Provided by: toulbar2_1.0.0+dfsg3-2build1_amd64 
NAME
toulbar2 - exactly solves discrete optimization problems on graphical models
SYNOPSIS
toulbar2 [options] file
DESCRIPTION
toulbar2 solves discrete optimization problems defined by a graphical model including Cost
Function Networks (solving the Weighted Constraint Satisfaction Problem), Markov Random
Fields (solving Maximum A Posteriori or MAP/MRF), Bayesian Networks (solving Maximum
Probability Explanation or MPE/BN), Quadratic Pseudo Boolean Optimization Problems (QPBO
or MAXCUT), Partial Weighted Maximum Satisfiability...
OPTIONS
GENERAL CONTROL
-a=[integer]
Finds at most a given number of solutions with a cost strictly lower than the
initial upper bound and stops, or if no integer is given, finds all solutions (or
counts the number of zero-cost satisfiable solutions in conjunction with BTD).
-D Approximate zero-cost solution count with BTD.
-logz Computes the log of probability of evidence (i.e. log partition function or log(Z)
or PR task). Restricted to stochastic graphical models (.uai format).
-seed=[integer]
Initializes the pseudo-random generator used inside toulbar2 with a fixed non-
negative integer argument. A negative argument instead specifies that the pseudo-
random generator be seeded by current time (default value is 1).
--stdin=[format]
Indicates that the problem should be read from the standard input instead of a file
(default format is cfn). Eg. cat example.uai | toulbar2 --stdin=uai
-timer=[integer]
Gives a cpu-time limit in seconds. Toulbar2 will stop after the specified amount
of CPU time has been consumed. The time limit is a CPU user time limit, not wall
clock time limit.
PREPROCESSING
-nopre Deactivates all preprocessing options (equivalent to -e: -p: -t: -f: -dec: -n:
-mst: -dee:).
-p=[integer]
Preprocessing only: activates variable elimination of variables of degree less than
or equal to the given value (default value is -1, with no elimination performed).
-t=[integer]
Preprocessing only: simulates restricted path consistency by adding ternary cost
functions on triangles of binary cost functions within a given maximum space limit
(in MB).
-f=[integer]
Preprocessing only: variable elimination of functional (f=1) (resp. bijective
(f=2)) variables (default value is 1)
-dec Preprocessing only: pairwise decomposition of cost functions with arity larger than
3 into smaller arity cost functions (default: activated)
-n=[integer]
Preprocessing only: projects n-ary cost functions on all binary cost functions if n
is lower than the given value (default value is 10).
-mst Find a maximum spanning tree ordering for DAC.
-M=[integer]
Apply the Min Sum Diffusion algorithm (default is off, with a number of iterations
of 0).
INITIAL UPPER BOUNDING
-ub=[decimal]
Gives a primal (upper in minimization mode, lower otherwise) bound on cost that a
solution must satisfies. If the file specifies a primal bound too, the tightest of
the two bounds is used. A tight primal bound can accelerate search or be used in
conjunction with -a to find all solutions of sufficient quality.
-l=[integer]
Activate limited discrepancy search. Use a negative value to stop search after the
given absolute number of discrepancies have been explored (discrepancy bound = 4 by
default).
-L=[integer]
Activate randomized (quasi-random variable ordering) search with restart (maximum
number of nodes = 10000 by default)
-i=["string"]
Use initial upper bound found by INCOP local search solver. The string parameter
is optional, using "0 1 3 idwa 100000 cv v 0 200 1 0 0" by default with the
following meaning: stoppinglowerbound randomseed nbiterations method nbmoves
neighborhoodchoice neighborhoodchoice minnbneighbors maxnbneighbors
neighborhoodchoice autotuning tracemode.
-x=[(,i=a)*]
Assigns variable of index i to value a (multiple assignments are separated by a
comma and no space). Without any argument, a complete assignment, used as initial
upper bound and as a value orderin heuristic, is read from default file "sol" or
from a filename given directly as an additional input filename with ".sol"
extension and without -x
TREE SEARCH ALGORITHMS AND TREE DECOMPOSITION SELECTION
-hbfs=[integer]
Use hybrid best-first search, restarting from the root after a given number of
backtracks (default value is 10000).
