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The family of judicious partitioning problems, introduced by Bollobàs and Scott to the field of extremal combinatorics, has been extensively studied from a structural point of view for over two decades. This rich realm of problems aims to counterbalance the objectives of classical partitioning problems such as Min Cut, Min Bisection, and Max Cut. While these classical problems focus solely on the minimization/maximization of the number of edges crossing the cut, judicious (bi)partitioning problems ask the natural question of the minimization/maximization of the number of edges lying in the (two) sides of the cut. In particular, Judicious Bipartition (JB) seeks a bipartition that is "judicious" in the sense that neither side is burdened by too many edges, and Balanced JB (BJB) also requires that the sizes of the sides themselves are "balanced" in the sense that neither of them is too large. Both of these problems were defined in the work by Bollobàs and Scott and have received notable scientific attention since then. In this paper, we shed light on the study of judicious partitioning problems from the viewpoint of algorithm design. Specifically, we prove that BJB is fixed parameter tractable (FPT) (which also proves that JB is FPT). [ABSTRACT FROM AUTHOR]

Bidimensionality theory was introduced by [E. D. Demaine et al., J. ACM, 52 (2005), pp. 866--893] as a tool to obtain subexponential time parameterized algorithms on H-minor-free graphs. In [E. D. Demaine and M. Hajiaghayi, Bidimensionality: New connections between FPT algorithms and PTASs, in Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, Philadelphia, 2005, pp. 590--601] this theory was extended in order to obtain polynomial time approximation schemes (PTASs) for bidimensional problems. In this work, we establish a third meta-algorithmic direction for bidimensionality theory by relating it to the existence of linear kernels for parameterized problems. In particular, we prove that every minor (resp., contraction) bidimensional problem that satisfies a separation property and is expressible in Countable Monadic Second Order Logic (CMSO) admits a linear kernel for classes of graphs that exclude a fixed graph (resp., an apex graph) H as a minor. Our results imply that a multitude of bidimensional problems admit linear kernels on the corresponding graph classes. For most of these problems no polynomial kernels on H-minor-free graphs were known prior to our work. [ABSTRACT FROM AUTHOR]

For a positive integer c, a graph G is said to be c-closed if every pair of nonadjacent vertices in G have at most c 1 neighbors in common. The closure of a graph G, denoted by cl(G), is the least positive integer c for which G is c-closed. The class of c-closed graphs was introduced by J. Fox, T. Roughgarden, C. Seshadhri, F. Wei, and N. Wein [Proceedings of the International Colloquium on Automata, Languages, and Programming (2018), 55; SIAM J. Comput., 49 (2020), pp. 448--464]. T. Koana, C. Komusiewicz, and F. Sommer [Proceedings of the European Symposium on Algorithms (2020), 65; SIAM J. Discrete Math., 36 (2022), pp. 2798--2821] started the study of using cl(G) as an additional structural parameter to design kernels for problems that are W-hard under standard parameterizations. In particular, they studied problems such as Independent Set, Induced Matching, Irredundant Set, and (Threshold) Dominating Set and showed that each of these problems admits a polynomial kernel when parameterized either by k + c or by k for each fixed value of c. Here, k is the solution size and c = cl(G). The work of Koana et al. left several questions open, one of which was whether the Perfect Code problem admits a fixed-parameter tractable (FPT) algorithm and a polynomial kernel on c-closed graphs. In this paper, among other results, we answer this question in the affirmative. Inspired by the FPT algorithm for Perfect Code, we further explore two more domination problems on the graphs of bounded closure. The other problems that we study are Connected Dominating Set and Partial Dominating Set. We show that Perfect Code and Connected Dominating Set are fixed-parameter tractable when parameterized by k + cl(G), whereas Partial Dominating Set, parameterized by k, is W[1]-hard even when cl(G) = 2. We also show that for each fixed c, Perfect Code admits a polynomial kernel on the class of c-closed graphs. And we observe that Connected Dominating Set has no polynomial kernel even on 2-closed graphs unless NP\subseteq co-NP/poly. [ABSTRACT FROM AUTHOR]

