1Department of Logistics, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa.
2Stellenbosch Unit for Operations Research in Engineering, Department of Industrial Engineering, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa.
For a graph G and for non-negative integers p, q, and r, the triplet is said to be an admissible triplet if . If G admits a decomposition into p cycles of length 3, q cycles of length 4, and r cycles of length 6 for every admissible triplet , then we say that G has a -decomposition. In this paper, the necessary conditions for the existence of -decomposition of are proved to be sufficient. This affirmatively answers the problem raised in Decomposing complete tripartite graphs into cycles of lengths 3 and 4, Discrete Math. 197/198 (1999), 123-135. As a corollary, we deduce the main results of Decomposing complete tripartite graphs into cycles of lengths 3 and 4, Discrete Math., 197/198, 123-135 (1999) and Decompositions of complete tripartite graphs into cycles of lengths 3 and 6, Austral. J. Combin., 73(1), 220-241 (2019).
Keywords: Cycle decompositions, Latin square, Complete tripartite graphs
1. Introduction and
Preliminaries
A subset of vertices of
a graph is
independent if no two vertices in are adjacent in . The cardinality of a largest
independent set of is denoted by
and is called the
vertex independence number of .
A subset of the
vertices of a graph is
dominating if every vertex of is an element of or is adjacent in to an element of . A dominating set of is minimal if no
proper subset of is a
dominating set in . The
cardinality of a largest minimal dominating set of is denoted by and is called the
upper domination number of .
The closed neighbourhood of a graph vertex, denoted by , is the subset of all vertices of
that are adjacent to in , together with the vertex itself. The closed
neighbourhood of a set of vertices of , denoted by , is simply . The set
of private neighbours of a vertex with respect to some
subset
is defined as . A subset of the vertices of is irredundant if
every vertex has at
least one private neighbour in
with respect to (that is,
if for all ). Finally, the cardinality of a largest irredundant set of
is denoted by and is called the
irredundance number of .
It holds for any graph that
a result dating back to 1978 that is
due to Cockayne et al. [1].
A bi-colouring of the edges of a graph, using the colours red and
blue, is called a red-blue edge colouring of the
graph. If and are the subgraphs induced by
respectively the red and the blue edges of such a colouring, the
colouring is denoted by the ordered pair , and is referred to as the red
subgraph, while is
called the blue subgraph.
The following six classes of Ramsey numbers have previously been
defined in terms of the graph theoretic notions of independence,
irredundance and domination.
The independent Ramsey number is the smallest natural number
such that in any red-blue edge
colouring of the complete
graph of order , it holds that or (or both). This
definition dates from 1930 and is due to Ramsey [2].
The irredundant Ramsey number is the smallest natural number
such that in any red-blue edge
colouring of the complete
graph of order , it holds that or (or both). This definition
dates from 1989 and is due to Brewster et al. [3].
The mixed irredundant Ramsey number is the smallest natural number
such that in any red-blue edge
colouring of the complete
graph of order , it holds that or (or both). This
definition dates from 1990 and is due to Cockayne et al. [4].
The upper domination Ramsey number is the smallest natural number
such that in any red-blue edge
colouring of the complete
graph of order , it holds that or (or both). This
definition dates from the early 1990s and is due to Oellermann and
Shreve [5].
The mixed domination Ramsey number is the smallest natural number
such that in any red-blue edge
colouring of the complete
graph of order , it holds that or (or both). This
definition also dates from the early 1990s and is due to Oellermann and
Shreve [5].
The irredundant-domination Ramsey number is the smallest natural number
such that in any red-blue edge
colouring of the complete
graph of order , it holds that or (or both). This
definition dates from 2013 is due to Burger and Van Vuuren [6].
The arguments of the independent, irredundant and upper domination
Ramsey numbers may be interchanged without altering the values of these
numbers, i.e., and for any integers . In the case of the mixed
Ramsey numbers , and , however, the arguments and do not necessarily commute,
i.e., ,
and in general.
