp$.

We devise a stronger simplification algorithm,

called $\bsimpStrong$, which can prune away

similar components in two regular expressions at the same 

alternative level,

even if these regular expressions are not exactly the same.

We call the lexer that uses this stronger simplification function

$\blexerStrong$.

We conjecture that both

\begin{center}

	$\blexerStrong \;r \; s = \blexer\; r\;s$

\end{center}

and

\begin{center}

	$\llbracket \bdersStrong{a}{s} \rrbracket = O(\llbracket a \rrbracket^3)$

\end{center}

hold, but formalising

them is still work in progress.

We give reasons why the correctness and cubic size bound proofs

can be achieved,

by exploring the connection between the internal

data structure of our $\blexerStrong$ and

Animirov's partial derivatives.\\

Secondly, we extend our $\blexersimp$

to support bounded repetitions ($r^{\{n\}}$).

We update our formalisation of 

the correctness and finiteness properties to

include this new construct. With bounded repetitions

we are able to out-compete other verified lexers such as

Verbatim++ on regular expressions which involve a lot of

\\

Last but not least, we present our efforts and challenges we met

in further improving the algorithm by data

structures such as zippers.

%----------------------------------------------------------------------------------------

%	SECTION strongsimp

%----------------------------------------------------------------------------------------

\section{A Stronger Version of Simplification}

%TODO: search for isabelle proofs of algorithms that check equivalence 

In our bitcoded lexing algorithm, (sub)terms represent (sub)matches.

For example, the regular expression 

\[

	aa \cdot a^*+ a \cdot a^* + aa\cdot a^*

\]

contains three terms, 

expressing three possibilities it will match future input.

The first and the third terms are identical, which means we can eliminate

the latter as we know it will not be picked up by $\bmkeps$.

In $\bsimps$, the $\distinctBy$ function takes care of this.

The criteria $\distinctBy$ uses for removing a duplicate

$a_2$ in the list

\begin{center}

	$rs_a@[a_1]@rs_b@[a_2]@rs_c$

\end{center}

is that 

\begin{center}

	$\rerase{a_1} = \rerase{a_2}$.

\end{center}

It can be characterised as the $LD$ 

rewrite rule in \ref{rrewriteRules}.\\

The problem , however, is that identical components

in two slightly different regular expressions cannot be removed:

\begin{figure}[H]

\[

	(a+b+d) \cdot r_1 + (a+c+e) \cdot r_1 \stackrel{?}{\rightsquigarrow} (a+b+d) \cdot r_1 + (c+e) \cdot r_1

\]

\caption{Desired simplification, but not done in $\blexersimp$}\label{partialDedup}

\end{figure}

\noindent

A simplification like this actually

cannot be omitted,

as without it the size could blow up even with our $\simp$ function:

\begin{figure}[H]

\centering

\begin{tikzpicture}

\begin{axis}[

    %xlabel={$n$},

    myplotstyle,

    xlabel={input length},

    ylabel={size},

    ]

\addplot[blue,mark=*, mark options={fill=white}] table {bsimpExponential.data};

\end{axis}

\end{tikzpicture}

\caption{Runtime of $\blexersimp$ for matching 

	$\protect((a^* + (aa)^* + \ldots + (aaaaa)^* )^*)^*$ 

	with strings 

	of the form $\protect\underbrace{aa..a}_{n}$.}\label{blexerExp}

\end{figure}

\noindent

We would like to apply the rewriting at some stage

\begin{figure}[H]

\[

	(a+b+d) \cdot r_1  \longrightarrow a \cdot r_1 + b \cdot r_1 + d \cdot r_1

\]

\caption{Desired simplification, but not done in $\blexersimp$}\label{desiredSimp}

\end{figure}

\noindent

in our $\simp$ function,

so that it makes the simplification in \ref{partialDedup} possible.

For example,

can a function like the below one be used?

%TODO: simp' that spills things

Unfortunately,

if we introduce them in our

setting we would lose the POSIX property of our calculated values. 

