1 Introduction
Polynomial time preprocessing is one of the widely used methods to tackle NPhardness in practice, and the area of kernelization has been extremely successful in laying down a mathematical framework for the design and rigorous analysis of preprocessing algorithms for decision problems. The central notion in kernelization is that of a kernel (also known as a kernelization), which is a preprocessing algorithm that takes as input a parameterized problem, which is a pair , where is the problem instance and is an integer called the parameter. A kernelization is required to run in polynomial time and convert a potentially large input into an equivalent instance such that and are both bounded by a function of the parameter . Over the last decade, the area of kernelization has seen the development of a wide range of tools to design preprocessing algorithms and a rich theory of lower bounds has been developed based on assumptions from complexity theory [1, 11, 2, 17, 10, 14, 19, 12, 18]. We refer the reader to the survey articles by Kratsch [22] or Lokshtanov et al. [28] for relatively recent developments, or the textbooks [8, 13], for an introduction to the field.
An ‘efficient preprocessing algorithm’ in this setting is referred to as a polynomial kernelization
and is simply a kernelization whose output has size bounded polynomially in the value of the parameter of the input. The central classification task in the area is to classify each NPhard problem as one which has a polynomial kernel, or as one that does not.
The Vertex Cover problem is one of the most frequently studied problems from the point of view of kernelization and buoyed by the rich literature on kernelization for Vertex Cover parameterized by the solution size, researchers have more recently turned their attention to the design of kernelization algorithms for Vertex Cover parameterized by smaller parameters. The results most relevant to us in this line of enquiry are the polynomial kernelization given by Jansen and Bodlaender [21] for Vertex Cover parameterized by the size of the feedback vertex set and the result of Cygan et al. [9], in which they showed that Vertex cover is unlikely to have a polynomial kernelization when parameterized by the deletion distance of the given graph to the class of graphs of treewidth at most , for any . Here, the deletion distance of the given graph to any graph class is the size of the smallest set such that .
On the other hand, the Connected Vertex Cover problem, where the solution is also required to induce a connected subgraph of the input graph, is known to exclude a polynomial kernelization already when parameterized by the solution size, under standard complexity theoretic hypotheses [12]. However, the study of preprocessing for this problem was handed a new lease of life by the recent work of Lokshtanov et al. [29], who aimed to facilitate the rigorous analysis of preprocessing algorithms in conjunction with approximation algorithms via the introduction of approximate kernels.
Informally speaking, an approximate kernel is a polynomialtime algorithm that, given an instance of a parameterized problem, outputs an instance such that for some computable function and any approximate solution to the instance can be turned in polynomial time into a approximate solution to the original instance .
As earlier, the notion of ‘efficiency’ in this context is captured by the function being polynomially bounded, in which case we call this algorithm, an approximate polynomial kernelization. We refer the reader to Section 2 for a formal definition of all terms related to (approximate) kernelization.
In their work, Lokshtanov et al. considered several problems which are known to exclude polynomial kernels and presented an approximate polynomial kernel for these problems for every fixed , also called a polynomial size approximate kernelization scheme (PSAKS, see Section 2 for formal definition). This implies that allowing for an arbitrarily small amount of error while preprocessing can drastically improve the extent to which the input instance can be reduced, even when dealing with problems for which polynomial kernels have been ruled out under the existing theory of lower bounds.
In particular, they showed that the Connected Vertex Cover problem admits an approximate polynomial kernel for every . We believe that their result provides a promising starting point towards a comprehensive study on approximate kernelizations for Connected Vertex Cover, with the aim being the replication of the success enjoyed by Vertex Cover in the domain of kernelization. Consequently, we consider the question of designing approximate kernelizations for Connected Vertex Cover in a systematic manner by considering as our parameter, the deletion distance of the given graph, to well understood super classes of edgeless graphs. We point out that we are not the first to attempt this. Krithika et al. [27] obtained a PSAKS for this problem when parameterized by the deletion distance of the input graph to the class of split graphs. The results we obtain in this paper generalize their result and also provide unified approximate kernelizations for this problem with respect to several parameters, including the deletion distance of the input graph to the class of split graphs and cographs.
