spot/spot/twaalgos/degen.hh
Alexandre Duret-Lutz 2bd2abd4c9 pdegen & toparity: minor refactor
* spot/twaalgos/degen.hh (is_partially_degeneralizable): Pass the
forbid vector by reference, and document it.  I hope that not passing
forbid by copy will get rid of a spurious "potential nullptr" warning
by gcc on Arch Linux.
* spot/twaalgos/degen.cc: Adjust, and refactor the code a bit.
* spot/twaalgos/toparity.cc: Likewise.
2024-05-14 10:41:18 +02:00

194 lines
8.4 KiB
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// -*- coding: utf-8 -*-
// Copyright (C) by the Spot authors, see the AUTHORS file for details.
//
// This file is part of Spot, a model checking library.
//
// Spot is free software; you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 3 of the License, or
// (at your option) any later version.
//
// Spot is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
// or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
// License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#pragma once
#include <spot/twa/twagraph.hh>
namespace spot
{
class scc_info;
/// \ingroup twa_acc_transform
/// \brief Degeneralize a generalized (co)Büchi automaton into an
/// equivalent (co)Büchi automaton.
///
/// There are two variants of the function. If the generalized
/// (co)Büchi acceptance uses N colors, degeneralize() algorithm
/// will builds a state-based (co)Büchi automaton that has at most
/// (N+1) times the number of states of the original automaton.
/// degeneralize_tba() builds a transition-based (co)Büchi automaton
/// that has at most N times the number of states of the original
/// automaton.
///
/// Additional options control optimizations described in
/// \cite babiak.13.spin . When \a use_z_lvl is set, the level of
/// the degeneralized automaton is reset every time an SCC is exited.
/// If \a use_cust_acc_orders is set, the degeneralization will
/// compute a custom acceptance order for each SCC (this option is
/// disabled by default because our benchmarks show that it usually
/// does more harm than good). If \a use_lvl_cache is set,
/// every time an SCC is entered on a state that as already been
/// associated to some level elsewhere, reuse that level (set it to
/// 2 to keep the smallest number, 3 to keep the largest level, and
/// 1 to keep the first level found). If \a ignaccsl is set, we do
/// not directly jump to the accepting level if the entering state
/// has an accepting self-loop. If \a remove_extra_scc is set (the
/// default) we ensure that the output automaton has as many SCCs as
/// the input by removing superfluous SCCs.
///
/// Any of these three options will cause the SCCs of the automaton
/// \a a to be computed prior to its actual degeneralization.
///
/// The mapping between each state of the resulting automaton
/// and the original state of the input automaton is stored in the
/// "original-states" named property of the produced automaton. Call
/// `aut->get_named_prop<std::vector<unsigned>>("original-states")`
/// to retrieve it. However be aware that if the input automaton
/// already defines the "original-states" named property, it will
/// be composed with the new one, so the "original-states" of the
/// degeneralized automaton will refer to the same automaton as the
/// "original-states" of the input automaton.
///
/// Note that these functions may return the original
/// automaton as-is if it is already degeneralized; in this case
/// the "original-states" property is not defined (or not changed).
///
/// Similarly, the property "degen-levels" keeps track of the degeneralization
/// levels. To retrieve it, call
/// `aut->get_named_prop<std::vector<unsigned>>("degen-levels")`.
///
/// As an alternative method to degeneralization, one may also
/// consider ACD transform. acd_transform() will never produce
/// larger automata than degeneralize_tba(), and
/// acd_transform_sbacc() produce smaller automata than
/// degeneralize() on the average. See \cite casares.22.tacas for
/// some comparisons.
///
/// \@{
SPOT_API twa_graph_ptr
degeneralize(const const_twa_graph_ptr& a, bool use_z_lvl = true,
bool use_cust_acc_orders = false,
int use_lvl_cache = 1,
bool skip_levels = true,
bool ignaccsl = false,
bool remove_extra_scc = true);
SPOT_API twa_graph_ptr
degeneralize_tba(const const_twa_graph_ptr& a, bool use_z_lvl = true,
bool use_cust_acc_orders = false,
int use_lvl_cache = 1,
bool skip_levels = true,
bool ignaccsl = false,
bool remove_extra_scc = true);
/// \@}
/// \ingroup twa_acc_transform
/// \brief Partial degeneralization of a TwA
///
/// Given an automaton whose acceptance contains a conjunction of
/// Inf terms, perform a partial degeneralization to replace this
/// conjunction by a single Inf term.
