summary |
shortlog |
changelog |
graph |
tags |
bookmarks |
branches |
files |
changeset |
file |
latest |
revisions |
annotate |
diff |
comparison |
raw |
help

doc-src/Tutorial/fp.tex

author | nipkow |

Wed, 26 Aug 1998 17:27:27 +0200 | |

changeset 5377 | efb799c5ed3c |

parent 5375 | 1463e182c533 |

child 5850 | 9712294e60b9 |

permissions | -rw-r--r-- |

*** empty log message ***

\chapter{Functional Programming in HOL} Although on the surface this chapter is mainly concerned with how to write functional programs in HOL and how to verify them, most of the constructs and proof procedures introduced are general purpose and recur in any specification or verification task. The dedicated functional programmer should be warned: HOL offers only what could be called {\em total functional programming} --- all functions in HOL must be total; lazy data structures are not directly available. On the positive side, functions in HOL need not be computable: HOL is a specification language that goes well beyond what can be expressed as a program. However, for the time being we concentrate on the computable. \section{An introductory theory} \label{sec:intro-theory} Functional programming needs datatypes and functions. Both of them can be defined in a theory with a syntax reminiscent of languages like ML or Haskell. As an example consider the theory in Fig.~\ref{fig:ToyList}. \begin{figure}[htbp] \begin{ttbox}\makeatother \input{ToyList/ToyList.thy}\end{ttbox} \caption{A theory of lists} \label{fig:ToyList} \end{figure} HOL already has a predefined theory of lists called \texttt{List} --- \texttt{ToyList} is merely a small fragment of it chosen as an example. In contrast to what is recommended in \S\ref{sec:Basic:Theories}, \texttt{ToyList} is not based on \texttt{Main} but on \texttt{Datatype}, a theory that contains everything required for datatype definitions but does not have \texttt{List} as a parent, thus avoiding ambiguities caused by defining lists twice. The \ttindexbold{datatype} \texttt{list} introduces two constructors \texttt{Nil} and \texttt{Cons}, the empty list and the operator that adds an element to the front of a list. For example, the term \texttt{Cons True (Cons False Nil)} is a value of type \texttt{bool~list}, namely the list with the elements \texttt{True} and \texttt{False}. Because this notation becomes unwieldy very quickly, the datatype declaration is annotated with an alternative syntax: instead of \texttt{Nil} and \texttt{Cons}~$x$~$xs$ we can write \index{#@{\tt[]}|bold}\texttt{[]} and \texttt{$x$~\#~$xs$}\index{#@{\tt\#}|bold}. In fact, this alternative syntax is the standard syntax. Thus the list \texttt{Cons True (Cons False Nil)} becomes \texttt{True \# False \# []}. The annotation \ttindexbold{infixr} means that \texttt{\#} associates to the right, i.e.\ the term \texttt{$x$ \# $y$ \# $z$} is read as \texttt{$x$ \# ($y$ \# $z$)} and not as \texttt{($x$ \# $y$) \# $z$}. \begin{warn} Syntax annotations are a powerful but completely optional feature. You could drop them from theory \texttt{ToyList} and go back to the identifiers \texttt{Nil} and \texttt{Cons}. However, lists are such a central datatype that their syntax is highly customized. We recommend that novices should not use syntax annotations in their own theories. \end{warn} Next, the functions \texttt{app} and \texttt{rev} are declared. In contrast to ML, Isabelle insists on explicit declarations of all functions (keyword \ttindexbold{consts}). (Apart from the declaration-before-use restriction, the order of items in a theory file is unconstrained.) Function \texttt{app} is annotated with concrete syntax too. Instead of the prefix syntax \texttt{app}~$xs$~$ys$ the infix $xs$~\texttt{\at}~$ys$ becomes the preferred form. Both functions are defined recursively. The equations for \texttt{app} and \texttt{rev} hardly need comments: \texttt{app} appends two lists and \texttt{rev} reverses a list. The keyword \texttt{primrec} indicates that the recursion is of a particularly primitive kind where each recursive call peels off a datatype constructor from one of the arguments (see \S\ref{sec:datatype}). Thus the recursion always terminates, i.e.\ the function is \bfindex{total}. The termination requirement is absolutely essential in HOL, a logic of total functions. If we were to drop it, inconsistencies could quickly arise: the ``definition'' $f(n) = f(n)+1$ immediately leads to $0 = 1$ by subtracting $f(n)$ on both sides. % However, this is a subtle issue that we cannot discuss here further. \begin{warn} As we have indicated, the desire for total functions is not a gratuitously imposed restriction but an essential characteristic of HOL. It is only because of totality that reasoning in HOL is comparatively easy. More generally, the philosophy in HOL is not to allow arbitrary axioms (such as function definitions whose totality has not been proved) because they quickly lead to inconsistencies. Instead, fixed constructs for introducing types and functions are offered (such as \texttt{datatype} and \texttt{primrec}) which are guaranteed to preserve consistency. \end{warn} A remark about syntax. The textual definition of a theory follows a fixed syntax with keywords like \texttt{datatype} and \texttt{end} (see Fig.~\ref{fig:keywords} in Appendix~\ref{sec:Appendix} for a full list). Embedded in this syntax are the types and formulae of HOL, whose syntax is extensible, e.g.\ by new user-defined infix operators (see~\ref{sec:infix-syntax}). To distinguish the two levels, everything HOL-specific should be enclosed in \texttt{"}\dots\texttt{"}. The same holds for identifiers that happen to be keywords, as in \begin{ttbox} consts "end" :: 'a list => 'a \end{ttbox} To lessen this burden, quotation marks around types can be dropped, provided their syntax does not go beyond what is described in \S\ref{sec:TypesTermsForms}. Types containing further operators, e.g.\ \texttt{*} for Cartesian products, need quotation marks. When Isabelle prints a syntax error message, it refers to the HOL syntax as the \bfindex{inner syntax}. \section{An introductory proof} \label{sec:intro-proof} Having defined \texttt{ToyList}, we load it with the ML command \begin{ttbox} use_thy "ToyList"; \end{ttbox} and are ready to prove a few simple theorems. This will illustrate not just the basic proof commands but also the typical proof process. \subsubsection*{Main goal: \texttt{rev(rev xs) = xs}} Our goal is to show that reversing a list twice produces the original list. Typing \begin{ttbox} \input{ToyList/thm}\end{ttbox} establishes a new goal to be proved in the context of the current theory, which is the one we just loaded. Isabelle's response is to print the current proof state: \begin{ttbox} {\out Level 0} {\out rev (rev xs) = xs} {\out 1. rev (rev xs) = xs} \end{ttbox} Until we have finished a proof, the proof state always looks like this: \begin{ttbox} {\out Level \(i\)} {\out \(G\)} {\out 1. \(G@1\)} {\out \(\vdots\)} {\out \(n\). \(G@n\)} \end{ttbox} where \texttt{Level}~$i$ indicates that we are $i$ steps into the proof, $G$ is the overall goal that we are trying to prove, and the numbered lines contain the subgoals $G@1$, \dots, $G@n$ that we need to prove to establish $G$. At \texttt{Level 0} there is only one subgoal, which is identical with the overall goal. Normally $G$ is constant and only serves as a reminder. Hence we rarely show it in this tutorial. Let us now get back to \texttt{rev(rev xs) = xs}. Properties of recursively defined functions are best established by induction. In this case there is not much choice except to induct on \texttt{xs}: \begin{ttbox} \input{ToyList/inductxs}\end{ttbox} This tells Isabelle to perform induction on variable \texttt{xs} in subgoal 1. The new proof state contains two subgoals, namely the base case (\texttt{Nil}) and the induction step (\texttt{Cons}): \begin{ttbox} {\out 1. rev (rev []) = []} {\out 2. !!a list. rev (rev list) = list ==> rev (rev (a # list)) = a # list} \end{ttbox} The induction step is an example of the general format of a subgoal: \begin{ttbox} {\out \(i\). !!