For example given the regular expression 

\begin{center}

	$(a + ab)(bc + c)$

\end{center}

and the string

\begin{center}

	$ab$,

\end{center}

then our algorithm generates the following

correct POSIX value

\begin{center}

	$\Seq \; (\Right \; ab) \; (\Right \; c)$.

\end{center}

Essentially it matches the string with the longer Right-alternative

in the first sequence (and

then the 'rest' with the character regular expression $c$ from the second sequence).

If we add the simplification above, then we obtain the following value

\begin{center}

	$\Left \; (\Seq \; a \; (\Left \; bc))$

\end{center}

where the $\Left$-alternatives get priority. 

However this violates the POSIX rules.

The reason for getting this undesired value

is that the new rule splits this regular expression up into 

\begin{center}

	$a\cdot(b c + c) + ab \cdot (bc + c)$,

\end{center}

which becomes a regular expression with a 

totally different structure--the original

was a sequence, and now it becomes an alternative.

With an alternative the maximum munch rule no longer works.\\

A method to reconcile this is to do the 

transformation in \ref{desiredSimp} ``non-invasively'',

meaning that we traverse the list of regular expressions

\begin{center}

	$rs_a@[a]@rs_c$

\end{center}

in the alternative

\begin{center}

	$\sum ( rs_a@[a]@rs_c)$

\end{center}

using  a function similar to $\distinctBy$,

but this time 

we allow a more general list rewrite:

\begin{mathpar}\label{cubicRule}

		\inferrule{\vspace{0mm} }{rs_a@[a]@rs_c 

			\stackrel{s}{\rightsquigarrow }

		rs_a@[\textit{prune} \; a \; rs_a]@rs_c }

\end{mathpar}

%L \; a_1' = L \; a_1 \setminus (\cup_{a \in rs_a} L \; a)

where $\textit{prune} \;a \; acc$ traverses $a$

without altering the structure of $a$, removing components in $a$

that have appeared in the accumulator $acc$.

For example

\begin{center}

	$\textit{prune} \;\;\; (r_a+r_f+r_g+r_h)r_d \;\; \; [(r_a+r_b+r_c)r_d, (r_e+r_f)r_d] $

\end{center}

should be equal to 

\begin{center}

	$(r_g+r_h)r_d$

\end{center}

because $r_gr_d$ and 

$r_hr_d$ are the only terms

that have not appeared in the accumulator list 

\begin{center}

$[(r_a+r_b+r_c)r_d, (r_e+r_f)r_d]$.

\end{center}

We implemented 

function $\textit{prune}$ in Scala,

and incorporated into our lexer,

by replacing the $\simp$ function 

with a stronger version called $\bsimpStrong$

that prunes regular expressions.

We call this lexer $\blexerStrong$.

$\blexerStrong$ is able to drastically reduce the

internal data structure size which could

trigger exponential behaviours in

$\blexersimp$.

\begin{figure}[H]

\centering

\begin{tabular}{@{}c@{\hspace{0mm}}c@{\hspace{0mm}}c@{}}

\begin{tikzpicture}

\begin{axis}[

    %xlabel={$n$},

    myplotstyle,

    xlabel={input length},

    ylabel={size},

    ]

\addplot[red,mark=*, mark options={fill=white}] table {strongSimpCurve.data};

\end{axis}

\end{tikzpicture}

  &

\begin{tikzpicture}

\begin{axis}[

    %xlabel={$n$},

    myplotstyle,

    xlabel={input length},

    ylabel={size},

    ]

\addplot[blue,mark=*, mark options={fill=white}] table {bsimpExponential.data};