-open=[integer]
Set hybrid best-first search limit on the number of stored open nodes (default
value is -1, no limit)
-B=[integer]
Use (0) DFBB, (1) BTD, (2) RDS-BTD, (3) RDS-BTD with path decomposition instead of
tree decomposition (default value is 0)
-O=[filename]
Read a variable elimination order from a file in order to build a tree
decomposition (if BTD-like and/or variable elimination methods are used). The order
is also used as a DAC ordering.
-O=[negativeinteger]
Build a tree decomposition (if BTD-like and/or variable elimination methods are
used) and also a compatible DAC ordering using either:
(-1) maximum cardinality search ordering
(-2) minimum degree ordering
(-3) minimum fill-in ordering,
(-4) maximum spanning tree ordering (see -mst),
(-5) reverse Cuthill-Mckee ordering,
(-6) approximate minimum degree ordering
If not specified, then the order in which variables appear in the problem file is
used.
-j=[integer]
Splits large clusters into a chain of smaller embedded clusters with a number of
proper variables less than this number (use options "-B=3 -j=1 -svo -k=1" for pure
RDS, use value 0 for no splitting) (default value is 0).
-r=[integer]
Set a limit on the maximum cluster separator size (merge cluster with its father
otherwise, use a negative value for no limit, default value is -1).
-X=[integer]
Set a limit on the minimum number of proper variables in a cluster (merge cluster
with its father otherwise, use a zero for no limit, default value is 0).
-E Merges leaf clusters with their father if small local treewidth (in conjunction
with option "-e").
-R=[integer]
Choose a specific cluster number as a root cluster.
-I=[integer]
Solve only a specific rooted cluster subtree (with RDS-BTD only).
VNS SEARCH
-vns unified decomposition guided variable neighborhood search (a problem decomposition
can be given as *.dec, *.cov, or *.order input files or using tree decomposition
options such as -O).
-vnsini=[integer]
Initial solution for VNS-like methods found (-1) at random, (-2) min domain values,
(-3) max domain values, (-4) first solution found by a complete method, (k=0 or
more) tree search with k discrepancy max (-4 by default)
-ldsmin=[integer]
Minimum discrepancy for VNS-like methods (1 by default).
-ldsmax=[integer]
Maximum discrepancy for VNS-like methods (number of problem variables multiplied by
maximum domain size -1 by default)
-ldsinc=[integer]
Discrepancy increment strategy for VNS-like methods using (1) Add1, (2) Mult2, (3)
Luby operator (2 by default).
-kmin=[integer]
Minimum neighborhood size for VNS-like methods (4 by default)
-kmax=[integer]
Maximum neighborhood size for VNS-like methods (number of problem variables by
default).
-kinc=[integer]
Neighborhood size increment strategy for VNS-like methods using (1) Add1, (2)
Mult2, (3) Luby operator (4) Add1/Jump (4 by default)
-best=[integer]
Stop VNS-like methods if a better solution is found (default value is 0)
NODE PROCESSING & BOUNDING OPTIONS
-e=[integer]
Perform "on the fly" variable elimination of variable with small degree (less than
or equal to a specified value. Default is 3, creating a maximum of ternary cost
functions).
-k=[integer]
Set the soft local consistency level enforced at preprocessing and at each node
during search:
0: Node Consistency with Strong Node Inverse Consistency for global cost
functions,
1: Generalized Arc Consistency
2: Directed Generalized Arc Consistency
3: Full Directed Generalized Arc Consistency
4: (weak) Existential Directed Generalized Arc Consistency
Default value is 4.
-A=[integer]
Enforce Virtual Arc Consistency at each search node with a search depth less than
the given value (default value is 0 which enforces VAC only at root node).
-T=[decimal]
Threshold cost value for VAC (default value is 1)
-P=[decimal]
Threshold cost value for VAC during the preprocessing phase (default value is 1)
-C=[float]
Multiplies all costs internally by this number when loading the problem (default
value is 1)
-S Preprocessing only: performs singleton consistency (only in conjunction with option
"-A")
-dee=[integer]
Enforce restricted dead-end elimination, or value pruning by dominance rule from
EAC value (dee>=1 and dee<=3) and soft neighborhood substitutability, in
preprocessing (dee=2 or dee=4) or during search (dee=3). Default value is 1.
-o Ensures an optimal worst-case time complexity of Directed and Existential Arc
Consistency (can be slower in practice).
BRANCHING, VARIABLE & VALUE ORDERING
-svo Use a static variable ordering heuristic. The variable order used will be the same
order as the DAC order.