The question of the existence of a polynomial kernelization of the Vertex Cover Above LP problem was a long-standing, notorious open problem in parameterized complexity. Some years ago, the breakthrough work by Kratsch and Wahlstrom on representative sets finally answered this question in the affirmative [FOCS 2012]. In this paper, we present an alternative, algebraic compression of the Vertex Cover Above LP problem into the Rank Vertex Cover problem. Here, the input consists of a graph G, a parameter k, and a bijection between V (G) and the set of columns of a representation of a matroid M, and the objective is to find a vertex cover whose rank is upper bounded by k. [ABSTRACT FROM AUTHOR]

The Cycle Packing problem asks whether a given undirected graph G = (V;E) con- tains k vertex-disjoint cycles. Since the publication of the classic Erd}os{Posa theorem in 1965, this problem received significant attention in the fields of graph theory and algorithm design. In particu- lar, this problem is one of the first problems studied in the framework of parameterized complexity. The nonuniform fixed-parameter tractability of Cycle Packing follows from the Robertson{Seymour theorem, a fact already observed by Fellows and Langston in the 1980s. In 1994, Bodlaender showed that Cycle Packing can be solved in time 2O(k2) jV j using exponential space. In the case a solu- tion exists, Bodlaender's algorithm also outputs a solution (in the same time). It has later become common knowledge that Cycle Packing admits a 2O(k log2 k) jV j-time (deterministic) algorithm using exponential space, which is a consequence of the Erd}os{Posa theorem. Nowadays, the design of this algorithm is given as an exercise in textbooks on parameterized complexity. Yet, no algorithm that runs in time 2o(k log2 k) jV jO(1), beating the bound 2O(k log2 k) jV jO(1), has been found. In light of this, it seems natural to ask whether the 2O(k log2 k) jV jO(1) bound is essentially optimal. In this paper, we answer this question negatively by developing a 2 O(k log2 k log log k) jV j-time (deterministic) algorithm for Cycle Packing. In the case a solution exists, our algorithm also outputs a solution (in the same time). Moreover, apart from beating the bound 2O(k log2 k) jV jO(1), our algorithm runs in time linear in jV j, and its space complexity is polynomial in the input size. [ABSTRACT FROM AUTHOR]

A vertex subset S in a graph G is a dominating set if every vertex not contained in S has a neighbor in S. A dominating set S is a connected dominating set if the subgraph G[S] induced by S is connected. A connected dominating set S is a minimal connected dominating set if no proper subset of S is also a connected dominating set. We prove that there exists a constant \epsilon ∊10-50 such that every graph G on n vertices has at most O(2(1-∊)n) minimal connected dominating sets. For the same ∊ we also give an algorithm with running time 2(1-∊)n· nO(1) to enumerate all minimal connected dominating sets in an input graph G. [ABSTRACT FROM AUTHOR]

A central problem in parameterized algorithms is to obtain algorithms with running time f(k) · nO(1) such that f is as slow growing a function of the parameter k as possible. In particular, a large number of basic parameterized problems admit parameterized algorithms where f(k) is single-exponential, that is, ck for some constant c, which makes aiming for such a running time a natural goal for other problems as well. However, there are still plenty of problems where the f(k) appearing in the best-known running time is worse than single-exponential and it remained "slightly superexponential" even after serious attempts to bring it down. A natural question to ask is whether the f(k) appearing in the running time of the best-known algorithms is optimal for any of these problems. In this paper, we examine parameterized problems where f(k) is kO(k) = 2O(k log k) in the best-known running time, and for a number of such problems we show that the dependence on k in the running time cannot be improved to single-exponential. More precisely we prove the following tight lower bounds, for four natural problems, arising from three different domains: (1) In the Closest String problem, given strings s1, ..., st over an alphabet Σ of length L each, and an integer d, the question is whether there exists a string s over Σ of length L, such that its hamming distance from each of the strings si, 1 ≤ i ≤ t, is at most d. The pattern matching problem Closest String is known to be solvable in times 2O(d log d) · nO(1) and 2O(d log |Σ|) · nO(1). We show that there are no 2o(d log d) · nO(1) or 2o(d log |Σ|) · nO(1) time algorithms, unless the Exponential Time Hypothesis (ETH) fails. (2) The graph embedding problem Distortion, that is, deciding whether a graph G has a metric embedding into the integers with distortion at most d can be solved in time 2O(d log d) · nO(1). We show that there is no 2o(d log d) · nO(1) time algorithm, unless the ETH fails. (3) The Disjoint Paths problem can be solved in time 2O(w log w) · nO(1) on graphs of treewidth at most w. We show that there is no 2o(w log w) · nO(1) time algorithm, unless the ETH fails. (4) The Chromatic Number problem can be solved in time 2O(w log w) ·nO(1) on graphs of treewidth at most w. We show that there is no 2o(w log w) · nO(1) time algorithm, unless the ETH fails. To obtain our results, we first prove the lower bound for variants of basic problems: finding cliques, independent sets, and hitting sets. These artificially constrained variants form a good starting point for proving lower bounds on natural problems without any technical restrictions and could be of independent interest. Several follow-up works have already obtained tight lower bounds by using our framework, and we believe it will prove useful in obtaining even more lower bounds in the future. [ABSTRACT FROM AUTHOR]

