%% $Id$

\documentclass[12pt,a4paper]{article}

\usepackage{latexsym,../iman,../extra,../ttbox,../proof,../pdfsetup}

\newif\ifshort%''Short'' means a published version, not the documentation

\shortfalse%%%%%\shorttrue

\title{A Fixedpoint Approach to\\ 

  (Co)Inductive and (Co)Datatype Definitions%

  \thanks{J. Grundy and S. Thompson made detailed comments.  Mads Tofte and

    the referees were also helpful.  The research was funded by the SERC

    grants GR/G53279, GR/H40570 and by the ESPRIT Project 6453 ``Types''.}}

\author{Lawrence C. Paulson\\{\tt lcp@cl.cam.ac.uk}\\

        Computer Laboratory, University of Cambridge, England}

\date{\today} 

\setcounter{secnumdepth}{2} \setcounter{tocdepth}{2}

\newcommand\sbs{\subseteq}

\let\To=\Rightarrow

\newcommand\defn[1]{{\bf#1}}

\newcommand\pow{{\cal P}}

\newcommand\RepFun{\hbox{\tt RepFun}}

\newcommand\cons{\hbox{\tt cons}}

\def\succ{\hbox{\tt succ}}

\newcommand\split{\hbox{\tt split}}

\newcommand\fst{\hbox{\tt fst}}

\newcommand\snd{\hbox{\tt snd}}

\newcommand\converse{\hbox{\tt converse}}

\newcommand\domain{\hbox{\tt domain}}

\newcommand\range{\hbox{\tt range}}

\newcommand\field{\hbox{\tt field}}

\newcommand\lfp{\hbox{\tt lfp}}

\newcommand\gfp{\hbox{\tt gfp}}

\newcommand\id{\hbox{\tt id}}

\newcommand\trans{\hbox{\tt trans}}

\newcommand\wf{\hbox{\tt wf}}

\newcommand\nat{\hbox{\tt nat}}

\newcommand\rank{\hbox{\tt rank}}

\newcommand\univ{\hbox{\tt univ}}

\newcommand\Vrec{\hbox{\tt Vrec}}

\newcommand\Inl{\hbox{\tt Inl}}

\newcommand\Inr{\hbox{\tt Inr}}

\newcommand\case{\hbox{\tt case}}

\newcommand\lst{\hbox{\tt list}}

\newcommand\Nil{\hbox{\tt Nil}}

\newcommand\Cons{\hbox{\tt Cons}}

\newcommand\lstcase{\hbox{\tt list\_case}}

\newcommand\lstrec{\hbox{\tt list\_rec}}

\newcommand\length{\hbox{\tt length}}

\newcommand\listn{\hbox{\tt listn}}

\newcommand\acc{\hbox{\tt acc}}

\newcommand\primrec{\hbox{\tt primrec}}

\newcommand\SC{\hbox{\tt SC}}

\newcommand\CONST{\hbox{\tt CONST}}

\newcommand\PROJ{\hbox{\tt PROJ}}

\newcommand\COMP{\hbox{\tt COMP}}

\newcommand\PREC{\hbox{\tt PREC}}

\newcommand\quniv{\hbox{\tt quniv}}

\newcommand\llist{\hbox{\tt llist}}

\newcommand\LNil{\hbox{\tt LNil}}

\newcommand\LCons{\hbox{\tt LCons}}

\newcommand\lconst{\hbox{\tt lconst}}

\newcommand\lleq{\hbox{\tt lleq}}

\newcommand\map{\hbox{\tt map}}

\newcommand\term{\hbox{\tt term}}

\newcommand\Apply{\hbox{\tt Apply}}

\newcommand\termcase{\hbox{\tt term\_case}}

\newcommand\rev{\hbox{\tt rev}}

\newcommand\reflect{\hbox{\tt reflect}}

\newcommand\tree{\hbox{\tt tree}}

\newcommand\forest{\hbox{\tt forest}}

\newcommand\Part{\hbox{\tt Part}}

\newcommand\TF{\hbox{\tt tree\_forest}}

\newcommand\Tcons{\hbox{\tt Tcons}}

\newcommand\Fcons{\hbox{\tt Fcons}}

\newcommand\Fnil{\hbox{\tt Fnil}}

\newcommand\TFcase{\hbox{\tt TF\_case}}

\newcommand\Fin{\hbox{\tt Fin}}

\newcommand\QInl{\hbox{\tt QInl}}

\newcommand\QInr{\hbox{\tt QInr}}

\newcommand\qsplit{\hbox{\tt qsplit}}

\newcommand\qcase{\hbox{\tt qcase}}

\newcommand\Con{\hbox{\tt Con}}

\newcommand\data{\hbox{\tt data}}

\binperiod     %%%treat . like a binary operator

\begin{document}

\pagestyle{empty}

\begin{titlepage}

\maketitle 

\begin{abstract}

  This paper presents a fixedpoint approach to inductive definitions.

  Instead of using a syntactic test such as ``strictly positive,'' the

  approach lets definitions involve any operators that have been proved

  monotone.  It is conceptually simple, which has allowed the easy

  implementation of mutual recursion and iterated definitions.  It also

  handles coinductive definitions: simply replace the least fixedpoint by a

  greatest fixedpoint.  

  The method has been implemented in two of Isabelle's logics, \textsc{zf} set

  theory and higher-order logic.  It should be applicable to any logic in

  which the Knaster-Tarski theorem can be proved.  Examples include lists of

  $n$ elements, the accessible part of a relation and the set of primitive

  recursive functions.  One example of a coinductive definition is

  bisimulations for lazy lists.  Recursive datatypes are examined in detail,

  as well as one example of a \defn{codatatype}: lazy lists.

  The Isabelle package has been applied in several large case studies,

  including two proofs of the Church-Rosser theorem and a coinductive proof of

  semantic consistency.  The package can be trusted because it proves theorems

  from definitions, instead of asserting desired properties as axioms.

\end{abstract}

%

\bigskip

\centerline{Copyright \copyright{} \number\year{} by Lawrence C. Paulson}

\thispagestyle{empty} 

\end{titlepage}

\tableofcontents\cleardoublepage\pagestyle{plain}

\setcounter{page}{1}

\section{Introduction}

Several theorem provers provide commands for formalizing recursive data

structures, like lists and trees.  Robin Milner implemented one of the first

of these, for Edinburgh \textsc{lcf}~\cite{milner-ind}.  Given a description

of the desired data structure, Milner's package formulated appropriate

definitions and proved the characteristic theorems.  Similar is Melham's

recursive type package for the Cambridge \textsc{hol} system~\cite{melham89}.

Such data structures are called \defn{datatypes}

below, by analogy with datatype declarations in Standard~\textsc{ml}\@.

