We use a dynamic programming algorithm to establish a new lower bound on the domination number of complete cylindrical grid graphs of the form , that is, the Cartesian product of a path and a cycle, when , and we establish a new upper bound equal to the lower bound, thus computing the exact domination number for these graphs.
Keywords: Dynamic programming algorithm, Grid graph, Domination number
1. Introduction
A set of vertices in a graph
is called a
dominating set if every vertex is either in or adjacent to a vertex in . The domination number of , , is the minimum size of a
dominating set.
Let denote the path on vertices and the cycle on vertices; the complete
cylindrical grid graph or cylinder is
the product . That
is, if we denote the vertices of by and the vertices of
by , then is the graph with vertices
, , , and
adjacent to if and is adjacent to or if and is adjacent to . It will be useful to think of this
graph as , with the
edge paths of length glued
together, that is, connected with new edges. We assume throughout that
and .
P. Pavlič and J. Žerovnik [1] established upper bounds for the
domination number of , and José Juan Carreño et al. [2] established non-trivial lower
bounds. For the
bounds agree, so the domination number is known exactly. In [3], we improved the lower bounds,
except of course in the case that . Using the same techniques, we improve the lower bound
slightly when , and
we show that this value is the true domination number by also slightly
improving the known upper bound.
2. Lower Bound
We summarize the technique for computing a lower bound; details are
in [3]. A vertex in dominates at most five
vertices, including itself, so certainly . If we
could keep the sets dominated by individual vertices from overlapping,
we could get a dominating set with approximately vertices, and indeed we can arrange
this for much of the graph, with the exception of the “edges”, that is,
the two copies of in which the
vertices have only 3 neighbors, and except when , we run into some
trouble where the edge paths of are connected to form .
Suppose is a subset of the
vertices of . Let
be the set of vertices that
are either in or adjacent to a
member of , that is, the vertices
dominated by . Define the
wasted domination of as , that is, the number of
vertices we could dominate with
vertices in the best case, less the number actually dominated. When
is a dominating set, , and if then . Our goal now is to find
a lower bound for .
Figure 1: Partitioned Cylinder
Suppose a cylinder is partitioned into subgraphs as indicated in Figure 1, where each is a subgraph . (We will refer to
and as edge subgraphs, and the others as
interior subgraphs.) Let be a
dominating set for and . Then Note that in computing we consider to be a subset of , not of (this affects the computation of
). To verify the inequality,
note that the following inequalities are equivalent: The last inequality is satisfied, since each
vertex in is counted at least
once by the expression on the right.
Note that is a set that
dominates all the vertices of
except possibly some vertices in the top or bottom row of (or in the cases of and , in the bottom row and top row,
respectively). Let us say that a set that dominates a cylinder , with the exception of some vertices on
the top or bottom edges, almost dominates. Given a cylinder (namely, one of the
), What we want to know is the
value of taking the minimum over sets that almost dominate and computing as if were a subset of a larger graph in which occupies the middle rows, or in the case of or , is a subset of in which occupies the top rows. If we can compute this minimum
for (small) fixed and any , we can choose through with a small number of rows and get
lower bounds on for any
dominating set of the original
.
3. The Algorithm
We use a dynamic programming algorithm to compute these lower bounds
on . This is done by
computing smallest almost-dominating sets for , , until periodicity is
detected. That is, we test for a point after which the minimum wasted
domination of is
that of plus a
constant . (Livingston and
Stout [4] and Fisher [5] independently thought of looking
for this sort of periodicity.) In [3], we found that the minimum wasted domination for
is . For an interior , the minimum wasted
domination is 0, 6, 5, 9, or 6 as
is 0, 1, 2, 3, or 4 . For
, this means that
for a dominating set of , we have so that Since need not be a multiple of 10, this
calculation effectively ignores one of the , namely, one in which the number of
rows is less than 10. When the number of rows is too small (less than 5,
as it turns out), the resulting lower bound is too small to be useful,
namely, 0. So we computed the corresponding minimum wasted domination
for an interior ,
for . We find that the
minima are 2, 3, 3, 4, and 4, respectively. This means, for example,
that if , To handle , ,
we eliminate one interior with
10 rows, and insert two new subgraphs, with 5 and 6 rows, 6 and 6 rows,
6 and 7 rows, or 6 and 8 rows, respectively. For example, for , we get
When we complete this task, the resulting lower bounds still turn out
to be a bit too low for some values of . To improve the bounds, we compute the
minimum wasted domination for edge subgraphs and with 11 rows, instead of 10. We find
that the minimum wasted domination for is , when . The resulting lower bound on the domination number of
becomes
where is the necessary addition as described
above when is not divisible by
10. For example, when , we get since now
.