1.1 Our results and significance of the chosen parameterizations
The parameters we consider in this paper are the deletion distances of the input graph to (a) bounded treewidth graphs, (b) split graphs or cographs, and (c) chordal graphs. Since an edgeless graph is contained in all of these graph classes, it follows that all of our parameters are upper bounded by the minimum vertex cover (note the removal of the connectivity requirement), in any given graph. Clearly, the size of the smallest vertex cover is in turn upper bounded by the size of the smallest connected vertex cover. Consequently, all our parameters are upper bounded by the standard parameter for Connected Vertex Cover, the solution size. Moreover, since the classes of bounded treewidth graphs, cographs, and chordal graphs are all pairwise incomparable, it follows that our parameters are also pairwise incomparable. See Figure 1 for a hierarchy of the parameters.
Parameterization by deletion distance bounded treewidth graphs. Our first technical result is a PSAKS for Connected Vertex Cover parameterized by the deletion distance to graphs with treewidth (Section 2 contains the formal definition of a PSAKS). We denote this problem by CVC(transversal).
In fact, we demonstrate the existence of something stronger – a time efficient PSAKS for a more general problem which we call CVC(Deletion), defined as below. Here, is a fixed hereditary graph class.
Input:  A graph , a vertex set of size such that , integer . 
Parameter:  
Problem:  Does have a connected vertex cover of size at most ? 
Theorem 1.
Suppose that a graph class is polynomialtime recognizable. For every , CVC(Deletion) admits a PSAKS with vertices if Connected Vertex Cover has a PTAS on the graph class . Moreover, if this PTAS is an Efficient PTAS, then the PSAKS is a time efficient PSAKS.
The class is simply the class of all graphs from which a single vertex can be removed to obtain a graph in . We refer the reader to [33] for the definitions of PTAS and Efficient PTAS. Now, as a consequence of Theorem 1 and the fact that Connected Vertex Cover has a linear time algorithm on graphs of constant treewidth, we have our second result.
Corollary 1.
For every fixed , CVC(transversal) has a time efficient PSAKS with vertices.
This result provides an interesting contrast to the result of Cygan et al. [9] which rules out polynomial kernelizations even for Vertex Cover (with no connectivity requirements) parameterized by the deletion distance to treewidth graphs (for ).
Parameterization by deletion distance to chordal graphs. For our third result, we consider the parameterization by the deletion distance of the input graph to another class of graphs which is incomparable with both bounded treewidth and bounded diameter graphs. This is the class of chordal graphs. The central idea driving this result is a new reduction rule for Connected Vertex Cover, the exhaustive application of which leaves us with an equivalent instance of CVC(transversal) for an appropriate depending only on . By combining this reduction rule with Corollary 1, we obtain the following result.
Theorem 2.
CVC(ChordalDel) has a PSAKS with vertices.
Parameterization by deletion distance to split graphs or cographs. After our result on CVC(ChordalDel), we obtain a PSAKS for Connected Vertex Cover parameterized by the deletion distance of the input graph to the class of split graphs and cographs. More specifically, we consider the parameter such that every connected component of is either a split graph or a cograph.
Corollary 2.
Connected Vertex Cover admits a time efficient PSAKS when parameterized by deletion distance to a graph whose connected components are either split graph, or cograph.
While the result in the case of split graphs provides an alternate proof to the one given by Krithika et al. [27], our PSAKS for Connected Vertex Cover parameterized by the deletion distance to cographs is the first such result.
Finally, we prove that our three main results can be in fact be unified under a single even stronger parameterization which is the deletion distance of the input graph to the class of graphs where every connected component is either a treewidth graph or a chordal graph or a cograph.
Theorem 3.
For every fixed , Connected Vertex Cover parameterized by the size of a modulator to the class of graphs where connected component is a cograph or a chordal graph or a graph with treewidth at most admits a time efficient PSAKS with vertices.
When this deletion set is part of the input we can directly utilize our framework to also encapsulate graph classes which are significantly more general than classes comprising graphs which have a small deletion distance to only one of . For instance, one can easily observe the existence of graphs from which an unbounded number of vertices must be deleted in order to move them into any one of but at the same time only a constant number of vertices need to be deleted from them in order to obtain a graph where each connected component lies in one of . We refer the reader to Section 5 for a more detailed discussion.