Some logics take datatypes as primitive; consider Boyer and Moore's shell

principle~\cite{bm79} and the Coq type theory~\cite{paulin-tlca}.

A datatype is but one example of an \defn{inductive definition}.  Such a

definition~\cite{aczel77} specifies the least set~$R$ \defn{closed under}

given rules: applying a rule to elements of~$R$ yields a result within~$R$.

Inductive definitions have many applications.  The collection of theorems in a

logic is inductively defined.  A structural operational

semantics~\cite{hennessy90} is an inductive definition of a reduction or

evaluation relation on programs.  A few theorem provers provide commands for

formalizing inductive definitions; these include Coq~\cite{paulin-tlca} and

again the \textsc{hol} system~\cite{camilleri92}.

The dual notion is that of a \defn{coinductive definition}.  Such a definition

specifies the greatest set~$R$ \defn{consistent with} given rules: every

element of~$R$ can be seen as arising by applying a rule to elements of~$R$.

Important examples include using bisimulation relations to formalize

equivalence of processes~\cite{milner89} or lazy functional

programs~\cite{abramsky90}.  Other examples include lazy lists and other

infinite data structures; these are called \defn{codatatypes} below.

Not all inductive definitions are meaningful.  \defn{Monotone} inductive

definitions are a large, well-behaved class.  Monotonicity can be enforced

by syntactic conditions such as ``strictly positive,'' but this could lead to

monotone definitions being rejected on the grounds of their syntactic form.

More flexible is to formalize monotonicity within the logic and allow users

to prove it.

This paper describes a package based on a fixedpoint approach.  Least

fixedpoints yield inductive definitions; greatest fixedpoints yield

coinductive definitions.  Most of the discussion below applies equally to

inductive and coinductive definitions, and most of the code is shared.  

The package supports mutual recursion and infinitely-branching datatypes and

codatatypes.  It allows use of any operators that have been proved monotone,

thus accepting all provably monotone inductive definitions, including

iterated definitions.

The package has been implemented in

Isabelle~\cite{paulson-markt,paulson-isa-book} using 

\textsc{zf} set theory \cite{paulson-set-I,paulson-set-II}; part of it has

since been ported to Isabelle/\textsc{hol} (higher-order logic).  The

recursion equations are specified as introduction rules for the mutually

recursive sets.  The package transforms these rules into a mapping over sets,

and attempts to prove that the mapping is monotonic and well-typed.  If

successful, the package makes fixedpoint definitions and proves the

introduction, elimination and (co)induction rules.  Users invoke the package

by making simple declarations in Isabelle theory files.

Most datatype packages equip the new datatype with some means of expressing

recursive functions.  This is the main omission from my package.  Its

fixedpoint operators define only recursive sets.  The Isabelle/\textsc{zf}

theory provides well-founded recursion~\cite{paulson-set-II}, which is harder

to use than structural recursion but considerably more general.

Slind~\cite{slind-tfl} has written a package to automate the definition of

well-founded recursive functions in Isabelle/\textsc{hol}.

\paragraph*{Outline.} Section~2 introduces the least and greatest fixedpoint

operators.  Section~3 discusses the form of introduction rules, mutual

recursion and other points common to inductive and coinductive definitions.

Section~4 discusses induction and coinduction rules separately.  Section~5

presents several examples, including a coinductive definition.  Section~6

describes datatype definitions.  Section~7 presents related work.

Section~8 draws brief conclusions.  \ifshort\else The appendices are simple

user's manuals for this Isabelle package.\fi

Most of the definitions and theorems shown below have been generated by the

package.  I have renamed some variables to improve readability.

\section{Fixedpoint operators}

In set theory, the least and greatest fixedpoint operators are defined as

follows:

\begin{eqnarray*}

   \lfp(D,h)  & \equiv & \inter\{X\sbs D. h(X)\sbs X\} \\

   \gfp(D,h)  & \equiv & \union\{X\sbs D. X\sbs h(X)\}

\end{eqnarray*}   

Let $D$ be a set.  Say that $h$ is \defn{bounded by}~$D$ if $h(D)\sbs D$, and

\defn{monotone below~$D$} if

$h(A)\sbs h(B)$ for all $A$ and $B$ such that $A\sbs B\sbs D$.  If $h$ is

bounded by~$D$ and monotone then both operators yield fixedpoints:

\begin{eqnarray*}

   \lfp(D,h)  & = & h(\lfp(D,h)) \\

   \gfp(D,h)  & = & h(\gfp(D,h)) 

\end{eqnarray*}   

These equations are instances of the Knaster-Tarski theorem, which states

that every monotonic function over a complete lattice has a

fixedpoint~\cite{davey-priestley}.  It is obvious from their definitions

that $\lfp$ must be the least fixedpoint, and $\gfp$ the greatest.

This fixedpoint theory is simple.  The Knaster-Tarski theorem is easy to

prove.  Showing monotonicity of~$h$ is trivial, in typical cases.  We must

also exhibit a bounding set~$D$ for~$h$.  Frequently this is trivial, as when

a set of theorems is (co)inductively defined over some previously existing set

of formul{\ae}.  Isabelle/\textsc{zf} provides suitable bounding sets for

infinitely-branching (co)datatype definitions; see~\S\ref{univ-sec}.  Bounding

sets are also called \defn{domains}.

The powerset operator is monotone, but by Cantor's theorem there is no

set~$A$ such that $A=\pow(A)$.  We cannot put $A=\lfp(D,\pow)$ because

there is no suitable domain~$D$.  But \S\ref{acc-sec} demonstrates

that~$\pow$ is still useful in inductive definitions.

\section{Elements of an inductive or coinductive definition}\label{basic-sec}

Consider a (co)inductive definition of the sets $R_1$, \ldots,~$R_n$, in

mutual recursion.  They will be constructed from domains $D_1$,

\ldots,~$D_n$, respectively.  The construction yields not $R_i\sbs D_i$ but

$R_i\sbs D_1+\cdots+D_n$, where $R_i$ is contained in the image of~$D_i$

under an injection.  Reasons for this are discussed

elsewhere~\cite[\S4.5]{paulson-set-II}.

The definition may involve arbitrary parameters $\vec{p}=p_1$,

\ldots,~$p_k$.  Each recursive set then has the form $R_i(\vec{p})$.  The

parameters must be identical every time they occur within a definition.  This

would appear to be a serious restriction compared with other systems such as

Coq~\cite{paulin-tlca}.  For instance, we cannot define the lists of

$n$ elements as the set $\listn(A,n)$ using rules where the parameter~$n$

varies.  Section~\ref{listn-sec} describes how to express this set using the

inductive definition package.

