Towards optimal kernel for edge-disjoint triangle packing

We study the kernelization of the Edge-Disjoint Triangle Packing (Etp) problem, in which we are asked to find k edge-disjoint triangles in an undirected graph. Etp is known to be fixed-parameter tractable with a 4k vertex kernel. The kernelization first finds a maximal triangle packing which contains at most 3k vertices, then the reduction rules are used to bound the size of the remaining graph. The constant in the kernel size is so small that a natural question arises: Could 4k be already the optimal vertex kernel size for this problem? In this paper, we answer the question negatively by deriving an improved 3.5k vertex kernel for the problem.     

Kernels for feedback arc set in tournaments

A tournament T=(V,A) is a directed graph in which there is exactly one arc between every pair of distinct vertices. Given a digraph on n vertices and an integer parameter k, the Feedback Arc Set problem asks whether the given digraph has a set of k arcs whose removal results in an acyclic digraph. The Feedback Arc Set problem restricted to tournaments is known as the k-Feedback Arc Set in Tournaments (k-FAST) problem. In this paper we obtain a linear vertex kernel for k-FAST. That is, we give a polynomial time algorithm which given an input instance T to k-FAST obtains an equivalent instance T^′ on O(k) vertices. In fact, given any fixed ?>0, the kernelized instance has at most (2+?)k vertices. Our result improves the previous known bound of O(k²) on the kernel size for k-FAST. Our kernelization algorithm solves the problem on a subclass of tournaments in polynomial time and uses a known polynomial time approximation scheme for k-FAST.     

Note on maximal bisection above tight lower bound

In a graph G=(V,E), a bisection (X,Y) is a partition of V into sets X and Y such that |X|?|Y|?|X|+1. The size of (X,Y) is the number of edges between X and Y. In the Max Bisection problem we are given a graph G=(V,E) and are required to find a bisection of maximum size. It is not hard to see that ⌈|E|/2⌉ is a tight lower bound on the maximum size of a bisection of G.We study parameterized complexity of the following parameterized problem called Max Bisection above Tight Lower Bound (Max-Bisec-ATLB): decide whether a graph G=(V,E) has a bisection of size at least ⌈|E|/2⌉+k, where k is the parameter. We show that this parameterized problem has a kernel with O(k²) vertices and O(k³) edges, i.e., every instance of Max-Bisec-ATLB is equivalent to an instance of Max-Bisec-ATLB on a graph with at most O(k²) vertices and O(k³) edges.     

Compression-based fixed-parameter algorithms for feedback vertex set and edge bipartization

We show that the NP-complete Feedback Vertex Set problem, which asks for the smallest set of vertices to remove from a graph to destroy all cycles, is deterministically solvable in O(ck⋅m) time. Here, m denotes the number of graph edges, k denotes the size of the feedback vertex set searched for, and c is a constant. We extend this to an algorithm enumerating all solutions in O(dk⋅m) time for a (larger) constant d. As a further result, we present a fixed-parameter algorithm with runtime O(k²⋅m²) for the NP-complete Edge Bipartization problem, which asks for at most k edges to remove from a graph to make it bipartite.     

Parameterized Complexity Results for General Factors in Bipartite Graphs with an Application to Constraint Programming

The NP-hard general factor problem asks, given a graph and for each vertex a list of integers, whether the graph has a spanning subgraph where each vertex has a degree that belongs to its assigned list. The problem remains NP-hard even if the given graph is bipartite with partition U?V, and each vertex in?U is assigned the list {1}; this subproblem appears in the context of constraint programming as the consistency problem for the extended global cardinality constraint. We show that this subproblem is fixed-parameter tractable when parameterized by the size of the second partite set?V. More generally, we show that the general factor problem for bipartite graphs, parameterized by |V|, is fixed-parameter tractable as long as all vertices in?U are assigned lists of length?1, but becomes $\text {\normalfont W[1]}$ -hard if vertices in?U are assigned lists of length at most?2. We establish fixed-parameter tractability by reducing the problem instance to a bounded number of acyclic instances, each of which can be solved in polynomial time by dynamic programming.     

Hypergraph recovery algorithms from a given vector of vertex degrees

Methods of obtaining certain classes of hypergraphs from a given integer vector of vertex degrees are considered. These classes are as follows: hyperedges with unit weight incident upon k vertices; hyperedges with unit weight incident upon k vertices in the case when the vertices may be non-unique; multiple hyperedges incident upon k vertices; and arbitrary hypergraph in which the edges can contain any set of k vertices. For each of these classes, an algorithm is proposed for constructing the hypergraph from an arbitrary vector. If the construction is impossible, the algorithm determines how much the vector should be reduced so that the hypergraph could be constructed.     