FEEDBACK VERTEX SET (FVS) is one of the most well-studied problems in the realm of parameterized complexity. In this problem we are given a graph G and a positive integer k and the objective is to test whether there exists S ⊆ V (G) of size at most k such that G - S is a forest. Thus, FVS is about deleting as few vertices as possible to get a forest. The main goal of this paper is to study the following interesting problem: How can we generalize the family of forests such that the nice structural properties of forests and the interesting algorithmic properties of FVS can be extended to problems on this class? Toward this we define a graph class, F1, that contains all graphs where each connected component can be transformed into a forest by deleting at most l edges. A graph in the class F1 is known as pseudoforest in the literature and we call a graph in Fl an l-pseudoforest. We study the problem of deleting k vertices to get into Fl, l-PSEUDOFOREST DELETION, in the realm of parameterized complexity. We show that l-PSEUDOFOREST DELETION admits an algorithm with running time clknO(1) and admits a kernel of size f(l)k2. Thus, for every fixed l we have a kernel of size O(k2). That is, we get a uniform polynomial kernel for l-pseudoforest Deletion. Our algorithms and uniform kernels involve the use of the expansion lemma and protrusion machinery. [ABSTRACT FROM AUTHOR]

In the Tree Deletion Set problem the input is a graph G together with an integer k. The objective is to determine whether there exists a set S of at most k vertices such that G \ S is a tree. The problem is NP-complete and even NP-hard to approximate within any factor of OPTc for any constant c. In this paper we give an O(k5) size kernel for the Tree Deletion Set problem. An appealing feature of our kernelization algorithm is a new reduction rule, based on systems of linear equations, that we use to handle the instances on which Tree Deletion Set is hard to approximate. [ABSTRACT FROM AUTHOR]

We prove the following theorem. Given a planar graph G and an integer k, it is possible in polynomial time to randomly sample a subset A of vertices of G with the following properties: A induces a subgraph of G of treewidth O(√k log k), and for every connected subgraph H of G on at most k vertices, the probability that A covers the whole vertex set of H is at least (2O(√k log² k)⋅nO(1))-1, where n is the number of vertices of G. Together with standard dynamic programming techniques for graphs of bounded treewidth, this result gives a versatile technique for obtaining (randomized) subexponential-time parameterized algorithms for problems on planar graphs, usually with running time bound 2O(√k log² k)nO(1). The technique can be applied to problems expressible as searching for a small, connected pattern with a prescribed property in a large host graph; examples of such problems include Directed k -Path, Weighted k -Path, Vertex Cover Local Search, and Subgraph Isomorphism, among others. Up to this point, it was open whether these problems could be solved in subexponential parameterized time on planar graphs, because they are not amenable to the classic technique of bidimensionality. Furthermore, all our results hold in fact on any class of graphs that exclude a fixed apex graph as a minor, in particular on graphs embeddable in any fixed surface. [ABSTRACT FROM AUTHOR]