\end{axis}

\end{tikzpicture}\\

\multicolumn{2}{l}{Graphs: Runtime for matching $((a^* + (aa)^* + \ldots + (aaaaa)^* )^*)^*$ with strings 

	   of the form $\underbrace{aa..a}_{n}$.}

\end{tabular}    

\caption{aaaaaStarStar} \label{fig:aaaaaStarStar}

\end{figure}

\begin{figure}[H]

\begin{lstlisting}

    def atMostEmpty(r: Rexp) : Boolean = r match {

      case ZERO => true

      case ONE => true

      case STAR(r) => atMostEmpty(r)

      case SEQ(r1, r2) => atMostEmpty(r1) && atMostEmpty(r2)

      case ALTS(r1, r2) => atMostEmpty(r1) && atMostEmpty(r2)

      case CHAR(_) => false

    }

    def isOne(r: Rexp) : Boolean = r match {

      case ONE => true

      case SEQ(r1, r2) => isOne(r1) && isOne(r2)

      case ALTS(r1, r2) => (isOne(r1) || isOne(r2)) && (atMostEmpty(r1) && atMostEmpty(r2))//rs.forall(atMostEmpty) && rs.exists(isOne)

      case STAR(r0) => atMostEmpty(r0)

      case CHAR(c) => false

      case ZERO => false

    }

    //r = r' ~ tail' : If tail' matches tail => returns r'

    def removeSeqTail(r: Rexp, tail: Rexp) : Rexp = r match {

      case SEQ(r1, r2) => 

        if(r2 == tail) 

          r1

        else

          ZERO

      case r => ZERO

    }

    def prune(r: ARexp, acc: Set[Rexp]) : ARexp = r match{

      case AALTS(bs, rs) => rs.map(r => prune(r, acc)).filter(_ != ZERO) match

      {

        //all components have been removed, meaning this is effectively a duplicate

        //flats will take care of removing this AZERO 

        case Nil => AZERO

        case r::Nil => fuse(bs, r)

        case rs1 => AALTS(bs, rs1)

      }

      case ASEQ(bs, r1, r2) => 

        //remove the r2 in (ra + rb)r2 to identify the duplicate contents of r1

        prune(r1, acc.map(r => removeSeqTail(r, erase(r2)))) match {

          //after pruning, returns 0

          case AZERO => AZERO

          //after pruning, got r1'.r2, where r1' is equal to 1

          case r1p if(isOne(erase(r1p))) => fuse(bs ++ mkepsBC(r1p), r2)

          //assemble the pruned head r1p with r2

          case r1p => ASEQ(bs, r1p, r2)

        }

        //this does the duplicate component removal task

      case r => if(acc(erase(r))) AZERO else r

    }

\end{lstlisting}

\caption{pruning function together with its helper functions}

\end{figure}

\noindent

The benefits of using 

$\textit{prune}$ such as refining the finiteness bound

to a cubic bound has not been formalised yet.

Therefore we choose to use Scala code rather than an Isabelle-style formal 

definition like we did for $\simp$, as the definitions might change

to suit proof needs.

In the rest of the chapter we will use this convention consistently.

\begin{figure}[H]

\begin{lstlisting}

  def distinctWith(rs: List[ARexp], 

    pruneFunction: (ARexp, Set[Rexp]) => ARexp, 

    acc: Set[Rexp] = Set()) : List[ARexp] = 

      rs match{

        case Nil => Nil

        case r :: rs => 

          if(acc(erase(r)))

            distinctWith(rs, pruneFunction, acc)

          else {

            val pruned_r = pruneFunction(r, acc)

            pruned_r :: 

            distinctWith(rs, 

              pruneFunction, 

              turnIntoTerms(erase(pruned_r)) ++: acc

              )

          }

      }

\end{lstlisting}

\caption{A Stronger Version of $\textit{distinctBy}$}

\end{figure}

\noindent

The function $\textit{prune}$ is used in $\distinctWith$.

