Idom.numCSup.bnd.std.tri {pcds} | R Documentation |
The indicator for k
being an upper bound for the domination number of Central Similarity Proximity
Catch Digraph (CS-PCD) by the exact algorithm - standard equilateral triangle case
Description
Returns domination number of CS-PCD is less than or equal to
k
where the vertices of the CS-PCD are the data points
Xp
,
that is, returns 1 if the domination number of CS-PCD is less than the prespecified value k
, returns 0
otherwise. It also provides the vertices (i.e., data points) in a dominating set of size k
of CS-PCD.
CS proximity region is constructed with respect to the standard equilateral triangle with
expansion parameter
and edge regions are based on the center
in Cartesian coordinates or
in barycentric coordinates in the interior of
;
default is
i.e., the center of mass of
(which is equivalent to the circumcenter of
).
Edges of ,
,
,
, are also labeled as 3, 1, and 2, respectively.
Loops are allowed in the digraph.
It takes a long time for large number of vertices (i.e., large number of row numbers).
See also (Ceyhan (2012)).
Usage
Idom.numCSup.bnd.std.tri(Xp, k, t, M = c(1, 1, 1))
Arguments
Xp |
A set of 2D points which constitute the vertices of CS-PCD. |
k |
A positive integer representing an upper bound for the domination number of CS-PCD. |
t |
A positive real number which serves as the expansion parameter in CS proximity region in the
standard equilateral triangle |
M |
A 2D point in Cartesian coordinates or a 3D point in barycentric coordinates
which serves as a center in the interior of the standard equilateral triangle |
Value
A list
with two elements
domUB |
The upper bound |
Idom.num.up.bnd |
The indicator for the upper bound for domination number of CS-PCD being the
specified value |
ind.domset |
The vertices (i.e., data points) in the dominating set of size |
Author(s)
Elvan Ceyhan
References
Ceyhan E (2012). “An investigation of new graph invariants related to the domination number of random proximity catch digraphs.” Methodology and Computing in Applied Probability, 14(2), 299-334.
See Also
Idom.numCSup.bnd.tri
, Idom.num.up.bnd
, Idom.numASup.bnd.tri
,
and dom.num.exact
Examples
A<-c(0,0); B<-c(1,0); C<-c(1/2,sqrt(3)/2);
Te<-rbind(A,B,C);
n<-10
set.seed(1)
Xp<-runif.std.tri(n)$gen.points
M<-as.numeric(runif.std.tri(1)$g) #try also M<-c(.6,.2)
t<-.5
Idom.numCSup.bnd.std.tri(Xp,1,t,M)
for (k in 1:n)
print(c(k,Idom.numCSup.bnd.std.tri(Xp,k,t,M)$Idom.num.up.bnd))
print(c(k,Idom.numCSup.bnd.std.tri(Xp,k,t,M)$domUB))