This paper establishes a connection between perfect matching width and directed treewidth in bipartite graphs, confirming Norine's conjecture that high perfect matching width implies the presence of large grid minors.
Contribution
It proves that for bipartite graphs, perfect matching width is equivalent to directed treewidth, linking it to the directed grid theorem and advancing the structural understanding of matching covered graphs.
Findings
01
Perfect matching width equals directed treewidth in bipartite graphs.
02
High perfect matching width implies the existence of large grid minors.
03
Confirmed Norine's conjecture for bipartite graphs.
Abstract
A connected graph G is called matching covered if every edge of G is contained in a perfect matching. Perfect matching width is a width parameter for matching covered graphs based on a branch decomposition. It was introduced by Norine and intended as a tool for the structural study of matching covered graphs, especially in the context of Pfaffian orientations. Norine conjectured that graphs of high perfect matching width would contain a large grid as a matching minor, similar to the result on treewidth by Robertson and Seymour. In this paper we obtain the first results on perfect matching width since its introduction. For the restricted case of bipartite graphs, we show that perfect matching width is equivalent to directed treewidth and thus the Directed Grid Theorem by Kawarabayashi and Kreutzer for directed \treewidth implies Norine's conjecture.
Equations14
E(D)
E(D)
∪{(w,xu,v)∣(w,u)∈E(D) or (w,v)∈E(D)}
∪{(xu,v,w)∣(u,w)∈E(D) or (v,w)∈E(D)}.
\displaystyle\operatorname{cp}(\mathchoice{\partial\!\left(t_{1}t_{2}\right)}{\partial\!\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)})\coloneqq\max_{\begin{subarray}{c}\mathcal{C}\text{ family of pairwise}\\
\text{disjoint directed cycles}\\
\text{in $D$}\end{subarray}}\Bigl{|}\mathchoice{\partial\!\left(t_{1}t_{2}\right)}{\partial\!\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)}\cap\bigcup_{C\in\mathcal{C}}\mathchoice{E\!\left(C\right)}{E\!\left(C\right)}{E\left(C\right)}{E\left(C\right)}\Bigr{|}.
\displaystyle\operatorname{cp}(\mathchoice{\partial\!\left(t_{1}t_{2}\right)}{\partial\!\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)})\coloneqq\max_{\begin{subarray}{c}\mathcal{C}\text{ family of pairwise}\\
\text{disjoint directed cycles}\\
\text{in $D$}\end{subarray}}\Bigl{|}\mathchoice{\partial\!\left(t_{1}t_{2}\right)}{\partial\!\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)}{\partial\left(t_{1}t_{2}\right)}\cap\bigcup_{C\in\mathcal{C}}\mathchoice{E\!\left(C\right)}{E\!\left(C\right)}{E\left(C\right)}{E\left(C\right)}\Bigr{|}.
γ′(e):={γ(e)β(t′) if e∈E(T) if e=(t,t′) for some t∈V(T)∖L(T).
γ′(e):={γ(e)β(t′) if e∈E(T) if e=(t,t′) for some t∈V(T)∖L(T).
mp(∂(X)):=M∈M(G)max∣M∩∂(X)∣.
mp(∂(X)):=M∈M(G)max∣M∩∂(X)∣.
pmw(G):=(T,δ) perfect matchingdecomposition of Gmine∈E(T)maxmp(∂(e)).
pmw(G):=(T,δ) perfect matchingdecomposition of Gmine∈E(T)maxmp(∂(e)).
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Full text
Cyclewidth and the Grid Theorem for
Perfect Matching Width
of Bipartite Graphs††thanks: This work has been supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC consolidator grant DISTRUCT, agreement No 648527).
A connected graph G is called matching covered if
every edge of G is contained in a perfect matching.
Perfect matching width is a width parameter for
matching covered graphs based on a branch decomposition. It
was introduced by Norine and intended as a tool for the
structural study of matching covered graphs, especially in
the context of Pfaffian orientations. Norine conjectured
that graphs of high perfect matching width would contain a
large grid as a matching minor, similar to the result on
treewidth by Robertson and Seymour.
In this paper we obtain the first results on perfect
matching width since its introduction. For the restricted
case of bipartite graphs, we show that perfect matching
width is equivalent to directed treewidth and thus the
Directed Grid Theorem by Kawarabayashi and Kreutzer for
directed treewidth implies Norine’s conjecture.
Keywords. Branch Decomposition; Perfect Matching; Directed Treewidth; Matching Minor
1 Introduction
The concept of width-parameters, or decompositions of graphs into tree like structures has proven to be a powerful tool in both structural graph theory and for coping with computational intractability.
The shining star among these concepts is the treewidth of undirected graphs introduced in its popular form in the Graph Minor series by Robertson and Seymour (see [RS10]).
Tree decompositions are a way to decompose a given graph into
loosely connected small subgraphs of bounded size that, in many algorithmic applications, can be dealt with individually instead of considering the graph as a whole.
This concept allows the use of dynamic programming and other
techniques to solve many hard computational problems, see for example [Bod96, Bod97, Bod05, DF16].
Treewidth was also successfully applied for non-algorithmic problems:
in the famous Graph Minor project by Robertson and Seymour treewidth plays a key role, in model
theory [Grä99, BtCS11, SC13, BBB17],
or in the proofs of the (general) Erdős-Pósa property for undirected graphs [RS86].
In the latter result, the Grid theorem plays an important role.
It says that if the treewidth of a graph is big, then it has a large
grid as a minor.
Proven by Robertson and Seymour in 1986 [RS86], the
Grid Theorem has given rise to many other interesting results.
An example of those is the algorithm design principle called
bidimensionality theory, that, roughly, consists in
distinguishing two cases for the given graph: small treewidth or large grid minor (see [DH07, DH04, FLST10] for examples).
As directed graphs pose a natural generalisation of graphs, soon the question arose whether a similar strategy would be useful for directed graphs and so Reed [Ree99] and Johnson et al. [JRST01] introduced directed treewidth along with the conjecture of a directed version of the Grid Theorem.
After being open for several years, the conjecture was finally proven
by Kawarabayashi and Kreutzer [KK15].
Similarly to the undirected case, it implies the Erdős-Pósa property for
large classes of directed graphs [AKKW16].
It is possible to go further and to consider even more general structures than directed graphs.
One of the ways to do this is to characterise (strongly connected) directed graphs by pairs of undirected bipartite graphs and perfect matchings.
The generalisation (up to strong connectivity) is then to drop the condition on the graphs to be bipartite.
There is a deep connection between the theory of matching minors in matching covered graphs and the theory of butterfly minors and strongly connected directed graphs.