$\distinctWith$ is a stronger version of $\distinctBy$

which not only removes duplicates as $\distinctBy$ would

do, but also uses the $\textit{pruneFunction}$

argument to prune away verbose components in a regular expression.\\

\begin{figure}[H]

\begin{lstlisting}

   //a stronger version of simp

    def bsimpStrong(r: ARexp): ARexp =

    {

      r match {

        case ASEQ(bs1, r1, r2) => (bsimpStrong(r1), bsimpStrong(r2)) match {

          //normal clauses same as simp

        case (AZERO, _) => AZERO

        case (_, AZERO) => AZERO

        case (AONE(bs2), r2s) => fuse(bs1 ++ bs2, r2s)

        //bs2 can be discarded

        case (r1s, AONE(bs2)) => fuse(bs1, r1s) //assert bs2 == Nil

        case (r1s, r2s) => ASEQ(bs1, r1s, r2s)

        }

        case AALTS(bs1, rs) => {

          //distinctBy(flat_res, erase)

          distinctWith(flats(rs.map(bsimpStrong(_))), prune) match {

            case Nil => AZERO

            case s :: Nil => fuse(bs1, s)

            case rs => AALTS(bs1, rs)

          }

        }

        //stars that can be treated as 1

        case ASTAR(bs, r0) if(atMostEmpty(erase(r0))) => AONE(bs)

        case r => r

      }

    }

\end{lstlisting}

\caption{The function $\bsimpStrong$ and $\bdersStrongs$}

\end{figure}

\noindent

$\distinctWith$, is in turn used in $\bsimpStrong$:

\begin{figure}[H]

\begin{lstlisting}

      //Conjecture: [| bdersStrong(s, r) |] = O([| r |]^3)

      def bdersStrong(s: List[Char], r: ARexp) : ARexp = s match {

        case Nil => r

        case c::s => bdersStrong(s, bsimpStrong(bder(c, r)))

      }

\end{lstlisting}

\caption{The function $\bsimpStrong$ and $\bdersStrongs$}

\end{figure}

\noindent

We conjecture that the above Scala function $\bdersStrongs$,

written $\bdersStrong{\_}{\_}$ as an infix notation, 

satisfies the following property:

\begin{conjecture}

	$\llbracket \bdersStrong{a}{s} \rrbracket = O(\llbracket a \rrbracket^3)$

\end{conjecture}

The stronger version of $\blexersimp$'s

code in Scala looks like: 

\begin{figure}[H]

\begin{lstlisting}

      def strongBlexer(r: Rexp, s: String) : Option[Val] = {

        Try(Some(decode(r, strong_blex_simp(internalise(r), s.toList)))).getOrElse(None)

      }

      def strong_blex_simp(r: ARexp, s: List[Char]) : Bits = s match {

        case Nil => {

          if (bnullable(r)) {

            mkepsBC(r)

          }

          else

            throw new Exception("Not matched")

        }

        case c::cs => {

          strong_blex_simp(strongBsimp(bder(c, r)), cs)

        }

      }

\end{lstlisting}

\end{figure}

\noindent

We would like to preserve the correctness like the one 

we had for $\blexersimp$:

\begin{conjecture}\label{cubicConjecture}

	$\blexerStrong \;r \; s = \blexer\; r\;s$

\end{conjecture}

\noindent

We introduce the new rule \ref{cubicRule}

and augment our proofs in chapter \ref{Bitcoded2}.

The idea is to maintain the properties like

$r \stackrel{*}{\rightsquigarrow} \textit{bsimp} \; r$ etc.

In the next section,

we will describe why we 

believe a cubic bound can be achieved.

We give an introduction to the

partial derivatives,

which was invented by Antimirov \cite{Antimirov95},

and then link it with the result of the function

$\bdersStrongs$.

\section{Antimirov's partial derivatives}

This suggests a link towrads "partial derivatives"

 introduced  The idea behind Antimirov's partial derivatives

is to do derivatives in a similar way as suggested by Brzozowski, 

but maintain a set of regular expressions instead of a single one:

%TODO: antimirov proposition 3.1, needs completion

 \begin{center}  

 \begin{tabular}{ccc}

 $\partial_x(a+b)$ & $=$ & $\partial_x(a) \cup \partial_x(b)$\\

$\partial_x(\ONE)$ & $=$ & $\phi$

\end{tabular}

\end{center}

Rather than joining the calculated derivatives $\partial_x a$ and $\partial_x b$ together

using the alternatives constructor, Antimirov cleverly chose to put them into

a set instead.  This breaks the terms in a derivative regular expression up, 

allowing us to understand what are the "atomic" components of it.