Related work on kernelizations for Vertex Cover with respect to parameters smaller than solution size. Kratsch and Wahlström [25] gave the first (randomized) polynomial kernelization for Vertex Cover parameterized above the optimum value of the standard LP relaxation. This result was later strengthened by Kratsch [23] who parameterized above an even stronger lower bound on the solution, , where denotes the optimum value of the standard LP relaxation and denotes the size of the maximum matching in the input graph. Majumdar et al. [30] considered as their parameter, the deletion distance to graphs of degree 2 and to cluster graphs where each clique has bounded size. Fomin and Strømme [16] considered the deletion distance to pseudoforests, which are graphs where every connected component has at most one cycle. They also showed that parameterizing Vertex Cover by the deletion distance even to cactus graphs is unlikely to lead to a polynomial kernel. More recently, Kratsch and Hols [24] generalized their positive result by choosing as their parameter, the deletion distance of the input graph to quasiforests, which are graphs where every connected component has a set of at most vertices whose deletion leaves a forest. Finally, Bougeret and Sau [3] focussed on the deletion distance to graphs of treedepth , for any fixed constant , and obtained a polynomial kernel for Vertex Cover.
2 Preliminaries
We use to denote the set .
Graph theoretic preliminaries. For , we denote by the path on vertices. All graphs studied in this paper are undirected. A graph is a cluster graph if each of its connected components is a clique. A graph is a cocluster graph if its complement is a cluster graph. A graph is a split graph if its vertex set can be partitioned into two sets, one of which induces a clique and the other, an independent set. A graph is called a cograph if it has no induced .
A graph is called chordal if it has no induced cycle of length more than . Given an undirected connected graph, for every pair of vertices , there exists a shortest path from to . We denote the length of this shortest path by . The diameter of is . In other words, the diameter of a graph is the largest among all shortest distances between any pair of vertices. Given a graph and vertex set , we define the operation of identifying the set as the construction of the following graph, . The vertex set of , , where is a new vertex not in . The edges of are defined as follows. For every , if , then as well. For every and , if , then . We ignore all edges of with both endpoints in . We introduce and define the following notation that we will use throughout the rest of the paper. We use to denote the cliquewidth of the graph (see [6] for the definition of cliquewidth).
Definition 1.
Let be a graph class. For , we denote by the class of graphs in which there is a set of at most vertices whose deletion results in a graph from . We denote by class of all graphs whose connected components lie in and by the class of graphs in which there is a set of at most vertices whose deletion results in a graph where every connected component is in .
Let be a set of vertices such that . Then we say that is a deletion set of .
Tree Decomposition and Treewidth. Let be a graph. A tree decomposition of is a pair where is a tree and is a collection of subsets of such that (a) and (b) is a nonempty connected subtree of . The width of is defined as and the treewidth of is the minimum width over all tree decompositions of and is denoted by . An transversal of a graph is a subset such that .
Parameterized algorithms and kernels. A parameterized problem is a subset of for some finite alphabet . An instance of a parameterized problem is a pair , where is called the parameter and is the input. We assume that is given in unary and without loss of generality, . The notion of kernelization is formally defined as follows.
Definition 2.
[Kernelization] Let be a parameterized problem and be a computable function. We say that admits a kernel of size if there exists an algorithm, referred to as a kernelization (or a kernel) that, given , outputs in time polynomial in , a pair such that

if and only if , and

.
When , we say that admits a polynomial kernel.
Parameterized optimization problems and approximate kernels.
Definition 3.
[29] A parameterized optimization (minimization or maximization) problem is a computable function
The instances of a parameterized optimization problem are pairs , and a solution to is simply a string , such that . The value of the solution is . Since the problems we deal with in this paper are all minimization problems, we state some of the definitions only in terms of minimization problems when the definition for maximization problems is analogous. As an illustrative example, we define the parameterized optimization version of Connected Vertex Cover parameterized by the solution size. This is a minimization problem, where the optimization function is defined as follows.
Definition 4.
[29] For a parameterized minimization problem , the optimum value of an instance is .
Consequently, in the case of Connected Vertex Cover above, we define . We now recall other relevant definitions from [29] regarding approximate kernels.
Definition 5.
[29] Let be a real number and be a parameterized minimization problem. An approximatepolynomialtime preprocessing algorithm for is a pair of polynomialtime algorithms. The first one is called the reduction algorithm, and computes a map . Given as input an instance of the reduction algorithm outputs another instance .
The second algorithm is called the solution lifting algorithm. This algorithm takes as input an instance of , the output instance of the reduction algorithm, and a solution to the instance . The solution lifting algorithm works in time polynomial in ,,, and , and outputs a solution to such that the following holds.
The size of a polynomialtime preprocessing algorithm is a function defined as
Definition 6.
