1 | |
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2 | (** This module provides a generic algorithm to compute the least |
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3 | solution of a system of monotonic equations. *) |
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4 | |
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5 | (**************************************************************************) |
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6 | (* *) |
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7 | (* Fix *) |
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8 | (* *) |
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9 | (* Author: François Pottier, INRIA Paris-Rocquencourt *) |
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10 | (* Version: 20091201 *) |
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11 | (* *) |
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12 | (* The copyright to this code is held by Institut National de Recherche *) |
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13 | (* en Informatique et en Automatique (INRIA). All rights reserved. This *) |
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14 | (* file is distributed under the license CeCILL-C (see file LICENSE). *) |
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15 | (* *) |
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16 | (**************************************************************************) |
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17 | |
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18 | (* This code is described in the paper ``Lazy Least Fixed Points in ML''. *) |
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19 | |
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20 | (* -------------------------------------------------------------------------- *) |
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21 | |
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22 | (* Maps. *) |
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23 | |
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24 | (* We require imperative maps, that is, maps that can be updated in place. |
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25 | An implementation of persistent maps, such as the one offered by ocaml's |
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26 | standard library, can easily be turned into an implementation of imperative |
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27 | maps, so this is a weak requirement. *) |
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28 | |
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29 | module type IMPERATIVE_MAPS = sig |
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30 | type key |
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31 | type 'data t |
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32 | val create: unit -> 'data t |
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33 | val clear: 'data t -> unit |
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34 | val add: key -> 'data -> 'data t -> unit |
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35 | val find: key -> 'data t -> 'data |
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36 | val iter: (key -> 'data -> unit) -> 'data t -> unit |
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37 | end |
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38 | |
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39 | (* -------------------------------------------------------------------------- *) |
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40 | |
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41 | (* Properties. *) |
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42 | |
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43 | (* Properties must form a partial order, equipped with a least element, and |
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44 | must satisfy the ascending chain condition: every monotone sequence |
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45 | eventually stabilizes. *) |
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46 | |
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47 | (* [is_maximal] determines whether a property [p] is maximal with respect to |
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48 | the partial order. Only a conservative check is required: in any event, it |
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49 | is permitted for [is_maximal p] to return [false]. If [is_maximal p] |
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50 | returns [true], then [p] must have no upper bound other than itself. In |
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51 | particular, if properties form a lattice, then [p] must be the top |
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52 | element. This feature, not described in the paper, enables a couple of |
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53 | minor optimizations. *) |
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54 | |
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55 | module type PROPERTY = sig |
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56 | type property |
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57 | val bottom: property |
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58 | val equal: property -> property -> bool |
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59 | val is_maximal: property -> bool |
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60 | end |
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61 | |
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62 | (* -------------------------------------------------------------------------- *) |
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63 | |
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64 | (* The code is parametric in an implementation of maps over variables and in |
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65 | an implementation of properties. *) |
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66 | |
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67 | module Make |
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68 | (M : IMPERATIVE_MAPS) |
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69 | (P : PROPERTY) |
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70 | : sig |
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71 | type variable = M.key |
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72 | type property = P.property |
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73 | |
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74 | (* A valuation is a mapping of variables to properties. *) |
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75 | type valuation = variable -> property |
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76 | |
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77 | (* A right-hand side, when supplied with a valuation that gives |
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78 | meaning to its free variables, evaluates to a property. More |
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79 | precisely, a right-hand side is a monotone function of |
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80 | valuations to properties. *) |
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81 | type rhs = valuation -> property |
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82 | |
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83 | (* A system of equations is a mapping of variables to right-hand |
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84 | sides. *) |
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85 | type equations = variable -> rhs |
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86 | |
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87 | (* [lfp eqs] produces the least solution of the system of monotone |
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88 | equations [eqs]. *) |
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89 | |
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90 | (* It is guaranteed that, for each variable [v], the application [eqs v] is |
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91 | performed at most once (whereas the right-hand side produced by this |
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92 | application is, in general, evaluated multiple times). This guarantee can |
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93 | be used to perform costly pre-computation, or memory allocation, when [eqs] |
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94 | is applied to its first argument. *) |
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95 | |
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96 | (* When [lfp] is applied to a system of equations [eqs], it performs no |
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97 | actual computation. It produces a valuation, [get], which represents |
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98 | the least solution of the system of equations. The actual fixed point |
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99 | computation takes place, on demand, when [get] is applied. *) |
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100 | val lfp: equations -> valuation |
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101 | end |
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102 | |
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103 | (** val compute_fixpoint : Fixpoints.fixpoint_computer **) |
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