Furthermore, these Ramsey numbers are related to each other by the
inequality chains which are
easily shown to hold for all in view of (1). It is unknown in what way (if
any) the Ramsey numbers and
are related.
Apart from the infinite classes of Ramsey numbers for all and all , only 45 members of the
above-mentioned six classes of Ramsey numbers are known exactly. These
numbers are shown in Table 1. Our aim in
this paper is to determine the two Ramsey numbers and .
Table 1: Exactly known Ramsey numbers from the literature, as well as
new numbers (in bold). Due to Brewster et al. [3], Burger and Van Vuuren [6], Dzido and Zakrzewska [7], Cockayne et al. [4], Henning and Oellermann [8], Greenwood and Gleason [9], Brewster et al. [10], Grobler [11], Graver and Yackel [12], Cockayne et al. [13], Cockayne et al. [14], Kalbfleisch [15], McKay and Min [16], Grinstead and Roberts [17], Burger et al. [18], McKay and Radziszowski [19].
Ramsey problems are known to be difficult, and it is accepted that
the use of computers is inevitable when solving these problems.
Considering all possible edge colourings works for smaller Ramsey
problems, but in larger cases a more judicious approach is required. The
algorithmic approach put forward in this paper for solving Ramsey
problems of medium size offers a technique not seen before, which can be
applied more widely than previous search tree approaches. It attempts to
uncover a red-blue graph colouring systematically by looking for edges
that necessarily have to be either red or blue graph, all the while
making use of a set of rules/lemmas. It branches only into different
cases when it can find no edges that are forced to be one colour or the
other at that point during the search, and it eliminates cases that lead
to contradictions. The chain of algorithmic deductions can, in fact, be
printed by the algorithm in the format of a human-readable proof. This
proof (by computer) can be shortened by using more sophisticated
rules/lemmas. For this paper we chose to keep the number of rules
limited and the nature of the rules themselves simple so as to avoid
long and technical arguments. The algorithm is, however, generic in
nature, in the sense that it can easily incorporate additional, newly
established rules. A significant advantage of this approach is that the
proof produced by the algorithm can be checked by another algorithm in a
much shorter time than would be required to generate the proof in the
first place.
The paper is organised as follows. After reviewing a number of
preliminary results in §2, we present a recursive enumeration algorithm
in §3 for determining bounds on any of the six types of Ramsey numbers
mentioned above. In §4, we then show how the algorithm may be applied to
determine the values of the two Ramsey numbers and . The numerical results returned by
the algorithm are presented and analysed in §5, after which the paper
closes with suggestions for further work in §6.
2. Preliminary Results
Let be a
Ramsey number. An colouring is a
red-blue edge colouring of
the complete graph of order
which satisfies neither of the
two sought-after chromatic conditions of the Ramsey number , hence showing that .
In [6], we computed
complete sets of and colourings for all , all and all admissible
values of . In that paper, we also
computed complete sets of
colourings for all and all admissible
values of . The red subgraphs of
the complete sets of
colourings of orders and are, for example, shown in Figure 1. The red subgraphs of
the complete sets of
colourings of orders , ,
and are similarly shown in
Figures 2, 3, 4 and 5,
respectively.
Figure 1 (a)–(c) The red subgraphs of the complete set of colourings, and (d) the red subgraph of the only colouringFigure 2 The red subgraphs of the complete set of colouringsFigure 3 The red subgraphs of the complete set of colouringsFigure 4 The red subgraphs of the complete set of colouringsFigure 5 The red subgraphs of the complete set of colourings
The following characterisation dates from 1989 and is due to Brewster
et al. [3].
Lemma 1 ([3]). The blue subgraph of a red-blue edge
colouring of a complete graph has an irredundant set of cardinality
if and only if there is a -cycle in the red subgraph or a -cycle in the red subgraph
with the edges , and in the blue subgraph.
