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introduce output_aborter, and use it in ltlcross

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* spot/twaalgos/alternation.cc, spot/twaalgos/alternation.hh,
spot/twaalgos/complement.cc, spot/twaalgos/complement.hh,
spot/twaalgos/determinize.cc, spot/twaalgos/determinize.hh,
spot/twaalgos/minimize.cc, spot/twaalgos/minimize.hh,
spot/twaalgos/postproc.cc, spot/twaalgos/postproc.hh,
spot/twaalgos/powerset.cc, spot/twaalgos/powerset.hh,
spot/twaalgos/product.cc, spot/twaalgos/product.hh: Use an
output_aborter argument to abort if the output is too large.
* bin/ltlcross.cc: Use complement() with an output_aborter
so that ltlcross will not attempt to build complement larger
than 500 states or 5000 edges.  Add --determinize-max-states
and --determinize-max-edges options.
* tests/core/ltlcross3.test, tests/core/ltlcrossce2.test,
tests/core/sccsimpl.test, tests/core/wdba2.test,
tests/python/stutter-inv.ipynb: Adjust test cases.
* NEWS: Document this.
* bin/spot-x.cc: Add documentation for postprocessor's
det-max-states and det-max-edges arguments.
* doc/org/ltlcross.org: Update description.
This commit is contained in:
Alexandre Duret-Lutz 2019-05-27 23:08:13 +02:00
parent 5c3a33f720
commit a85045091b
23 changed files with 568 additions and 287 deletions
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bin/spot-x.cc
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@ -103,6 +103,14 @@ for the presence of an accepting self-loop.") },
{ DOC("degen-remscc", "If non-zero (the default), make sure the output \
of the degenalization has as many SCCs as the input, by removing superfluous \
ones.") },
{ DOC("det-max-states", "When defined to a positive integer N, \
determinizations will be aborted whenever the number of generated \
states would exceed N. In this case a non-deterministic automaton \
will be returned.")},
{ DOC("det-max-edges", "When defined to a positive integer N, \
determinizations will be aborted whenever the number of generated \
edges would exceed N. In this case a non-deterministic automaton \
will be returned.")},
{ DOC("det-scc", "Set to 0 to disable scc-based optimizations in \
the determinization algorithm.") },
{ DOC("det-simul", "Set to 0 to disable simulation-based optimizations in \