An undirected graph G is d-degenerate if every subgraph of G has a vertex of degree at most d. By the classical theorem of Erdös and Gallai from 1959, every graph of degeneracy d > 1 contains a cycle of length at least d + 1. The proof of Erdös and Gallai is constructive and can be turned into a polynomial time algorithm constructing a cycle of length at least d + 1. But can we decide in polynomial time whether a graph contains a cycle of length at least d + 2? An easy reduction from Hamiltonian Cycle provides a negative answer to this question: Deciding whether a graph has a cycle of length at least d+2 is NP-complete. Surprisingly, the complexity of the problem changes drastically when the input graph is 2-connected. In this case we prove that deciding whether G contains a cycle of length at least d + k can be done in time 2O(k) .| V (G)| O(1). In other words, deciding whether a 2-connected n-vertex G contains a cycle of length at least d+log n can be done in polynomial time. Similar algorithmic results hold for long paths in graphs. We observe that deciding whether a graph has a path of length at least d+1 is NP-complete. However, we prove that if graph G is connected, then deciding whether G contains a path of length at least d+k can be done in time 2O(k) .nO(1). We complement these results by showing that the choice of degeneracy as the ``above guarantee parameterization"" is optimal in the following sense: For any ε > 0 it is NP-complete to decide whether a connected (2-connected) graph of degeneracy d has a path (cycle) of length at least (1 + ε )d. [ABSTRACT FROM AUTHOR]

In the classic Minimum Bisection problem we are given as input an undirected graph G and an integer k. The task is to determine whether there is a partition of V (G) into two parts A and B such that | | A| | B| | \leq 1 and there are at most k edges with one endpoint in A and the other in B. In this paper we give an algorithm for Minimum Bisection with running time 2O(k3)n³ log³ n. This is the first fixed parameter tractable algorithm for Minimum Bisection parameterized by k. At the core of our algorithm lies a new decomposition theorem that states that every graph G can be decomposed by small separators into parts where each part is "highly connected"" in the following sense: any separator of bounded size can separate only a limited number of vertices from each part of the decomposition. Our techniques generalize to the weighted setting, where we seek a bisection of minimum weight among solutions that contain at most k edges. [ABSTRACT FROM AUTHOR]

In the EDGE EDITING TO CONNECTED f-Degree Graph problem we are given a graph G, an integer fc, and a function f assigning integers to vertices of G. The task is to decide whether there is a connected graph F on the same vertex set as G, such that for every vertex v, its degree in F is f(v), and the number of edges in E(G)ΔE(F), the symmetric difference of E(G) and E(F), is at most k. We show that Edge EDITING TO CONNECTED f-DEGREE GRAPH is fixed- parameter tractable (FPT) by providing an algorithm solving the problem on an n-vertex graph in time2O(k)nO(1). We complement this result by showing that the weighted version of the problem with costs 1 and 0 is W[l]-hard when parameterized by k and the maximum value of f even when the input graph is a tree. Our FPT algorithm is based on a nontrivial combination of color-coding and fast computations of representative families over the direct sum matroid of l-elongation of the co-graphic matroid associated with G and a uniform matroid over the set of nonedges of G. We believe that this combination could be useful in designing parameterized algorithms for other edge editing and connectivity problems. [ABSTRACT FROM AUTHOR]

In the Steiner Tree problem, we are given as input a connected n-vertex graph with edge weights in {1,2,...,W}, and a set of k terminal vertices. Our task is to compute a minimum-weight tree that contains all of the terminals. The main result of the paper is an algorithm solving Steiner Tree in time O(7.97k·n4·logW) and using O(n3·lognW·logk) space. This is the first single-exponential time, polynomial space FPT algorithm for the weighted Steiner Tree problem. Whereas our main result seeks to optimize the polynomial dependency in n for both the running time and space usage, it is possible to trade between polynomial dependence in n and the single-exponential dependence in k to obtain faster running time as a function of k, but at the cost of increased running time and space usage as a function of n. In particular, we show that there exists a polynomial space algorithm for Steiner Tree running in O(6.751knO(1)logW) time. Finally, by pushing such a trade-off between a polynomial in n and an exponential in k dependencies, we show that for any ϵ > 0 there is an n O(f(ϵ))logW space 4(1+ϵ)kn O(f(ϵ))logW time algorithm for Steiner Tree, where f is a computable function depending only on ϵ. [ABSTRACT FROM AUTHOR]