Improved algorithms for recognizing p-Helly and hereditary p-Helly hypergraphs

A hypergraph H is set of vertices V together with a collection of nonempty subsets of it, called the hyperedges of H. A partial hypergraph of H is a hypergraph whose hyperedges are all hyperedges of H, whereas for V^′⊆V the subhypergraph (induced by V^′) is a hypergraph with vertices V^′ and having as hyperedges the subsets obtained as nonempty intersections of V^′ and each of the hyperedges of H. For p?1 say that H is p-intersecting when every subset formed by p hyperedges of H contain a common vertex. Say that H is p-Helly when every p-intersecting partial hypergraph H^′ of H contains a vertex belonging to all the hyperedges of H^′. A hypergraph is hereditary p-Helly when every (induced) subhypergraph of it is p-Helly. In this paper we describe new characterizations for hereditary p-Helly hypergraphs and discuss the recognition problems for both p-Helly and hereditary p-Helly hypergraphs. The proposed algorithms improve the complexity of the existing recognition algorithms.     

Towards Optimal and Expressive Kernelization for d-Hitting Set

A sunflower in a hypergraph is a set of hyperedges pairwise intersecting in exactly the same vertex set. Sunflowers are a useful tool in polynomial-time data reduction for problems formalizable as d-Hitting Set, the problem of covering all hyperedges (whose cardinality is bounded from above by a constant d) of a hypergraph by at most k vertices. Additionally, in fault diagnosis, sunflowers yield concise explanations for “highly defective structures”. We provide a linear-time algorithm that, by finding sunflowers, transforms an instance of d-Hitting Set into an equivalent instance comprising at most O(k ^d ) hyperedges and vertices. In terms of parameterized complexity, we show a problem kernel with asymptotically optimal size (unless \(\operatorname {coNP}\subseteq \operatorname {NP/poly}\) ) and provide experimental results that show the practical applicability of our algorithm. Finally, we show that the number of vertices can be reduced to O(k ^d?1) with additional processing in O(k ^1.5d ) time—nontrivially combining the sunflower technique with problem kernels due to Abu-Khzam and Moser.     

Fixed-Parameter Tractability of Satisfying Beyond the Number of Variables

We consider a CNF formula F as a multiset of clauses: F={c ₁,…,c _m }. The set of variables of F will be denoted by V(F). Let B _F denote the bipartite graph with partite sets V(F) and F and with an edge between v∈V(F) and c∈F if v∈c or $\bar{v} \in c$ . The matching number ν(F) of F is the size of a maximum matching in B _F . In our main result, we prove that the following parameterization of MaxSat (denoted by (ν(F)+k)-SAT) is fixed-parameter tractable: Given a formula F, decide whether we can satisfy at least ν(F)+k clauses in F, where k is the parameter. A formula F is called variable-matched if ν(F)=|V(F)|. Let δ(F)=|F|?|V(F)| and δ ^?(F)=max _F′?F δ(F′). Our main result implies fixed-parameter tractability of MaxSat parameterized by δ(F) for variable-matched formulas F; this complements related results of Kullmann (IEEE Conference on Computational Complexity, pp. 116–124, 2000) and Szeider (J. Comput. Syst. Sci. 69(4):656–674, 2004) for MaxSat parameterized by δ ^?(F). To obtain our main result, we reduce (ν(F)+k)-SAT into the following parameterization of the Hitting Set problem (denoted by (m?k)-Hitting Set): given a collection $\mathcal{C}$ of m subsets of a ground set U of n elements, decide whether there is X?U such that C∩X≠? for each $C\in \mathcal{C}$ and |X|≤m?k, where k is the parameter. Gutin, Jones and Yeo (Theor. Comput. Sci. 412(41):5744–5751, 2011) proved that (m?k)-Hitting Set is fixed-parameter tractable by obtaining an exponential kernel for the problem. We obtain two algorithms for (m?k)-Hitting Set: a deterministic algorithm of runtime $O((2e)^{2k+O(\log^{2} k)} (m+n)^{O(1)})$ and a randomized algorithm of expected runtime $O(8^{k+O(\sqrt{k})} (m+n)^{O(1)})$ . Our deterministic algorithm improves an algorithm that follows from the kernelization result of Gutin, Jones and Yeo (Theor. Comput. Sci. 412(41):5744–5751, 2011).     

