- bb_cost(++Handle, -Cost)
- Low-level primitive for building branch-and-bound-style search procedures
- bb_finish(++Handle)
- Low-level primitive for building branch-and-bound-style search procedures
- bb_init(++ExtremeCost, -Handle)
- Low-level primitive for building branch-and-bound-style search procedures
- bb_min(+Goal, ?Cost, +Options)
- Find one or all minimal solutions using the branch-and-bound method
- bb_min(+Goal, ?Cost, ?Template, ?Solution, ?Optimum, ?Options)
- Find a minimal solution using the branch-and-bound method
- bb_min_cost(+Goal, ?Cost, -Optimum, +Options)
- Find the minimal cost using the branch-and-bound method
- bb_probe(++From, ++To, +Goal, ?Template, ?Cost, ++Handle, ++Module)
- Low-level primitive for building branch-and-bound-style search procedures
- bb_solution(++Handle, -Solution)
- Low-level primitive for building branch-and-bound-style search procedures
- minimize(+Goal, ?Cost)
- Find a minimal solution using the branch-and-bound method
- struct bb_options(strategy, from, to, delta, factor, timeout, probe_timeout, solutions, report_success, report_failure, report_timeout)
- No description available
This is a solver-independent library implementing branch-and-bound optimization. It can be used with any nondeterministic search routine that instantiates a cost variable when a solution is found. The cost variable can be an arbitrary numerical domain variable or even a simple domain-less Prolog variable.
The main primitive is bb_min/3. Assume we have the following collection of facts:
        % item(Food, Calories, Price)
        item(bread,  500, 1.50).
        item(eggs,   600, 1.99).
        item(milk,   400, 0.99).
        item(apples, 200, 1.39).
        item(butter, 800, 1.89).
   Then we can find a minimum-calorie solution as follows:
        ?- bb_min(item(Food,Cal,Price), Cal, _).
        Found a solution with cost 500
        Found a solution with cost 400
        Found a solution with cost 200
        Found no solution with cost -1.0Inf .. 199
        Food = apples
        Cal = 200
        Price = 1.39
        Yes (0.00s cpu)
    In this example, the item/3 predicate serves as a nondeterministic
    generator of solutions with different values for the variable Cal,
    which we have chosen as our cost variable.  As can be seen from the
    progress messages, the optimization procedure registers increasingly
    good solutions (i.e. solutions with smaller cost), and finally delivers
    the minimum-cost solution with Cal=200.
Alternatively, we can minimize the item price:
        ?- bb_min(item(Food,Cal,Price), Price, bb_options{delta:0.05}).
        Found a solution with cost 1.5
        Found a solution with cost 0.99
        Found no solution with cost -1.0Inf .. 0.94
        Food = milk
        Cal = 400
        Price = 0.99
        Yes (0.00s cpu)
    Because the price is non-integral, we had to adjust the step-width
    of the optimization procedure using the delta-option.
This library is designed to work together with arbitrary constraint solvers, for instance library(ic). The principle there is to wrap the solver's nondeterministic search procedure into a bb_min/3 call. This turns a program that finds all solutions into one that finds the best solution. For example:
        ?- [X,Y,Z] #:: 1..5,                   % constraints (model)
           X+Z #>= Y,
           C #= 3*X - 5*Y + 7*Z,               % objective function
           bb_min(labeling([X,Y,Z]), C, _).    % nondet search + b&b
        Found a solution with cost 5
        Found a solution with cost 0
        Found a solution with cost -2
        Found a solution with cost -4
        Found a solution with cost -6
        Found no solution with cost -15.0 .. -7.0
        X = 4
        Y = 5
        Z = 1
        C = -6
        Yes (0.00s cpu)
    The code shows the general template for such an optimization solver:
    All constraints should be set up BEFORE the call to bb_min/3,
    while the nondeterministic search procedure (here labeling/1)
    must be invoked WITHIN bb_min/3.  The branch-and-bound procedure
    only works if it envelops all nondeterminism.
The cost variable (here C) must be defined in such a way that it is instantiated (possibly by propagation) whenever the search procedure succeeds with a solution. Moreover, good, early bounds on the cost variable are important for efficiency, as they help the branch-and-bound procedure to prune the search. Redundant constraints on the cost variable can sometimes help.
The library allows the cost to be instantiated to a number of type breal. This is useful e.g. when using lib(ic) to solve problems with continuous variables. When the variable domains have been narrowed sufficiently, the problem variables (in particular the cost variable) should be instantiated to a bounded real, e.g. using the following idiom:
	    X is breal_from_bounds(get_min(X),get_max(X))
    
    Bounded reals contain some uncertainty about their true value. If
    this uncertainty is too large, the branch-and-bound procedure may
    not be able to compare the quality of two solutions. In this case,
    a warning is issued and the search terminated prematurely.  The
    problem can be solved by increasing the delta-parameter, or by
    locating the cost value more precisely.