\(x@1 \dots x@n\). {\it assumptions} ==> {\it conclusion}} \end{ttbox}\index{==>@{\tt==>}|bold} The prefix of bound variables \texttt{!!\(x@1 \dots x@n\)} can be ignored most of the time, or simply treated as a list of variables local to this subgoal. Their deeper significance is explained in \S\ref{sec:PCproofs}. The {\it assumptions} are the local assumptions for this subgoal and {\it conclusion} is the actual proposition to be proved. Typical proof steps that add new assumptions are induction or case distinction. In our example the only assumption is the induction hypothesis \texttt{rev (rev list) = list}, where \texttt{list} is a variable name chosen by Isabelle. If there are multiple assumptions, they are enclosed in the bracket pair \texttt{[|}\index{==>@\ttlbr|bold} and \texttt{|]}\index{==>@\ttrbr|bold} and separated by semicolons. Let us try to solve both goals automatically: \begin{ttbox} \input{ToyList/autotac}\end{ttbox} This command tells Isabelle to apply a proof strategy called \texttt{Auto_tac} to all subgoals. Essentially, \texttt{Auto_tac} tries to `simplify' the subgoals. In our case, subgoal~1 is solved completely (thanks to the equation \texttt{rev [] = []}) and disappears; the simplified version of subgoal~2 becomes the new subgoal~1: \begin{ttbox}\makeatother {\out 1. !!a list. rev(rev list) = list ==> rev(rev list @ a # []) = a # list} \end{ttbox} In order to simplify this subgoal further, a lemma suggests itself. \subsubsection*{First lemma: \texttt{rev(xs \at~ys) = (rev ys) \at~(rev xs)}} We start the proof as usual: \begin{ttbox}\makeatother \input{ToyList/lemma1}\end{ttbox} There are two variables that we could induct on: \texttt{xs} and \texttt{ys}. Because \texttt{\at} is defined by recursion on the first argument, \texttt{xs} is the correct one: \begin{ttbox} \input{ToyList/inductxs}\end{ttbox} This time not even the base case is solved automatically: \begin{ttbox}\makeatother by(Auto_tac); {\out 1. rev ys = rev ys @ []} {\out 2. \dots} \end{ttbox} We need another lemma. \subsubsection*{Second lemma: \texttt{xs \at~[] = xs}} This time the canonical proof procedure \begin{ttbox}\makeatother \input{ToyList/lemma2}\input{ToyList/inductxs}\input{ToyList/autotac}\end{ttbox} leads to the desired message \texttt{No subgoals!}: \begin{ttbox}\makeatother {\out Level 2} {\out xs @ [] = xs} {\out No subgoals!} \end{ttbox} Now we can give the lemma just proved a suitable name \begin{ttbox} \input{ToyList/qed2}\end{ttbox} and tell Isabelle to use this lemma in all future proofs by simplification: \begin{ttbox} \input{ToyList/addsimps2}\end{ttbox} Note that in the theorem \texttt{app_Nil2} the free variable \texttt{xs} has been replaced by the unknown \texttt{?xs}, just as explained in \S\ref{sec:variables}. Going back to the proof of the first lemma \begin{ttbox}\makeatother \input{ToyList/lemma1}\input{ToyList/inductxs}\input{ToyList/autotac}\end{ttbox} we find that this time \texttt{Auto_tac} solves the base case, but the induction step merely simplifies to \begin{ttbox}\makeatother {\out 1. !!a list.} {\out rev (list @ ys) = rev ys @ rev list} {\out ==> (rev ys @ rev list) @ a # [] = rev ys @ rev list @ a # []} \end{ttbox} Now we need to remember that \texttt{\at} associates to the right, and that \texttt{\#} and \texttt{\at} have the same priority (namely the \texttt{65} in the definition of \texttt{ToyList}). Thus the conclusion really is \begin{ttbox}\makeatother {\out ==> (rev ys @ rev list) @ (a # []) = rev ys @ (rev list @ (a # []))} \end{ttbox} and the missing lemma is associativity of \texttt{\at}. \subsubsection*{Third lemma: \texttt{(xs \at~ys) \at~zs = xs \at~(ys \at~zs)}} This time the canonical proof procedure \begin{ttbox}\makeatother \input{ToyList/lemma3}\end{ttbox} succeeds without further ado. Again we name the lemma and add it to the set of lemmas used during simplification: \begin{ttbox} \input{ToyList/qed3}\end{ttbox} Now we can go back and prove the first lemma \begin{ttbox}\makeatother \input{ToyList/lemma1}\input{ToyList/inductxs}\input{ToyList/autotac}\end{ttbox} add it to the simplification lemmas \begin{ttbox} \input{ToyList/qed1}\end{ttbox} and then solve our main theorem: \begin{ttbox}\makeatother \input{ToyList/thm}\input{ToyList/inductxs}\input{ToyList/autotac}\end{ttbox} \subsubsection*{Review} This is the end of our toy proof. It should have familiarized you with \begin{itemize} \item the standard theorem proving procedure: state a goal; proceed with proof until a new lemma is required; prove that lemma; come back to the original goal. \item a specific procedure that works well for functional programs: induction followed by all-out simplification via \texttt{Auto_tac}. \item a basic repertoire of proof commands. \end{itemize} \section{Some helpful commands} \label{sec:commands-and-hints} This section discusses a few basic commands for manipulating the proof state and can be skipped by casual readers. There are two kinds of commands used during a proof: the actual proof commands and auxiliary commands for examining the proof state and controlling the display. Proof commands are always of the form \texttt{by(\textit{tactic});}\indexbold{tactic} where \textbf{tactic} is a synonym for ``theorem proving function''. Typical examples are \texttt{induct_tac} and \texttt{Auto_tac} --- the suffix \texttt{_tac} is merely a mnemonic. Further tactics are introduced throughout the tutorial. %Tactics can also be modified. For example, %\begin{ttbox} %by(ALLGOALS Asm_simp_tac); %\end{ttbox} %tells Isabelle to apply \texttt{Asm_simp_tac} to all subgoals. For more on %tactics and how to combine them see~\S\ref{sec:Tactics}. The most useful auxiliary commands are: \begin{description} \item[Printing the current state] Type \texttt{pr();} to redisplay the current proof state, for example when it has disappeared off the screen. \item[Limiting the number of subgoals] Typing \texttt{prlim $k$;} tells Isabelle to print only the first $k$ subgoals from now on and redisplays the current proof state. This is helpful when there are many subgoals. \item[Undoing] Typing \texttt{undo();} undoes the effect of the last tactic. \item[Context switch] Every proof happens in the context of a \bfindex{current theory}. By default, this is the last theory loaded. If you want to prove a theorem in the context of a different theory \texttt{T}, you need to type \texttt{context T.thy;}\index{*context|bold} first. Of course you need to change the context again if you want to go back to your original theory. \item[Displaying types] We have already mentioned the flag \ttindex{show_types} above. It can also be useful for detecting typos in formulae early on. For example, if \texttt{show_types} is set and the goal \texttt{rev(rev xs) = xs} is started, Isabelle prints the additional output \begin{ttbox} {\out