For example, To compute what regular expression $x^*(xx + y)^*$'s 

derivative against $x$ is made of, one can do a partial derivative

of it and get two singleton sets $\{x^* \cdot (xx + y)^*\}$ and $\{x \cdot (xx + y) ^* \}$

from $\partial_x(x^*) \cdot (xx + y) ^*$ and $\partial_x((xx + y)^*)$.

To get all the "atomic" components of a regular expression's possible derivatives,

there is a procedure Antimirov called $\textit{lf}$, short for "linear forms", that takes

whatever character is available at the head of the string inside the language of a

regular expression, and gives back the character and the derivative regular expression

as a pair (which he called "monomial"):

 \begin{center}  

 \begin{tabular}{ccc}

 $\lf(\ONE)$ & $=$ & $\phi$\\

$\lf(c)$ & $=$ & $\{(c, \ONE) \}$\\

 $\lf(a+b)$ & $=$ & $\lf(a) \cup \lf(b)$\\

 $\lf(r^*)$ & $=$ & $\lf(r) \bigodot \lf(r^*)$\\

\end{tabular}

\end{center}

%TODO: completion

There is a slight difference in the last three clauses compared

with $\partial$: instead of a dot operator $ \textit{rset} \cdot r$ that attaches the regular 

expression $r$ with every element inside $\textit{rset}$ to create a set of 

sequence derivatives, it uses the "circle dot" operator $\bigodot$ which operates 

on a set of monomials (which Antimirov called "linear form") and a regular 

expression, and returns a linear form:

 \begin{center}  

 \begin{tabular}{ccc}

 $l \bigodot (\ZERO)$ & $=$ & $\phi$\\

 $l \bigodot (\ONE)$ & $=$ & $l$\\

 $\phi \bigodot t$ & $=$ & $\phi$\\

 $\{ (x, \ZERO) \} \bigodot t$ & $=$ & $\{(x,\ZERO) \}$\\

 $\{ (x, \ONE) \} \bigodot t$ & $=$ & $\{(x,t) \}$\\

  $\{ (x, p) \} \bigodot t$ & $=$ & $\{(x,p\cdot t) \}$\\

 $\lf(a+b)$ & $=$ & $\lf(a) \cup \lf(b)$\\

 $\lf(r^*)$ & $=$ & $\lf(r) \cdot \lf(r^*)$\\

\end{tabular}

\end{center}

%TODO: completion

 Some degree of simplification is applied when doing $\bigodot$, for example,

 $l \bigodot (\ZERO) = \phi$ corresponds to $r \cdot \ZERO \rightsquigarrow \ZERO$,

 and  $l \bigodot (\ONE) = l$ to $l \cdot \ONE \rightsquigarrow l$, and

  $\{ (x, \ZERO) \} \bigodot t = \{(x,\ZERO) \}$ to $\ZERO \cdot x \rightsquigarrow \ZERO$,

  and so on.

  With the function $\lf$ one can compute all possible partial derivatives $\partial_{UNIV}(r)$ of a regular expression $r$ with 

  an iterative procedure:

   \begin{center}  

 \begin{tabular}{llll}

$\textit{while}$ & $(\Delta_i \neq \phi)$  &                &          \\

 		       &  $\Delta_{i+1}$           &        $ =$ & $\lf(\Delta_i) - \PD_i$ \\

		       &  $\PD_{i+1}$              &         $ =$ & $\Delta_{i+1} \cup \PD_i$ \\

$\partial_{UNIV}(r)$ & $=$ & $\PD$ &		     

\end{tabular}

\end{center}