To avoid clutter below, the recursive sets are shown as simply $R_i$

instead of~$R_i(\vec{p})$.

\subsection{The form of the introduction rules}\label{intro-sec}

The body of the definition consists of the desired introduction rules.  The

conclusion of each rule must have the form $t\in R_i$, where $t$ is any term.

Premises typically have the same form, but they can have the more general form

$t\in M(R_i)$ or express arbitrary side-conditions.

The premise $t\in M(R_i)$ is permitted if $M$ is a monotonic operator on

sets, satisfying the rule 

\[ \infer{M(A)\sbs M(B)}{A\sbs B} \]

The user must supply the package with monotonicity rules for all such premises.

The ability to introduce new monotone operators makes the approach

flexible.  A suitable choice of~$M$ and~$t$ can express a lot.  The

powerset operator $\pow$ is monotone, and the premise $t\in\pow(R)$

expresses $t\sbs R$; see \S\ref{acc-sec} for an example.  The \emph{list of}

operator is monotone, as is easily proved by induction.  The premise

$t\in\lst(R)$ avoids having to encode the effect of~$\lst(R)$ using mutual

recursion; see \S\ref{primrec-sec} and also my earlier

paper~\cite[\S4.4]{paulson-set-II}.

Introduction rules may also contain \defn{side-conditions}.  These are

premises consisting of arbitrary formul{\ae} not mentioning the recursive

sets. Side-conditions typically involve type-checking.  One example is the

premise $a\in A$ in the following rule from the definition of lists:

\[ \infer{\Cons(a,l)\in\lst(A)}{a\in A & l\in\lst(A)} \]

\subsection{The fixedpoint definitions}

The package translates the list of desired introduction rules into a fixedpoint

definition.  Consider, as a running example, the finite powerset operator

$\Fin(A)$: the set of all finite subsets of~$A$.  It can be

defined as the least set closed under the rules

\[  \emptyset\in\Fin(A)  \qquad 

    \infer{\{a\}\un b\in\Fin(A)}{a\in A & b\in\Fin(A)} 

\]

The domain in a (co)inductive definition must be some existing set closed

under the rules.  A suitable domain for $\Fin(A)$ is $\pow(A)$, the set of all

subsets of~$A$.  The package generates the definition

\[  \Fin(A) \equiv \lfp(\pow(A), \,

  \begin{array}[t]{r@{\,}l}

      \lambda X. \{z\in\pow(A). & z=\emptyset \disj{} \\

                  &(\exists a\,b. z=\{a\}\un b\conj a\in A\conj b\in X)\})

  \end{array}

\]

The contribution of each rule to the definition of $\Fin(A)$ should be

obvious.  A coinductive definition is similar but uses $\gfp$ instead

of~$\lfp$.

The package must prove that the fixedpoint operator is applied to a

monotonic function.  If the introduction rules have the form described

above, and if the package is supplied a monotonicity theorem for every

$t\in M(R_i)$ premise, then this proof is trivial.\footnote{Due to the

  presence of logical connectives in the fixedpoint's body, the

  monotonicity proof requires some unusual rules.  These state that the

  connectives $\conj$, $\disj$ and $\exists$ preserve monotonicity with respect

  to the partial ordering on unary predicates given by $P\sqsubseteq Q$ if and

  only if $\forall x.P(x)\imp Q(x)$.}

The package returns its result as an \textsc{ml} structure, which consists of named

components; we may regard it as a record.  The result structure contains

the definitions of the recursive sets as a theorem list called {\tt defs}.

It also contains some theorems; {\tt dom\_subset} is an inclusion such as 

$\Fin(A)\sbs\pow(A)$, while {\tt bnd\_mono} asserts that the fixedpoint

definition is monotonic.

Internally the package uses the theorem {\tt unfold}, a fixedpoint equation

such as

\[

  \begin{array}[t]{r@{\,}l}

     \Fin(A) = \{z\in\pow(A). & z=\emptyset \disj{} \\

             &(\exists a\,b. z=\{a\}\un b\conj a\in A\conj b\in \Fin(A))\}

  \end{array}

\]

In order to save space, this theorem is not exported.  

\subsection{Mutual recursion} \label{mutual-sec}

In a mutually recursive definition, the domain of the fixedpoint construction

is the disjoint sum of the domain~$D_i$ of each~$R_i$, for $i=1$,

\ldots,~$n$.  The package uses the injections of the

binary disjoint sum, typically $\Inl$ and~$\Inr$, to express injections

$h_{1n}$, \ldots, $h_{nn}$ for the $n$-ary disjoint sum $D_1+\cdots+D_n$.

As discussed elsewhere \cite[\S4.5]{paulson-set-II}, Isabelle/\textsc{zf} defines the

operator $\Part$ to support mutual recursion.  The set $\Part(A,h)$

contains those elements of~$A$ having the form~$h(z)$:

\[ \Part(A,h)  \equiv \{x\in A. \exists z. x=h(z)\}. \]   

For mutually recursive sets $R_1$, \ldots,~$R_n$ with

$n>1$, the package makes $n+1$ definitions.  The first defines a set $R$ using

a fixedpoint operator. The remaining $n$ definitions have the form

\[ R_i \equiv \Part(R,h_{in}), \qquad i=1,\ldots, n.  \] 

It follows that $R=R_1\un\cdots\un R_n$, where the $R_i$ are pairwise disjoint.

\subsection{Proving the introduction rules}

The user supplies the package with the desired form of the introduction

rules.  Once it has derived the theorem {\tt unfold}, it attempts

to prove those rules.  From the user's point of view, this is the

trickiest stage; the proofs often fail.  The task is to show that the domain 

$D_1+\cdots+D_n$ of the combined set $R_1\un\cdots\un R_n$ is

closed under all the introduction rules.  This essentially involves replacing

each~$R_i$ by $D_1+\cdots+D_n$ in each of the introduction rules and

attempting to prove the result.

Consider the $\Fin(A)$ example.  After substituting $\pow(A)$ for $\Fin(A)$

in the rules, the package must prove

\[  \emptyset\in\pow(A)  \qquad 

    \infer{\{a\}\un b\in\pow(A)}{a\in A & b\in\pow(A)} 

\]

Such proofs can be regarded as type-checking the definition.\footnote{The

  Isabelle/\textsc{hol} version does not require these proofs, as \textsc{hol}

  has implicit type-checking.} The user supplies the package with

type-checking rules to apply.  Usually these are general purpose rules from

the \textsc{zf} theory.  They could however be rules specifically proved for a

particular inductive definition; sometimes this is the easiest way to get the

definition through!

The result structure contains the introduction rules as the theorem list {\tt

intrs}.