-b Use binary branching (as a default) instead of k-ary branching. Uses binary
branching for interval domains and small domains and dichotomic branching for large
enumerated domains (see option -d).
-c Use binary branching with last conflict backjumping variable ordering heuristic.
-q=[integer]
Use weighted degree variable ordering heuristic if the number of cost functions is
less than the given value (default value is 10000).
-var=[integer]
Searches by branching only on the first [given value] decision variables, assuming
the remaining variables are intermediate variables that will be completely assigned
by the decision variables (use a zero if all variables are decision variables).
Default value is 0.
-m=[integer]
Use a variable ordering heuristic that preferably selects variables such that the
sum of the mean (m=1) or median (m=2) cost of all incident cost functions is
maximum (in conjunction with weighted degree heuristic -q). Default value is 0:
unused.
-d=[integer]
Searches using dichotomic branching. The default d=1 splits domains in the middle
of domain range while d=2 splits domains in the middle of the sorted domain based
on unary costs.
-sortd Sort domains in preprocessing based on increasing unary costs (works only for
binary CFN).
-V VAC-based value ordering heuristic (default option, only in conjunction with
option "-A")
CONSOLE OUTPUT
-help Show default help message that toulbar2 prints when it gets no argument.
-v=[integer]
Set the verbosity level (default 0).
-Z=[integer]
Debug mode (save problem at each node if verbosity option -v=num>= 1 and
-Z=num>=3).
-s Shows each solution found during search. The solution is printed on one line,
giving the value (integer) of each variable successively in increasing order of
definition in the model file.
FILE OUTPUT
-w=[filename]
Writes last solution found in the specified filename (or "sol" if no parameter is
given). The current directory is used as a relative path.
-z=[filename]
Saves problem in wcsp format in filename (or "problem.wcsp" if no parameter is
given).
Writes also the graphviz .dot file and the degree distribution of the input
problem.
-z=[integer]
1: saves original instance (by default), 2: saves
after preprocessing (this option can be used in combination with -z=filename)
-x=[(,i=a)*]
Assigns variable of index i to value a (multiple assignments are separated by a
comma and no space). Without any argument, a complete assignment, used as initial
upper bound and as a value orderin heuristic, is read from default file "sol" or
from a filename given directly as an additional input filename with ".sol"
extension and without -x.
PROBABILITY REPRESENTATION AND NUMERICAL CONTROL
-precision=[integer]
Probability/real log10 precision conversion factor (a power of ten) for
representing probabilities as fixed decimal point numbers. Default value is 7.
-epsilon=[float]
Approximation factor for computing the partition function (default value is 1000
representing epsilon=1/1000).
RANDOM PROBLEM GENERATION
-random=[benchprofile]
Benchmark profile must be specified as follows, where n and d are respectively the
number of variable and the maximum domain size of the random problem.
bin-{n}-{d}-{t1}-{p2}-{seed}
t1 is the tightness in percentage of random binary cost functions
p2 is the number of binary cost functions to include
the seed parameter is optional
binsub-{n}-{d}-{t1}-{p2}-{p3}-{seed} binary random submodular cost
functions
t1 is the tightness in percentage of random cost functions
p2 is the number of binary cost functions to include
p3 is the percentage of submodular cost functions among p2 cost
functions (plus 10 permutations of two randomly-chosen values for
each domain).
tern-{n}-{d}-{t1}-{p2}-{p3}-{seed}
p3 is the number of ternary cost functions
nary-{n}-{d}-{t1}-{p2}-{p3}...-{pn}-{seed}
pn is the number of n-ary cost functions
salldiff-{n}-{d}-{t1}-{p2}-{p3}...-{pn}-{seed}
pn is the number of salldiff global cost functions (p2 and p3 still
being used for the number of random binary and ternary cost
functions). salldiff can be replaced by gcc or regular keywords with
three possible forms ( e.g., sgcc, sgccdp, wgcc).
FILE FORMATS
toulbar2 can read .cfn, .cfn.gz,.wcsp, .uai, .LG, .pre, .cnf, .wcnf, .bep files. See the
full user documentation for a description of these file formats.
SEE ALSO
A more complete user documentation should be available on your system, in
/usr/share/doc/toulbar2/userdoc.pdf or can be otherwise downloaded from
http://www.inra.fr/mia/T/toulbar2.
AUTHORS
See https://github.com/toulbar2/toulbar2
TOULBAR2(1)