This connection can be used to show structural results on directed graphs by using matching theory (see [McC00, GT11]).
The corresponding branch of graph theory was developed from the theory of
tight cuts and tight cut decompositions of
matching covered graphs introduced by Kotzig, Lovász and
Plummer [Lov87, LP09, Kot60].
A graph is matching covered if it is connected and each of its edges is contained in a perfect matching.
One of the main forces behind the development of the field is the question of Pfaffian orientations; see [McC04, Tho06] for an overview on the subject.
Matching minors can be used to characterise the bipartite Pfaffian graphs [McC04, RST99]. The characterisation implies a polynomial time algorithm for the problem to decide whether a matching covered bipartite graph is Pfaffian or not.
In addition, there are powerful generation methods for the building blocks of matching covered graphs obtained by the tight cut decomposition: the bricks and braces, which are an analogue of Tutte’s theorem on the generation of 3-connected graphs from wheels.
Here the bipartite (brace) case was solved by McGuaig [McC01] while the non-bipartite (brick) case was solved by Norine in his PhD thesis [Nor05].
The goal of this thesis was to find an analogue to the bipartite characterisation of Pfaffian graphs in the non-bipartite case.
However, while a bipartite matching covered graph turns out to be Pfaffian if and only if it does not contain K3,3 as a matching minor, Norine discovered an infinite antichain of non-Pfaffian bricks.
While no polynomial time algorithm for Pfaffian graphs is known,
Norine defined a branch decomposition for matching covered graphs and
found an algorithm that decides whether a graph from a class of bounded perfect matching width is Pfaffian in XP-time.
This branch decomposition for matching covered graphs and perfect matching width are similar to branch decompositions and branchwidth, again introduced by Robertson and Seymour [RS91].
Norine and Thomas also conjectured a grid theorem for their new width parameter (see [Nor05, Tho06]).
Based on the above mentioned ties between bipartite matching covered graphs and
directed graphs, Norine conjectures in his thesis that the
Grid Theorem for digraphs, which was still open at that time, would at least imply the conjecture in the bipartite case.
Whether perfect matching width and directed treewidth could be seen as equivalent was unknown at that time.
Contribution.
We settle the Matching Grid Conjecture for the bipartite case.
To do so, in section 3, we construct a branch decomposition and a corresponding new width parameter for directed graphs: the cyclewidth and prove its equivalence to directed treewidth.
Cyclewidth itself seems to be an interesting parameter as it appears to be more natural and gives further insight in the difference between undirected and directed treewidth: while the undirected case considers local properties of the graph, the directed version is forced to have a more global point of view.
We also prove that cyclewidth is closed under butterfly minors, which
is not true for directed treewidth as shown by Adlern [Adl07].
The introduction of cyclewidth leads to a straightforward proof of
the Matching Grid Theorem for bipartite graphs. In section 4 we show that cyclewidth and perfect matching width are within a constant factor of each other. This immediately implies the Matching Grid Theorem for bipartite graphs. Our proofs are algorithmic and thus also imply an approximation algorithm for perfect matching width on bipartite graphs, which is the first known result on this matter.
Norine proposes the quadratic
planar grid as the right matching minor witnessing high perfect
matching width as in the original Grid Theorem by Robertson and
Seymour. In this work we give an argument that cylindrical grids are a more natural grid-like structure in the context of matching covered
graphs. Moreover, to better fit into the canonical matching theory, we
would like our grid to be a brace. McGuaig proved that every brace
either contains K3,3, or the cube as a matching minor (see
[McC01]). Additionally, one of the three infinite
families of braces from which every brace can be generated are the
even prisms (or planar ladders as McGuaig calls them in his paper) of
which the cube is the smallest one.
The bipartite matching grid, which we define in this work can be seen
as a generalisation of the even prism and again the cube is the
smallest among them.
Width parameters for directed graphs similar to directed treewidth are exclusively concerned with directed cycles.
So, when studying cyclewidth and related topics one might restrict themselves to strongly connected digraphs.
Similarly, an edge that is not contained in any perfect matching is, in most cases, irrelevant for the matching theoretic properties of the graph.
For this reason it is common to only consider matching covered graphs as this does not pose a loss of generality.
Finally, we show that the perfect matching widths of a
matching covered bipartite graph and of any matching minor of
the graph are within a constant factor of each other.
Let us remark that we took the freedom to rename Norine’s matching-width [Nor05] to perfect matching width to better distinguish it from related parameters such as maximum matching width (see [JST18]).
2 Preliminaries
We consider finite graphs and digraphs without multiple edges and use
standard notation (see [Die17]). For a
graph G, its vertex set is denoted by V(G) and its edge set by
E(G), and similarly for digraphs where we call arcs
edges. For a (directed) tree T, we write L(T) for the set
of its leaves.
Let X⊆V(G) be a non-empty set of vertices in a graph G.
The cut at X is the set ∂(X)⊆E(G) of all edges
joining vertices of X to vertices of V(G)∖X.
We call X and V(G)∖X the shores of
∂(X). A set E⊆E(G) is a cut if E=∂(X) for some X. Note that in connected graphs the shores are
uniquely defined. In such cases, a cut is said to be odd if both shores have odd cardinality and we call a cut trivial if one of the two shores only contains one vertex.
A matching of a graph G is a set M⊆E(G) such that no two edges in M share a common endpoint.
If e=xy∈M, e is said to cover the two vertices x and y.
A matching M is called perfect if every vertex of G is covered by an edge of M.
We denote by M(G) the set of all perfect matchings of a graph G.
A restriction of a matching M to a set S⊆V(G) or to a
subgraph G′⊆G is defined by M∣S:={xy∈M∣x,y∈S} and M∣G′:=M∣V(G′).
Definition 2.1**.**
Let G=(A∪B,E) be a bipartite graph and let M∈M(G) be a perfect matching of G.
The M-directionD(G,M) of G is defined as follows (see fig. 1 for an illustration).
Let M={a1b1,…,a∣M∣b∣M∣}
with ai∈A,bi∈B for 1≤i≤∣M∣.
Then,
(i)
V(D(G,M)):={v1,…,v∣M∣} and
2. (ii)
E(D(G,M)):={(vi,vj)∣aibj∈E(G)}.
Thus, the M-directionD(G,M) of G is
defined by contracting the edges of M, and orienting the
edges of G that do not belong to M from A to B.
A graph G is called matching covered if G is connected and for every edge e∈E(G) there is an M∈M(G) with e∈M.
The following is a well known observation on M-directions.
Observation 2.2**.**
A digraph D is strongly connected if and only if there is an, up to
isomorphism unique, pair consisting of a bipartite matching covered graph G and a perfect matching M∈M(G) such that D is isomorphic to D(G,M).
