SYNOPSIS
lps2torx [OPTION]… input.lps
DESCRIPTION
lps2torx provides access to a labelled transition system from a specification provided in input.lps via the TorXExplorer textual interface on stdin and stdout. Specifications are in lps format and are commonly generated by mcrl22lps(1).
OPTIONS
mCRL2 Options
 mcrl2=OPTIONS

Pass options to the mCRL2 library. Defaults to "
rewriter=jittyc
".The "
rewriter=<rewriter>
" option is the only recognized option. Possible rewriters arejitty
andjittyc
.  mcrl2finitetypes

Use mCRL2 finite type information.
Enabling this option may cause premature termination in case nonnormalform instances of finite types occur in the state space. This will be the case, e.g., when the specification has been preprocessed using lpsparunfold(1).
 mcrl2readableedgelabels

Use human readable edge labels.
Enabling this option may cause problems during bisimulation reduction, e.g., the edge labels l(0) with 0 of type Nat and l(0) with 0 of type Pos will be mapped to the same string.
PINS Options
 labels

Print state variable, type and value names, and state and action labels. Then exit. Useful for writing predicate (
invariant
), LTL (ltl
), CTL/CTL* (ctl
), and mucalculus (mu
) expressions.  matrix

Print the dependency matrix and exit.
 c, cache

Enable caching of greybox calls.
If this option is used, the state space generator makes calls to the short version of the greybox nextstate function and memoizes the results. If the nextstate function is expensive this will yield substantial speedups.
 pinsguards

Use guards in combination with the long nextstate function to speed up the nextstate function.
 allowundefinededges

Allow values for edge variables in atomic predicates to be unreachable.
 allowundefinedvalues

Allow undefined values in atomic predicates for enums.
 r, regroup=SPEC

Enable regrouping optimizations on the dependency matrix.
SPEC is a commaseparated sequence of transformations <(T,)+> which are applied in this order to the dependency matrix. The following transformations T are available:
 gs

Group Safely; macro for "gc,gr,cw,rs"; almost always a win.
 ga

Group Aggressively (row subsumption); macro for "gc,rs,ru,cw,rs"; can be a huge win, but in some cases causes slower state space generation.
 gsa

Group Simulated Annealing; macro for "gc,gr,csa,rs"; almost always a win; usually better than gs.
 gc

Group Columns; macro for "cs,cn".
 gr

Group Rows; macro for "rs,rn".
 cs

Column Sort; sort columns lexicographically.
 cn

Column Nub; (temporarily) group duplicate columns, thereby making ca more tractable. Requires cs.
 cw

Column sWap; minimize distance between columns by swapping them heuristically. This reordering improves performance of the symbolic data structures.
 ca

Column All permutations; try to find the column permutation with the best cost metric. Potentially, this is an expensive operation.
 csa

Column Simulated Annealing; minimize distance between columns by swapping them using simulated annealing.
 rs

Row Sort; sort rows lexicographically.
 rn

Row Nub; remove duplicate rows from the dependency matrix. This is always a win. Requires rs.
 ru

Row sUbsume; try to remove more rows than nubbing, thereby trading speed for memory. Requires rs.
 w2W

Overapproximate all mustwrite to maywrite. Maywrite supports the copy () dependency.
 r2+

Overapproximate read to read+write. Allows read dependencies to also subsume write dependencies.
 w2+

Overapproximate mustwrite to read+write. Allows mustwrite dependencies to also subsume read dependencies.
 W2+

Overapproximate maywrite to read+write. Allows mustwrite dependencies to also subsume read dependencies.
 2r

Overapproximate copy to read. May be useful for testing whether the dependency matrix is correct.
 rb4w

Use special heuristics to move read dependencies before write dependences. Often a win in symbolic state space generation.
 mm

Writes metrics of the selected (sr, sw, sc) matrix to stdout. The following metrics are printed:

Event span: the total distance between the minimum and maximum column of rows.

Normalized event span: the event span divided by the size of the matrix (rows x columns).

Weighted event span: the weighted event span, the event span, including a moment signifying the location of the span. See, Siminiceanu et al., we use moment 1.

Normalized weighted event span: the weighted event span divided by the size of the matrix (rows x column).

 sr

Select the read matrix for cs, csa, cw, ca, rs, bcm, bs, bk, vcm, vacm, vgps and mm.
 sw

Select the write matrix (default) for cs, csa, cw, ca, rs, bcm, bs, bk, vcm, vacm, vgps and mm. The write matrix is the default selection, because only write dependencies can create new nodes in decision diagrams. A bad variable order in the write matrix thus leads to a large number of peak nodes during reachability analysis. A bad variable order in the read matrix can also lead to a slow reachability analysis, but typically not as severe as a bad variable order in the write matrix. Slow reachability analysis due to a bad variable order in the read matrix causes many recursive calls to the relational product operation. Typically it is best that read dependencies are moved to the top DD level, thus left most in the read matrix.
 sc