We call the second substructure in the above lemma a red
-cycle with blue
diagonals. The following result relates colourings and colourings for different types
of Ramsey
numbers.
Lemma 2 ([6]). Let be integers, and let and be two Ramsey numbers in (2)
satisfying . Then
any colouring is also a
colouring.
The minimum degree of the subgraph [, resp.] in a colouring is referred to as the
minimum red [blue,
resp.] degree and is denoted by [, resp.]. Similar definitions
hold for the maximum degrees of
and , denoted by and , respectively.
Lemma 3 ([20]). Let be integers. Then, and in any colouring .
The following corollary follows from Lemma 3.
Corollary 1. Let be integers and let be a Ramsey number.
Then, and
in any colouring .
Finally, the following useful result immediately follows from
Corollary 1.
Corollary 2. Let be integers and let be a Ramsey number.
Then, and
in any
colouring .
3. Enumeration of Colourings
We present a recursive algorithm for deciding whether or not a
partial red-blue edge colouring of a complete graph of order can be completed to form an colouring, where is a Ramsey number. A
pseudo-code listing of the algorithm is presented as Algorithm 1.
The algorithm takes as global input parameter a natural number and as local input variables a set
of red edges, a set of blue edges and a set of uncoloured edges of a
complete graph of order and
essentially follows a branch-and-bound approach to yield the boolean
variable output True if the edges in can be coloured red or blue in
some combination to form an colouring, or
False otherwise.
The external function is repeatedly
called (step 3) during the repeat-until loop (steps 1–5). This function
examines each edge in in
turn, determining whether perhaps it must necessarily be coloured either
red or blue in order to avoid violating a so-called bounding
list of properties of an colouring. An example of such a
list of properties in the context of the Ramsey number is the bounding list in Table 2. If a colouring of any edge is thus necessitated, the
edge is removed from and inserted into or (whichever is appropriate). In
addition to this set update, the function also updates the value of a global boolean variable
OK, originally initialised as having the value
True, to the value if it is determined that some
edge can be coloured
neither red nor blue (as a result of avoiding the contradiction of some
pair of properties in the bounding list). This process of calling the
function is repeated until the colouring of no
further edges in is
necessitated by the bounding list.
Table 2 Bounding list of properties used to illustrate that .
Property
Description
a
No red triangle is formed
b
No blue clique of order is formed
c
The red degree of each vertex is at most
by Corollary 1
d
The blue degree of each vertex is at most
by Corollary 1
If all edges have either been coloured red or blue ( in step 6), a valid
colouring has been found,
in which case the boolean value True is
returned. Otherwise, an edge is randomly selected (step 7) and branched upon, attempting
to colour it either red or blue by calling the algorithm recursively
with the updated input variable triples or (step 8). If this cannot be
achieved, then the boolean value False is
returned (step 9).
For some value and a fixed
vertex , the set may be initialised without loss
of generality in the context of seeking an colouring by taking arbitrary edges incident to
according to Corollary 2. The set may similarly be initialised by
taking any of the
remaining edges incident to . This
is illustrated in Figure 6(a) for in the context of an colouring, upon which Algorithm
1 returns the boolean
value True, resulting in the colouring shown in Figure 6(b), which shows that
.
Figure 6 (a) Initialisation of the input sets and to Algorithm 1 in pursuit of an colouring. (b) The colouring returned by Algorithm 1 with input sets and initialised as in (a). (c) Initialisation of the input sets and to Algorithm 1 in pursuit of an colouring.
(d) A larger initialisation of the input sets and to Algorithm 1, without loss of generality, in pursuit of an colouring.
If, however, the same initialisation process is followed in the
context of an colouring,
as shown in Figure 6(c), Algorithm 1 returns the boolean
value False as a result of not being able to
compute an colouring,
because . Although a
conceptually simple algorithm, the ranges of possible values of the
smallest unknown Ramsey numbers are currently so large that direct
application of Algorithm 1 to determine these
Ramsey numbers is not technologically possible. The rapid growth in the
number of branches and associated computing times required to determine
the Ramsey numbers for via Algorithm 1 is, for example,
illustrated in Table 3.