A set S⊆V(G) of vertices is called conformal if G−S has a perfect matching.
Given a matching M∈M(G), a set S⊆V(G) is called M-conformal if M∣G−S is a perfect matching of G−S and
M∣S is a perfect matching of G[S].
A subgraph H⊆G is conformal if V(H) is a conformal set.
H is called M-conformal for a perfect matching M∈M(G) if H is conformal and M∣H is a perfect matching of H.
If a cycle C is M-conformal, there is another perfect matching M′=M with E(C)∖M⊆M′.
Hence, if needed, we say C is M-M′-conformal to indicate that M and M′ form a partition of the edges of C.
3 Directed Treewidth and Cyclewidth
Directed treewidth is, similar to treewidth on undirected graphs, an important tool in the structure theory of directed graphs.
For treewidth there is an equivalent (up to a constant factor) concept of branch-width that also allows a structural comparison of an undirected graph to a tree.
To the best of our knowledge, so far there is no branch decomposition like concept for digraphs that can be seen as an equivalent to directed treewidth.
For the proof of our main result, we introduce such a decomposition, which we call cyclewidth and prove some basic properties of it such as its equivalence to directed treewidth and that it is closed under butterfly minors.
This section is divided into two subsections.
First we introduce cyclewidth and show that it provides a lower bound on the directed treewidth with a linear function.
Then, in a second step, we show that cyclewidth is bounded from below by the cyclewidth of its butterfly minors and, moreover, that large cylindrical grids have large cyclewidth.
The Directed Grid Theorem implies that there exists a function that bounds the cyclewidth of a digraph from below by its directed treewidth.
3.1 Cyclewidth: A Branch Decomposition for Digraphs
We first recall the directed tree decomposition by Reed [Ree99], and Johnson et al. [JRST01].
An arborescence is a directed tree T with a root r0 and all edges directed away from r0.
For r,r′∈V(T) we say that r′ is belowr and r is abover′ in T and write r′>r if r′=r and r′ is reachable from r in T.
For e∈E(T) with head r we write r′>e if either r′=r, or r′>r.
Definition 3.1**.**
Let T be an arborescence or a rooted undirected tree and let e∈E(T).
Then T⋉e:=(T1,T2) where T1 is the sub-arborescence (subtree) of T−e containing the root of T and T2 is the other sub-arborescence (subtree) with the head of e as the new root.
By a slight abuse of notation an undirected tree T (without a root) and an edge e=tt′ in it, we write T⋉e:=(T1,T2) where T1 is the subtree containing t and T2 the subtree containing t′ in T−e.
Let D be a digraph and let Z⊆V(D).
A set S⊆V(D)−Z is Z-normal if
there is no directed walk in D−Z with the first and the last vertex in S that uses a vertex of D−(Z∪S).
Definition 3.2**.**
A directed tree decomposition of a digraph D is a
triple (T,β,γ), where T is an
arborescence and β:V(T)→2V(D) and γ:E(T)→2V(D) are functions such that
(i)
{β(t)∣t∈V(T)} is a
partition of V(D) into possibly empty sets
(a near partition) and
2. (ii)
if e∈E(T), then ⋃{β(t)∣t∈V(T),t>e} is γ(e)-normal.
For any t∈V(T) we define Γ(t):=β(t)∪⋃{γ(e)∣e∈E(T),e∼t}, where e∼t if e is incident with t.
The width of (T,δ,γ) is
maxt∈V(T)∣Γ(t)∣−1.
The directed treewidthdtw(D) of D is the least
integer w such that D has a directed tree decomposition of width w.
The sets β(t) are called bags and the sets γ(e) are called the guards of the directed tree decomposition.
We also apply the bag-function β on subtrees instead of single vertices to refer to the union over all bags in the subtree, i.e. β(T′):=⋃v∈V(T)β(v) for T′ being a subtree T.
If a vertex v of D is contained in β(T′) for some subtree T′⊆T, we say that T′containsv.
Directed treewidth is a generalisation of the undirected version treewidth.
Similar to the undirected case one can find certain structural obstructions witnessing that a digraph has high directed treewidth.
An important result among these is the Directed Grid Theorem by Kawarabayashi and Kreutzer [KK15].
It states, roughly, that a graph has high directed treewidth if and
only if it contains a large cylindrical grid as a butterfly minor.
In order to formally state the Directed Grid Theorem, we need some further definitions.
Definition 3.3** (Butterfly Minor).**
Let D be a digraph.
An edge e=(u,v)∈E(D) is butterfly-contractible if e is the only outgoing edge of u or the only incoming edge of v.
In this case the graph D′ obtained from D by butterfly contractinge is the graph with vertex set (V(D)∖{u,v})∪{xu,v}, where xu,v is a new vertex and the edge set
[TABLE]
We denote the result of butterfly contracting an edge e in D by D/e.
A digraph D′ is a butterfly-minor of D if it can be obtained from a subgraph of D by butterfly contractions.
Definition 3.4** (Cylindrical Grid).**
A cylindrical gridDk↻ of order k consists of k concentric directed cycles and 2k paths
connecting the cycles in alternating directions, see fig. 2.
Now we can state the Directed Grid Theorem.
Theorem 3.5** (Kawarabayashi and Kreutzer, 2015 [KK15]).**
There is a function f:\mathdsN→\mathdsN such that every digraph D either satisfies dtw(D)≤f(k), or contains the cylindrical grid of order k as a butterfly minor.
The goal of this section is the introduction of a branch decomposition for digraphs.
Branch decompositions usually work as follows.
They are defined as a tuple (T,δ) such that T is a cubic tree and δ is a bijection between the leaves of T and the vertices of the graph G that is decomposed by (T,δ).
Therefore every edge of T induces a bipartition of the vertex set of G, which can be seen as the two shores of an edge cut.
The width of the decomposition depends on the function that evaluates the edge cut.
In the case of directed treewidth, this concept faces a challenge.
While edge cuts are very local objects, the guards of a directed tree decomposition are not.
In fact, one of the main issues of directed treewidth is that the guards can appear almost everywhere in the graph.
In order to approach this problem, we need our evaluation function for the edge cuts given by our decomposition to measure a more global property.
We define cuts, shores and trivial cuts
for digraphs as for undirected graphs. Recall that L(T) is
the set of leaves of a tree T.
Definition 3.6** (Cyclewidth).**
Let D be a digraph.
A cycle decomposition of D is a tuple (T,φ),
where T is a cubic tree (i.e. all inner vertices have
degree three) and φ:L(T)→V(D) a bijection.
For a subtree T′ of T we use φ(T′):={φ(t)∣t∈V(T′)∩L(T)}.
