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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]

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 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]

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]

We show that for a fixed r, the number of maximal r-regular induced subgraphs in any graph with n vertices is upper bounded by O(cn), where c is a positive constant strictly less than 2. This bound generalizes the well-known result of Moon and Moser, who showed an upper bound of 3n/3 on the number of maximal independent sets of a graph on n vertices. We complement this upper bound result by obtaining an almost tight lower bound on the number of (possible) maximal r-regular induced subgraphs possible in a graph on n vertices. Our upper bound results are algorithmic. That is, we can enumerate all the maximal r-regular induced subgraphs in time (cnnO(1)). A related question is that of finding a maximum-sized r-regular induced subgraph. Given a graph G =(V,E) on n vertices, the Maximum r-Regular Induced Subgraph (M-r-RIS) problem asks for a maximum-sized subset of vertices, R ⊆ V, such that the induced subgraph on R is r-regular. As a by-product of the enumeration algorithm, we get a O (cn) time algorithm for this problem for any fixed constant r, where c is a positive constant strictly less than 2. Furthermore, we use the techniques and results obtained in the paper to obtain improved exact algorithms for a special case of the Induced Subgraph Isomorphism problem, namely, the Induced r-Regular Subgraph Isomorphism problem, where r is a constant, the δ-Separating Maximum Matching problem and the Efficient Edge Dominating Set problem. [ABSTRACT FROM AUTHOR]

We introduce a generic approach for counting subgraphs in a graph. The main idea is to relate counting subgraphs to counting graph homomorphisms. This approach provides new algorithms and unifies several well-known results in algorithms and combinatorics, including the recent algorithm of Björklund, Husfeldt, and Koivisto for computing the chromatic polynomial, the classical algorithm of Kohn et al. for counting Hamiltonian cycles, Ryser's formula for counting perfect matchings of a bipartite graph, and color-coding-based algorithms of Alon, Yuster, and Zwick. By combining our method with known combinatorial bounds, ideas from succinct data structures, partition functions, and the color coding technique, we obtain the following new results. The number of optimal bandwidth permutations of a graph on n vertices excluding a fixed graph as a minor can be computed in time 2n+o(n)), in particular, in time O(2nn³) for trees and in time 2n+O(√n) for planar graphs. Counting all maximum planar subgraphs, subgraphs of bounded genus, or more generally subgraphs excluding a fixed graph M as a minor can be done in 2O(n) time. Counting all subtrees with a given maximum degree (a generalization of counting Hamiltonian paths) of a given graph can be done in time 2O(n). A generalization of Ryser's formula is, Let G be a graph with an independent set of size I. Then the number of perfect matchings in G can be found in time O(2n-ℓn³). Let H be a graph class excluding a fixed graph M as a minor. Then the maximum number of vertex disjoint subgraphs from H in a graph G on n vertices can be found in time 2O(n). In order to show this, we prove that there exists a constant CM depending only on M such that the number of nonisomorphic n-vertex graphs in H is at most cnM. Let F be a k-vertex graph of treewidth t and let G be an n-vertex graph. A subgraph of G isomorphic to F (if one exists) can be found in O(4.32k⋅k⋅t⋅nt+1) expected time using O(log k ⋅ nt+1 space. [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 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 show that Edge Dominating Set, Hamiltonian Cycle, and Graph Coloring are W[1]-hard parameterized by clique-width. It was an open problem, explicitly mentioned in several papers, whether any of these problems is fixed parameter tractable when parameterized by the clique-width, that is, solvable in time g(k) ∙ nO(1) on n-vertex graphs of clique-width k, where g is some function of k only. Our results imply that the running time O(nf(k)) of many clique-widthbased algorithms is essentially the best we can hope for (up to a widely believed assumption from parameterized complexity, namely FPT ≠ W[1]). [ABSTRACT FROM AUTHOR]

We show that Edge Dominating Set, Hamiltonian Cycle, and Graph Coloring are W[1]-hard parameterized by clique-width. It was an open problem, explicitly mentioned in several papers, whether any of these problems is fixed parameter tractable when parameterized by the clique-width, that is, solvable in time g(k) · nO(1) on n-vertex graphs of clique-width k, where g is some function of k only. Our results imply that the running time O(nf(k)) of many clique-widthbased algorithms is essentially the best we can hope for (up to a widely believed assumption from parameterized complexity, namely FPT ≠ W[1]). [ABSTRACT FROM AUTHOR]

We show that Edge Dominating Set, Hamiltonian Cycle, and Graph Coloring are W[1]-hard parameterized by clique-width. It was an open problem, explicitly mentioned in several papers, whether any of these problems is fixed parameter tractable when parameterized by the clique-width, that is, solvable in time g(k) · nO(1) on η-vertex graphs of clique-width k, where g is some function of k only. Our results imply that the running time O(nf(k)) of many clique-widthbased algorithms is essentially the best we can hope for (up to a widely believed assumption from parameterized complexity, namely FPT ≠ W[1]). [ABSTRACT FROM AUTHOR]