Variables:} {\out xs :: 'a list} \end{ttbox} which tells us that Isabelle has correctly inferred that \texttt{xs} is a variable of list type. On the other hand, had we made a typo as in \texttt{rev(re xs) = xs}, the response \begin{ttbox} Variables: re :: 'a list => 'a list xs :: 'a list \end{ttbox} would have alerted us because of the unexpected variable \texttt{re}. \item[(Re)loading theories]\index{loading theories}\index{reloading theories} Initially you load theory \texttt{T} by typing \ttindex{use_thy}~\texttt{"T";}, which loads all parent theories of \texttt{T} automatically, if they are not loaded already. If you modify \texttt{T.thy} or \texttt{T.ML}, you can reload it by typing \texttt{use_thy~"T";} again. This time, however, only \texttt{T} is reloaded. If some of \texttt{T}'s parents have changed as well, type \ttindexbold{update}\texttt{();} to reload all theories that have changed. \end{description} Further commands are found in the Reference Manual. \section{Datatypes} \label{sec:datatype} Inductive datatypes are part of almost every non-trivial application of HOL. First we take another look at a very important example, the datatype of lists, before we turn to datatypes in general. The section closes with a case study. \subsection{Lists} Lists are one of the essential datatypes in computing. Readers of this tutorial and users of HOL need to be familiar with their basic operations. Theory \texttt{ToyList} is only a small fragment of HOL's predefined theory \texttt{List}\footnote{\texttt{http://www.in.tum.de/\~\relax isabelle/library/HOL/List.html}}. The latter contains many further operations. For example, the functions \ttindexbold{hd} (`head') and \ttindexbold{tl} (`tail') return the first element and the remainder of a list. (However, pattern-matching is usually preferable to \texttt{hd} and \texttt{tl}.) Theory \texttt{List} also contains more syntactic sugar: \texttt{[}$x@1$\texttt{,}\dots\texttt{,}$x@n$\texttt{]} abbreviates $x@1$\texttt{\#}\dots\texttt{\#}$x@n$\texttt{\#[]}. In the rest of the tutorial we always use HOL's predefined lists. \subsection{The general format} \label{sec:general-datatype} The general HOL \texttt{datatype} definition is of the form \[ \mathtt{datatype}~(\alpha@1, \dots, \alpha@n) \, t ~=~ C@1~\tau@{11}~\dots~\tau@{1k@1} ~\mid~ \dots ~\mid~ C@m~\tau@{m1}~\dots~\tau@{mk@m} \] where $\alpha@i$ are type variables (the parameters), $C@i$ are distinct constructor names and $\tau@{ij}$ are types; it is customary to capitalize the first letter in constructor names. There are a number of restrictions (such as the type should not be empty) detailed elsewhere~\cite{Isa-Logics-Man}. Isabelle notifies you if you violate them. Laws about datatypes, such as \verb$[] ~= x#xs$ and \texttt{(x\#xs = y\#ys) = (x=y \& xs=ys)}, are used automatically during proofs by simplification. The same is true for the equations in primitive recursive function definitions. \subsection{Primitive recursion} Functions on datatypes are usually defined by recursion. In fact, most of the time they are defined by what is called \bfindex{primitive recursion}. The keyword \texttt{primrec} is followed by a list of equations \[ f \, x@1 \, \dots \, (C \, y@1 \, \dots \, y@k)\, \dots \, x@n = r \] such that $C$ is a constructor of the datatype $t$ and all recursive calls of $f$ in $r$ are of the form $f \, \dots \, y@i \, \dots$ for some $i$. Thus Isabelle immediately sees that $f$ terminates because one (fixed!) argument becomes smaller with every recursive call. There must be exactly one equation for each constructor. Their order is immaterial. A more general method for defining total recursive functions is explained in \S\ref{sec:recdef}. \begin{exercise} Given the datatype of binary trees \begin{ttbox} \input{Misc/tree}\end{ttbox} define a function \texttt{mirror} that mirrors the structure of a binary tree by swapping subtrees (recursively). Prove \texttt{mirror(mirror(t)) = t}. \end{exercise} \subsection{\texttt{case}-expressions} \label{sec:case-expressions} HOL also features \ttindexbold{case}-expressions for analyzing elements of a datatype. For example, \begin{ttbox} case xs of [] => 0 | y#ys => y \end{ttbox} evaluates to \texttt{0} if \texttt{xs} is \texttt{[]} and to \texttt{y} if \texttt{xs} is \texttt{y\#ys}. (Since the result in both branches must be of the same type, it follows that \texttt{y::nat} and hence \texttt{xs::(nat)list}.) In general, if $e$ is a term of the datatype $t$ defined in \S\ref{sec:general-datatype} above, the corresponding \texttt{case}-expression analyzing $e$ is \[ \begin{array}{rrcl} \mbox{\tt case}~e~\mbox{\tt of} & C@1~x@{11}~\dots~x@{1k@1} & \To & e@1 \\ \vdots \\ \mid & C@m~x@{m1}~\dots~x@{mk@m} & \To & e@m \end{array} \] \begin{warn} {\em All} constructors must be present, their order is fixed, and nested patterns are not supported. Violating these restrictions results in strange error messages. \end{warn} \noindent Nested patterns can be simulated by nested \texttt{case}-expressions: instead of \begin{ttbox} case xs of [] => 0 | [x] => x | x#(y#zs) => y \end{ttbox} write \begin{ttbox} case xs of [] => 0 | x#ys => (case ys of [] => x | y#zs => y) \end{ttbox} Note that \texttt{case}-expressions should be enclosed in parentheses to indicate their scope. \subsection{Structural induction} Almost all the basic laws about a datatype are applied automatically during simplification. Only induction is invoked by hand via \texttt{induct_tac}, which works for any datatype. In some cases, induction is overkill and a case distinction over all constructors of the datatype suffices. This is performed by \ttindexbold{exhaust_tac}. A trivial example: \begin{ttbox} \input{Misc/exhaust.ML}{\out1. xs = [] ==> (case xs of [] => [] | y # ys => xs) = xs} {\out2. !!a list. xs = a # list ==> (case xs of [] => [] | y # ys => xs) = xs} \input{Misc/autotac.ML}\end{ttbox} Note that this particular case distinction could have been automated completely. See~\S\ref{sec:SimpFeatures}. \begin{warn} Induction is only allowed on a free variable that should not occur among the assumptions of the subgoal. Exhaustion works for arbitrary terms. \end{warn} \subsection{Case study: boolean expressions} \label{sec:boolex} The aim of this case study is twofold: it shows how to model boolean expressions and some algorithms for manipulating them, and it demonstrates the constructs introduced above. \subsubsection{How can we model boolean expressions?} We want to represent boolean expressions built up from variables and constants by negation and conjunction. The following datatype serves exactly that purpose: \begin{ttbox} \input{Ifexpr/boolex}\end{ttbox} The two constants are represented by the terms \texttt{Const~True} and \texttt{Const~False}. Variables are represented by terms of the form \texttt{Var}~$n$, where $n$ is a natural number (type \texttt{nat}). For example, the formula $P@0 \land \neg P@1$ is represented by the term \texttt{And~(Var~0)~(Neg(Var~1))}. \subsubsection{What is the value of boolean expressions?