Node:Projection, Next:Why Disequations, Previous:Numerical Precision, Up:CLPQR
Once a derivation succeeds, the Prolog system presents the bindings for the
variables in the query. In a CLP system, the set of answer constraints is
presented in analogy. A complication in the CLP context are variables and
associated
constraints that were not mentioned in the query. A motivating example is
the familiar mortgage relation:
%
% from file: library('clpqr/examples/mg')
%
mg(P,T,I,B,MP):-
{
T = 1,
B + MP = P * (1 + I)
}.
mg(P,T,I,B,MP):-
{
T > 1,
P1 = P * (1 + I) - MP,
T1 = T - 1
},
mg(P1, T1, I, B, MP).
A sample query yields:
clp(r) ?- use_module(library('clpqr/examples/mg')).
clp(r) ?- mg(P,12,0.01,B,Mp).
{B=1.1268250301319698*P-12.682503013196973*Mp}
Without projection of the answer constraints onto the query variables we
would observe the following interaction:
clp(r) ?- mg(P,12,0.01,B,Mp).
{B=12.682503013196973*_A-11.682503013196971*P},
{Mp= -(_A)+1.01*P},
{_B=2.01*_A-1.01*P},
{_C=3.0301*_A-2.0301*P},
{_D=4.060401000000001*_A-3.0604009999999997*P},
{_E=5.101005010000001*_A-4.10100501*P},
{_F=6.152015060100001*_A-5.152015060099999*P},
{_G=7.213535210701001*_A-6.213535210700999*P},
{_H=8.285670562808011*_A-7.285670562808009*P},
{_I=9.368527268436091*_A-8.36852726843609*P},
{_J=10.462212541120453*_A-9.46221254112045*P},
{_K=11.566834666531657*_A-10.566834666531655*P}
The variables _A ... _K are not part of the query, they
originate from the mortgage program proper. Although the latter answer
is equivalent to the former in terms of linear algebra, most users would
prefer the former.