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:

-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:

-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.

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