A Cubic Kernel for Feedback Vertex Set and Loop Cutset

The Feedback Vertex Set problem on unweighted, undirected graphs is considered. Improving upon a result by Burrage et al. (Proceedings 2nd International Workshop on Parameterized and Exact Computation, pp. 192–202, 2006), we show that this problem has a kernel with O(k ³) vertices, i.e., there is a polynomial time algorithm, that given a graph G and an integer k, finds a graph G′ with O(k ³) vertices and integer k′≤k, such that G has a feedback vertex set of size at most k, if and only if G′ has a feedback vertex set of size at most k′. Moreover, the algorithm can be made constructive: if the reduced instance G′ has a feedback vertex set of size k′, then we can easily transform a minimum size feedback vertex set of G′ into a minimum size feedback vertex set of G. This kernelization algorithm can be used as the first step of an FPT algorithm for Feedback Vertex Set, but also as a preprocessing heuristic for Feedback Vertex Set.     

Vertex Cover Kernelization Revisited

An important result in the study of polynomial-time preprocessing shows that there is an algorithm which given an instance (G,k) of Vertex Cover outputs an equivalent instance (G′,k′) in polynomial time with the guarantee that G′ has at most 2k′ vertices (and thus $\mathcal{O}((k')^{2})$ edges) with k′≤k. Using the terminology of parameterized complexity we say that k-Vertex Cover has a kernel with 2k vertices. There is complexity-theoretic evidence that both 2k vertices and Θ(k ²) edges are optimal for the kernel size. In this paper we consider the Vertex Cover problem with a different parameter, the size $\mathop{\mathrm{\mbox{\textsc{fvs}}}}(G)$ of a minimum feedback vertex set for G. This refined parameter is structurally smaller than the parameter k associated to the vertex covering number $\mathop{\mathrm{\mbox {\textsc{vc}}}}(G)$ since $\mathop{\mathrm{\mbox{\textsc{fvs}}}}(G)\leq\mathop{\mathrm{\mbox{\textsc{vc}}}}(G)$ and the difference can be arbitrarily large. We give a kernel for Vertex Cover with a number of vertices that is cubic in $\mathop{\mathrm{\mbox{\textsc{fvs}}}}(G)$ : an instance (G,X,k) of Vertex Cover, where X is a feedback vertex set for G, can be transformed in polynomial time into an equivalent instance (G′,X′,k′) such that |V(G′)|≤2k and $|V(G')| \in\mathcal{O}(|X'|^{3})$ . A similar result holds when the feedback vertex set X is not given along with the input. In sharp contrast we show that the Weighted Vertex Cover problem does not have a polynomial kernel when parameterized by the cardinality of a given vertex cover of the graph unless NP ? coNP/poly and the polynomial hierarchy collapses to the third level.     

On the k-tuple domination of generalized de Brujin and Kautz digraphs

A vertex u in a digraph G = (V, A) is said to dominate itself and vertices v such that (u, v) ∈ A. For a positive integer k, a k-tuple dominating set of G is a subset D of vertices such that every vertex in G is dominated by at least k vertices in D. The k-tuple domination number of G is the minimum cardinality of a k-tuple dominating set of G. This paper deals with the k-tuple domination problem on generalized de Bruijn and Kautz digraphs. We establish bounds on the k-tuple domination number for the generalized de Bruijn and Kautz digraphs and we obtain some conditions for the k-tuple domination number attaining the bounds.     

Spanners of bounded degree graphs

A k-spanner of a graph G is a spanning subgraph of G in which the distance between any pair of vertices is at most k times the distance in G. We prove that for fixed k,w, the problem of deciding if a given graph has a k-spanner of treewidth w is fixed-parameter tractable on graphs of bounded degree. In particular, this implies that finding a k-spanner that is a tree (a tree k-spanner) is fixed-parameter tractable on graphs of bounded degree. In contrast, we observe that if the graph has only one vertex of unbounded degree, then Treek-Spanner is NP-complete for k?4.     

A linear-time algorithm for paired-domination problem in strongly chordal graphs

Let G=(V,E) be a simple graph without isolated vertices. A vertex set S⊆V is a paired-dominating set if every vertex in V−S has at least one neighbor in S and the induced subgraph G[S] has a perfect matching. In this paper, we present a linear-time algorithm to find a minimum paired-dominating set in strongly chordal graphs if the strong (elimination) ordering of the graph is given in advance.     