[29] [Approximate Kernelization] An approximate kernelization (or approximate kernel) for a parameterized optimization problem , and real , is an approximatepolynomialtime preprocessing algorithm for such that is upper bounded by a computable function . We say that is an approximate polynomial kernelization if is a polynomial function.
Definition 7.
[29] [Approximate Kernelization Schemes] A polynomial size approximate kernelization scheme (PSAKS) for a parameterized optimization problem is a family of approximate polynomial kernelization algorithms, with one such algorithm for every .
Definition 8.
[Time Efficient PSAKS] A PSAKS is said to be time efficient if both the reduction algorithm and the solution lifting algorithm run in time for some function and constant independent of and .
For further details on approximate kernelizations, we refer the reader to [29].
3 Connected Vertex Cover parameterized by Deletion Number
This section is devoted to the proof of our main theorem, Theorem 1. Recall that the decision version of CVC(Deletion) is formally defined as follows.
Input:  A graph , a vertex set of size such that , integer . 
Parameter:  
Problem:  Does have a connected vertex cover of size at most ? 
Note that in our problem description, we explicitly require a deletion set to be given with the input. The formal definition of the parameterized optimization version of this problem is as follows, where the input is the tuple .
We use to denote the malformed input instances and to denote the infeasible solutions. We need the polynomialtime recognizability of the graph class to identify the malformed input instances. Additionally, we need to assume that the recognition problem for the graph class is polynomialtime solvable in order to identify any malformed input instance. We let denote the size of a smallest connected vertex cover of a connected graph . When is clear from the context, we simply write OPT. The following observation is a property of optimal connected vertex covers we will use throughout the paper.
Observation 1.
Let be a connected graph and . Let be the graph obtained from by identifying the vertex set into . Then, .
Proof.
Let be an optimal connected vertex cover of the connected graph . By construction of , we know that . If , then we know that and is still a connected vertex cover of . On the other hand, if , then we construct . Clearly, all edges of incident to are incident to . Furthermore, identifying a set of vertices into a single vertex preserves connectivity and . So, is still a connected vertex cover of . Hence, . ∎
The above observation guarantees that the identification of a set of vertices does not increase the optimal solution size. Using Observation 1, we prove the following lemma that will be crucial for the main theorem of this section, i.e. Theorem 1. The following lemma guarantees that whenever the deletion set (this means that ) is known to be sufficiently small compared to a approximate connected vertex cover of , we can compute a approximate connected vertex cover of containing in polynomial time for every fixed . Note that and throughout the paper, we use and interchangeably.
Lemma 1.
Let and be a connected graph and such that . If where is a approximate connected vertex cover of , then there exists a polynomialtime algorithm which takes as input and and satisfies the following properties.

If Connected Vertex Cover admits a PTAS on the graph class , then Algorithm runs in time for some computable functions and and outputs a connected vertex cover of that contains and whose size is at most .

If Connected Vertex Cover admits an EPTAS on the graph class , Algorithm runs in time for some computable function and outputs a connected vertex cover of that contains and whose size is at most .
Proof.
As where is a approximate connected vertex cover of , we have that . Let be the graph obtained by identifying the vertex set into a single vertex .
If Condition 2 holds true, then we know that given , there exists an algorithm that runs in time and outputs a connected vertex cover of size at most .
If Condition 1 holds true, then we know that given , there exists an algorithm that runs in time and outputs a connected vertex cover of size at most .
We know by Observation 1 that . If , then consider the set . As , we know that the set of edges of incident on are covered by . On the other hand, if , then . In that case, we consider the set . In either case, we know that is a vertex cover of . But need not be a connected subgraph of . First, we have to ensure that in either case, there are at most connected components in . In the first case, when and , we know that has at least one neighbor in . So, at least one vertex of has at least one neighbor in . Otherwise, and in such situation, we have . In either case, has at most connected components. In the latter case, when , we know that . So, in that case, has at most connected components as is already connected. In order to convert into a connected subgraph of , we now add some additional vertices from . We know that is an independent set and is connected. If has more than one connected component, there exists a vertex such that is adjacent to at least two different connected components of . We find such a vertex and add it to . We continue this process until we have that induces a connected subgraph. As there are at most connected components in , we will need to repeat this step at most times. So, the size of the final connected vertex cover we generate is at most .
