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bb_min(+Goal, ?Cost, ?Template, ?Solution, ?Optimum, ?Options)

Find a minimal solution using the branch-and-bound method
Goal
The (nondeterministic) search goal
Cost
A (usually numeric domain) variable representing the cost
Template
A term containing all or some problem variables
Solution
A term which will be unified with the optimized Template
Optimum
A variable which will be set to the optimum value of Cost
Options
A bb_options structure or variable

Description

A solution of the goal Goal is found that minimizes the value of Cost. Cost should be a variable that is affected, and eventually instantiated, by Goal. Usually, Goal is the search procedure of a constraint problem and Cost is the variable representing the cost. The solution is found using the branch and bound method: as soon as a solution is found, it gets remembered and the search is continued or restarted with an additional constraint on the Cost variable which requires the next solution to be better than the previous one. Iterating this process yields an optimal solution in the end.

The possible options are

strategy:
continue (default)
after finding a solution, continue search with the newly found bound imposed on Cost
restart
after finding a solution, restart the whole search with the newly found bound imposed on Cost
step
a synonym for 'restart'
dichotomic
after finding a solution, split the remaining cost range and restart search to find a solution in the lower sub-range. If that fails, assume the upper sub-range as the remaining cost range and split again.
The new bound (or the split point, respectively), are computed from the current best solution, taking into account the parameters delta and factor.
from:
number - an initial lower bound for the cost (default -1.0Inf)
to:
number - an initial upper bound for the cost (default +1.0Inf)
delta:
number - minimal absolute improvement required for each step (default 1.0), applies to all strategies
factor:
number - minimal improvement ratio (with respect to the lower cost bound) for strategies 'continue' and 'restart' (default 1.0), or split factor for strategy 'dichotomic' (default 0.5)
timeout:
number - maximum seconds of cpu time to spend (default: no limit)
report_success:
GoalPrefix/N - this specifies a goal to be invoked whenever the branch-and-bound process finds a better solution. GoalPrefix is a callable term (atom or compound) and N is an integer between 0 and 3. The invoked goal is constructed by adding N optional arguments to GoalPrefix: Cost, Handle and Module. Cost is a float number representing the cost of the solution found, Handle is a handle as accepted by bb_cost/2 or bb_solution/2, and Module is the context module of the minimisation. To disable any reporting, choose report_success:true/0. The default handler prints a message to log_output.
report_failure:
GoalPrefix/N - this specifies a goal to be invoked whenever the branch-and-bound process cannot find a solution in a cost range. GoalPrefix is a callable term (atom or compound) and N is an integer between 0 and 3. The invoked goal is constructed by adding N optional arguments to GoalPrefix: Cost, Handle and Module. Cost is a From..To structure representing the range of cost in which no solution could be found, Handle is a handle as accepted by bb_cost/2 or bb_solution/2, and Module is the context module of the minimisation. To disable any reporting, choose report_failure:true/0. The default handler prints a message to log_output.
report_timeout:
GoalPrefix/N - this specifies a goal to be invoked when the branch-and-bound process times out. GoalPrefix is a callable term (atom or compound) and N is an integer between 0 and 3. The invoked goal is constructed by adding N optional arguments to GoalPrefix: Cost, Handle and Module. Cost is a float number representing the cost of the best solution found, Handle is a handle as accepted by bb_cost/2 or bb_solution/2, and Module is the context module of the minimisation. To disable any reporting, choose report_timeout:true/0. The default handler prints a message to log_output.
The default options can be selected by passing a free variable as the Options-argument. To specify other options, pass a bb_options- structure in struct-syntax, e.g. 
	bb_options{strategy:dichotomic, timeout:60}
In order to maximize instead of minimizing, introduce a negated cost variable in your model and minimize that instead.

Unlike bb_min/3, bb_min/6 does not affect Goal or Cost after the optimum has been found. Instead, the optimum cost value is returned in Optimum, and the Solution argument gets unified with an instance of Template where the variables have the values that correspond to the optimal solution. Note that bb_min/3 is actually based on bb_min/6 and can (for the one-solution case) be defined as: 

	bb_min(Goal, Cost, Options) :-
	    bb_min(Goal, Cost, Goal, Goal, Cost, Options).

Modes and Determinism

Modules

This predicate is sensitive to its module context (tool predicate, see @/2).

Fail Conditions

Goal has no solutions

See Also

bb_min / 3