It’s useful, but in my opinion not yet ideal because I cannot cut duplicate paths at the point of their creation.
Consider, with the complete graph K_n:
n_complete(N, Es) :-
numlist(1, N, Ns),
phrase(pairs(Ns), Es).
adjacent(Edges, X, Y) :- member(edge(X, Y), Edges).
pairs([]) --> [].
pairs([N|Ns]) --> edges(Ns, N), pairs(Ns).
edges([], _) --> [].
edges([N|Ns], X) --> [edge(X,N),edge(N,X)], edges(Ns, X).
The following query now has super-exponential runtime, although the closure can actually be found in polynomial time:
?- length(_, N), n_complete(N, Es), portray_clause(N),
time(findall(Y, closure0(adjacent(Es), 1, Y), Ys)),
false.
1.
16 inferences, 0.000 CPU in 0.000 seconds (97% CPU, 1982161 Lips)
2.
54 inferences, 0.000 CPU in 0.000 seconds (98% CPU, 4548901 Lips)
3.
259 inferences, 0.000 CPU in 0.000 seconds (97% CPU, 14499244 Lips)
4.
1,479 inferences, 0.000 CPU in 0.000 seconds (100% CPU, 16219595 Lips)
5.
9,599 inferences, 0.000 CPU in 0.000 seconds (100% CPU, 27691393 Lips)
6.
70,465 inferences, 0.002 CPU in 0.002 seconds (100% CPU, 28911161 Lips)
7.
581,283 inferences, 0.020 CPU in 0.020 seconds (100% CPU, 29397339 Lips)
8.
5,343,059 inferences, 0.181 CPU in 0.181 seconds (100% CPU, 29488001 Lips)
9.
54,252,559 inferences, 1.809 CPU in 1.808 seconds (100% CPU, 29994536 Lips)
10.
603,682,989 inferences, 19.870 CPU in 19.865 seconds (100% CPU, 30381451 Lips)
It would be great if a more efficient way to determine the closure could also be expressed with this meta-predicate.
For example, one would normally simply use Warshall’s algorithm to compute the closure in cubic time, with code similar to:
node_edges_closure(Node, Edges, Closure) :-
warshall_fixpoint(Edges, [Node], Closure).
warshall_fixpoint(Edges, Nodes0, Closure) :-
findall(Y, (member(X, Nodes0), adjacent(Edges, X, Y)), Nodes1, Nodes0),
sort(Nodes1, Nodes),
( Nodes == Nodes0 -> Closure = Nodes0
; warshall_fixpoint(Edges, Nodes, Closure)
).
Yielding (with all drawbacks in comparison to the nicely declarative closure0/3):
?- length(_, N), n_complete(N, Es), portray_clause(N),
time(node_edges_closure(1, Es, Ys)),
false.
1.
% 16 inferences, 0.000 CPU in 0.000 seconds (75% CPU, 533333 Lips)
2.
% 43 inferences, 0.000 CPU in 0.000 seconds (85% CPU, 1228571 Lips)
3.
% 69 inferences, 0.000 CPU in 0.000 seconds (85% CPU, 1769231 Lips)
4.
% 115 inferences, 0.000 CPU in 0.000 seconds (89% CPU, 2346939 Lips)
5.
% 187 inferences, 0.000 CPU in 0.000 seconds (91% CPU, 2968254 Lips)
6.
% 291 inferences, 0.000 CPU in 0.000 seconds (92% CPU, 3548780 Lips)
7.
% 433 inferences, 0.000 CPU in 0.000 seconds (95% CPU, 3866071 Lips)
8.
% 619 inferences, 0.000 CPU in 0.000 seconds (96% CPU, 4268966 Lips)
9.
% 855 inferences, 0.000 CPU in 0.000 seconds (97% CPU, 4500000 Lips)
10.
% 1,147 inferences, 0.000 CPU in 0.000 seconds (98% CPU, 4720165 Lips)
etc.