Table 3 The number of branches and associated computing times required
to determine the Ramsey numbers for . The times are given in
seconds and were measured on an Intel core of eight 3.4GHz, 7.7Gb RAM
processors running in a 64 bit Linux operating system.
Ramsey
Best lower bound
Best upper bound
number
Value
Branches
Time
Value
Branches
Time
2
0
12
234
1 034
0
7 969 625
276
6 944 615 486
416 233
There is, however, a better way of initialising the input sets and to Algorithm 1 than the
initialisations shown in Figure 6 (a) and (c).
Knowledge about smaller avoidance colourings may be utilised in the
initialisations. The subgraph induced in any eventual colouring by the vertex subset
in Figure 6(c), for example, is
necessarily an colouring,
for if this were not the case, then the subgraph induced in the colouring by the vertex subset
would contain
either a red triangle or a clique of order as subgraph, a contradiction.
Similarly, the subgraph induced in any eventual colouring by the vertex subset
in Figure 6(c) is necessarily an
colouring, for otherwise
the subgraph induced in the colouring by the vertex subset
would contain a red
triangle as subgraph, again a contradiction. The input sets and to Algorithm 1 may therefore be
initialised, without loss of generality, as shown in Figure 6(d), because there is
only one colouring (a blue
triangle) and only one
colouring (consisting of a red five-cycle embedded within a blue
five-cycle, as shown in Figure 1(d)).
When initialising the input sets and to
Algorithm 1 as shown in Figure 6(d), the branching
process illustrated in Figure 7 results,
which shows that . In
the figure, branching nodes are denoted by circles (containing the edges
that are branched upon in coordinate form), while bounding nodes are
denoted by rectangles (containing the reasons for contradictions
preventing further growth of the tree in the form ,!xy which means that the edge can be coloured neither red because of
reason x in Table 2 nor blue because of reason y in
Table 2). Square brackets are used to
present lists of edges whose colours are fixed by a branching decision,
where a string of the form ,Rx means that the edge is necessarily red so as to avoid
contradicting property x in Table 2, and
similarly for blue edges. Angular brackets are used at the top of the
tree to present a list of edge colours corresponding to the
initialisation of the input sets and to
Algorithm 1, where ,B
means that edge is coloured
blue, and similarly for red edges.
Figure 7 The branch-and-bound process followed by
Algorithm 1 to show that .
Note that because of the extended initialisation in Figure 6(d), the tree in
Figure 7 has merely eight branches instead of
the branches that result
according to Table 3 when the sparser initialisation in Figure
6(c) is used. But even
with the denser initialisation procedure described above, which utilises
information about smaller avoidance colourings, the rapid growth in the
number of branches and associated computing times required by Algorithm
1 places the computation
of most Ramsey numbers of the form for out of the current reach of
computing technology. It is, nevertheless, possible to apply the
algorithm judiciously in order to determine some of these Ramsey number
values, as we show in the next sections.
4. Enumeration of colourings
We denote the subgraph of the blue [red, resp.] subgraph of a -colouring induced by some set
by [, resp.]. Let
[ or , resp.] be the set of
vertices at distance
[at a distance greater than or at
a distance at least , resp.] from
some specified vertex in the
red subgraph of a -colouring. Since we avoid an
irredundant set of cardinality in
the blue subgraph, we have the following useful result, adapted from
[20].
Lemma 4 ([20]). Let be any vertex of a -colouring.
If is a path in
and ,
then and are joined by red edges to a common
vertex in .
If contains at most one vertex of , then is
bipartite.
The following generalisation of the result in Lemma 4(a) is
useful.
Corollary 3. Let be a star in with centre in and endpoints in . Then all the endpoints of this
star have a common neighbour in .