Let t1t2 be an edge in T and let (T1,T2):=T⋉t1t2. Let ∂(t1t2):=∂(φ(T1)).
The cyclic porosity of the edge t1t2 is
[TABLE]
The width of a cycle decomposition (T,φ) is given by maxt1t2∈E(T)cp(∂(t1t2)) and the cyclewidth of D is then defined as
[TABLE]
Note that the cycle porosity is the number of cycle edges crossing a
cut. Note also that for different edges of the decomposition tree,
different cycle families may constitute the cycle porosity.
Moreover, let D be a digraph and (T,δ) a cycle decomposition for D of width k.
Let D′ be the digraph obtained from D by reversing the orientation of all edges, then (T,δ) is a cycle decomposition for D′ of width k.
Our next goal is to prove that cyclewidth is bounded from above by a function in the directed treewidth.
For this, we transform a directed tree decomposition into a cycle
decomposition in two steps. First, we push all vertices contained in
bags of inner vertices of the arborescence into leaf bags, and then
transform the result into a cubic tree.
Definition 3.7** (Leaf Directed Tree Decomposition).**
A directed tree decomposition (T,β,γ) of a digraph D is called a leaf directed tree decomposition if β(t)=∅ for all t∈V(T)∖L(T).
So first, we show that a directed tree decomposition can be turned
into a leaf decomposition without changing its width.
Lemma 3.8**.**
Let (T,β,γ) be a directed tree decomposition of a digraph
D. There is a linear time algorithm that computes a leaf directed
tree decomposition of D of the same width.
Proof.****.
For every inner vertex t∈V(T) such that
β(t)=∅ we add a new leave t′
adjacent to t (and no other vertices of T) and thus obtain a
new tree T′. The new bags are defined by β′:=V(T′)→2V(D) with β′(t):=β(t) for
t∈L(T), and β′(t):=∅ and β′(t′):=β(t) for t∈V(T)∖L(T). The new
guards are defined by
[TABLE]
We prove that (T′,β′,γ′) is still a directed tree decomposition.
The bags given by β′ still provide a near partition of V(D).
For all edges e∈E(T) it is still the case that ⋃{β(s)∣s∈V(T′),s>e} is γ(e)-normal and for the new edges the normality is obvious.
Finally, if Γ′ is defined for (T′,β′,γ′) as Γ for (T,β,γ), then Γ′(t)=Γ(t) for all t∈V(T) and for all t′ we have Γ′(t′)⊆Γ(t). Hence, the width of the decomposition did not change. Clearly, the new decomposition can be computed in linear time.
□
So whenever we are given a directed tree decomposition of a digraph D, we can manipulate it such that exactly the leaf-bags are non-empty.
This is still not enough since a cycle decomposition requires every leaf to be mapped to exactly one vertex – so every bag has to be of size at most one – and also the decomposition tree itself has to be cubic.
The following lemma shows how a leaf directed tree
decomposition can be further manipulated to meet the above
requirements, again in polynomial time and without changing
the width. We call a directed tree decomposition subcubic if
its arborescence is subcubic.
Lemma 3.9**.**
Let D be a digraph. If there exists a directed tree decomposition of
width k for D, there also exists a subcubic leaf directed
tree decomposition of width k for D where every bag has size at most one.
Proof.****.
Let D be a digraph and (T′′,β,γ) a directed
tree decomposition of width k. Then, due to lemma 3.8, there
exists a leaf directed tree decomposition (T,β,γ) of D of
the same width. Algorithm 1 takes this as input and
transforms it into a subcubic leaf directed
tree decomposition (T′,β′,γ′).
Clearly the resulting tree T′ is subcubic and only the leave bags
contain vertices. Now we want to check whether the output of the
algorithm again yields a proper directed tree decomposition of desired width.
In the first part we split the bag of each leaf up into bags of
single vertices which are added as new children. We show that such
a split of a leaf l does not destroy the properties of the
directed tree decomposition. The new vertices lv obtain bags of size
1. The new edge (l,lv) obtains the guard
β(l)∪γ(x,lv).
Due to
∣β(l)∪γ(x,lv)∪{v}∣=∣β(l)∪γ(x,l)∣≤k+1, the width of
the new decomposition is still at most k.
The guard of the edge going to lv contains v, therefore every
walk in D starting and ending at v intersects the guard. So
after the first part of the algorithm (T′,β′,γ′) is
still a proper directed tree decomposition.
In the second part we split high degree vertices into paths. For
every vertex t of (total) degree d+1>3 we introduce d−1 new
vertices t1,…,td−1. Let c1,…,cd be the children
of t. We can assume without loss of generality that the children
are ordered by the topological order of their bags. That is if
i<j, then every path from β(Tcj) to β(Tci)
intersects Γ(v). The subtrees rooted at the children
stay intact and are attached differently to the subtree above
T−Tt. To do this we first remove t from T obtaining subtrees
Tr containing the root and the parent x of t as a leaf, and
Tci for every child of t. We now add the new vertices as
follows. The former parent x builds a path with the new vertices
ti in increasing order. Then every ti is mapped to the
corresponding ci, leaving cd which is also mapped to td−1
which only has two neighbours so far, since its the last on the
path.
For all the subtrees that stay the same during the construction it
is clear that no walk can leave them and come back without
intersecting a guard. But we introduce new subtrees that contain
several child-subtrees of t. Let Tti′ be such a subtree.
Assume there is a walk W in D starting and ending in
β(Tti) containing a vertex from
β(T−Tti) but no vertex of γ(t), which
is the guard for the edge towards ti. There are two
possibilities. Either W contains a vertex of T′−Tt1=T−Tt,
which directly yields a contradiction to (T,β,γ) being
a proper directed tree decomposition. Or W contains a vertex from
β(Ttj) for some j<i. But this would imply that
there is a path from β(Tci) to
β(Tcj), which contradicts the topological ordering.
Thus, the output of the algorithm is again a proper directed
tree decomposition.
□
So given a directed tree decomposition of a digraph D, we can transform it into a subcubic leaf directed tree decomposition (T,β,γ) of D in linear time without changing its width.
It remains to show that if we forget about the orientation of the edges of the arborescence T, (T,β) defines a cycle decomposition of bounded width.
Proposition 3.10**.**
For every digraph D we have cyw(D)≤2dtw(D).
Proof.****.
Let k:=dtw(D). Due to lemma 3.9
there exists a subcubic leaf directed tree decomposition (T,β,γ) of D of width k such that every
leaf bag contains at most one vertex. We want to show that
(T,β) yields a cycle decomposition of width at most 2k.