} The value of a boolean expressions depends on the value of its variables. Hence the function \texttt{value} takes an additional parameter, an {\em environment} of type \texttt{nat~=>~bool}, which maps variables to their values: \begin{ttbox} \input{Ifexpr/value}\end{ttbox} \subsubsection{If-expressions} An alternative and often more efficient (because in a certain sense canonical) representation are so-called \textit{If-expressions\/} built up from constants (\texttt{CIF}), variables (\texttt{VIF}) and conditionals (\texttt{IF}): \begin{ttbox} \input{Ifexpr/ifex}\end{ttbox} The evaluation if If-expressions proceeds as for \texttt{boolex}: \begin{ttbox} \input{Ifexpr/valif}\end{ttbox} \subsubsection{Transformation into and of If-expressions} The type \texttt{boolex} is close to the customary representation of logical formulae, whereas \texttt{ifex} is designed for efficiency. Thus we need to translate from \texttt{boolex} into \texttt{ifex}: \begin{ttbox} \input{Ifexpr/bool2if}\end{ttbox} At last, we have something we can verify: that \texttt{bool2if} preserves the value of its argument. \begin{ttbox} \input{Ifexpr/bool2if.ML}\end{ttbox} The proof is canonical: \begin{ttbox} \input{Ifexpr/proof.ML}\end{ttbox} In fact, all proofs in this case study look exactly like this. Hence we do not show them below. More interesting is the transformation of If-expressions into a normal form where the first argument of \texttt{IF} cannot be another \texttt{IF} but must be a constant or variable. Such a normal form can be computed by repeatedly replacing a subterm of the form \texttt{IF~(IF~b~x~y)~z~u} by \texttt{IF b (IF x z u) (IF y z u)}, which has the same value. The following primitive recursive functions perform this task: \begin{ttbox} \input{Ifexpr/normif} \input{Ifexpr/norm}\end{ttbox} Their interplay is a bit tricky, and we leave it to the reader to develop an intuitive understanding. Fortunately, Isabelle can help us to verify that the transformation preserves the value of the expression: \begin{ttbox} \input{Ifexpr/norm.ML}\end{ttbox} The proof is canonical, provided we first show the following lemma (which also helps to understand what \texttt{normif} does) and make it available for simplification via \texttt{Addsimps}: \begin{ttbox} \input{Ifexpr/normif.ML}\end{ttbox} But how can we be sure that \texttt{norm} really produces a normal form in the above sense? We have to prove \begin{ttbox} \input{Ifexpr/normal_norm.ML}\end{ttbox} where \texttt{normal} expresses that an If-expression is in normal form: \begin{ttbox} \input{Ifexpr/normal}\end{ttbox} Of course, this requires a lemma about normality of \texttt{normif} \begin{ttbox} \input{Ifexpr/normal_normif.ML}\end{ttbox} that has to be made available for simplification via \texttt{Addsimps}. How does one come up with the required lemmas? Try to prove the main theorems without them and study carefully what \texttt{Auto_tac} leaves unproved. This has to provide the clue. The necessity of universal quantification (\texttt{!t e}) in the two lemmas is explained in \S\ref{sec:InductionHeuristics} \begin{exercise} We strengthen the definition of a {\em normal\/} If-expression as follows: the first argument of all \texttt{IF}s must be a variable. Adapt the above development to this changed requirement. (Hint: you may need to formulate some of the goals as implications (\texttt{-->}) rather than equalities (\texttt{=}).) \end{exercise} \section{Some basic types} \subsection{Natural numbers} The type \ttindexbold{nat} of natural numbers is predefined and behaves like \begin{ttbox} datatype nat = 0 | Suc nat \end{ttbox} In particular, there are \texttt{case}-expressions, for example \begin{ttbox} case n of 0 => 0 | Suc m => m \end{ttbox} primitive recursion, for example \begin{ttbox} \input{Misc/natsum}\end{ttbox} and induction, for example \begin{ttbox} \input{Misc/NatSum.ML}\ttbreak {\out sum n + sum n = n * Suc n} {\out No subgoals!} \end{ttbox} The usual arithmetic operations \ttindexbold{+}, \ttindexbold{-}, \ttindexbold{*}, \ttindexbold{div} and \ttindexbold{mod} are predefined, as are the relations \ttindexbold{<=} and \ttindexbold{<}. There is even a least number operation \ttindexbold{LEAST}. For example, \texttt{(LEAST n.$\,$1 < n) = 2} (HOL does not prove this completely automatically). \begin{warn} The operations \ttindexbold{+}, \ttindexbold{-}, \ttindexbold{*} are overloaded, i.e.\ they are available not just for natural numbers but at other types as well (see \S\ref{sec:TypeClasses}). For example, given the goal \texttt{x+y = y+x}, there is nothing to indicate that you are talking about natural numbers. Hence Isabelle can only infer that \texttt{x} and \texttt{y} are of some arbitrary type where \texttt{+} is declared. As a consequence, you will be unable to prove the goal (although it may take you some time to realize what has happened if \texttt{show_types} is not set). In this particular example, you need to include an explicit type constraint, for example \texttt{x+y = y+(x::nat)}. If there is enough contextual information this may not be necessary: \texttt{x+0 = x} automatically implies \texttt{x::nat}. \end{warn} \subsection{Products} HOL also has pairs: \texttt{($a@1$,$a@2$)} is of type \texttt{$\tau@1$ * $\tau@2$} provided each $a@i$ is of type $\tau@i$. The components of a pair are extracted by \texttt{fst} and \texttt{snd}: \texttt{fst($x$,$y$) = $x$} and \texttt{snd($x$,$y$) = $y$}. Tuples are simulated by pairs nested to the right: \texttt{($a@1$,$a@2$,$a@3$)} and \texttt{$\tau@1$ * $\tau@2$ * $\tau@3$} stand for \texttt{($a@1$,($a@2$,$a@3$))} and \texttt{$\tau@1$ * ($\tau@2$ * $\tau@3$)}. Therefore \texttt{fst(snd($a@1$,$a@2$,$a@3$)) = $a@2$}. It is possible to use (nested) tuples as patterns in abstractions, for example \texttt{\%(x,y,z).x+y+z} and \texttt{\%((x,y),z).x+y+z}. In addition to explicit $\lambda$-abstractions, tuple patterns can be used in most variable binding constructs. Typical examples are \begin{ttbox} let (x,y) = f z in (y,x) case xs of [] => 0 | (x,y)\#zs => x+y \end{ttbox} Further important examples are quantifiers and sets. \begin{warn} Abstraction over pairs and tuples is merely a convenient shorthand for a more complex internal representation. Thus the internal and external form of a term may differ, which can affect proofs. If you want to avoid this complication, use \texttt{fst} and \texttt{snd}, i.e.\ write \texttt{\%p.~fst p + snd p} instead of \texttt{\%(x,y).~x + y}. See~\S\ref{} for theorem proving with tuple patterns. \end{warn} \section{Definitions} \label{sec:Definitions} A definition is simply an abbreviation, i.e.\ a new name for an existing construction. In particular, definitions cannot be recursive. Isabelle offers definitions on the level of types and terms. Those on the type level are called type synonyms, those on the term level are called (constant) definitions. \subsection{Type synonyms} \indexbold{type synonyms} Type synonyms are similar to those found in ML. Their syntax is fairly self explanatory: \begin{ttbox} \input{Misc/types}\end{ttbox}\indexbold{*types} The synonym \texttt{alist} shows that in general the type on the right-hand side needs to be enclosed in double quotation marks (see the end of~\S\ref{sec:intro-theory}). Internally all synonyms are fully expanded. As a consequence Isabelle's output never contains synonyms. Their main purpose is to improve the readability of theory definitions. Synonyms can be used just like any other type: \begin{ttbox} \input{Misc/consts}\end{ttbox} \subsection{Constant definitions} \label{sec:ConstDefinitions} The above constants \texttt{nand} and \texttt{exor} are non-recursive and can therefore be defined directly by \begin{ttbox} \input{Misc/defs}\end{ttbox}\indexbold{*defs} where \texttt{defs} is a keyword and \texttt{nand_def} and \texttt{exor_def} are arbitrary user-supplied names. The symbol \texttt{==}\index{==>@{\tt==}|bold} is a special form of equality that should only be used in constant definitions. Declarations and definitions can also be merged \begin{ttbox} \input{Misc/constdefs}\end{ttbox}\indexbold{*constdefs} in which case the default name of each definition is $f$\texttt{_def}, where $f$ is the name of the defined constant. Note that pattern-matching is not allowed, i.e.