Proof. By induction on the number of endpoints of the star.
The result of Lemma 4(a) serves as the base case for the
induction. Suppose, as induction hypothesis, that all the endpoints in
of any red star with centre in have a common neighbour
in . We show by
contradiction that all the endpoints in of any red star with centre in have a common neighbour
in . Let be the set of endpoints of such a star
with centre , and let and be two vertices in . Suppose, to the contrary, that this
star does not have a common neighbour in . Let and be subsets of cardinality of such that , as shown
in Figure 8. Then it follows by the induction
hypothesis that all vertices in
have a common neighbour in , and similarly for the vertices
in . Let these common neighbours
be and , respectively. Then the edges and must both be blue (since the
vertices in do not all have a
common neighbour in ). But
then is a red
six-cycle with blue diagonals , and — contradicting Lemma 1.
Figure 8 The neighbours of the endpoints of a star
in .
It follows from [18, Figure 4.1]
that colourings exist.
These colourings have, however, not yet been enumerated. Let and be respectively the red
and blue subgraphs of a
colouring, and denote the minimum and maximum degrees of by and , respectively. Suppose is a vertex of red degree in this colouring. Then the
colourings in Figures 2–5 are
relevant in the context of the colouring as a result of the
following observation.
Observation 1. The colouring induced in by the vertices in
is a colouring.
The subgraph also has the
following properties.
Lemma 5 ([18, Lemmas 5–6)]. is an independent set of , , and .
By Corollary 2, we therefore have the bounds .
Note, however, that , for otherwise we would have a cubic graph of odd order. Therefore, The subgraph of
is bipartite by Lemma 4(b). Suppose
contains components and denote
the bipartitions of these components by for all . We call a component of the
form an component, and
we assume, without loss of generality, that for all . Define and . Then .
4.1. Four-step enumeration
process
Our approach toward enumerating colourings consists of the
following four steps:
Step 1. For each possible pair
satisfying , we
determine all non-isomorphic
components in . That is,
connected, bipartite subgraphs with partite sets of cardinalities and satisfying the above inequality
chain.
Step 2. We consider each combination of values for the triple satisfying the
inequalities in Lemma 5 and in (3). These
combinations are listed as the eleven subcases in Table 4 where, of course, . In each subcase
of the table, we insert red edges in according to all permissable
combinations of component
combinations determined in Step 1.
Table 4 Subcases considered in Step 2 of our colouring enumeration
procedure.
Step 3. For each of the red subgraphs on in Step 2 above, we consider all
non-isomorphic ways in which to join vertices in to vertices in by means of red edges,
satisfying the requirements of Corollary 3. The number of
these non-isomorphic red-edge connections between and is given by a Stirling number of
the second kind 1. These cases are pruned by ensuring
that the properties in Table 5 are
satisfied.
Step 4. For each graph found in Step 3 above, and for each permissible
avoidance graph on (that
is, for each colouring in
Figures 2–5 in the case
where , or for each
colouring in Figure 1 in the case where ), we employ our recursive
branching algorithm of §3 to decide the colours of the remaining edges
joining vertices in to
as well as edges joining
vertices in to , as illustrated in Figure 9. For the branching process we use
the bounding properties in Table 6.
Table 5 Pruning rules applied to the Stirling edges of Step
3.
Property
Description
e
No red triangle is formed; see Lemma 1
f
No red -cycle with blue diagonals is formed;
see Lemma 1
g
The red degree of each vertex is at most
; see (3)
h
Every vertex in is joined by a red edge to some
vertex in
Figure 9 Edges branched upon in Step 4 of our
colouring enumeration
procedure.
Table 6 Bounding list of properties used for the colouring of edges
between vertices in and
vertices in as well as
between vertices in and
vertices in (in Step
4).