Now note that if Condition 2 holds true, then the running time of the algorithm to compute is . On the other hand if Condition 1 holds true, then the running time of the algorithm to compute is . All subsequent steps of our algorithm run in polynomial time. As a result, this proves our claimed bound on the running time and completes the proof of the lemma. ∎
Now, we consider the case when , where is a approximate connected vertex cover of . In this case, we have that . So, we know that . In that case, we can modify the PSAKS provided by Lokshtanov et al. [29], but with parameter value . We give a proof of this in the following lemma.
Lemma 2.
Let be the given instance of CVC(Deletion). Given a approximate connected vertex cover of , say , if , then one can compute a graph in polynomial time such that the following statements hold.

,

has a connected vertex cover of size at most .

Every inclusionwise minimal connected vertex cover of is a connected vertex cover of .
Proof.
If , it follows that . That is, . The graph is the output of a slightly modified version of the PSAKS for Connected Vertex Cover parameterized by solution size [29] but with the slightly different parameter value . Since, is linearly bounded in , we will be able to conclude that is .We present a brief sketch of the construction of for the sake of completeness.
Let denote the set of vertices of whose degree is at least . Observe that every vertex cover of of size at most must contain every vertex in . Moreover, since has a vertex cover of size OPT and OPT is upper bounded by , we conclude that .
Let denote the set of vertices in which have at least one neighbor which is not in . Since all such vertices have degree at most and has a vertex cover of size at most , we conclude that .
Let . By definition, is an independent set. The PSAKS of Lokshtanov et al. [29] uses as a subroutine, an efficient algorithm that computes a set of size such that satisfies the properties required by the lemma. This subroutine essentially does the following. First of all, for every , if has at most neighbors in , then it marks all of these neighbors. Otherwise, it marks an arbitrarily chosen set of neighbors of in . This is done simply to preserve the status of vertices in as vertices of degree at least . It then repeatedly executes the following step as long as possible. If there is an unmarked vertex such that is neighbor to at least distinct connected components of , then mark this vertex, contract all edges incident on , and add the resulting new vertex to . Observe that this will reduce the number of connected components of by at least and so this step will not be repeated more than times. When this procedure terminates, we go over the remaining unmarked vertices in and for every set of vertices with the same neighborhood in , mark one of these vertices and remove all others. Since any surviving vertex has degree at most into the (modified) set , the number of vertices marked in this procedure is these vertices form the set . Finally, we add pendants to every vertex in to force them into every connected vertex cover of . It follows from the definition of that a connected vertex cover of of size OPT can be converted to one of size at most for and consequently, every inclusionwise minimal connected vertex cover of (which must be disjoint from the pendant vertices added in the end) is also a connected vertex cover of . This completes the proof of the lemma. ∎
See 1
Proof.
Let be the given instance of CVC(Deletion), where . We first state the kernelization algorithm. Recall that the kernelization algorithm must have two parts. The first part is the Reduction Algorithm and the second is the Solution Lifting Algorithm.

Reduction Algorithm: We use the algorithm of [32] to compute a 2approximate connected vertex cover of , call it . If , then we return a trivial instance (of constant size) of CVC(Deletion) and otherwise, we invoke Lemma 2 to compute the subgraph and return the instance , where . Note that if is not completely contained in , then we may simply add it back to . Since is a subgraph of and is hereditary, we know that is also a deletion set of . Clearly, the size of the output satisfies the required bound. It only remains to prove the correctness of the reduction by providing a solution lifting algorithm.

Solution Lifting Algorithm: Recall that the solution lifting algorithm has access to . We may also assume without loss of generality that it has access to the set , which was computed by the reduction algorithm. Let be the given approximate solution for the instance output by the reduction algorithm. We may assume without loss of generality that is inclusionwise minimal.
There are two cases, one where Connected Vertex Cover admits a PTAS and the other where Connected Vertex Cover admits an EPTAS on every graph . In each case, we have managed to convert a approximate solution of to a approximate solution of . Now, we prove the items in the given order.

On the other hand, suppose that Connected Vertex Cover admits an EPTAS on . If , we know by Lemma 1 that there exists an algorithm that runs in time that computes a approximate connected vertex cover of . So, in this case, the reduction algorithms and solution lifting algorithms run in time. Hence, we have a time efficient PSAKS.
This completes the proof of the theorem. ∎
3.1 Connected Vertex Cover parameterized by Transversal Number
Now, we consider the problem CVC(transversal) whose decision version is as follows.
Input:  A graph , an transversal of size , integer . 