The resulting lower bounds for and are: With a little bit of algebraic manipulation, we
find that all of these bounds can be expressed as
These bounds apply when ,
; and when . The first restriction is due to when the periodicity
described earlier first appears; the second because the bounds are based
on two edge subgraphs with 11 rows and at least one interior subgraph
with at least 5 rows.
Crevals [6] computes exact
values for and all , and also for and all . Our bounds agree with Crevals’
exact values for , and
for (remember that our
bounds are for
only). Note well that our bounds agree with the Crevals values when
and when , even though our derivation
of the lower bounds does not apply to these values.
This gave us some confidence that our lower bounds would prove
correct in all cases except when . To show this, we will need to find upper bounds that match
our lower bounds, and we also need to fill in the gap not covered by either our
lower bounds or the Crevals values. So the first order of business is to
provide lower bounds for these missing values.
This we can do in a way similar to what we have already done, but we
need to compute minima for edge subgraphs of the form and . When we do this, we find that the minimum wasted
domination for both is , when
. Then we compute lower bounds as before, using two edge
subgraphs and no interior subgraphs. To get bounds for we use subgraphs with 11
and 12 rows; 12 and 12 rows; 12 and 13 rows; and 13 and 13 rows;
respectively. We find that the resulting lower bounds are given by the
formulas we already have. For example, for and we have which agrees with the
value in equation 3, , since .
4. Upper Bound
The best known upper bound, due to Pavlič and
Žerovnik [1], for
is . This
is slightly larger than our lower bound. We need to show that can in fact be dominated
by the number of vertices given by our lower bound. We can easily modify
our computer programs to provide almost-dominating sets of the component
subgraphs with the minimum
possible wasted domination. We would then hope that these subgraphs can
be pieced together in a way that dominates the entire cylinder; we can
attempt to do this for a particular value of , say . To then extend the upper bound to
all values of , we would need
these sample graphs to exhibit a periodicity that allows them to be
extended to any . With a little
bit of trial and error, we succeeded.
We discovered that the sample interior graphs that fit together
nicely are the ones with an even number of rows. So to begin, we need to
show that we can get dominating sets of cylinders with rows, for all values of , using only such graphs. We can
potentially get these for all even using two edge graphs of 11 rows, for
(because the graphs with
fewer than 6 rows are not helpful). To get odd values of , we use one edge graph of 11 rows
and one of 10 rows. For , we use two edge graphs of 11, 12, or 13 rows as needed, and
for we use two edge graphs
with 10 rows and one interior graph with 6 rows. It then suffices to
provide examples for ,
since for larger than this it
will be clear that any number of additional interior subgraphs with 10
rows can be added. For , it will be apparent that some graphs with between 33 and 42 can easily be
modified (by removing one interior subgraph) to verify the upper bounds
for between 27 and 32. There was
one slight surprise: for even,
, one of the two edge graphs
with 11 rows must have a single vertex added to form a dominating
set.
In Figures 2 to 4 we show the
interior graphs used to construct dominating sets. In each we show a
five column portion of the graph that can be repeated as necessary to
get a cylinder with larger . We
have omitted the edges in these graphs as the structure is apparent; the
small dots are vertices, the large dots are vertices in the dominating
sets.