The function β already provides a bijection between
L(T) and V(D). So next we show that every edge
e∈E(T) satisfies cp(e)≤2∣γ(e)∣.
Afterwards we make the subcubic decomposition cubic.
Let C be a minimal family of pairwise disjoint directed
cycles in D and let e∈E(T). We show that
∣∂(e)∩E(C)∣≤2∣γ(e)∣. Let
X1,X2⊆V(D) be the two shores of the cut ∂(e) such
that X1=β(T′) where T′⊆T is the subtree of
T not containing the root. Furthermore, let
Y1⊆V(C)∩X1 be the vertices of the cycles
in C incident with an edge of
∂(e)∩E(C).
Let W be the collection of directed walks Wv,w from
a vertex v∈Y1 to some w∈Y1 such that
(i)
Wv,w is a subwalk of some cycle in C, and
2. (ii)
∅=V(Wv1)∖{v1,v2}⊆X2.
In other words, W is the set of walks (paths or cycles)
starting in some v∈Y1 and going along the cycle in C
that contains v and ending in the first vertex in X1 after
leaving it from v1. Clearly, the walks in W are not
necessarily vertex disjoint as the paths may share common endpoints in
Y1.
Let W1,W2∈W be two walks with
V(W1)∩V(W2)=∅, then there is a cycle
C∈C such that both W1 and W2 are subwalks of C.
Hence ∣V(W1)∩V(W2)∣≤2.
As γ(e) is a guard in the
directed tree decomposition, it must contain a vertex of every walk in
W. Every vertex can guard at most two paths, hence
cp(∂(e))=∣W∣≤2∣γ(e)∣.
Now note that there still might be vertices of degree two in T.
Since they are not leaves, β does not map them to any vertex of
V(G). Therefore, the two edges incident to a vertex of degree two
induce the same cut and we can contract one of them to reduce the
number of vertices of degree two. Let (T′,β) be the
decomposition obtained in this way, then (T′,β) is cubic and
all cuts induced by edges still have porosity at most 2k. Thus,
(T′,β) is a cycle decomposition of D of width at most 2k.
□
Hence, given a directed tree decomposition of width k, we can compute a
cycle decomposition of width at most 2k in polynomial time using the
algorithms from the proofs of
lemmas 3.8 and 3.9. The proofs of other
known obstructions to directed treewidth such as well-linked
sets or havens (see
[Ree99, JRST01]) yield constant factor
approximation fixed-parameter tractable algorithms for directed
tree decompositions as described in [DKESP14]. (Given a
computational problem and a parameter (in our case, directed
treewidth), an algorithm solving it is fixed-parameter tractable if
its running time is bounded by a function of the form
f(k)⋅nO(1) where f is a computable function.) If we combine
this with our results from above we obtain the following concluding
corollary.
Corollary 3.11**.**
There is a fixed-parameter tractable approximation algorithm for cyclewidth.
Please note that at this point we cannot make any statement on the
quality of the approximation since we do not know lower bounds for
cyclewidth.
3.2 A Grid Theorem for Cyclewidth
As we have seen in the previous subsection, cyclewidth is bounded
from above by directed treewidth. The goal of this subsection is to
establish a lower bound. Here we face a special challenge. While
most width parameters, including directed treewidth, imply
separations of bounded size, namely in the width of the decomposition,
cyclewidth does not immediately imply the existence of a bounded size
separation. Moreover, it is not immediately clear whether there
exists a function f:\mathdsN→\mathdsN such that, given a digraph
D and a cut ∂(X) in D, there is always a set
S⊆V(D) that hits all directed cycles crossing
∂(X) and satisfying ∣S∣≤f(cp(∂(X))).
We will take a different approach. To show that directed treewidth poses, qualitatively, as a lower bound for cyclewidth, it suffices to
show that an obstruction for directed treewidth also gives a lower
bound on cyclewidth. This will imply that any graph of large
directed treewidth must also have high cyclewidth.
In order to establish such a result we will show that the cyclewidth of a digraph D is an upper bound on the cyclewidth of any
butterfly minor H of D. Then, in a second step we will show that
the cyclewidth of the cylindrical grid depends on its order. By
using the Directed Grid Theorem and proposition 3.10 we will
then obtain the equivalence of cyclewidth and directed treewidth as
desired.
Theorem 3.12**.**
If D is a digraph and D′ is a butterfly minor of D, then
cyw(D′)≤cyw(D).
Proof.****.
We first note that the cyclewidth is closed under taking subgraphs.
To see this let D′⊆D be a subgraph of D and
(T,φ) a cycle decomposition of D. We delete every leaf
that corresponds to a vertex in V(D)∖V(D′) and
eliminate vertices of degree two if needed as in the proof of
proposition 3.10 to obtain a new cycle decomposition
(T′,φ′) of D′ whose maximal cycle porosity is at most the
maximal cycle porosity of (T,φ). So
cyw(D′)≤cyw(D).
Next, we want to show that butterfly contracting an edge in D does
not increase the cyclewidth. Let D′:=D/e for some edge
e=(u,v)∈E(D). Since e is butterfly contractible it is
the only outgoing edge from u or the only ingoing edge of v. We
assume the former case first. Note that every cycle containing u also
contains v.
We obtain (T′,φ′) from (T,φ) by deleting the leaf
ℓ of T mapped to u and contracting one of the two edges in
T incident with the unique neighbour of ℓ in T in order to
obtain the cubic tree T′. Let xu,v be the contraction
vertex, we set φ′(φ−1(v)):=xu,v
while leaving the mapping of the other leaves intact.
All cuts in the decomposition for which u and v lie on the same
side do not change their porosity. So consider a cut ∂(X),
v∈X, induced by (T,φ) that separates u and v, then
(T′,φ′) induces a cut ∂(X′) where
X′=(X∖{v})∪{xu,v} and
V(D/e)∖X′=V(D)∖(X∪{u}).
Suppose there is a family of pairwise disjoint directed cycles
C in D that contains a cycle C with u∈V(C)
and satisfies ∣∂(X)∩E(C)∣=cp(∂(X)) as
well as ∂(X)∩E(C)=∅. Let C′ be the cycle in
D/e obtained from C after the contraction of e.
Let C′ be a family of directed cycles in D/e. At most
one cycle C contains xu,v. If C also exists in D, then
C′ is a family of cycles in D as well. Otherwise, as
(u,v) is the only edge leaving u, the predecessor of xu,c
on C has an edge to u in D. Thus we can construct a cycle C′
in D from C by replacing the edge (y,xu,v) by a path
y,u,v. Then C′ crosses ∂(X) at least as often as C and
the cycle porosity of (C∖(C))∪{C′}
is at least as high as the porosity of C.