///
/// For instance if the input has acceptance
/// (Fin(0)&Inf(1)&Inf(3))|Fin(2)
/// calling partial_degeneralize with \a todegen set to `{1,3}`
/// will build an equivalent automaton with acceptance
/// (Fin(0)&Inf(2))|Fin(1)
///
/// where Inf(2) tracks the acceptance of the original
/// Inf(1)&Inf(3), and Fin(1) tracks the acceptance of the original
/// Fin(2).
///
/// Cases where the sets listed in \a todegen also occur outside
/// of the Inf-conjunction are also supported. Subformulas that
/// are disjunctions of Fin(.) terms (e.g., Fin(1)|Fin(2)) will
/// be degeneralized as well.
///
/// If this functions is called with a value of \a todegen that does
/// not match a conjunction of Inf(.), or a disjunction of Fin(.),
/// an std::runtime_error exception is thrown.
///
/// The version of the function that has no \a todegen argument will
/// perform all possible partial degeneralizations, and may return
/// the input automaton unmodified if no partial degeneralization is
/// possible.
///
/// The "original-state" and "degen-levels" named properties are
/// updated as for degeneralize() and degeneralize_tba().
/// @{
SPOT_API twa_graph_ptr
partial_degeneralize(const const_twa_graph_ptr& a,
acc_cond::mark_t todegen);
SPOT_API twa_graph_ptr
partial_degeneralize(twa_graph_ptr a);
/// @}
/// \brief Is the automaton partially degeneralizable?
///
/// Return a mark `M={m₁, m₂, ..., mₙ}` such that either
/// `Inf(m₁)&Inf(m₂)&...&Inf(mₙ)` or `Fin(m₁)|Fin(m₂)|...|Fin(mₙ)`
/// appears in the acceptance condition of \a aut.
///
/// If multiple such marks exist the smallest such mark is returned.
/// (This is important in case of overlapping options. E.g., in the
/// formula `Inf(0)&Inf(1)&Inf(3) | (Inf(0)&Inf(1))&Fin(2)` we have
/// two possible degeneralizations options `{0,1,3}`, and `{0,1}`.
/// Degeneralizing for `{0,1,3}` and then `{0,1}` could enlarge the
/// automaton by a factor 6, while degeneralizing by `{0,1}` and
/// then some `{x,y}` may enlarge the automaton only by a factor 4.)
///
/// Return an empty mark otherwise if the automaton is not partially
/// degeneralizable.
///
/// The optional arguments \a allow_inf and \a allow_fin, can be set
/// to false to disallow one type of match.
///
/// If you need to disallow certain marks from being returned, pass
/// them in the \a forbid vector.
SPOT_API acc_cond::mark_t
is_partially_degeneralizable(const const_twa_graph_ptr& aut,
bool allow_inf = true, bool allow_fin = true,
const std::vector<acc_cond::mark_t>&
forbid = {});
/// \ingroup twa_algorithms
/// \brief Propagate marks around the automaton
///
/// For each state of the automaton, marks that are common
/// to all input transitions will be pushed on the outgoing
/// transitions, and marks that are common to all outgoing
/// transitions will be pulled to the input transitions.
/// This considers only transitions that are not self-loops
/// and that belong to some SCC. If an scc_info has already
/// been built, pass it as \a si to avoid building it again.
///
/// Two variants of the algorithm are provided. One modifies
/// the automaton in place; the second returns a vector of marks
/// indexed by transition numbers.
///
/// @{
SPOT_API std::vector<acc_cond::mark_t>
propagate_marks_vector(const const_twa_graph_ptr& aut,
const scc_info* si = nullptr);
SPOT_API void
propagate_marks_here(twa_graph_ptr& aut,
const scc_info* si = nullptr);
/// @}
}