\subsection{The case analysis rule}

The elimination rule, called {\tt elim}, performs case analysis.  It is a

simple consequence of {\tt unfold}.  There is one case for each introduction

rule.  If $x\in\Fin(A)$ then either $x=\emptyset$ or else $x=\{a\}\un b$ for

some $a\in A$ and $b\in\Fin(A)$.  Formally, the elimination rule for $\Fin(A)$

is written

\[ \infer{Q}{x\in\Fin(A) & \infer*{Q}{[x=\emptyset]}

                 & \infer*{Q}{[x=\{a\}\un b & a\in A &b\in\Fin(A)]_{a,b}} }

\]

The subscripted variables $a$ and~$b$ above the third premise are

eigenvariables, subject to the usual ``not free in \ldots'' proviso.

\section{Induction and coinduction rules}

Here we must consider inductive and coinductive definitions separately.  For

an inductive definition, the package returns an induction rule derived

directly from the properties of least fixedpoints, as well as a modified rule

for mutual recursion.  For a coinductive definition, the package returns a

basic coinduction rule.

\subsection{The basic induction rule}\label{basic-ind-sec}

The basic rule, called {\tt induct}, is appropriate in most situations.

For inductive definitions, it is strong rule induction~\cite{camilleri92}; for

datatype definitions (see below), it is just structural induction.  

The induction rule for an inductively defined set~$R$ has the form described

below.  For the time being, assume that $R$'s domain is not a Cartesian

product; inductively defined relations are treated slightly differently.

The major premise is $x\in R$.  There is a minor premise for each

introduction rule:

\begin{itemize}

\item If the introduction rule concludes $t\in R_i$, then the minor premise

is~$P(t)$.

\item The minor premise's eigenvariables are precisely the introduction

rule's free variables that are not parameters of~$R$.  For instance, the

eigenvariables in the $\Fin(A)$ rule below are $a$ and $b$, but not~$A$.

\item If the introduction rule has a premise $t\in R_i$, then the minor

premise discharges the assumption $t\in R_i$ and the induction

hypothesis~$P(t)$.  If the introduction rule has a premise $t\in M(R_i)$

then the minor premise discharges the single assumption

\[ t\in M(\{z\in R_i. P(z)\}). \] 

Because $M$ is monotonic, this assumption implies $t\in M(R_i)$.  The

occurrence of $P$ gives the effect of an induction hypothesis, which may be

exploited by appealing to properties of~$M$.

\end{itemize}

The induction rule for $\Fin(A)$ resembles the elimination rule shown above,

but includes an induction hypothesis:

\[ \infer{P(x)}{x\in\Fin(A) & P(\emptyset)

        & \infer*{P(\{a\}\un b)}{[a\in A & b\in\Fin(A) & P(b)]_{a,b}} }

\] 

Stronger induction rules often suggest themselves.  We can derive a rule for

$\Fin(A)$ whose third premise discharges the extra assumption $a\not\in b$.

The package provides rules for mutual induction and inductive relations.  The

Isabelle/\textsc{zf} theory also supports well-founded induction and recursion

over datatypes, by reasoning about the \defn{rank} of a

set~\cite[\S3.4]{paulson-set-II}.

\subsection{Modified induction rules}

If the domain of $R$ is a Cartesian product $A_1\times\cdots\times A_m$

(however nested), then the corresponding predicate $P_i$ takes $m$ arguments.

The major premise becomes $\pair{z_1,\ldots,z_m}\in R$ instead of $x\in R$;

the conclusion becomes $P(z_1,\ldots,z_m)$.  This simplifies reasoning about

inductively defined relations, eliminating the need to express properties of

$z_1$, \ldots,~$z_m$ as properties of the tuple $\pair{z_1,\ldots,z_m}$.

Occasionally it may require you to split up the induction variable

using {\tt SigmaE} and {\tt dom\_subset}, especially if the constant {\tt

  split} appears in the rule.

The mutual induction rule is called {\tt

mutual\_induct}.  It differs from the basic rule in two respects:

\begin{itemize}

\item Instead of a single predicate~$P$, it uses $n$ predicates $P_1$,

\ldots,~$P_n$: one for each recursive set.

\item There is no major premise such as $x\in R_i$.  Instead, the conclusion

refers to all the recursive sets:

\[ (\forall z.z\in R_1\imp P_1(z))\conj\cdots\conj

   (\forall z.z\in R_n\imp P_n(z))

\]

Proving the premises establishes $P_i(z)$ for $z\in R_i$ and $i=1$,

\ldots,~$n$.

\end{itemize}

%

If the domain of some $R_i$ is a Cartesian product, then the mutual induction

rule is modified accordingly.  The predicates are made to take $m$ separate

arguments instead of a tuple, and the quantification in the conclusion is over

the separate variables $z_1$, \ldots, $z_m$.

\subsection{Coinduction}\label{coind-sec}

A coinductive definition yields a primitive coinduction rule, with no

refinements such as those for the induction rules.  (Experience may suggest

refinements later.)  Consider the codatatype of lazy lists as an example.  For

suitable definitions of $\LNil$ and $\LCons$, lazy lists may be defined as the

greatest set consistent with the rules

\[  \LNil\in\llist(A)  \qquad 

    \infer[(-)]{\LCons(a,l)\in\llist(A)}{a\in A & l\in\llist(A)}

\]

The $(-)$ tag stresses that this is a coinductive definition.  A suitable

domain for $\llist(A)$ is $\quniv(A)$; this set is closed under the variant

forms of sum and product that are used to represent non-well-founded data

structures (see~\S\ref{univ-sec}).

The package derives an {\tt unfold} theorem similar to that for $\Fin(A)$. 

Then it proves the theorem {\tt coinduct}, which expresses that $\llist(A)$

is the greatest solution to this equation contained in $\quniv(A)$:

\[ \infer{x\in\llist(A)}{x\in X & X\sbs \quniv(A) &

    \infer*{

     \begin{array}[b]{r@{}l}

       z=\LNil\disj 

       \bigl(\exists a\,l.\, & z=\LCons(a,l) \conj a\in A \conj{}\\

                             & l\in X\un\llist(A) \bigr)

     \end{array}  }{[z\in X]_z}}

\]

This rule complements the introduction rules; it provides a means of showing

$x\in\llist(A)$ when $x$ is infinite.  For instance, if $x=\LCons(0,x)$ then

applying the rule with $X=\{x\}$ proves $x\in\llist(\nat)$.  (Here $\nat$

is the set of natural numbers.)

Having $X\un\llist(A)$ instead of simply $X$ in the third premise above

represents a slight strengthening of the greatest fixedpoint property.  I

discuss several forms of coinduction rules elsewhere~\cite{paulson-coind}.