Property
Description
i
No red triangle is formed; see Lemma 1
j
No red -cycle with blue diagonals is formed;
see Lemma 1
k
The red degree of each vertex is at most
; see (3)
l
The endpoints in of a star in with centre in
all have a common neighbour in ; see Corollary 3
m
No odd red cycle is formed; see Lemma 4(b)
n
No blue complete graph of order is formed in
It is possible to rule out certain types of components in . We
start, however, by noting that in order to avoid a clique of order in , the
following result holds.
Observation 2. If , then .
We also have the following restriction on small components of .
Lemma 6. If , and has
exactly one component, then
is not a subgraph of .
Proof. By contradiction. Suppose, to the contrary, that
and that has
exactly one component in
. Then any vertex is joined to the component by a red edge, for
otherwise contains a clique of order . The subgraph is
therefore edgeless (so as to avoid triangles in ). But then contains a clique
of order (and hence of order
), a contradiction.
We similarly have the following restriction on large components of
.
Lemma 7. is not a component.
Proof. By contradiction. Suppose, to the contrary, that
is a
component. Since , it follows
from Observation 2 that . Every vertex
is therefore joined
to by a red edge, for otherwise
contains a clique of order . Hence all edges from to in are blue (so as to
avoid odd cycles in ). But then contains a clique of order , by Lemma 4(b), since there
is at least one blue edge for
some pair , a
contradiction.
4.2. Results for the case
Suppose . Then
and by Lemma 5. Furthermore,
does not contain a component
(because of the red degree restriction on each vertex).
Table 7 Results obtained for Subcase IVb in Table 4.
No
X
Y
Time
(i)
1 1 3
0 0 3
14
2
0
143.3
(ii)
1 1 2 1
0 0 2 1
18
2
0
659.4
(iii)
1 1 1 1 1
0 0 1 1 1
10
2
0
212.2
(iv)
1 2 2
0 1 2
12
2
0
1 405.7
(v)
1 2 1 1
0 1 1 1
15
2
0
317.5
(vi)
1 3 1
0 2 1
19
2
0
84.1
(vii)
1 4
0 3
3
2
0
1.6
(viii)
2 2 1
1 1 1
9
2
3
274.3
(ix)
2 3
1 2
8
2
1
43.0
(x)
3 2
1 2
4
2
0
10.4
(xi)
3 1 1
1 1 1
2
2
0
7.7
(xii)
4 1
2 1
3
2
0
1.7
(xiii)
5
3
0
—
0
0.1
The results obtained when applying our four-step enumeration process
described above to subcases I–IVc in Table 4 are
presented in [21]. Table 7 is reproduced here as an example. In
these tables, and
denote respectively
the set of valid
subcolourings on the vertices in and the set of valid subcolourings on the vertices
in found upon
completion of Steps 1–3 in our four-step enumeration process of §4.1.
Furthermore, denotes the set
of valid subcolourings
involving only edges between vertices in and , and between vertices in and , as a result of applying
our branching algorithm of §3 (i.e. Step 4 of our
four-step enumeration process of §4.1). The times reported in the tables
are measured in seconds and represent the times required to carry out
the four-step enumeration process on the processor described in the
caption of Table 3.
Consider, for example, Subcase IVb(viii) in Table 7. An example of a result obtained upon
having applied the first three steps of our four-step enumeration
process in this case (i.e. before the branching of
Step 4 commences) is shown in Figure 10. Note that
one of the two possible
colourings in Figure 5, namely
, appears as in Figure 10. All non-red edges entirely within the
sets , and are blue, and so are the edges
between and , between and , and between and . Branching occurs only on the
remaining edges between
and as well as on the
edges between and , eventually resulting in the
colouring shown in Figure 11.
4.3. Results for the case
Suppose now . Then
and by Lemma 5. Furthermore, we have the following
restriction on the number of small components in .
Lemma 8. If , , and
has exactly two components,
then is not a subgraph of .
