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For a fixed finite collection of graphs F, the F-M-DELETION problem is as follows: given an n-vertex input graph G, find the minimum number of vertices that intersect all minor models in G of the graphs in F. by Courcelle's Theorem, this problem can be solved in time fF(tw) - n°(1), where tw is the treewidth of G for some function ƒ depending on F. In a recent series of articles, we have initiated the program of optimizing asymptotically the function ff. Here we provide an algorithm showing that fF(tw) = 2° (tw.log tw) for every collection F. Prior to this work, the best known function ƒ was double- exponential in tw. In particular, our algorithm vastly extends the results of Jansen, Lokshtanov, and Saurabh [Proc. of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, 2014, pp. 1802-1811] for the particular case F = {K5, K3,3} and of Kociumaka and Pilipczuk [Algorithmica, 81 (2019), pp. 3655-3691] for graphs of bounded genus, and answers an open problem posed by Cygan et al. [Inform. Comput., 256 (2017), pp. 62-82]. We combine several ingredients such as the machinery of boundaried graphs in dynamic programming via representatives, the Flat Wall Theorem, bidimensionality, the irrelevant vertex technique, treewidth modulators, and protrusion replacement. Together with our previous results providing single-exponential algorithms for particular collections F [J. Baste, I. Sau, and D. M. Thilikos, Theoret. Comput. Sci., 814 (2020), pp. 135-152] and general lower bounds [J. Baste, I. Sau, and D. M. Thilikos, J. Comput. Syst. Sci., 109 (2020), pp. 56-77], our algorithm yields the following complexity dichotomy when F = {H} contains a single connected graph H, assuming the Exponential Time Hypothesis: ƒH(tw) = 2Θ(tw) if H is a contraction of the chair or the banner, and fH(tw) = 2Θ(tw-log tw) otherwise. [ABSTRACT FROM AUTHOR]

In the Directed Disjoint Paths problem, we are given a digraph D and a set of requests { (s 1 , t 1) , ... , (s k , t k) } , and the task is to find a collection of pairwise vertex-disjoint paths { P 1 , ... , P k } such that each P i is a path from s i to t i in D. This problem is NP -complete for fixed k = 2 and W [1]-hard with parameter k in DAGs. A few positive results are known under restrictions on the input digraph, such as being planar or having bounded directed tree-width, or under relaxations of the problem, such as allowing for vertex congestion. Positive results are scarce, however, for general digraphs. In this article we propose a novel global congestion metric for the problem: we only require the paths to be "disjoint enough", in the sense that they must behave properly not in the whole graph, but in an unspecified part of size prescribed by a parameter. Namely, in the Disjoint Enough Directed Paths problem, given an n -vertex digraph D , a set of k requests, and non-negative integers d and s , the task is to find a collection of paths connecting the requests such that at least d vertices of D occur in at most s paths of the collection. We study the parameterized complexity of this problem for a number of choices of the parameter, including the directed tree-width of D. Among other results, we show that the problem is W [1]-hard in DAGs with parameter d and, on the positive side, we give an algorithm in time O (n d + 2 ⋅ k d ⋅ s) and a kernel of size d ⋅ 2 k − s ⋅ ( k s ) + 2 k in general digraphs. This latter result has consequences for the Steiner Network problem: we show that it is FPT parameterized by the number k of terminals and p , where p = n − q and q is the size of the solution. [ABSTRACT FROM AUTHOR]

For a finite collection of graphs F, the F-M-DELETION problem consists in, given a graph G and an integer k, deciding whether there exists S ⊆ V (G) with |S| ≤ k such that G\S does not contain any of the graphs in F as a minor. We are interested in the parameterized complexity of F-M-Deletion when the parameter is the treewidth of G, denoted by tw. Our objective is to determine, for a fixed F, the smallest function fF such that F-M-DELETION can be solved in time fF(tw) . nO(1) on n-vertex graphs. We prove that fF(tw) = 2²O(tw.logtw) for every collection F, that fF(tw) = 2O(tw.log tw) if F contains a planar graph, and that fF(tw) = 2O(tw) if in addition the input graph G is planar or embedded in a surface. We also consider the version of the problem where the graphs in F are forbidden as topological minors, called F-TM-Deletion. We prove similar results for this problem, except that in the last two algorithms, instead of requiring F to contain a planar graph, we need it to contain a subcubic planar graph. This is the first of a series of articles on this topic. [ABSTRACT FROM AUTHOR]

For a finite fixed collection of graphs F , the F -M-Deletion problem consists in, given a graph G and an integer k , decide whether there exists S ⊆ V (G) with | S | ≤ k such that G ∖ S does not contain any of the graphs in F as a minor. We provide lower bounds under the ETH on the smallest function f F such that F -M-Deletion can be solved in time f F (tw) ⋅ n O (1) on n -vertex graphs, where tw denotes the treewidth of G. We first prove that for any F containing connected graphs of size at least two, f F (tw) = 2 Ω (tw) , even if G is planar. Our main result is that if F contains a single connected graph H that is either P 5 or is not a minor of the b a n n e r , then f F (tw) = 2 Ω (tw ⋅ log ⁡ tw). [ABSTRACT FROM AUTHOR]