The clumsy form of the third premise makes the rule hard to use, especially in

large definitions.  Probably a constant should be declared to abbreviate the

large disjunction, and rules derived to allow proving the separate disjuncts.

\section{Examples of inductive and coinductive definitions}\label{ind-eg-sec}

This section presents several examples from the literature: the finite

powerset operator, lists of $n$ elements, bisimulations on lazy lists, the

well-founded part of a relation, and the primitive recursive functions.

\subsection{The finite powerset operator}

This operator has been discussed extensively above.  Here is the

corresponding invocation in an Isabelle theory file.  Note that

$\cons(a,b)$ abbreviates $\{a\}\un b$ in Isabelle/\textsc{zf}.

\begin{ttbox}

Finite = Arith + 

consts      Fin :: i=>i

inductive

  domains   "Fin(A)" <= "Pow(A)"

  intrs

    emptyI  "0 : Fin(A)"

    consI   "[| a: A;  b: Fin(A) |] ==> cons(a,b) : Fin(A)"

  type_intrs  empty_subsetI, cons_subsetI, PowI

  type_elims "[make_elim PowD]"

end

\end{ttbox}

Theory {\tt Finite} extends the parent theory {\tt Arith} by declaring the

unary function symbol~$\Fin$, which is defined inductively.  Its domain is

specified as $\pow(A)$, where $A$ is the parameter appearing in the

introduction rules.  For type-checking, we supply two introduction

rules:

\[ \emptyset\sbs A              \qquad

   \infer{\{a\}\un B\sbs C}{a\in C & B\sbs C}

\]

A further introduction rule and an elimination rule express both

directions of the equivalence $A\in\pow(B)\bimp A\sbs B$.  Type-checking

involves mostly introduction rules.  

Like all Isabelle theory files, this one yields a structure containing the

new theory as an \textsc{ml} value.  Structure {\tt Finite} also has a

substructure, called~{\tt Fin}.  After declaring \hbox{\tt open Finite;} we

can refer to the $\Fin(A)$ introduction rules as the list {\tt Fin.intrs}

or individually as {\tt Fin.emptyI} and {\tt Fin.consI}.  The induction

rule is {\tt Fin.induct}.

\subsection{Lists of $n$ elements}\label{listn-sec}

This has become a standard example of an inductive definition.  Following

Paulin-Mohring~\cite{paulin-tlca}, we could attempt to define a new datatype

$\listn(A,n)$, for lists of length~$n$, as an $n$-indexed family of sets.

But her introduction rules

\[ \hbox{\tt Niln}\in\listn(A,0)  \qquad

   \infer{\hbox{\tt Consn}(n,a,l)\in\listn(A,\succ(n))}

         {n\in\nat & a\in A & l\in\listn(A,n)}

\]

are not acceptable to the inductive definition package:

$\listn$ occurs with three different parameter lists in the definition.

The Isabelle version of this example suggests a general treatment of

varying parameters.  It uses the existing datatype definition of

$\lst(A)$, with constructors $\Nil$ and~$\Cons$, and incorporates the

parameter~$n$ into the inductive set itself.  It defines $\listn(A)$ as a

relation consisting of pairs $\pair{n,l}$ such that $n\in\nat$

and~$l\in\lst(A)$ and $l$ has length~$n$.  In fact, $\listn(A)$ is the

converse of the length function on~$\lst(A)$.  The Isabelle/\textsc{zf} introduction

rules are

\[ \pair{0,\Nil}\in\listn(A)  \qquad

   \infer{\pair{\succ(n),\Cons(a,l)}\in\listn(A)}

         {a\in A & \pair{n,l}\in\listn(A)}

\]

The Isabelle theory file takes, as parent, the theory~{\tt List} of lists.

We declare the constant~$\listn$ and supply an inductive definition,

specifying the domain as $\nat\times\lst(A)$:

\begin{ttbox}

ListN = List +

consts  listn :: i=>i

inductive

  domains   "listn(A)" <= "nat*list(A)"

  intrs

    NilI  "<0,Nil>: listn(A)"

    ConsI "[| a:A; <n,l>:listn(A) |] ==> <succ(n), Cons(a,l)>: listn(A)"

  type_intrs "nat_typechecks @ list.intrs"

end

\end{ttbox}

The type-checking rules include those for 0, $\succ$, $\Nil$ and $\Cons$.

Because $\listn(A)$ is a set of pairs, type-checking requires the

equivalence $\pair{a,b}\in A\times B \bimp a\in A \conj b\in B$.  The

package always includes the rules for ordered pairs.

The package returns introduction, elimination and induction rules for

$\listn$.  The basic induction rule, {\tt listn.induct}, is

\[ \infer{P(z_1,z_2)}{\pair{z_1,z_2}\in\listn(A) & P(0,\Nil) &

             \infer*{P(\succ(n),\Cons(a,l))}

                {[a\in A & \pair{n,l}\in\listn(A) & P(n,l)]_{a,l,n}}}

\]

This rule lets the induction formula to be a 

binary property of pairs, $P(n,l)$.  

It is now a simple matter to prove theorems about $\listn(A)$, such as

\[ \forall l\in\lst(A). \pair{\length(l),\, l}\in\listn(A) \]

\[ \listn(A)``\{n\} = \{l\in\lst(A). \length(l)=n\} \]

This latter result --- here $r``X$ denotes the image of $X$ under $r$

--- asserts that the inductive definition agrees with the obvious notion of

$n$-element list.  

A ``list of $n$ elements'' really is a list, namely an element of ~$\lst(A)$.

It is subject to list operators such as append (concatenation).  For example,

a trivial induction on $\pair{m,l}\in\listn(A)$ yields

\[ \infer{\pair{m\mathbin{+} m',\, l@l'}\in\listn(A)}

         {\pair{m,l}\in\listn(A) & \pair{m',l'}\in\listn(A)} 

\]

where $+$ denotes addition on the natural numbers and @ denotes append.

\subsection{Rule inversion: the function \texttt{mk\_cases}}

The elimination rule, {\tt listn.elim}, is cumbersome:

\[ \infer{Q}{x\in\listn(A) & 

          \infer*{Q}{[x = \pair{0,\Nil}]} &

          \infer*{Q}

             {\left[\begin{array}{l}

               x = \pair{\succ(n),\Cons(a,l)} \\

               a\in A \\

               \pair{n,l}\in\listn(A)

               \end{array} \right]_{a,l,n}}}

\]

The \textsc{ml} function {\tt listn.mk\_cases} generates simplified instances of

this rule.  It works by freeness reasoning on the list constructors:

$\Cons(a,l)$ is injective in its two arguments and differs from~$\Nil$.  If

$x$ is $\pair{i,\Nil}$ or $\pair{i,\Cons(a,l)}$ then {\tt listn.mk\_cases}

deduces the corresponding form of~$i$;  this is called rule inversion.  