Proof. By contradiction. Suppose, to the contrary, that
, , and
has exactly two components,
but that is a subgraph of . It follows from Observation 2 that
and hence
from Observation 2 that is the red subgraph of a colouring. Furthermore, each
vertex is joined to a
component by a red edge, for
otherwise contains a clique of order . Consequently one of the components is joined to at least
three vertices of by red
edges. These three vertices form a triangle in in
order to avoid forming a triangle in , but this contradicts the fact that
is the red subgraph of a
colouring.
Figure 10 An example of the result of applying the
first three steps of the enumeration process to Subcase IVb(viii) of
Table 7.
The results obtained upon having applied our four-step enumeration
process of §4.1 to subcases V–VIb in Table 4 are
presented in [21, Tables 15-17].
4.4. Validation of results
It is known that there is only one colouring, two colourings, six colourings and thirty four
colourings [6, Table 3]. We validated our computer
implementation of the four-step enumeration process described in §4.1 by
verifying these results independently. We also verified that the results
obtained by this process are independent of the choice of the vertex
in each case.
4.5. The sets of
colourings and colourings
A total of 298
colourings were uncovered during the enumeration process described in
the previous sections. These colourings have not been enumerated before
and are available online [22].
Furthermore, a total of six colourings were similarly
found, as summarised in [21]. Among these colourings
there are, however, only three pairwise non-isomorphic colourings — all
corresponding to the case where . The red subgraphs of these
colourings are shown in Figure 11.
Figure 11 The red subgraphs of the complete set of colourings. A minimal dominating (maximal irredundant) set is indicated by means of solid vertices for each graph
5. The Ramsey numbers and
Since the circulant graph in
Figure 12(a) contains no irredundant
set of cardinality and the
circulant graph in Figure 12(b) contains no irredundant
set of cardinality , it follows
that the pair in Figure 12 forms an colouring.
Figure 12 An colouring in which both and are circulant graphs
It therefore follows by (2)
and Table 1 that
Lemma 2 implies that sets of and colourings may be found from
among the set of
colourings enumerated in §4. The graphs in Figure 11 each
contains a minimal dominating (or maximal irredundant) set of
cardinality , so that none of
these graphs represent
colourings, or
colourings, and hence we have the following result in view of (4).
Theorem 3. .
6. Further work
In order to pave the way for the computation of the Ramsey numbers
and , we establish the following
bounds.
Theorem 4. .
Proof. Using the notation introduced in Figure 12, the circulant graph
contains no irredundant set of cardinality and the circulant graph
contains no irredundant set of cardinality . The pair therefore forms an colouring and hence .
In order to establish the desired upper bound on , we show by contradiction that no
colouring exists.
Suppose, to the contrary, that such a colouring indeed exists, and let
be its red subgraph. Then
it follows from Corollary 2 that .
It also follows from Corollary 1 that , and so
, ,
or .
Let be a vertex of maximum
degree in and suppose
. Then is empty in order to avoid
a clique of order in the blue
subgraph . But
then has cardinality and forms a clique of order in by Lemma 4(b), a contradiction.
Suppose next that has degree
or . Then by an argument similar to the one
above, we may assume that is
not empty (so as to avoid a large set ). But then it follows from Lemma
4(b) that has fewer than vertices in order to avoid a clique of
order in (on the vertices of a
partite set of the bipartite graph in together with ). Furthermore, similar to Observation
2,
we also have that has
fewer than
vertices, and so has fewer
than
vertices. For or
, this value is or , respectively — contradicting the fact
that has order .
It follows that is a
-regular graph, which is a
contradiction, because it has odd order.
As further work, we propose that the values of and be computed according to our
enumeration strategy in §4.1. In order to render this strategy feasible
for graphs of orders –, however, we anticipate that our
branch-and-bound procedure in §3 will have to be refined, possibly by
selecting the branching order judiciously. We furthermore expect that
the development of new theoretical results will be required in order to
extend the bounding list of Table 6.
Declaration of competing
interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
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