Seymour's decomposition theorem for regular matroids is a fundamental result with a number of combinatorial and algorithmic applications. In this work we demonstrate how this theorem can be used in the design of parameterized algorithms on regular matroids. We consider the problem of covering a set of vectors of a given finite dimensional linear space (vector space) by a subspace generated by a set of vectors of minimum size. Specifically, in the SPACE COVER problem, we are given a matrix M and a subset of its columns T; the task is to find a minimum set F of columns of M disjoint with T such that the linear span of F contains all vectors of T. For graphic matroids this problem is essentially Steiner Forest and for cographic matroids this is a generalization of MULTIWAY CUT. Our main result is the algorithm with running time 2Oh(k). | | M| |Oh(1) solving SPACE COVER in the case when M is a totally unimodular matrix over rationals, where k is the size of F. In other words, we show that on regular matroids the problem is fixed-parameter tractable parameterized by the rank of the covering subspace. [ABSTRACT FROM AUTHOR]

A key result in the field of kernelization, a subfield of parameterized complexity, states that the classic Disjoint Cycle Packing problem, i.e., finding k vertex disjoint cycles in a given graph G, admits no polynomial kernel unless NP ⊆ coNP/poly. However, very little is known about this problem beyond the aforementioned kernelization lower bound (within the parameterized complexity framework). In the hope of clarifying the picture and better understanding the types of constraints that separate kernelizable from nonkernelizable variants of Disjoint Cycle Packing, we investigate two relaxations of the problem. The first variant, which we call Almost Disjoint Cycle Packing, introduces a global relaxation parameter t. That is, given a graph G and integers k and t, the goal is to find at least k distinct cycles such that every vertex of G appears in at most t of the cycles. The second variant, Pairwise Disjoint Cycle Packing, introduces a local relaxation parameter, and we seek at least k distinct cycles such that every two cycles intersect in at most t vertices. While the Pairwise Disjoint Cycle Packing problem admits a polynomial kernel for all t ≥ 1, the kernelization complexity of Almost Disjoint Cycle Packing reveals an interesting spectrum of upper and lower bounds. In particular, for t = k/c, where c could be a function of k, we obtain a kernel of size O(2c² k7+c log³ k) whenever c ∈ o(√k). Thus the kernel size varies from being subexponential when c ∈ o(√k), to quasi-polynomial when c ∈ o(logℓ k), ℓ ∈ R+, and polynomial when c ∈ O(1). We complement these results for Almost Disjoint Cycle Packing by showing that the problem does not admit a polynomial kernel whenever t ∈ O(k∈) for any 0≤∈< 1, unless NP 埦 coNP/poly. [ABSTRACT FROM AUTHOR]

Conflict-free coloring of hypergraphs is a very well studied question of theoretical and practical interest. For a hypergraph H = (U,F), a conflict-free coloring of H refers to a vertex coloring where every hyperedge has a vertex with a unique color, distinct from all other vertices in the hyperedge. In this paper, we initiate a study of a natural maximization version of this problem, namely, Max-CFC: For a given hypergraph H and a fixed r = 2, color the vertices of U using r colors so that the number of hyperedges that are conflict-free colored is maximized. By previously known hardness results for conflict-free coloring, this maximization version is NPhard. We study this problem in the context of both exact and parameterized algorithms. In the parameterized setting, we study this problem with respect to a natural parameter--the solution size. In particular, the question we study is the following: p-CFC: For a given hypergraph, can we conflict-free color at least k hyperedges with at most r colors, the parameter being the solution size k. We show that this problem is fixed parameter tractable by designing an algorithm with running time 2O(k log log k+k log r)(n + m)O(1) using a novel connection to the Unique Coverage problem and applying the method of color coding in a nontrivial manner. For the special case for hypergraphs induced by graph neighborhoods we give a polynomial kernel. Finally, we give an exact algorithm for Max-CFC running in O(2n+m) time. All our algorithms, with minor modifications, work for a stronger version of conflict-free coloring, Unique Maximum Coloring. [ABSTRACT FROM AUTHOR]