Here is a sample session: 

\begin{ttbox}

listn.mk_cases "<i,Nil> : listn(A)";

{\out "[| <?i, []> : listn(?A); ?i = 0 ==> ?Q |] ==> ?Q" : thm}

listn.mk_cases "<i,Cons(a,l)> : listn(A)";

{\out "[| <?i, Cons(?a, ?l)> : listn(?A);}

{\out     !!n. [| ?a : ?A; <n, ?l> : listn(?A); ?i = succ(n) |] ==> ?Q }

{\out  |] ==> ?Q" : thm}

\end{ttbox}

Each of these rules has only two premises.  In conventional notation, the

second rule is

\[ \infer{Q}{\pair{i, \Cons(a,l)}\in\listn(A) & 

          \infer*{Q}

             {\left[\begin{array}{l}

               a\in A \\ \pair{n,l}\in\listn(A) \\ i = \succ(n)

               \end{array} \right]_{n}}}

\]

The package also has built-in rules for freeness reasoning about $0$

and~$\succ$.  So if $x$ is $\pair{0,l}$ or $\pair{\succ(i),l}$, then {\tt

listn.mk\_cases} can deduce the corresponding form of~$l$. 

The function {\tt mk\_cases} is also useful with datatype definitions.  The

instance from the definition of lists, namely {\tt list.mk\_cases}, can

prove that $\Cons(a,l)\in\lst(A)$ implies $a\in A $ and $l\in\lst(A)$:

\[ \infer{Q}{\Cons(a,l)\in\lst(A) & 

                 & \infer*{Q}{[a\in A &l\in\lst(A)]} }

\]

A typical use of {\tt mk\_cases} concerns inductive definitions of evaluation

relations.  Then rule inversion yields case analysis on possible evaluations.

For example, Isabelle/\textsc{zf} includes a short proof of the

diamond property for parallel contraction on combinators.  Ole Rasmussen used

{\tt mk\_cases} extensively in his development of the theory of

residuals~\cite{rasmussen95}.

\subsection{A coinductive definition: bisimulations on lazy lists}

This example anticipates the definition of the codatatype $\llist(A)$, which

consists of finite and infinite lists over~$A$.  Its constructors are $\LNil$

and~$\LCons$, satisfying the introduction rules shown in~\S\ref{coind-sec}.  

Because $\llist(A)$ is defined as a greatest fixedpoint and uses the variant

pairing and injection operators, it contains non-well-founded elements such as

solutions to $\LCons(a,l)=l$.

The next step in the development of lazy lists is to define a coinduction

principle for proving equalities.  This is done by showing that the equality

relation on lazy lists is the greatest fixedpoint of some monotonic

operation.  The usual approach~\cite{pitts94} is to define some notion of 

bisimulation for lazy lists, define equivalence to be the greatest

bisimulation, and finally to prove that two lazy lists are equivalent if and

only if they are equal.  The coinduction rule for equivalence then yields a

coinduction principle for equalities.

A binary relation $R$ on lazy lists is a \defn{bisimulation} provided $R\sbs

R^+$, where $R^+$ is the relation

\[ \{\pair{\LNil,\LNil}\} \un 

   \{\pair{\LCons(a,l),\LCons(a,l')} . a\in A \conj \pair{l,l'}\in R\}.

\]

A pair of lazy lists are \defn{equivalent} if they belong to some

bisimulation.  Equivalence can be coinductively defined as the greatest

fixedpoint for the introduction rules

\[  \pair{\LNil,\LNil} \in\lleq(A)  \qquad 

    \infer[(-)]{\pair{\LCons(a,l),\LCons(a,l')} \in\lleq(A)}

          {a\in A & \pair{l,l'}\in \lleq(A)}

\]

To make this coinductive definition, the theory file includes (after the

declaration of $\llist(A)$) the following lines:

\bgroup\leftmargini=\parindent

\begin{ttbox}

consts    lleq :: i=>i

coinductive

  domains "lleq(A)" <= "llist(A) * llist(A)"

  intrs

    LNil  "<LNil,LNil> : lleq(A)"

    LCons "[| a:A; <l,l'>:lleq(A) |] ==> <LCons(a,l),LCons(a,l')>: lleq(A)"

  type_intrs  "llist.intrs"

\end{ttbox}

\egroup

The domain of $\lleq(A)$ is $\llist(A)\times\llist(A)$.  The type-checking

rules include the introduction rules for $\llist(A)$, whose 

declaration is discussed below (\S\ref{lists-sec}).

The package returns the introduction rules and the elimination rule, as

usual.  But instead of induction rules, it returns a coinduction rule.

The rule is too big to display in the usual notation; its conclusion is

$x\in\lleq(A)$ and its premises are $x\in X$, 

${X\sbs\llist(A)\times\llist(A)}$ and

\[ \infer*{z=\pair{\LNil,\LNil}\disj \bigl(\exists a\,l\,l'.\,

     \begin{array}[t]{@{}l}

       z=\pair{\LCons(a,l),\LCons(a,l')} \conj a\in A \conj{}\\

       \pair{l,l'}\in X\un\lleq(A) \bigr)

     \end{array}  

    }{[z\in X]_z}

\]

Thus if $x\in X$, where $X$ is a bisimulation contained in the

domain of $\lleq(A)$, then $x\in\lleq(A)$.  It is easy to show that

$\lleq(A)$ is reflexive: the equality relation is a bisimulation.  And

$\lleq(A)$ is symmetric: its converse is a bisimulation.  But showing that

$\lleq(A)$ coincides with the equality relation takes some work.

\subsection{The accessible part of a relation}\label{acc-sec}

Let $\prec$ be a binary relation on~$D$; in short, $(\prec)\sbs D\times D$.

The \defn{accessible} or \defn{well-founded} part of~$\prec$, written

$\acc(\prec)$, is essentially that subset of~$D$ for which $\prec$ admits

no infinite decreasing chains~\cite{aczel77}.  Formally, $\acc(\prec)$ is

inductively defined to be the least set that contains $a$ if it contains

all $\prec$-predecessors of~$a$, for $a\in D$.  Thus we need an

introduction rule of the form 

\[ \infer{a\in\acc(\prec)}{\forall y.y\prec a\imp y\in\acc(\prec)} \]

Paulin-Mohring treats this example in Coq~\cite{paulin-tlca}, but it causes

difficulties for other systems.  Its premise is not acceptable to the

inductive definition package of the Cambridge \textsc{hol}

system~\cite{camilleri92}.  It is also unacceptable to the Isabelle package

(recall \S\ref{intro-sec}), but fortunately can be transformed into the

acceptable form $t\in M(R)$.