The spanning connectivity of folded hypercubes

A k-container of a graph G is a set of k internally disjoint paths between u and v. A k-container of G is a k∗-container if it contains all vertices of G. A graph G is k∗-connected if there exists a k∗-container between any two distinct vertices, and a bipartite graph G is k∗-laceable if there exists a k∗-container between any two vertices u and v from different partite sets of G for a given k. A k-connected graph (respectively, bipartite graph) G is f-edge fault-tolerant spanning connected (respectively, laceable) if G − F is w∗-connected for any w with 1 ? w ? k − f and for any set F of f faulty edges in G. This paper shows that the folded hypercube FQ_n is f-edge fault-tolerant spanning laceable if n(?3) is odd and f ? n − 1, and f-edge fault-tolerant spanning connected if n (?2) is even and f ? n − 2.     

Bandwidth on AT-free graphs

We study the classical Bandwidth problem from the viewpoint of parametrised algorithms. Given a graph G=(V,E) and a positive integer k, the Bandwidth problem asks whether there exists a bijective function β:{1,…,∣V∣}→V such that for every edge uv∈E, ∣β⁻¹(u)−β⁻¹(v)∣≤k. It is known that under standard complexity assumptions, no algorithm for Bandwidth with running time of the form f(k)n^O(1) exists, even when the input is restricted to trees. We initiate the search for classes of graphs where such algorithms do exist. We present an algorithm with running time n⋅2^O(klogk) for Bandwidth on AT-free graphs, a well-studied graph class that contains interval, permutation, and cocomparability graphs. Our result is the first non-trivial algorithm that shows fixed-parameter tractability of Bandwidth on a graph class on which the problem remains NP-complete.     

On induced-universal graphs for the class of bounded-degree graphs

For a family F of graphs, a graph U is said to be F-induced-universal if every graph of F is an induced subgraph of U. We give a construction for an induced-universal graph for the family of graphs on n vertices with degree at most k. For k even, our induced-universal graph has O(nk/2) vertices and for k odd it has O(n⌈k/2⌉−1/klog2+2/kn) vertices. This construction improves a result of Butler by a multiplicative constant factor for the even case and by almost a multiplicative n1/k factor for the odd case. We also construct induced-universal graphs for the class of oriented graphs with bounded incoming and outgoing degree, slightly improving another result of Butler.     

The super laceability of the hypercubes

A k-containerC(u,v) of a graph G is a set of k disjoint paths joining u to v. A k-container C(u,v) is a k∗-container if every vertex of G is incident with a path in C(u,v). A bipartite graph G is k∗-laceable if there exists a k∗-container between any two vertices u, v from different partite set of G. A bipartite graph G with connectivity k is super laceable if it is i∗-laceable for all i?k. A bipartite graph G with connectivity k is f-edge fault-tolerant super laceable if G−F is i∗-laceable for any 1?i?k−f and for any edge subset F with |F|=f<k−1. In this paper, we prove that the hypercube graph Q_r is super laceable. Moreover, Q_r is f-edge fault-tolerant super laceable for any f?r−2.     

Computing simple-path convex hulls in hypergraphs

In a connected hypergraph a vertex set X is simple-path convex (sp-convex, for short) if either |X|?1 or X contains every vertex on every simple path between two vertices in X (Faber and Jamison, 1986 [7]), and the sp-convex hull of a vertex set X is the minimal superset of X that is sp-convex. In this paper, we give a polynomial algorithm to compute sp-convex hulls in an arbitrary hypergraph.     

A note on computing set overlap classes

Let V be a finite set of n elements and F={X₁,X₂,…,Xm} a family of m subsets of V. Two sets Xi and Xj of F overlap if Xi∩Xj≠∅, Xj?Xi≠∅, and Xi?Xj≠∅. Two sets X,Y∈F are in the same overlap class if there is a series X=X₁,X₂,…,Xk=Y of sets of F in which each XiXi+1 overlaps. In this note, we focus on efficiently identifying all overlap classes in time. We thus revisit the clever algorithm of Dahlhaus [E. Dahlhaus, Parallel algorithms for hierarchical clustering and applications to split decomposition and parity graph recognition, J. Algorithms 36 (2) (2000) 205-240] of which we give a clear presentation and that we simplify to make it practical and implementable in its real worst case complexity. An useful variant of Dahlhaus's approach is also explained.