For a target rank r, the rigidity of a matrix A over a field F is the minimum Hamming distance between A and a matrix of rank at most r. Rigidity is a classical concept in computational complexity theory: constructions of rigid matrices are known to imply lower bounds of significant importance relating to arithmetic circuits. Yet, from the viewpoint of parameterized complexity, the study of central properties of matrices in general, and of the rigidity of a matrix in particular, has been neglected. In this paper, we conduct a comprehensive study of different aspects of the computation of the rigidity of general matrices in the framework of parameterized complexity. Naturally, given parameters r and k, the Matrix Rigidity problem asks whether the rigidity of A for the target rank r is at most k. We show that in the case F = R or F is any finite field, this problem is fixed-parameter tractable with respect to k+r. To this end, we present a dimension reduction procedure, which may be a valuable primitive in future studies of problems of this nature. We also employ central tools in real algebraic geometry, which are not well known in parameterized complexity, as a black box. In particular, we view the output of our dimension reduction procedure as an algebraic variety. Our main results are complemented by a W[1]-hardness result and a subexponential-time parameterized algorithm for a special case of Matrix Rigidity, highlighting the different flavors of this problem. [ABSTRACT FROM AUTHOR]

In the Subset Odd Cycle Transversal (Subset OCT) problem, the input is a graph G, a subset of vertices T and a positive integer k and the objective is to determine whether there exists a k-sized vertex subset that intersects every odd cycle containing a vertex from T. Clearly, Subset OCT is a generalization of the classic Odd Cycle Transversal problem where the objective is to determine whether there exists a k-sized vertex subset that intersects every odd cycle in the given graph. We remark that Subset OCT also generalizes the well known Multiway Cut problem, as well as a parity constrained variant, the Odd Multiway Cut problem. Recently, Kakimura, Kawarabayashi, and Kobayashi [Proceedings of SODA, 2012, pp. 1726{1736] proposed a fixed parameter tractable (FPT) algorithm for this problem that runs in time ƒ(k)mn³ using the theory of graph minors, where ƒ is some function, and n and m denote the number of vertices and edges in the graph. However, the dependence of this function on k is at least triple exponential. In this paper, we give the first FPT algorithm for this problem where the exponential dependence of the running time of the algorithm on k is polynomial. Our algorithm avoids the use of the theory of graph minors, is self contained, and runs in time 2O(k³ log k)mn² log² n, thus improving upon the algorithm of Kakimura and co-authors with respect to both the parameter as well as the input size. Our algorithm utilizes a recursive application of "generalized" important separators to reduce the subset version of this problem to the standard version of the problem. [ABSTRACT FROM AUTHOR]

We study the parameterized complexity of t he directed variant of the classical STEINER Tree problem on various classes of directed sparse graphs. While the parameterized complexity of STEINER Tree parameterized by the number of terminals is well understood, not much is known about the parameterization by the number of nonterminals in the solution tree. All that is known for this parameterization is that both the directed and the undirected versions are W[2]-hard on general graphs and hence unlikely to be fixed parameter tractable (FPT). The undirected STEINER Tree problem becomes FPT when restricted to sparse classes of graphs such as planar graphs, but the techniques used to show this result break down on directed planar graphs. In this article we precisely chart the tractability border for DIRECTED STEINER Tree (DST) on sparse graphs parameterized by the number of nonterminals in the solution tree. Specifically, we show that the problem is FPT on graphs excluding a topological minor but becomes W[2]-hard on graphs of degeneracy 2. On the other hand we show that if the subgraph induced by the terminals is acyclic, then the problem becomes FPT on graphs of bounded degeneracy. We further show that our algorithm achieves the best possible asymptotic running time dependence on the solution size and degeneracy of the input graph, under standard complexity theoretic assumptions. Using the ideas developed for DST, we also obtain improved algorithms for DOMINATING SET on sparse undirected graphs. These algorithms are asymptotically optimal. [ABSTRACT FROM AUTHOR]

We give a fixed-parameter tractable algorithm that, given a parameter k and two graphs G1, G2, either concludes that one of these graphs has treewidth at least k or determines whether G1 and G2 are isomorphic. The running time of the algorithm on an n-vertex graph is ...⋅n5, and this is the first fixed-parameter algorithm for GRAPH ISOMORPHISM parameterized by treewidth. Our algorithm in fact solves the more general canonization problem. We namely design a procedure working in ...⋅n5 time that, for a given graph G on n vertices, either concludes that the treewidth of G is at least k or (i) finds in an isomorphic-invariant way a graph c(G) that is isomorphic to G; (ii) finds an isomorphism-invariant construction term--an algebraic expression that encodes G together with a tree decomposition of G of width less than k. Hence, the isomorphism test reduces to verifying whether the computed isomorphic copies or the construction terms for G1 and G2 are equal. [ABSTRACT FROM AUTHOR]