Parameter:  
Problem:  Does have a connected vertex cover of size at most ? 
The optimization version of CVC(transversal) is as follows.
We know that Connected Vertex Cover is polynomialtime solvable on graphs of bounded treewidth. Let be a given instance where and for some constant . So, we have Corollary 1 as a consequence of Theorem 1.
See 1
3.2 Connected Vertex Cover parameterized by Chordal Deletion Number
Now, we consider the following problem CVC(ChordalDel) whose decision version is stated as follows.
Input:  A graph , a chordal vertex deletion set of size , integer . 
Parameter:  
Problem:  Does have a connected vertex cover of size at most ? 
Note that as in the case of CVC(Deletion), we explicitly require a chordal vertex deletion set to be provided as part of the input. However, this requirement can be removed and replaced with an execution of the polynomialtime factor approximation algorithm for Chordal Vertex Deletion of Jansen and Pilipczuk [20].
The formal definition of the parameterized optimization version of this problem is as follows:
Let . We apply the following reduction rule.
Reduction Rule 1.
Let be the given instance of CVC(ChordalDel), where is a chordal graph. Let such that and is a maximal clique. Contract the edges of to obtain a new vertex and add a pendant vertex adjacent to . Let denote the graph resulting from this operation.
The intuition behind this reduction rule comes from the fact that since any vertex cover must contain all but at most one vertex of this clique, we can also force the remaining vertex of the clique (if one exists) into the solution at the cost of a small but manageable error without violating the connectivity requirement.
Lemma 3.
There is a polynomialtime algorithm that, given a approximate connected vertex cover of , returns a connected vertex cover of whose size is at most where .
Proof.
Let be the given connected vertex cover of and let denote the size of a smallest connected vertex cover of . Recall that denotes the size of a smallest connected vertex cover of . Note that because it has a pendant neighbor . In addition, since is the unique neighbor of , we may assume without loss of generality that . We now argue that is the required connected vertex cover of . It is straightforward to see that is a connected vertex cover of .
It remains to prove that . Since any vertex cover of must contain at least vertices of the clique , we infer that . And by definition, . Combining these two inequalities, we obtain the following.
Hence we conclude that . This completes the proof of the lemma. ∎
When Reduction Rule 1 is not applicable, it must be the case that all the maximal cliques of are of size at most . This gives us the following lemma.
Lemma 4.
If has a set such that is chordal and has no cliques of size , then is also an transversal for .
Proof.
The lemma follows from the fact that the treewidth of the chordal graph is bounded by the size of the maximum clique in , which is at most . As a result, is also an transversal of . ∎
See 2
Proof.
Let be the given instance of the optimization version of CVC(ChordalDel). Recall that . We apply Reduction Rule 1 exhaustively on to obtain a graph which is a subgraph of such that is a chordal vertex deletion set of and has no cliques of size . Moreover, it follows from the description of the rule that if is connected, so is .
By Lemma 3, it follows that any given approximate connected vertex cover of can be converted to a connected vertex cover of whose size is at most in polynomial time, where . In addition, from Lemma 4, we know that , implying that we may now treat as a meaningful instance of CVC(transversal).
We now invoke Corollary 1 and return as our output, the output of the associated PSAKS when given the instance as input. The correctness as well as the bound on the size of the returned output follow from those of Corollary 1. We note that even if is the size of a smallest chordal vertex deletion set of the original input graph and is only a factor approximation, it follows that the size of the output is still bounded polynomially in , since would be bounded polynomially in . This completes the proof of the theorem. ∎
4 Connected Vertex Cover parameterized by the deletion distance to split graphs, or cographs
In this section we present a PSAKS for Connected Vertex Cover parameterized by the size of a minimum deletion set into disjoint union of split graphs, and cographs.
Lemma 5.
Let be a graph such that there exists a vertex such that every connected component of is either a split graph, or a cograph. Then, Connected Vertex Cover is polynomialtime solvable on . Furthermore, there exists a polynomialtime algorithm that outputs a smallest connected vertex cover containing .
Proof.
It is sufficient for us to provide a polynomialtime algorithm to solve inputs of Connected Vertex Cover on the class for each . Recall that the inputs we are interested in, have the property that and we are only interested in smallest connected vertex covers of .
