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1.4. FD Constraints 19

3. The third group demands to ﬁnd all the solutions (all) or only one solution to

maximize (resp. minimize) the domain of a variable X in L (toMinimize

X) (resp. toMaximize X).

4. The fourth group controls the number of assumptions K (choices) made

during the search (assumptions K).

• Example at the TOY(FD) command level:

Next goal looks (by backtracking) for assignments to all variables in list L and

follows a ﬁrst fail strategy.

TOY> L == [X,Y], domain L 1 3 == T,

labeling [ff] L == true

yes

L == [ 1, 1 ]

X == 1

Y == 1

T == true

Elapsed time: 0 ms.

more solutions (y/n/d) [y]?

......

1.4.7 Statistics Constraints

TOY(FD) also provides a set of constraints that allow to recover information about

constrained FD variables and their associated domains during the solving of a goal. In

this group we have the statistics constraints to generate execution statistics that are

useful in many forms as, for example, to provide information about the solving of a goal.

A remark about statistics constraints. Statistics functions pose no problem for

our functional logic programming framework since they behave as usual TOY functions,

returning values that, in general, depend on the computation state.

fd statistics’/0

• Type declaration:

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Ascend FD-23R Bedienungsanleitung Seite 31