An instance of the (n-k)-Set Cover or the (n-k)-Test Cover problems is of the form (U; S; k), where U is a set with n elements, S ... 2U with |S| = m, and k is the parameter. The instance is a YES-instance of (n-k)-Set Cover if and only if there exists S'... S with |S'| ≤ n - k such that every element of U is contained in some set in S0. Similarly, it is a YES-instance of (n - k)-Test Cover if and only if there exists S' ... S with |S'| n - k such that for any pair of elements from U, there exists a set in S' that contains one of them but not the other. It is known in the literature that both (n - k)-Set Cover and (n - k)-Test Cover do not admit polynomial kernels (under some well-known complexity theoretic assumptions). However, in this paper we show that they do admit "partially polynomial kernels" we give polynomial time algorithms that take as input an instance (U; S; k) of (n - k)-Set Cover (respectively, (n - k)-Test Cover) and return an equivalent instance ( U; S; k) of (n-k)-Set Cover (respectively, (n-k)-Test Cover) with ... ≤ k and |U| = O(k²) (respectively, |U| = O(k7)). These results allow us to generalize, improve, and unify several results known in the literature. For example, these immediately imply traditional kernels when input instances satisfy certain "sparsity properties." Using a part of our partial kernelization algorithm for (n - k)-Set Cover, we also get an improved fixed-parameter tractable algorithm for this problem which runs in time O(4kkO(1)(m + n) + mn) improving over the previous best of O(8k+o(k)(m+n)O(1)). On the other hand, the partially polynomial kernel for (n-k)-Test Cover gives an algorithm with running time O(2O(k2)(m + n)O(1)). We believe such an approach could also be useful for other covering problems. [ABSTRACT FROM AUTHOR]

We study a general class of problems called F-DELETION problems. In an F-DELETION problem, we are asked whether a subset of at most k vertices can be deleted from a graph G such that the resulting graph does not contain as a minor any graph from the family F of forbidden minors. We study the problem parameterized by k, using p-F-DELETION to refer to the parameterized version of the problem. We obtain a number of algorithmic results on the p-F-DELETION problem when F contains a planar graph. We give a linear vertex kernel on graphs excluding t-claw K1,t, the star with t leaves, as an induced subgraph, where t is a fixed integer and an approximation algorithm achieving an approximation ratio of O(log3/2 OPT), where OPT is the size of an optimal solution on general undirected graphs. Finally, we obtain polynomial kernels for the case when F only contains graph θc as a minor for a fixed integer c. The graph θc consists of two vertices connected by c parallel edges. Even though this may appear to be a very restricted class of problems it already encompasses well-studied problems such as VERTEX COVER, FEEDBACK VERTEX SET, and DIAMOND HITTING SET. The generic kernelization algorithm is based on a nontrivial application of protrusion techniques, previously used only for problems on topological graph classes. [ABSTRACT FROM AUTHOR]

We obtain asymptotically tight algorithmic bounds for Max-Cut and Edge Dominating Set problems on graphs of bounded clique-width. We show that on an n-vertex graph of clique-width t both problems (1) cannot be solved in time f(t)no(t) for any function f of t unless exponential time hypothesis fails, and (2) can be solved in time nO(t). [ABSTRACT FROM AUTHOR]

The c-pumpkin is the graph with two vertices linked by c ≥ 1 parallel edges. A c-pumpkin-model in a graph G is a pair {A, B} of disjoint subsets of vertices of G, each inducing a connected subgraph of G, such that there are at least c edges in G between A and B. We focus on hitting and packing c-pumpkin-models in a given graph in the realm of approximation algorithms and parameterized algorithms. We give a fixed-parameter tractable (FPT) algorithm running in time 2...(κ)(n...(1) deciding, for any fixed c ≥ 1, whether all c-pumpkin-models can be hit by at most k vertices. This generalizes known single-exponential FPT algorithms for Vertex Cover and Feedback Vertex SET, which correspond to the cases c = 1, 2 respectively. Finally, we present an ...(log n)-approximation algorithm for both the problems of hitting all c-pumpkin-models with a smallest number of vertices and packing a maximum number of vertex-disjoint c-pumpkin-models. [ABSTRACT FROM AUTHOR]