 Case 1:

. Consider a graph in the class and let denote a vertex in such that is a split graph. The proof idea is similar to the way presented by Krithika et al. [27], but we provide it here for completeness. Let be the given instance of Connected Vertex Cover and let denote the partition of into a clique and independent set respectively. We will now construct a connected vertex cover by making a constant number of nondeterministic choices as follows. Initialize when we include in the vertex cover, and otherwise we initialize (when we consider not to include in the vertex cover). Since any vertex cover must pick at least vertices from , we guess whether or not contains all of and in the latter case, we guess the unique vertex . In case, we decide not to pick in the vertex cover, we pick its neighbors present in and . If the current set is a connected vertex cover of , then we simply return it. Otherwise, it must be the case that it has two different components. They can be connected by using the only possible remaining vertex in that we might not have picked. So, we add that vertex to and check if that forms a connected vertex cover. If that forms a connected vertex cover, then we return , or otherwise we simply return to indicate that the graph has no connected vertex cover.
When has more than one connected component, then must always be there in any connected vertex cover. In such case, let be a connected component of . Consider any minimum connected vertex cover of . Consider . must also be a vertex cover of . Furthermore, also must be connected as well since is a cut vertex in also. In such case, we compute minimum connected vertex cover for each of , and finally we put together to get a minimum connected vertex cover of .
On the other hand, if has only one component, then there could have existed a connected vertex cover that does not contain .
 Case 2:

. For the case of cographs, note that they are a subclass of distancehereditary graphs and hence have rankwidth 1 [31]. Therefore, Connected Vertex Cover is polynomialtime solvable on the class when , is a direct consequence of the fact that this problem is expressible in , the result of Courcelle, Makowsky, and Rotics [5], and the fact that adding a constant number of vertices to a graph of constant rankwidth keeps the rankwidth of the resulting graph constant [31, 5]. In fact, by the same result of Courcelle, Makowsky, and Rotics [5], we can find a smallest connected vertex cover containing in polynomialtime. We note that this argument cannot be used in the previous case since split graphs can have arbitrarily large rankwidth [31, 5].
Suppose that some connected component of is a split graph, and some other connected component is a cograph. Since is connected, without loss of generality, we can assume that for at least two connected components , and of , there is an edge in as well as in . Then any (smallest) connected vertex cover of must contain . In that case, for each connected component , we first compute minimum connected vertex cover of containing , and compute their union to get a minimum connected vertex cover of . This completes the proof of the lemma. ∎
Input:  A graph , a set of size such that every connected component of is either a split graph, or a cograph, and an integer . 
Parameter:  
Problem:  Does have a connected vertex cover of size at most ? 
As in the case of CVC(transversal), the explicit requirement on the deletion set being provided in the input can be removed for as we have a greedy approximation algorithm to find this deletion set.
The formal definition of the parameterized optimization version of this problem is as follows:
Let be an instance of CVCSplitCographDeletion. Let be the graph constructed by identifying the vertex set into a single vertex . We know from Lemma 5 that Connected Vertex Cover is polynomialtime solvable on . Now, using Theorem 1 and Lemma 5, we prove Corollary 2.
See 2
We have designed our kernelization algorithms in Sections 3 and 4 in such a way, that our lossy kernels can be unified under a single parameterization. Hence, we consider a single parameterization, the deletion distance of the input graph to the class of graph where every connected component is either a treewidth graph, or a chordal graph, or a cograph. So, essentially we consider CVC(Deletion) where . We assume that the deletion set is given with the input. This assumption is important as no polynomialtime approximation algorithm is known to find such a deletion set.
We know that the rankwidth and cliquewidth of a graph are equivalent. It means that, if a graph has bounded cliquewidth, then it also has bounded rankwidth and vice versa. Towards this, we first prove the following lemma, proving which, we will be able to prove Theorem 3.
Lemma 6.
Let be a constant and be a connected graph having a vertex such that every connected component of is either a chordal graph, or a graph with cliquewidth . Then, Connected Vertex Cover is polynomialtime solvable on .
Proof.
First, we partition the set of connected components of as follows.

– connected components of that are chordal graphs.

– connected components of that have cliquewidth at most but not in .
For every , we denote by the graph induced by the vertex set spanned by the connected components in plus the vertex . As is connected, without loss of generality, we can assume that both the graphs and have an edge. So, is part of any (optimal) connected vertex cover of . For every , let be a smallest connected vertex cover of that contains .