\ each definition must be of the form $f\,x@1\,\dots\,x@n$\texttt{~==~}$t$. Section~\S\ref{sec:Simplification} explains how definitions are used in proofs. \begin{warn} A common mistake when writing definitions is to introduce extra free variables on the right-hand side as in the following fictitious definition: \begin{ttbox} defs prime_def "prime(p) == (m divides p) --> (m=1 | m=p)" \end{ttbox} Isabelle rejects this `definition' because of the extra {\tt m} on the right-hand side, which would introduce an inconsistency. What you should have written is \begin{ttbox} defs prime_def "prime(p) == !m. (m divides p) --> (m=1 | m=p)" \end{ttbox} \end{warn} \chapter{More Functional Programming} The purpose of this chapter is to deepen the reader's understanding of the concepts encountered so far and to introduce an advanced method for defining recursive functions. The first two sections give a structured presentation of theorem proving by simplification (\S\ref{sec:Simplification}) and discuss important heuristics for induction (\S\ref{sec:InductionHeuristics}). They can be skipped by readers less interested in proofs. They are followed by a case study, a compiler for expressions (\S\ref{sec:ExprCompiler}). Finally we present a very general method for defining recursive functions that goes well beyond what \texttt{primrec} allows (\S\ref{sec:recdef}). \section{Simplification} \label{sec:Simplification} So far we have proved our theorems by \texttt{Auto_tac}, which `simplifies' all subgoals. In fact, \texttt{Auto_tac} can do much more than that, except that it did not need to so far. However, when you go beyond toy examples, you need to understand the ingredients of \texttt{Auto_tac}. This section covers the tactic that \texttt{Auto_tac} always applies first, namely simplification. Simplification is one of the central theorem proving tools in Isabelle and many other systems. The tool itself is called the \bfindex{simplifier}. The purpose of this section is twofold: to introduce the many features of the simplifier (\S\ref{sec:SimpFeatures}) and to explain a little bit how the simplifier works (\S\ref{sec:SimpHow}). Anybody intending to use HOL should read \S\ref{sec:SimpFeatures}, and the serious student should read \S\ref{sec:SimpHow} as well in order to understand what happened in case things do not simplify as expected. \subsection{Using the simplifier} \label{sec:SimpFeatures} In its most basic form, simplification means repeated application of equations from left to right. For example, taking the rules for \texttt{\at} and applying them to the term \texttt{[0,1] \at\ []} results in a sequence of simplification steps: \begin{ttbox}\makeatother (0#1#[]) @ [] \(\leadsto\) 0#((1#[]) @ []) \(\leadsto\) 0#(1#([] @ [])) \(\leadsto\) 0#1#[] \end{ttbox} This is also known as {\em term rewriting} and the equations are referred to as {\em rewrite rules}. This is more honest than `simplification' because the terms do not necessarily become simpler in the process. \subsubsection{Simpsets} To facilitate simplification, each theory has an associated set of simplification rules, known as a \bfindex{simpset}. Within a theory, proofs by simplification refer to the associated simpset by default. The simpset of a theory is built up as follows: starting with the union of the simpsets of the parent theories, each occurrence of a \texttt{datatype} or \texttt{primrec} construct augments the simpset. Explicit definitions are not added automatically. Users can add new theorems via \texttt{Addsimps} and delete them again later by \texttt{Delsimps}. You may augment a simpset not just by equations but by pretty much any theorem. The simplifier will try to make sense of it. For example, a theorem \verb$~$$P$ is automatically turned into \texttt{$P$ = False}. The details are explained in \S\ref{sec:SimpHow}. As a rule of thumb, rewrite rules that really simplify a term (like \texttt{xs \at\ [] = xs} and \texttt{rev(rev xs) = xs}) should be added to the current simpset right after they have been proved. Those of a more specific nature (e.g.\ distributivity laws, which alter the structure of terms considerably) should only be added for specific proofs and deleted again afterwards. Conversely, it may also happen that a generally useful rule needs to be removed for a certain proof and is added again afterwards. The need of frequent temporary additions or deletions may indicate a badly designed simpset. \begin{warn} Simplification may not terminate, for example if both $f(x) = g(x)$ and $g(x) = f(x)$ are in the simpset. It is the user's responsibility not to include rules that can lead to nontermination, either on their own or in combination with other rules. \end{warn} \subsubsection{Simplification tactics} There are four main simplification tactics: \begin{ttdescription} \item[\ttindexbold{Simp_tac} $i$] simplifies the conclusion of subgoal~$i$ using the theory's simpset. It may solve the subgoal completely if it has become trivial. For example: \begin{ttbox}\makeatother {\out 1. [] @ [] = []} by(Simp_tac 1); {\out No subgoals!} \end{ttbox} \item[\ttindexbold{Asm_simp_tac}] is like \verb$Simp_tac$, but extracts additional rewrite rules from the assumptions of the subgoal. For example, it solves \begin{ttbox}\makeatother {\out 1. xs = [] ==> xs @ xs = xs} \end{ttbox} which \texttt{Simp_tac} does not do. \item[\ttindexbold{Full_simp_tac}] is like \verb$Simp_tac$, but also simplifies the assumptions (without using the assumptions to simplify each other or the actual goal). \item[\ttindexbold{Asm_full_simp_tac}] is like \verb$Asm_simp_tac$, but also simplifies the assumptions. In particular, assumptions can simplify each other. For example: \begin{ttbox}\makeatother \out{ 1. [| xs @ zs = ys @ xs; [] @ xs = [] @ [] |] ==> ys = zs} by(Asm_full_simp_tac 1); {\out No subgoals!} \end{ttbox} The second assumption simplifies to \texttt{xs = []}, which in turn simplifies the first assumption to \texttt{zs = ys}, thus reducing the conclusion to \texttt{ys = ys} and hence to \texttt{True}. (See also the paragraph on tracing below.) \end{ttdescription} \texttt{Asm_full_simp_tac} is the most powerful of this quartet of tactics. In fact, \texttt{Auto_tac} starts by applying \texttt{Asm_full_simp_tac} to all subgoals. The only reason for the existence of the other three tactics is that sometimes one wants to limit the amount of simplification, for example to avoid nontermination: \begin{ttbox}\makeatother {\out 1. ! x. f x = g (f (g x)) ==> f [] = f [] @ []} \end{ttbox} is solved by \texttt{Simp_tac}, but \texttt{Asm_simp_tac} and \texttt{Asm_full_simp_tac} loop because the rewrite rule \texttt{f x = g(f(g x))} extracted from the assumption does not terminate. Isabelle notices certain simple forms of nontermination, but not this one. \subsubsection{Modifying simpsets locally} If a certain theorem is merely needed in one proof by simplification, the pattern \begin{ttbox} Addsimps [\(rare_theorem\)]; by(Simp_tac 1); Delsimps [\(rare_theorem\)]; \end{ttbox} is awkward. Therefore there are