The powerset operator is monotonic, and $t\in\pow(R)$ is equivalent to

$t\sbs R$.  This in turn is equivalent to $\forall y\in t. y\in R$.  To

express $\forall y.y\prec a\imp y\in\acc(\prec)$ we need only find a

term~$t$ such that $y\in t$ if and only if $y\prec a$.  A suitable $t$ is

the inverse image of~$\{a\}$ under~$\prec$.

The definition below follows this approach.  Here $r$ is~$\prec$ and

$\field(r)$ refers to~$D$, the domain of $\acc(r)$.  (The field of a

relation is the union of its domain and range.)  Finally $r^{-}``\{a\}$

denotes the inverse image of~$\{a\}$ under~$r$.  We supply the theorem {\tt

  Pow\_mono}, which asserts that $\pow$ is monotonic.

\begin{ttbox}

consts    acc :: i=>i

inductive

  domains "acc(r)" <= "field(r)"

  intrs

    vimage  "[| r-``\{a\}: Pow(acc(r)); a: field(r) |] ==> a: acc(r)"

  monos      Pow_mono

\end{ttbox}

The Isabelle theory proceeds to prove facts about $\acc(\prec)$.  For

instance, $\prec$ is well-founded if and only if its field is contained in

$\acc(\prec)$.  

As mentioned in~\S\ref{basic-ind-sec}, a premise of the form $t\in M(R)$

gives rise to an unusual induction hypothesis.  Let us examine the

induction rule, {\tt acc.induct}:

\[ \infer{P(x)}{x\in\acc(r) &

     \infer*{P(a)}{\left[

                   \begin{array}{r@{}l}

                     r^{-}``\{a\} &\, \in\pow(\{z\in\acc(r).P(z)\}) \\

                                a &\, \in\field(r)

                   \end{array}

                   \right]_a}}

\]

The strange induction hypothesis is equivalent to

$\forall y. \pair{y,a}\in r\imp y\in\acc(r)\conj P(y)$.

Therefore the rule expresses well-founded induction on the accessible part

of~$\prec$.

The use of inverse image is not essential.  The Isabelle package can accept

introduction rules with arbitrary premises of the form $\forall

\vec{y}.P(\vec{y})\imp f(\vec{y})\in R$.  The premise can be expressed

equivalently as 

\[ \{z\in D. P(\vec{y}) \conj z=f(\vec{y})\} \in \pow(R) \] 

provided $f(\vec{y})\in D$ for all $\vec{y}$ such that~$P(\vec{y})$.  The

following section demonstrates another use of the premise $t\in M(R)$,

where $M=\lst$. 

\subsection{The primitive recursive functions}\label{primrec-sec}

The primitive recursive functions are traditionally defined inductively, as

a subset of the functions over the natural numbers.  One difficulty is that

functions of all arities are taken together, but this is easily

circumvented by regarding them as functions on lists.  Another difficulty,

the notion of composition, is less easily circumvented.

Here is a more precise definition.  Letting $\vec{x}$ abbreviate

$x_0,\ldots,x_{n-1}$, we can write lists such as $[\vec{x}]$,

$[y+1,\vec{x}]$, etc.  A function is \defn{primitive recursive} if it

belongs to the least set of functions in $\lst(\nat)\to\nat$ containing

\begin{itemize}

\item The \defn{successor} function $\SC$, such that $\SC[y,\vec{x}]=y+1$.

\item All \defn{constant} functions $\CONST(k)$, such that

  $\CONST(k)[\vec{x}]=k$. 

\item All \defn{projection} functions $\PROJ(i)$, such that

  $\PROJ(i)[\vec{x}]=x_i$ if $0\leq i<n$. 

\item All \defn{compositions} $\COMP(g,[f_0,\ldots,f_{m-1}])$, 

where $g$ and $f_0$, \ldots, $f_{m-1}$ are primitive recursive,

such that

\[ \COMP(g,[f_0,\ldots,f_{m-1}])[\vec{x}] = 

   g[f_0[\vec{x}],\ldots,f_{m-1}[\vec{x}]]. \] 

\item All \defn{recursions} $\PREC(f,g)$, where $f$ and $g$ are primitive

  recursive, such that

\begin{eqnarray*}

  \PREC(f,g)[0,\vec{x}] & = & f[\vec{x}] \\

  \PREC(f,g)[y+1,\vec{x}] & = & g[\PREC(f,g)[y,\vec{x}],\, y,\, \vec{x}].

\end{eqnarray*} 

\end{itemize}

Composition is awkward because it combines not two functions, as is usual,

but $m+1$ functions.  In her proof that Ackermann's function is not

primitive recursive, Nora Szasz was unable to formalize this definition

directly~\cite{szasz93}.  So she generalized primitive recursion to

tuple-valued functions.  This modified the inductive definition such that

each operation on primitive recursive functions combined just two functions.

\begin{figure}

\begin{ttbox}

Primrec_defs = Main +

consts SC :: i

 \(\vdots\)

defs

 SC_def    "SC == lam l:list(nat).list_case(0, \%x xs.succ(x), l)"

 \(\vdots\)

end

Primrec = Primrec_defs +

consts prim_rec :: i

inductive

 domains "primrec" <= "list(nat)->nat"

 intrs

   SC     "SC : primrec"

   CONST  "k: nat ==> CONST(k) : primrec"

   PROJ   "i: nat ==> PROJ(i) : primrec"

   COMP   "[| g: primrec; fs: list(primrec) |] ==> COMP(g,fs): primrec"

   PREC   "[| f: primrec; g: primrec |] ==> PREC(f,g): primrec"

 monos       list_mono

 con_defs    SC_def, CONST_def, PROJ_def, COMP_def, PREC_def

 type_intrs "nat_typechecks @ list.intrs @                      

             [lam_type, list_case_type, drop_type, map_type,    

              apply_type, rec_type]"

end

\end{ttbox}

\hrule

\caption{Inductive definition of the primitive recursive functions} 

\label{primrec-fig}

\end{figure}

\def\fs{{\it fs}} 

Szasz was using \textsc{alf}, but Coq and \textsc{hol} would also have

problems accepting this definition.  Isabelle's package accepts it easily

since $[f_0,\ldots,f_{m-1}]$ is a list of primitive recursive functions and

$\lst$ is monotonic.  There are five introduction rules, one for each of the

five forms of primitive recursive function.  Let us examine the one for

$\COMP$:

\[ \infer{\COMP(g,\fs)\in\primrec}{g\in\primrec & \fs\in\lst(\primrec)} \]

The induction rule for $\primrec$ has one case for each introduction rule.