We define by the class of all connected graphs that contain a vertex whose removal results in a chordal graph. Note that . Suppose that a new vertex , and an edge such that are added to (called pendant addition by Escoffier et al. [15]). Even then also is a chordal graph. So, is closed under pendant addition. So, each of the biconnected components of also has one vertex whose deletion results in a chordal graph. Hence, all the biconnected components of are also in the graph class . Using Lemma 4 of Escoffier et al. [15], we know that finding minimum connected vertex cover, and finding smallest connected vertex cover containing are polynomially equivalent in .
Now, we explain how Connected Vertex Cover is polynomialtime solvable on . The idea is standard and goes along the line of the proof of Theorem 23 by Krithika et al. [26]. It is known that every chordal graph admits a clique tree decomposition. Now, adding to every bag gives a tree decomposition of . Let be such a tree decomposition of . Every bag of the tree decomposition has a vertex whose deletion results in a clique. Consider a standard dynamic programming over a rooted tree decomposition of . For a node of , let be the subtree rooted at and denotes the union of all bags rooted at . Furthermore, let be the subgraph of induced by the vertex set . For a node of , and , and a partition into at most parts, let denote a minimum vertex cover of such that , and has exactly connected components where for all . Moreover, if , then is required to be connected in . So, consider the root node of a nice tree decomposition. We know that . Now, is a minimum connected vertex cover of . But, every bag has one vertex whose deletion results in a clique. So, any connected vertex cover of can avoid at most two vertices from where (from every bag of ). Hence, the total number of valid states per node is . So, each entry can be computed in polynomial time. Hence, Connected Vertex Cover is polynomialtime solvable on . As Connected Vertex Cover and finding a smallest connected vertex cover containing are polynomially equivalent in , we can find in polynomial time.
We know that , we know due to Courcelle’s Theorem [6, 7] that Connected Vertex Cover is polynomialtime solvable on . In fact, due to the same result by Courcelle [6, 7], a smallest connected vertex cover containing can also be computed in polynomial time in . So, can be found in polynomial time.
Hence, is an optimal connected vertex cover of . In this process, we can solve Connected Vertex Cover in polynomial time in . ∎
Note that a graph with treewidth has cliquewidth at most . Since cographs have bounded rankwidth, it has bounded cliquewidth too. Lemma 6 gives a guarantee that graph class with satisfies the fact that Connected Vertex Cover is polynomialtime solvable on any graph . Now, using the above lemma (Lemma 6) and Theorem 1, we give a proof of Theorem 3.
See 3
Proof.
Let be the graph constructed by identifying the vertex set into a single vertex . Let be an arbitrary connected component of that have treewidth at most . Due to Cornell and Routics [4], we know that if , then . So, we have that every connected component of has is either a cograph or a graph with cliquewidth at most or a chordal graph. But, cographs also has constant cliquewidth. So, we know that every connected component of is of either bounded cliquewidth or chordal graph. We know from Lemma 6 that there exists a polynomialtime algorithm that constructs an optimal connected vertex cover of . Now, using Theorem 1, we know that Connected Vertex Cover parameterized by admits a time efficient PSAKS with vertices. ∎
5 Conclusion
In this paper we obtained the first polynomial size approximate kernelization schemes for the Connected Vertex Cover problem when parameterized by the deletion distance of the input graph to the class of cographs, the class of bounded treewidth graphs, and the class of all chordal graphs. Moreover, they are in fact time efficient PSAKSes and this raises the natural question of whether one can obtain a size efficient PSAKS for Connected Vertex Cover even when parameterized by solution size. The output of a size efficient PSAKS is required to be bounded by instead of .
We designed our kernelizations in such a way as to ensure that our results have been unified under a single parameterization, the deletion distance of the input graph to the class of graphs where every connected component is either a treewidth graph or a chordal graph or a cograph.
This has allowed our framework to capture graph classes which are significantly more general than classes which have a small deletion distance to only one of . For instance, consider the graph obtained by taking the disjoint union of cycles of length each and cliques of size each. Observe that the deletion distance of to any one of is at least . On the other hand, the deletion distance of to the class of graphs where every connected component is either a treewidth graph or a chordal graph is 0. Our framework therefore allows one to obtain a approximate kernel of constant size for Connected Vertex Cover on since .
As a final remark, we point out that in order to generalize our results in this way for parameterization by deletion distance to even in the absence of the deletion set in the input, one must first design a polynomialtime factor approximation algorithm to compute such a deletion set. We leave this as an interesting problem for future research. Such an algorithm would have interesting implications in the study of graph modification problems.
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