lower-case versions of the simplification tactics (\ttindexbold{simp_tac}, \ttindexbold{asm_simp_tac}, \ttindexbold{full_simp_tac}, \ttindexbold{asm_full_simp_tac}) and of the simpset modifiers (\ttindexbold{addsimps}, \ttindexbold{delsimps}) that do not access or modify the implicit simpset but explicitly take a simpset as an argument. For example, the above three lines become \begin{ttbox} by(simp_tac (simpset() addsimps [\(rare_theorem\)]) 1); \end{ttbox} where the result of the function call \texttt{simpset()} is the simpset of the current theory and \texttt{addsimps} is an infix function. The implicit simpset is read once but not modified. This is far preferable to pairs of \texttt{Addsimps} and \texttt{Delsimps}. Local modifications can be stacked as in \begin{ttbox} by(simp_tac (simpset() addsimps [\(rare_theorem\)] delsimps [\(some_thm\)]) 1); \end{ttbox} \subsubsection{Rewriting with definitions} Constant definitions (\S\ref{sec:ConstDefinitions}) are not automatically included in the simpset of a theory. Hence such definitions are not expanded automatically either, just as it should be: definitions are introduced for the purpose of abbreviating complex concepts. Of course we need to expand the definitions initially to derive enough lemmas that characterize the concept sufficiently for us to forget the original definition completely. For example, given the theory \begin{ttbox} \input{Misc/Exor.thy}\end{ttbox} we may want to prove \verb$exor A (~A)$. Instead of \texttt{Goal} we use \begin{ttbox} \input{Misc/exorgoal.ML}\end{ttbox} which tells Isabelle to expand the definition of \texttt{exor}---the first argument of \texttt{Goalw} can be a list of definitions---in the initial goal: \begin{ttbox} {\out exor A (~ A)} {\out 1. A & ~ ~ A | ~ A & ~ A} \end{ttbox} In this simple example, the goal is proved by \texttt{Simp_tac}. Of course the resulting theorem is insufficient to characterize \texttt{exor} completely. In case we want to expand a definition in the middle of a proof, we can simply add the definition locally to the simpset: \begin{ttbox} \input{Misc/exorproof.ML}\end{ttbox} You should normally not add the definition permanently to the simpset using \texttt{Addsimps} because this defeats the whole purpose of an abbreviation. \begin{warn} If you have defined $f\,x\,y$\texttt{~==~}$t$ then you can only expand occurrences of $f$ with at least two arguments. Thus it is safer to define $f$\texttt{~==~\%$x\,y$.}$\;t$. \end{warn} \subsubsection{Simplifying \texttt{let}-expressions} Proving a goal containing \ttindex{let}-expressions invariably requires the \texttt{let}-constructs to be expanded at some point. Since \texttt{let}-\texttt{in} is just syntactic sugar for a defined constant (called \texttt{Let}), expanding \texttt{let}-constructs means rewriting with \texttt{Let_def}: %context List.thy; %Goal "(let xs = [] in xs@xs) = ys"; \begin{ttbox}\makeatother {\out 1. (let xs = [] in xs @ xs) = ys} by(simp_tac (simpset() addsimps [Let_def]) 1); {\out 1. [] = ys} \end{ttbox} If, in a particular context, there is no danger of a combinatorial explosion of nested \texttt{let}s one could even add \texttt{Let_def} permanently via \texttt{Addsimps}. \subsubsection{Conditional equations} So far all examples of rewrite rules were equations. The simplifier also accepts {\em conditional\/} equations, for example \begin{ttbox} xs ~= [] ==> hd xs # tl xs = xs \hfill \((*)\) \end{ttbox} (which is proved by \texttt{exhaust_tac} on \texttt{xs} followed by \texttt{Asm_full_simp_tac} twice). Assuming that this theorem together with %\begin{ttbox}\makeatother \texttt{(rev xs = []) = (xs = [])} %\end{ttbox} are part of the simpset, the subgoal \begin{ttbox}\makeatother {\out 1. xs ~= [] ==> hd(rev xs) # tl(rev xs) = rev xs} \end{ttbox} is proved by simplification: the conditional equation $(*)$ above can simplify \texttt{hd(rev~xs)~\#~tl(rev~xs)} to \texttt{rev xs} because the corresponding precondition \verb$rev xs ~= []$ simplifies to \verb$xs ~= []$, which is exactly the local assumption of the subgoal. \subsubsection{Automatic case splits} Goals containing \ttindex{if}-expressions are usually proved by case distinction on the condition of the \texttt{if}. For example the goal \begin{ttbox} {\out 1. ! xs. if xs = [] then rev xs = [] else rev xs ~= []} \end{ttbox} can be split into \begin{ttbox} {\out 1. ! xs. (xs = [] --> rev xs = []) \& (xs ~= [] --> rev xs ~= [])} \end{ttbox} by typing \begin{ttbox} \input{Misc/splitif.ML}\end{ttbox} Because this is almost always the right proof strategy, the simplifier performs case-splitting on \texttt{if}s automatically. Try \texttt{Simp_tac} on the initial goal above. This splitting idea generalizes from \texttt{if} to \ttindex{case}: \begin{ttbox}\makeatother {\out 1. (case xs of [] => zs | y#ys => y#(ys@zs)) = xs@zs} \end{ttbox} becomes \begin{ttbox}\makeatother {\out 1. (xs = [] --> zs = xs @ zs) &} {\out (! a list. xs = a # list --> a # list @ zs = xs @ zs)} \end{ttbox} by typing \begin{ttbox} \input{Misc/splitlist.ML}\end{ttbox} In contrast to \texttt{if}-expressions, the simplifier does not split \texttt{case}-expressions by default because this can lead to nontermination in case of recursive datatypes. Nevertheless the simplifier can be instructed to perform \texttt{case}-splits by adding the appropriate rule to the simpset: \begin{ttbox} by(simp_tac (simpset() addsplits [split_list_case]) 1); \end{ttbox}\indexbold{*addsplits} solves the initial goal outright, which \texttt{Simp_tac} alone will not do. In general, every datatype $t$ comes with a rule \texttt{$t$.split} that can be added to the simpset either locally via \texttt{addsplits} (see above), or permanently via \begin{ttbox} Addsplits [\(t\).split]; \end{ttbox}\indexbold{*Addsplits} Split-rules can be removed globally via \ttindexbold{Delsplits} and locally via \ttindexbold{delsplits} as, for example, in \begin{ttbox} by(simp_tac (simpset() addsimps [\(\dots\)] delsplits [split_if]) 1); \end{ttbox} \subsubsection{Permutative rewrite rules} A rewrite rule is {\bf permutative} if the left-hand side and right-hand side are the same up to renaming of variables. The most common permutative rule is commutativity: $x+y = y+x$. Another example is $(x-y)-z = (x-z)-y$. Such rules are problematic because once they apply, they can be used forever. The simplifier is aware of this danger and treats permutative rules separately. For details see~\cite{Isa-Ref-Man}. \subsubsection{Tracing} \indexbold{tracing the simplifier} Using the simplifier effectively may take a bit of experimentation. Set the \verb$trace_simp$ flag to get a better idea of what is going on: \begin{ttbox}\makeatother {\out 1. rev [x] = []} \ttbreak set trace_simp; by(Simp_tac 1); \ttbreak\makeatother {\out Applying instance of rewrite rule:} {\out rev (?x # ?xs) == rev ?xs @ [?x]} {\out Rewriting:} {\out rev [x] == rev [] @ [x]} \ttbreak {\out Applying instance of rewrite rule:} {\out rev [] == []} {\out Rewriting:} {\out rev [] == []} \ttbreak\makeatother {\out Applying instance of rewrite rule:} {\out [] @ ?y == ?y} {\out Rewriting:} {\out [] @ [x] == [x]} \ttbreak {\out Applying instance of rewrite rule:} {\out ?x # ?t = ?t == False} {\out Rewriting:} {\out [x] = [] == False} \ttbreak {\out Level 1} {\out rev [x] = []} {\out 1. False} \end{ttbox} In more complicated cases, the trace can be enormous, especially since invocations of the simplifier are often nested (e.g.