Due to the use of $\lst$ as a monotone operator, the composition case has

an unusual induction hypothesis:

 \[ \infer*{P(\COMP(g,\fs))}

          {[g\in\primrec & \fs\in\lst(\{z\in\primrec.P(z)\})]_{\fs,g}} 

\]

The hypothesis states that $\fs$ is a list of primitive recursive functions,

each satisfying the induction formula.  Proving the $\COMP$ case typically

requires structural induction on lists, yielding two subcases: either

$\fs=\Nil$ or else $\fs=\Cons(f,\fs')$, where $f\in\primrec$, $P(f)$, and

$\fs'$ is another list of primitive recursive functions satisfying~$P$.

Figure~\ref{primrec-fig} presents the theory file.  Theory {\tt Primrec}

defines the constants $\SC$, $\CONST$, etc.  These are not constructors of

a new datatype, but functions over lists of numbers.  Their definitions,

most of which are omitted, consist of routine list programming.  In

Isabelle/\textsc{zf}, the primitive recursive functions are defined as a subset of

the function set $\lst(\nat)\to\nat$.

The Isabelle theory goes on to formalize Ackermann's function and prove

that it is not primitive recursive, using the induction rule {\tt

  primrec.induct}.  The proof follows Szasz's excellent account.

\section{Datatypes and codatatypes}\label{data-sec}

A (co)datatype definition is a (co)inductive definition with automatically

defined constructors and a case analysis operator.  The package proves that

the case operator inverts the constructors and can prove freeness theorems

involving any pair of constructors.

\subsection{Constructors and their domain}\label{univ-sec}

A (co)inductive definition selects a subset of an existing set; a (co)datatype

definition creates a new set.  The package reduces the latter to the former.

Isabelle/\textsc{zf} supplies sets having strong closure properties to serve

as domains for (co)inductive definitions.

Isabelle/\textsc{zf} defines the Cartesian product $A\times

B$, containing ordered pairs $\pair{a,b}$; it also defines the

disjoint sum $A+B$, containing injections $\Inl(a)\equiv\pair{0,a}$ and

$\Inr(b)\equiv\pair{1,b}$.  For use below, define the $m$-tuple

$\pair{x_1,\ldots,x_m}$ to be the empty set~$\emptyset$ if $m=0$, simply $x_1$

if $m=1$ and $\pair{x_1,\pair{x_2,\ldots,x_m}}$ if $m\geq2$.

A datatype constructor $\Con(x_1,\ldots,x_m)$ is defined to be

$h(\pair{x_1,\ldots,x_m})$, where $h$ is composed of $\Inl$ and~$\Inr$.

In a mutually recursive definition, all constructors for the set~$R_i$ have

the outer form~$h_{in}$, where $h_{in}$ is the injection described

in~\S\ref{mutual-sec}.  Further nested injections ensure that the

constructors for~$R_i$ are pairwise distinct.  

Isabelle/\textsc{zf} defines the set $\univ(A)$, which contains~$A$ and

furthermore contains $\pair{a,b}$, $\Inl(a)$ and $\Inr(b)$ for $a$,

$b\in\univ(A)$.  In a typical datatype definition with set parameters

$A_1$, \ldots, $A_k$, a suitable domain for all the recursive sets is

$\univ(A_1\un\cdots\un A_k)$.  This solves the problem for

datatypes~\cite[\S4.2]{paulson-set-II}.

The standard pairs and injections can only yield well-founded

constructions.  This eases the (manual!) definition of recursive functions

over datatypes.  But they are unsuitable for codatatypes, which typically

contain non-well-founded objects.

To support codatatypes, Isabelle/\textsc{zf} defines a variant notion of

ordered pair, written~$\pair{a;b}$.  It also defines the corresponding variant

notion of Cartesian product $A\otimes B$, variant injections $\QInl(a)$

and~$\QInr(b)$ and variant disjoint sum $A\oplus B$.  Finally it defines the

set $\quniv(A)$, which contains~$A$ and furthermore contains $\pair{a;b}$,

$\QInl(a)$ and $\QInr(b)$ for $a$, $b\in\quniv(A)$.  In a typical codatatype

definition with set parameters $A_1$, \ldots, $A_k$, a suitable domain is

$\quniv(A_1\un\cdots\un A_k)$.  Details are published

elsewhere~\cite{paulson-mscs}.

\subsection{The case analysis operator}

The (co)datatype package automatically defines a case analysis operator,

called {\tt$R$\_case}.  A mutually recursive definition still has only one

operator, whose name combines those of the recursive sets: it is called

{\tt$R_1$\_\ldots\_$R_n$\_case}.  The case operator is analogous to those

for products and sums.

Datatype definitions employ standard products and sums, whose operators are

$\split$ and $\case$ and satisfy the equations

\begin{eqnarray*}

  \split(f,\pair{x,y})  & = &  f(x,y) \\

  \case(f,g,\Inl(x))    & = &  f(x)   \\

  \case(f,g,\Inr(y))    & = &  g(y)

\end{eqnarray*}

Suppose the datatype has $k$ constructors $\Con_1$, \ldots,~$\Con_k$.  Then

its case operator takes $k+1$ arguments and satisfies an equation for each

constructor:

\[ R\hbox{\_case}(f_1,\ldots,f_k, {\tt Con}_i(\vec{x})) = f_i(\vec{x}),

    \qquad i = 1, \ldots, k

\]

The case operator's definition takes advantage of Isabelle's representation of

syntax in the typed $\lambda$-calculus; it could readily be adapted to a

theorem prover for higher-order logic.  If $f$ and~$g$ have meta-type $i\To i$

then so do $\split(f)$ and $\case(f,g)$.  This works because $\split$ and

$\case$ operate on their last argument.  They are easily combined to make

complex case analysis operators.  For example, $\case(f,\case(g,h))$ performs

case analysis for $A+(B+C)$; let us verify one of the three equations:

\[ \case(f,\case(g,h), \Inr(\Inl(b))) = \case(g,h,\Inl(b)) = g(b) \]

Codatatype definitions are treated in precisely the same way.  They express

case operators using those for the variant products and sums, namely

$\qsplit$ and~$\qcase$.

\medskip

To see how constructors and the case analysis operator are defined, let us

examine some examples.  Further details are available

elsewhere~\cite{paulson-set-II}.

\subsection{Example: lists and lazy lists}\label{lists-sec}

Here is a declaration of the datatype of lists, as it might appear in a theory

file:

\begin{ttbox} 

consts  list :: i=>i

datatype "list(A)" = Nil | Cons ("a:A", "l: list(A)")

\end{ttbox}

And here is a declaration of the codatatype of lazy lists:

\begin{ttbox}