Select the combined matrix for cs, csa, cw, ca, rs, bcm, bs, bk, vcm, vacm, vgps and mm. The combined matrix is the logical or of the read and write matrix.
 bg

Use a bipartite graph (default) for bcm, bk, bs, vcm, vacm and vgps.
 tg

Create a total graph of the bipartite graph for bcm, bk, bs, vcm, vacm and vgps. This adds more vertices and edges thus increasing computation time, but sometimes provides a better ordering.
Below, the sparse matrix algorithms prefixed with b are only available when LTSmin is compiled with Boost. Algorithms prefixed with v are only available when LTSmin is compiled with ViennaCL.
 bcm

Apply Boost’s CuthillMcKee ordering.
 bk

Apply Boost’s King ordering.
 bs

Apply Boost’s Sloan ordering.
 vcm

Apply ViennaCL’s CuthillMcKee ordering.
 vacm

Apply ViennaCL’s advanced CuthillMcKee ordering.
 vgps

Apply ViennaCl’s GibbsPooleStockmeyer ordering.
 f

Apply FORCE ordering.
 rowperm=PERM

Apply row permutation PERM, where PERM is a sequence of row numbers, separated by a comma. E.g. the vector 2,1,0 will swap row 2 with row 0.
 colperm=PERM

Apply column permutation PERM, where PERM is a sequence of column numbers, separated by a comma. E.g. the vector 2,1,0 will swap column 2 with column 0.
 colins=PAIRS

Insert columns before other columns in the dependency matrix.
PAIRS is a commaseparated sequence of pairs <(C.C,)+>'. E.g. colins=1.0 will insert column 1 before column 0. Each pair contains a source column C and a target column C'. During the application of the whole sequence, C will always be the column number that corresponds with the column before the application of the whole sequence. The column number C' will always be the column during the application of the whole sequence. This means that in for example colins=2.0,1.0, first column 2 is inserted at position 0, then column 1 is inserted at position 0. The result will be that the original column 2 will be at position 1. Another important detail is that when colins is used, all source columns will temporarily be "removed" during reordering from the dependency matrix, i.e. when the r,regroup option is given. After reordering is done, the columns will be inserted at the desired target position. In other words, reordering algorithms given by the option r,regroup, will only be applied on the dependency matrix with source columns removed.
 sloanw1=WEIGHT1

Use WEIGHT1 as the first weight for the Sloan algorithm, see https://www.boost.org/doc/libs/1_66_0/libs/graph/doc/sloan_ordering.htm.
 sloanw2=WEIGHT2

Use WEIGHT2 as the second weight for the Sloan algorithm, see https://www.boost.org/doc/libs/1_66_0/libs/graph/doc/sloan_ordering.htm.
 graphmetrics

Print Boost’s and ViennaCL’s graph metrics (only available when LTSmin is compiled with Boost or ViennaCL).
 regroupexit

Exit with 0 when regrouping is done.
 regrouptime

Print timing information of each transformation, given in sequence regroup (r).
 mucalc=FILEFORMULA

Compute a parity game for the mucalculus formula.
The mucalculus formula is provided in the file FILE or directly as a string FORMULA. The syntax and tool support are described in ltsminmucalc(5).
 por=heurdel

Activate partialorder reduction
PartialOrder Reduction (POR) can reduce the state space when searching for deadlocks (
d
) or accepting cycles (ltl
). Two POR algorithms are available: heur

Uses a costbased heuristic beamsearch to find the smallest stubborn set
 del

Uses Valmari’s deletion algorithm to find the smallest stubborn set by iteratively removing transitions while maintaining the constraints.
 weak

Use weak commutativity in partialorder reduction. Possibly yielding better reductions.
 leap

Use leaping partialorder reduction, by combining several disjoint stubborn sets sequentially.
Environment Variables
LTSmin supports the following list of environment variables.
Name  Unit  Description 

LTSMIN_MEM_SIZE 
bytes 
Sets the amount of system memory to the given value. 
LTSMIN_NUM_CPUS 
constant 
Sets the amount of CPUs to the given value. 
The variables LTSMIN_MEM_SIZE, and LTSMIN_NUM_CPUS are particularly relevant when neither sysconf(3) nor cgroups(7) is able to properly detect these limits, e.g. when LTSmin runs on Travis CI in a docker container.
General Options
 v

Increase the level of verbosity
 q

Be quiet; do not print anything to the terminal.
 debug=<file.c>

Enable debugging output for file.c (option allowed multiple times).
 version

Print version string of this tool.
 h, help

Print help text
 usage

Print short usage summary.
EXIT STATUS
 0

Successful termination.
 255

Some error occurred.
SUPPORT
Send questions, bug reports, comments and feature suggestions to the LTSmin Support Team.
SEE ALSO
ltsmin(7), ltsminconvert(1), etf(5), muCRL, mCRL2, DiVinE2.2, and prom.