\ when solving conditions of rewrite rules). \subsection{How it works} \label{sec:SimpHow} \subsubsection{Higher-order patterns} \subsubsection{Local assumptions} \subsubsection{The preprocessor} \section{Induction heuristics} \label{sec:InductionHeuristics} The purpose of this section is to illustrate some simple heuristics for inductive proofs. The first one we have already mentioned in our initial example: \begin{quote} {\em 1. Theorems about recursive functions are proved by induction.} \end{quote} In case the function has more than one argument \begin{quote} {\em 2. Do induction on argument number $i$ if the function is defined by recursion in argument number $i$.} \end{quote} When we look at the proof of \begin{ttbox}\makeatother (xs @ ys) @ zs = xs @ (ys @ zs) \end{ttbox} in \S\ref{sec:intro-proof} we find (a) \texttt{\at} is recursive in the first argument, (b) \texttt{xs} occurs only as the first argument of \texttt{\at}, and (c) both \texttt{ys} and \texttt{zs} occur at least once as the second argument of \texttt{\at}. Hence it is natural to perform induction on \texttt{xs}. The key heuristic, and the main point of this section, is to generalize the goal before induction. The reason is simple: if the goal is too specific, the induction hypothesis is too weak to allow the induction step to go through. Let us now illustrate the idea with an example. We define a tail-recursive version of list-reversal, i.e.\ one that can be compiled into a loop: \begin{ttbox} \input{Misc/Itrev.thy}\end{ttbox} The behaviour of \texttt{itrev} is simple: it reverses its first argument by stacking its elements onto the second argument, and returning that second argument when the first one becomes empty. We need to show that \texttt{itrev} does indeed reverse its first argument provided the second one is empty: \begin{ttbox} \input{Misc/itrev1.ML}\end{ttbox} There is no choice as to the induction variable, and we immediately simplify: \begin{ttbox} \input{Misc/induct_auto.ML}\ttbreak\makeatother {\out1. !!a list. itrev list [] = rev list\(\;\)==> itrev list [a] = rev list @ [a]} \end{ttbox} Just as predicted above, the overall goal, and hence the induction hypothesis, is too weak to solve the induction step because of the fixed \texttt{[]}. The corresponding heuristic: \begin{quote} {\em 3. Generalize goals for induction by replacing constants by variables.} \end{quote} Of course one cannot do this na\"{\i}vely: \texttt{itrev xs ys = rev xs} is just not true --- the correct generalization is \begin{ttbox}\makeatother \input{Misc/itrev2.ML}\end{ttbox} If \texttt{ys} is replaced by \texttt{[]}, the right-hand side simplifies to \texttt{rev xs}, just as required. In this particular instance it is easy to guess the right generalization, but in more complex situations a good deal of creativity is needed. This is the main source of complications in inductive proofs. Although we now have two variables, only \texttt{xs} is suitable for induction, and we repeat our above proof attempt. Unfortunately, we are still not there: \begin{ttbox}\makeatother {\out 1. !!a list.} {\out itrev list ys = rev list @ ys} {\out ==> itrev list (a # ys) = rev list @ a # ys} \end{ttbox} The induction hypothesis is still too weak, but this time it takes no intuition to generalize: the problem is that \texttt{ys} is fixed throughout the subgoal, but the induction hypothesis needs to be applied with \texttt{a \# ys} instead of \texttt{ys}. Hence we prove the theorem for all \texttt{ys} instead of a fixed one: \begin{ttbox}\makeatother \input{Misc/itrev3.ML}\end{ttbox} This time induction on \texttt{xs} followed by simplification succeeds. This leads to another heuristic for generalization: \begin{quote} {\em 4. Generalize goals for induction by universally quantifying all free variables {\em(except the induction variable itself!)}.} \end{quote} This prevents trivial failures like the above and does not change the provability of the goal. Because it is not always required, and may even complicate matters in some cases, this heuristic is often not applied blindly. A final point worth mentioning is the orientation of the equation we just proved: the more complex notion (\texttt{itrev}) is on the left-hand side, the simpler \texttt{rev} on the right-hand side. This constitutes another, albeit weak heuristic that is not restricted to induction: \begin{quote} {\em 5. The right-hand side of an equation should (in some sense) be simpler than the left-hand side.} \end{quote} The heuristic is tricky to apply because it is not obvious that \texttt{rev xs \at\ ys} is simpler than \texttt{itrev xs ys}. But see what happens if you try to prove the symmetric equation! \section{Case study: compiling expressions} \label{sec:ExprCompiler} The task is to develop a compiler from a generic type of expressions (built up from variables, constants and binary operations) to a stack machine. This generic type of expressions is a generalization of the boolean expressions in \S\ref{sec:boolex}. This time we do not commit ourselves to a particular type of variables or values but make them type parameters. Neither is there a fixed set of binary operations: instead the expression contains the appropriate function itself. \begin{ttbox} \input{CodeGen/expr}\end{ttbox} The three constructors represent constants, variables and the combination of two subexpressions with a binary operation. The value of an expression w.r.t.\ an environment that maps variables to values is easily defined: \begin{ttbox} \input{CodeGen/value}\end{ttbox} The stack machine has three instructions: load a constant value onto the stack, load the contents of a certain address onto the stack, and apply a binary operation to the two topmost elements of the stack, replacing them by the result. As for \texttt{expr}, addresses and values are type parameters: \begin{ttbox} \input{CodeGen/instr}\end{ttbox} The execution of the stack machine is modelled by a function \texttt{exec} that takes a store (modelled as a function from addresses to values, just like the environment for evaluating expressions), a stack (modelled as a list) of values and a list of instructions and returns the stack at the end of the execution --- the store remains unchanged: \begin{ttbox} \input{CodeGen/exec}\end{ttbox} Recall that \texttt{hd} and \texttt{tl} return the first element and the remainder of a list. Because all functions are total, \texttt{hd} is defined even for the empty list, although we do not know what the result is. Thus our model of the machine always terminates properly, although the above definition does not tell us much about the result in situations where \texttt{Apply} was executed with fewer than two elements on the stack. The compiler is a function from expressions to a list of instructions. Its definition is pretty much obvious: \begin{ttbox}\makeatother \input{CodeGen/comp}\end{ttbox} Now we have to prove the correctness of the compiler, i.e.