For handling the case that the edge e is the only ingoing edge of
v, we show that the cyclewidth does not change if we reverse all
directions of the edges in the graph. That is because we still get
exactly the same cycles just with reversed direction and they still
cross the same cuts. Therefore the decomposition stays exactly the
same with the same porosities for all cuts.
By these arguments cyw(D′)≤cyw(D) holds for every
butterfly minor D′ of D.
□
The reverse direction follows from the Directed Grid Theorem. It says
that, whenever the directed treewidth of a digraph is large enough,
one can find a specific butterfly minor of large width.
In order to use the Directed Grid Theorem for our purposes we need to
show that the cylindrical grid has unbounded cyclewidth. It actually
suffices to prove a statement that gives a lower bound on the
cyclewidth of a cylindrical grid of order n in terms of its order.
To do this we have to analyse how the cycles in such a grid behave
when a reasonably large part of it is separated from the rest.
We call a cut ∂(X) in a digraph Dbalanced if
∣X∣≥3∣V(D)∣ and
∣V(D)∖X∣≥3∣V(D)∣.
Lemma 3.13**.**
The cylindrical grid of order k has cyclewidth at least
32k.
Proof.****.
Let Dk↻ be the cylindrical grid of order k and let
(T,φ) be an optimal cycle decomposition of Dk↻.
First we need to show that T contains an edge such that the cut
induced by this edge is balanced in Dk↻. Such a cut can
be found as follows. We direct every edge of T such that it
points in direction of the subtree of T containing more vertices
of Dk↻. If both sides contain exactly half the vertices
we found the balanced cut. Otherwise every edge can be directed and
no two edges can point away from each other. Also all leaf edges
point away from the leaf. So there has to be an inner vertex v
with only ingoing edges, this vertex defines three subtrees of T,
each separated from v by one of its three incident edges.
Each two subtrees contain together at least half of the
vertices. If there is a subtree with less than one third of the
vertices, then the other two edges induce balanced cuts. Otherwise
all three subtrees contain exactly one third of the vertices and all
three edges induce balanced cuts.
Consider such a balanced cut f=∂(e) in Dk↻ induced
by an edge e∈E(T). There are two cases: either each shore of
f contains one of the concentric cycles of Dk↻ or one of
its shores does not contain any of the concentric cycles of the grid
completely.
In the case where each shore of f contains one of the concentric
cycles of Dk↻ we are able to construct a cycle C that
contains 2k edges of f. Let Cin be one of the
concentric cycles of Dk↻ that is completely on one of the
shores of f, which we will call the inner shore, and
Cout a concentric cycle of Dk↻ on the other
shore. We call the shortest paths from Cout to Cin ingoing
and the shortest paths in the other direction outgoing.
We start on a vertex of Cout where it intersects an
ingoing path of Dk↻. Then we walk along the ingoing path
until we meet Cin and walk along it for an edge. There
we meet an outgoing path and walk along it until we intersect the
outer cycle again. This we repeat until we reach the starting
vertex. Since we used all in- and outgoing paths of Dk↻,
there are 2k subpaths of C crossing f at least once,
therefore C contains at least 2k edges of f. Thus f
has cycle porosity at least 2k>32k.
Next, consider the case that there is a shore of f that does not
contain any concentric cycle of Dk↻. The other shore of
f can contain at most two third of the concentric cycles of
Dk↻ as f is balanced. Therefore, the remaining at least
3k cycles of Dk↻ cross f. Since they are
cycles, each of them meets f in at least two edges and thus
cp(∂(e))≥32k. So, finally
cyw(Dk↻)≥32k.
□
Together with theorem 3.12 this implies the following corollary.
Corollary 3.14**.**
If a digraph D has the cylindrical grid of order k as a butterfly minor, then its cyclewidth is at least 32k.
Theorem 3.15**.**
A class D of digraphs is a class of bounded directed treewidth, if and only if it is a class of bounded cyclewidth.
Proof.****.
Let D be a class of digraphs and let f
be the function from theorem 3.5.
Suppose C has unbounded directed treewidth, then for each n∈\mathdsN there is a digraph Dn′∈D such that dtw(D′)≥f(n).
By theorem 3.5, there is a digraph Dn∈D that contains the cylindrical grid of order n as a butterfly minor.
Therefore, cyw(Dn)≥32n by
corollary 3.14 and thus C has also
unbounded cyclewidth. The other direction is proposition 3.10.
□
We conclude this section by reformulation of theorem 3.5 into a grid theorem for cyclewidth.
This is a direct corollary of the main result of this section.
Theorem 3.16**.**
There is a function f:\mathdsN→\mathdsN such that every digraph D either satisfies cyw(D)≤f(k), or contains the cylindrical grid of order k as a butterfly minor.
4 Perfect Matching Width
We will now leave the world of directed graphs and consider undirected graphs with perfect matchings.
As we have seen in 2.2, strongly connected
directed graphs correspond to matching covered bipartite graphs with a
fixed perfect matching. We discuss this correspondence in more detail
in this section.
In this section we establish a connection between the perfect matching width of bipartite matching covered graphs and the directed treewidth of their M-directions.
This is done in two steps.
First we introduce perfect matching width and relate it to the directed treewidth of M-directions.
Then, using the relation between matching minors and butterfly minors of M-directions of bipartite graphs, we deduce the Bipartite Matching Grid Theorem.
4.1 Perfect Matching Width and Directed Cycles
Definition 4.1** (matching-porosity).**
Let G be a matching covered graph and X⊆V(G).
We define the matching-porosity of ∂(X) as follows:
[TABLE]
A perfect matching M∈M(G) is maximal with respect to a cut ∂(X) if there is no perfect matching M′∈M(G) such that ∂(X)∩M⊊∂(X)∩M′.
A perfect matching M∈M(G)maximises a cut ∂(X) if mp(∂(X))=∣M∩∂(X)∣.
Definition 4.2** (Perfect Matching Width).**
Let G be a matching covered graph.
A perfect matching decomposition of G is a
tuple (T,δ), where T is a cubic tree and
δ:L(T)→V(G) is a bijection.
The width of (T,δ) is given by maxe∈E(T)mp(e) and the perfect matching width of G is then defined as
[TABLE]
As every vertex is contained in exactly one perfect matching edge, we
obtain the following observation.
Observation 4.3**.**
For all matching covered graphs G, for all X⊆V(G) and all x∈V(G)∖X,
mp(∂(X))−1≤mp(∂(X∪{x}))≤mp(∂(X))+1.
If we are given a perfect matching M∈M(G) of a matching covered graph G and a cut ∂(X) of matching porosity k, then there are at most k vertices in X that are incident with edges in M∩∂(X).
