Information Technology Reference
In-Depth Information
x 2
g 1
10
g 2
0
x 1
0
1
Fig. 1. Slow convergence in constraint propagation.
4
Implementation and Results
An implementation has been realized in
. In the following we give
details of the encoding of constraint projections. In order to illustrate the effects
of our approach in consistency reasonings, a set of experimental results is also
discussed.
RealPaver
4.1
Implementation Details
First of all let us note that every constraint can be expressed as a projection
constraint: f = 0 corresponds to f
[0 , 0] and f
0to f
[
−∞
, 0]. Projec-
tions on terms are computed by the
algorithm, which is a chain rule
enforcing elementary constraint inversion steps [7].
HC4revise
Example 5. Let f be a term and let C be the constraint ( f + y )
1=0.Given
10 , 10] 2
the box [
let us implement
to compute an interval I such
HC4revise
that the projection constraint f
I holds. Constraint C is rewritten as the
projection constraint ( f + y )
1
[0 , 0]. The first inversion step eliminates the
minus operation: f + y
[0 , 0] + 1 = [1 , 1]. The second inversion step removes
the plus operation: f
[1 , 1]
y and y is replaced with its domain: f
9 , 11].
[
Now, given the constraint f
9 , 11] box consistency may be enforced over f .
[
Giventwoprojectionconstraints f
I and g
J every redundant constraint
ψ ( ϕ ( f, g ))
φ I ( I,J ) is represented as follows. Term ψ ( ϕ ( f, g )) is represented in
explicit form in the DAG. φ I ( I,J ) is implemented as an interval function taking
as input the intervals I and J available at the root nodes of f and g in the
DAG. This function is located in the root node of term ψ ( ϕ ( f, g )). This way φ I
is evaluated as fast as possible and memory consumption is kept in reasonable
bounds. If the expression of ψ ( ϕ ( f, g )) is simple then the computation of box
consistency may be precise and cheap.
 
Search WWH ::




Custom Search