In Figures 5 to 8 we show the
edge graphs used to construct dominating sets. Again, in each we show a
five column portion of the graph that can be repeated as necessary to
get a cylinder with larger .
Finally, we show some examples of cylinders put together from these
pieces. In the case of even , as
mentioned above, we need an extra vertex in the dominating set; this
vertex is shown as a star. The extra vertex is always in the bottom row
of the top edge graph. Note also that to make the subgraphs fit together
properly, so that we get a dominating set for the whole cylinder, some
of the subgraphs must be rotated so that they line up properly at the
boundaries, which are shown with dashed lines. (We produced in all
twenty of these graphs, covering .)
To produce a dominated cylinder of arbitrary size, we first determine
which of the individual subgraphs we will need, namely, two edge
subgraphs, some number of interior subgraphs with 10 rows, and one or
two additional graphs with 6 or 8 rows. Then using the repeatable
sections in the graphs of Figures 2 to 8, we expand each of these graphs to the
desired number of columns, and finally we rotate the graphs as necessary
so that they fit together properly.
We can now write down an upper bound for each and . For example, for and , we use edge graphs with 11 and
10 rows, an interior graph with 6 rows, and as many additional interior
subgraphs of 10 rows as needed. Let and . The number of vertices
contributed by the edge graph with 11 rows is , since the
repeatable section, from Figure 6, contributes
12 vertices to the dominating set. Similarly, the edge graph with 10
vertices contributes , an interior
graph with 10 rows contributes , and an interior graph
with 6 rows contributes . Then the total number of
vertices in a dominating set is which matches the corresponding lower bound in
equation 3. We repeat this process for all other
in , with , and in each case we find that
the results match the values in equation 3. We also do
as special cases,
with no surprises.
5. Summary
For and , the domination number of is now known when (as a result of P. Pavlič and J.
Žerovnik [1] and
José Juan Carreño et al. [2]) and . For the latter, when or , the domination number is
as shown here. For and
, the domination numbers are
given by the formulas found in Crevals [6].
Unfortunately, the technique used here seems unlikely to settle the
remaining cases, when is 1, 3, or
. The lower bounds we get
are below the upper bounds of [] by a small multiple of . When we look at a few sample subgraphs
generated by our algorithm, they do not fit together nicely at the
boundaries (that is, some vertices are left undominated), and so do not
give upper bounds matching our lower bounds. Indeed, it seems quite
remarkable that for
the subgraphs can be combined to dominate the entire graph.
It is possible that our technique might work using somewhat larger
subgraphs, though the algorithm we used would have to be improved or
replaced with a substantially faster one. Computing the minimum wasted
domination for fewer than 13 rows was reasonably fast on an Intel Core
i7, although for 12 rows it did take a week or two. The 13 row graphs
took considerably longer. We split the problem into 10 pieces by hand
and ran each on a separate Intel Core i5 computer that was otherwise
idle. The division wasn’t perfect, so some finished earlier than others;
the whole process took over three months. The programs were written in
C++ and compiled with the Gnu gcc compiler with optimization.
References:
Pavlic, P.O.L.O.N.A. and Zerovnik, J., 2013. A note
on the domination number of the Cartesian products of paths and cycles.
Kragujevac Journal of Mathematics, 37(2), pp.275-285.
Carreño, J.J., Martínez, J.A. and Puertas, M.L., 2020. A general
lower bound for the domination number of cylindrical graphs.
Bulletin of the Malaysian Mathematical Sciences Society, 43,
pp.1671-1684.
Guichard, D.R., 2022. A New Lower Bound for the Domination Number of
Complete Cylindrical Grid Graphs. arXiv preprint
arXiv:2207.14414.
Livingston, M. and Stout, Q.F., 1994. Constant time computation of
minimum dominating sets. Congressus Numerantium,
pp.116-128.
Fisher, D.C., 1993. The domination number of complete grid graphs.
preprint.