Hence we have to move at most k vertices from one shore to the other in order to obtain a new cut where both shores are M-conformal.
This leads to the following proposition.
Proposition 4.4**.**
Let G be a matching covered graph, X⊆V(G) and M∈M(G).
Then there is an M-conformal set X′⊆V(G) such that
(i)
X⊆X′,
2. (ii)
∣X′∣≤∣X∣+mp(∂(X)) and
3. (iii)
mp(∂(X′))≤2mp(∂(X)).
Now, if we look at the M-direction of a matching covered bipartite graph G with M∈M(G), then any cycle decomposition (T,φ) of D(G,M) can be interpreted as a decomposition of G where φ is a bijection between L(T) and M.
Then every edge in T induces a bipartition of V(G) into M-conformal sets.
The next definition relates this observation to perfect matching decompositions.
Definition 4.5** (M-Perfect Matching Width).**
Let G be a matching covered graph and M∈M(G).
The M-perfect matching width, M-pmw, is defined as the smallest width of a perfect matching decomposition of G such that for every inner edge e holds if (T1,T2)=T⋉e, then δ(L(T1)) and δ(L(T2)) are M-conformal.
Proposition 4.6**.**
Let G be a matching covered graph and M∈M(G).
Then pmw(G)≤M-pmw(G)≤2pmw(G).
Proof.****.
Clearly pmw(G)≤M-pmw(G), because the M-pmw is the width of a perfect matching decomposition.
Next, we prove M-pmw(G)≤2pmw(G). Let
(T,δ) be a perfect matching decomposition of G of
minimum width. Now let X⊆V(M) such that
for all e∈M holds ∣e∩X∣=1. Denote by
xM the vertex y with xy∈M and let
X′⊆X be the set of vertices x∈X such that
the path from δ−1(x) to δ−1(xM) in T contains an inner
edge (i.e. an edge not incident with a leaf).
Now we construct a new decomposition (T′,δ′).
We remove δ−1(xM) and add two new leaves to the vertex δ−1(x) in T′.
Moreover, the deletion of δ−1(xM) left a vertex of degree 2, in order to maintain a cubic tree we contract one of the two edges incident with said degree 2 vertex.
Now δ−1(x) has two new neighbours a and b which we map to the vertices xM and x such that δ′(a):=xM and δ′(b):=x.
The vertex δ−1(x) now is an inner vertex of T′
therefore δ′ is not defined on it.
The only additional inner edges in T′ are those where the corresponding cut separates a pair of leaves mapped to a matching edge of M containing a vertex in X′ from the rest of the graph.
So these induce cuts of matching porosity at most 2 and M-conformal shores.
Now consider an inner edge e from T and the two shores
X and V(G)∖X it induces.
The edges of M′⊆M that have vertices in both shores are at most pmw(G) many.
Therefore by 4.3 the porosity of the induced cut is at most doubled.
□
We now need the following observation.
Let G=(A∪B,E) be a bipartite matching covered graph and M,M′∈M(G) two distinct perfect matchings.
Then the graph induced by M∪M′ consists only of isolated edges and M-M′-conformal cycles.
Moreover, the isolated edges are exactly the set M∩M′.
Let C be such an M-M′-conformal cycle.
Then in both, D(G,M) and D(G,M′), C corresponds to a directed cycle.
On the other hand let N∈M(G) and let C be a directed cycle in D(G,N).
Then C corresponds to an N-conformal cycle CN in G of exactly double the length, where E(C) and E(CN)∖N coincide (up to the direction of the edges in C).
Thus (N∖E(CN))∪(E(CN)∖N) is a perfect matching of G.
So there is a one-to-one correspondence between the directed cycles in D(G,M) and the M-conformal cycles in G.
Using this insight we can translate an M-perfect matching decomposition of G to a cycle decomposition of D(G,M) and back.
Lemma 4.7**.**
Let G be a bipartite and matching covered graph and M∈M(G).
Then M-pmw(G)=cyw(D(G,M)).
Proof.****.
We first prove that M-pmw(G)≥cyw(D(G,M)).
Assume M-pmw(G)=k for some k∈\mathdsN.
Then there is a perfect matching decomposition (T,δ) of width k such that all shores of the cuts induced by inner edges are M-conformal.
We construct a cycle decomposition (T′,φ) of D:=D(G,M).
In (T,δ) the leaves containing two vertices matched by M share a neighbour.
We define T′:=T−L(T). Recall that matching
edges become vertices in D(G,M). For
xy∈M let txy be the common neighbour of
φ−1(x) and φ−1(y). We define φ(txy):=xy.
Now assume this decomposition has an edge e∈T′ that induces a cut ∂(X) of cycle porosity at least k+1.
Then there is a family of directed cycles C in D witnessing this.
This corresponds to a family of M-alternating cycles C′ in G that also has at least k+1 edges in the cut ∂(X′) induced by e∈T, note that V(T′)⊆V(T).
Since X′ is M-conformal, M∩(E[C′]∩∂(X′))=∅, that is none of the edges of C′ that lie in the cut are from M.
Let M′ be the matching we obtain by switching M along all the cycles in C′, that is M′:=(M∖E[C′])∪(E[C′]∖M).
Now M′ has at least k+1 edges in ∂(e)
contradicting that (T,δ) has width k. Therefore (T′,φ) is a cycle decomposition of D of width at most k.
Now we prove that M-pmw(G)≤cyw(D).
Let cyw(D)=k for some k∈\mathdsN.
Then there is a cycle decomposition (T,φ) of D with width k.
We construct a perfect matching decomposition (T′,δ) of G.
The construction basically works the other way around as in the first part of the proof.
For every leaf in T we introduce two new child vertices that are mapped to the two endpoints of the matching edge which is contracted into a vertex of contracted in D.
Formally, \mathchoice{V\!\left(T^{\prime}\right)}{V\!\left(T^{\prime}\right)}{V\left(T^{\prime}\right)}{V\left(T^{\prime}\right)}\coloneqq\mathchoice{V\!\left(T\right)}{V\!\left(T\right)}{V\left(T\right)}{V\left(T\right)}\cup\bigl{\{}t_{i}~{}:~{}t\in\mathchoice{L\!\left(T\right)}{L\!\left(T\right)}{L\left(T\right)}{L\left(T\right)},i\in\left\{\ell,r\right\}\bigr{\}} and E(T′):=E(T)∪{tti:t∈L(T),i∈{ℓ,r}} where all tr and
tℓ are new vertices.
Now for all t∈L(T) if φ(t) is the vertex xy∈M of D,
then δ(tℓ):=x and δ(tr):=y.