\ that the execution of a compiled expression results in the value of the expression: \begin{ttbox} exec s [] (comp e) = [value s e] \end{ttbox} This is generalized to \begin{ttbox} \input{CodeGen/goal2.ML}\end{ttbox} and proved by induction on \texttt{e} followed by simplification, once we have the following lemma about executing the concatenation of two instruction sequences: \begin{ttbox}\makeatother \input{CodeGen/goal2.ML}\end{ttbox} This requires induction on \texttt{xs} and ordinary simplification for the base cases. In the induction step, simplification leaves us with a formula that contains two \texttt{case}-expressions over instructions. Thus we add automatic case splitting as well, which finishes the proof: \begin{ttbox} \input{CodeGen/simpsplit.ML}\end{ttbox} We could now go back and prove \texttt{exec s [] (comp e) = [value s e]} merely by simplification with the generalized version we just proved. However, this is unnecessary because the generalized version fully subsumes its instance. \section{Total recursive functions} \label{sec:recdef} \index{*recdef|(} Although many total functions have a natural primitive recursive definition, this is not always the case. Arbitrary total recursive functions can be defined by means of \texttt{recdef}: you can use full pattern-matching, recursion need not involve datatypes, and termination is proved by showing that each recursive call makes the argument smaller in a suitable (user supplied) sense. \subsection{Defining recursive functions} Here is a simple example, the Fibonacci function: \begin{ttbox} consts fib :: nat => nat recdef fib "measure(\%n. n)" "fib 0 = 0" "fib 1 = 1" "fib (Suc(Suc x)) = fib x + fib (Suc x)" \end{ttbox} The definition of \texttt{fib} is accompanied by a \bfindex{measure function} \texttt{\%n.$\;$n} that maps the argument of \texttt{fib} to a natural number. The requirement is that in each equation the measure of the argument on the left-hand side is strictly greater than the measure of the argument of each recursive call. In the case of \texttt{fib} this is obviously true because the measure function is the identity and \texttt{Suc(Suc~x)} is strictly greater than both \texttt{x} and \texttt{Suc~x}. Slightly more interesting is the insertion of a fixed element between any two elements of a list: \begin{ttbox} consts sep :: "'a * 'a list => 'a list" recdef sep "measure (\%(a,xs). length xs)" "sep(a, []) = []" "sep(a, [x]) = [x]" "sep(a, x#y#zs) = x # a # sep(a,y#zs)" \end{ttbox} This time the measure is the length of the list, which decreases with the recursive call; the first component of the argument tuple is irrelevant. Pattern matching need not be exhaustive: \begin{ttbox} consts last :: 'a list => bool recdef last "measure (\%xs. length xs)" "last [x] = x" "last (x#y#zs) = last (y#zs)" \end{ttbox} Overlapping patterns are disambiguated by taking the order of equations into account, just as in functional programming: \begin{ttbox} recdef sep "measure (\%(a,xs). length xs)" "sep(a, x#y#zs) = x # a # sep(a,y#zs)" "sep(a, xs) = xs" \end{ttbox} This defines exactly the same function \texttt{sep} as further above. \begin{warn} Currently \texttt{recdef} only accepts functions with a single argument, possibly of tuple type. \end{warn} When loading a theory containing a \texttt{recdef} of a function $f$, Isabelle proves the recursion equations and stores the result as a list of theorems $f$.\texttt{rules}. It can be viewed by typing \begin{ttbox} prths \(f\).rules; \end{ttbox} All of the above examples are simple enough that Isabelle can determine automatically that the measure of the argument goes down in each recursive call. In that case $f$.\texttt{rules} contains precisely the defining equations. In general, Isabelle may not be able to prove all termination conditions automatically. For example, termination of \begin{ttbox} consts gcd :: "nat*nat => nat" recdef gcd "measure ((\%(m,n).n))" "gcd (m, n) = (if n=0 then m else gcd(n, m mod n))" \end{ttbox} relies on the lemma \texttt{mod_less_divisor} \begin{ttbox} 0 < n ==> m mod n < n \end{ttbox} that is not part of the default simpset. As a result, Isabelle prints a warning and \texttt{gcd.rules} contains a precondition: \begin{ttbox} (! m n. 0 < n --> m mod n < n) ==> gcd (m, n) = (if n=0 \dots) \end{ttbox} We need to instruct \texttt{recdef} to use an extended simpset to prove the termination condition: \begin{ttbox} recdef gcd "measure ((\%(m,n).n))" simpset "simpset() addsimps [mod_less_divisor]" "gcd (m, n) = (if n=0 then m else gcd(n, m mod n))" \end{ttbox} This time everything works fine and \texttt{gcd.rules} contains precisely the stated recursion equation for \texttt{gcd}. When defining some nontrivial total recursive function, the first attempt will usually generate a number of termination conditions, some of which may require new lemmas to be proved in some of the parent theories. Those lemmas can then be added to the simpset used by \texttt{recdef} for its proofs, as shown for \texttt{gcd}. Although all the above examples employ measure functions, \texttt{recdef} allows arbitrary wellfounded relations. For example, termination of Ackermann's function requires the lexicographic product \texttt{**}: \begin{ttbox} recdef ack "measure(\%m. m) ** measure(\%n. n)" "ack(0,n) = Suc n" "ack(Suc m,0) = ack(m, 1)" "ack(Suc m,Suc n) = ack(m,ack(Suc m,n))" \end{ttbox} For details see~\cite{Isa-Logics-Man} and the examples in the library. \subsection{Deriving simplification rules} Once we have succeeded to prove all termination conditions, we can start to derive some theorems. In contrast to \texttt{primrec} definitions, which are automatically added to the simpset, \texttt{recdef} rules must be included explicitly, for example as in \begin{ttbox} Addsimps fib.rules; \end{ttbox} However, some care is necessary now, in contrast to \texttt{primrec}. Although \texttt{gcd} is a total function, its defining equation leads to nontermination of the simplifier, because the subterm \texttt{gcd(n, m mod n)} on the right-hand side can again be simplified by the same equation, and so on. The reason: the simplifier rewrites the \texttt{then} and \texttt{else} branches of a conditional if the condition simplifies to neither \texttt{True} nor \texttt{False}. Therefore it is recommended to derive an alternative formulation that replaces case distinctions on the right-hand side by conditional equations. For \texttt{gcd} it means we have to prove \begin{ttbox} gcd (m, 0) = m n ~= 0 ==> gcd (m, n) = gcd(n, m mod n) \end{ttbox} To avoid nontermination during those proofs, we have to resort to some low level tactics: \begin{ttbox} Goal "gcd(m,0) = m"; by(resolve_tac [trans] 1); by(resolve_tac gcd.rules 1); by(Simp_tac 1); \end{ttbox} At this point it is not necessary to understand what exactly \texttt{resolve_tac} is doing. The main point is that the above proof works not just for this one example but in general (except that we have to use \texttt{Asm_simp_tac} and $f$\texttt{.rules} in general). Try the second \texttt{gcd}-equation above! \subsection{Induction} Assuming we have added the recursion equations (or some suitable derived equations) to the simpset, we might like to prove something about our function. Since the function is recursive, the natural proof principle is again induction. But this time the structural form of induction that comes with datatypes is unlikely to work well---otherwise we could have defined the function by \texttt{primrec}. Therefore \texttt{recdef} automatically proves a suitable induction rule $f$\texttt{.induct} that follows the recursion pattern of the particular function $f$. Roughly speaking, it requires you to prove for each \texttt{recdef} equation that the property you are trying to establish holds for the left-hand side provided it holds for all recursive calls on the right-hand side. Applying $f$\texttt{.induct} requires its explicit instantiation. See \S\ref{sec:explicit-inst} for details. \index{*recdef|)}