Since now all pairs of vertices that are matched by M have a common parent vertex in T′ the shores of the cuts induced by inner edges are M-conformal.
Therefore the width of (T′,δ) yields an upper bound on M-pmw(G).
Assume there is an edge e∈E(T′) and a matching M′ such that ∣M′∩∂(e)∣≥k+1.
We consider the subgraph of G only containing edges from M and M′.
It consists only of disjoint cycles and independent edges.
Because none of the edges in M lie in ∂(e), all edges of M′∩∂(e) lie on M-M′-conformal cycles.
Therefore there is a family of M-conformal cycles with more than k edges in ∂(e).
This corresponds to a family of directed cycles in D having more than k edges in the cut induced by e in D.
This yields a contradiction to (T,φ) having width k.
Therefore (T′,δ) is a perfect matching decomposition of G of width k.
□
By combining proposition 4.6 and lemma 4.7 we obtain the following result as an immediate corollary.
Theorem 4.8**.**
Let G be a bipartite and matching covered graph and M∈M(G).
Then pmw(G)≤cyw(D(G,M))≤2pmw(G).
The proof of lemma 4.7 provides an algorithm to translate a cycle decomposition of D(G,M) into a perfect matching decomposition of G.
The cycle decomposition itself can be computed in FPT-time from a directed tree decomposition by corollary 3.11 and thus we obtain the following corollary.
Corollary 4.9**.**
There is an approximation algorithm for perfect matching width on bipartite matching covered graphs running in FPT-time.
Please note here that the quality of the approximation provided by corollary 4.9 depends on the function of the lower bound for cyclewidth in terms of directed treewidth.
Since the lower bound presented in this paper is essentially the function from the Directed Grid Theorem, we can only state that there is some function bounding the quality of this approximation.
It would be interesting to see whether this can be turned into a constant factor.
4.2 The Bipartite Matching Grid
The standard concept of contractions in graphs reduces the number of vertices by exactly one.
Thus it does not preserve the property of a graph to contain a perfect matching.
However, if we always consider conformal subgraphs and contract two
edges at a time,
we can find a specialised version of minors that preserve the property
of being matching covered.
The idea of matching minors appears in the work of McGuaig [McC01], but the formal framework and the actual name were introduced by Norine and Thomas in [NT07].
Definition 4.10** (Bicontraction).**
Let G be a graph and let v0 be a vertex of G of degree two incident to the edges e1=v0v1 and e2=v0v2.
Let H be obtained from G by contracting both e1 and e2 and deleting all resulting parallel edges.
We say that H is obtained from G by bicontraction or bicontracting the vertex v0.
Definition 4.11** (Matching Minor).**
Let G and H be graphs.
We say that H is a matching minor of G if H can be obtained from a conformal subgraph of G by repeatedly bicontracting vertices of degree two.
There is a strong relation between matching minors of bipartite
matching covered graphs and butterfly minors of strongly connected digraphs.
Let G and H be bipartite matching covered graphs.
Then H is a matching minor of G if and only if there exist perfect matchings M∈M(G) and M′∈M(H) such that D(H,M′) is a butterfly minor of D(G,M).
We want to establish a relation between the perfect matching
widths of
matching covered graphs and their matching minors.
At this point we do not know whether the perfect matching width in general is closed under matching minors.
We are, however, able to at least show a qualitative closure by using our result on the relation of the cyclewidth of digraphs and their butterfly minors.
Proposition 4.13**.**
Let G and H be matching covered bipartite graphs.
If H is a matching minor of G, then pmw(H)≤2pmw(G).
Proof.****.
Let H be a matching minor of G.
Then lemma 4.12 provides the existence of perfect matchings M∈M(G) and M′∈M(H) such that D(H,M′) is a butterfly minor of D(G,M).
The M-perfect matching width of G is at most 2pmw(G) by proposition 4.6 and, by lemma 4.7, M-pmw(G)=cyw(D(G,M)).
Since D(H,M′) is a butterfly minor of D(G,M), theorem 3.12 gives us cyw(D(H,M′))≤cyw(D(G,M)).
At last, using lemma 4.7 again and combining the
above inequalities we obtain pmw(H)≤2pmw(G).
□
As we are going for a cylindrical grid and derive the grid theorem for bipartite matching covered graphs from the Directed Grid Theorem anyway, it makes sense to derive our grid from the directed case as well.
The following definition defines the bipartite matching grid
by providing a procedure that allows us to obtain it from the
directed cylindrical grid. Let Eo be a set of edges of the
outermost cycle containing every second edge. Let Ei be the
set of edges of the innermost cycle containing every second
edge such that for every ei∈Ei there is an eo∈Eo
such that the shortest path from the tail of ei to the tail
of eo has length k−1. The last condition assures that the
chosen edges in both cycles are not “shifted”.
Definition 4.14** (Bipartite Matching Grid).**
Let k∈\mathdsN be a positive integer.
Let Dk↻ be the digraph obtained from
Dk↻ by butterfly contracting every edge from
Eo and every edge from Ei.
The bipartite matching grid of order k is the unique bipartite matching covered graph GkM that has a perfect matching M∈M(GkM) such that D(GkM,M)=Dk↻.
The uniqueness of GkM and M follows from 2.2.
As an example see fig. 3.
Here we construct G3M from D3↻.
The edges from Eo∪Ei are marked.
Lemma 4.15**.**
pmw(GnM)≥31k.
Proof.****.
Let k∈\mathdsN be a positive integer and Dk↻ the cylindrical grid of order k.
By lemma 3.13, 31k≤21cyw(Dk↻).
Then the graph Dk↻ obtained from Dk↻ by contracting every second edge in the innermost and the outermost of its consecutive cycles as in the definition of the bipartite matching grid also has cyclewidth at most 32k as we are only contracting already contractible edges and therefore do not create new directed cycles in our graph.
By applying theorem 4.8, we obtain 31k≤21cyw(Dk↻)≤pmw(GkM).
□
With this last observation at hand, we can derive our main
theorem. Assume that a graph G has high perfect matching
width. By theorem 3.16 and theorem 4.8 this implies high cyclewidth for all M-directions of G. This, in turn, implies large cylindrical
grids as butterfly minors on those M-directions.
Now lemma 4.12 lets us translate these cylindrical grids into matching minors of G and as the perfect matching width of G is bounded from below by the width of its matching minors as we have observed in proposition 4.13, we obtain the grid theorem for bipartite matching covered graphs.
Theorem 4.16**.**
There is a function f:\mathdsN→\mathdsN such that every matching
covered bipartite graph G either satisfies pmw(G)≤f(k), or
contains the bipartite matching grid of order k as a matching minor.
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