For a finite collection of graphs F , the F -M-Deletion (resp. F -TM-Deletion) problem consists in, given a graph G and an integer k , decide whether there exists S ⊆ V (G) with | S | ≤ k such that G ∖ S does not contain any of the graphs in F as a minor (resp. topological minor). We are interested in the parameterized complexity of both problems when the parameter is the treewidth of G , denoted by tw , and specifically in the cases where F contains a single connected planar graph H. We present algorithms running in time 2 O (tw) ⋅ n O (1) , called single-exponential , when H is either P 3 , P 4 , C 4 , the paw , the chair , and the banner for both { H } -M-Deletion and { H } -TM-Deletion , and when H = K 1 , i , with i ≥ 1 , for { H } -TM-Deletion. Some of these algorithms use the rank-based approach introduced by Bodlaender et al. (2015) [7]. This is the second of a series of articles on this topic, and the results given here together with other ones allow us, in particular, to provide a tight dichotomy on the complexity of { H } -M-Deletion in terms of H. [ABSTRACT FROM AUTHOR]

We study the minimum diameter spanning tree problem under the reload cost model (Diameter‐Tree for short) introduced by Wirth and Steffan. In this problem, given an undirected edge‐colored graph G, reload costs on a path arise at a node where the path uses consecutive edges of different colors. The objective is to find a spanning tree of G of minimum diameter with respect to the reload costs. We initiate a systematic study of the parameterized complexity of the Diameter‐Tree problem by considering the following parameters: the cost of a solution, and the treewidth and the maximum degree Δ of the input graph. We prove that Diameter‐Tree is para‐NP‐hard for any combination of two of these three parameters, and that it is FPT parameterized by the three of them. We also prove that the problem can be solved in polynomial time on cactus graphs. This result is somehow surprising since we prove Diameter‐Tree to be NP‐hard on graphs of treewidth two, which is best possible as the problem can be trivially solved on forests. When the reload costs satisfy the triangle inequality, Wirth and Steffan proved that the problem can be solved in polynomial time on graphs with Δ = 3, and Galbiati proved that it is NP‐hard if Δ = 4. Our results show, in particular, that without the requirement of the triangle inequality, the problem is NP‐hard if Δ = 3, which is also best possible. Finally, in the case where the reload costs are polynomially bounded by the size of the input graph, we prove that Diameter‐Tree is in XP and W[1]‐hard parameterized by the treewidth plus Δ. [ABSTRACT FROM AUTHOR]

Very recently, Thomassé et al. (2017) have given an FPT algorithm for Weighted Independent Set in bull-free graphs parameterized by the weight of the solution, running in time 2 O ( k 5 ) ⋅ n 9 . In this article we improve this running time to 2 O ( k 2 ) ⋅ n 7 . As a byproduct, we also improve the previous Turing-kernel for this problem from O ( k 5 ) to O ( k 2 ) . Furthermore, for the subclass of bull-free graphs without holes of length at most 2 p − 1 for p ≥ 3 , we speed up the running time to 2 O ( k ⋅ k 1 p − 1 ) ⋅ n 7 . As p grows, this running time is asymptotically tight in terms of k , since we prove that for each integer p ≥ 3 , Weighted Independent Set cannot be solved in time 2 o ( k ) ⋅ n O ( 1 ) in the class of { bull , C 4 , … , C 2 p − 1 } -free graphs unless the ETH fails. [ABSTRACT FROM AUTHOR]

A proper k-coloring of a graph G is a partition c = ( S i ) i ∈ [ 1 , k ] of V ( G ) into k stable sets S 1 , … , S k . Given a weight function w : V ( G ) → R + , the weight of a color S i is defined as w ( i ) = max v ∈ S i ⁡ w ( v ) and the weight of a coloring c as w ( c ) = ∑ i = 1 k w ( i ) . Guan and Zhu [Inf. Process. Lett., 1997] defined the weighted chromatic number of a pair ( G , w ), denoted by σ ( G , w ) , as the minimum weight of a proper coloring of G . For a positive integer r , they also defined σ ( G , w ; r ) as the minimum of w ( c ) among all proper r -colorings c of G . The complexity of determining σ ( G , w ) when G is a tree was open for almost 20 years, until Araújo et al. [SIAM J. Discrete Math., 2014] recently proved that the problem cannot be solved in time n o ( log ⁡ n ) on n -vertex trees unless the Exponential Time Hypothesis (ETH) fails. The objective of this article is to provide hardness results for computing σ ( G , w ) and σ ( G , w ; r ) when G is a tree or a forest, relying on complexity assumptions weaker than the ETH. Namely, we study the problem from the viewpoint of parameterized complexity, and we assume the weaker hypothesis FPT ≠ W [ 1 ] . Building on the techniques of Araújo et al. , we prove that when G is a forest, computing σ ( G , w ) is W [ 1 ] -hard parameterized by the size of a largest connected component of G , and that computing σ ( G , w ; r ) is W [ 2 ] -hard parameterized by r . Our results rule out the existence of FPT algorithms for computing these invariants on trees or forests for many natural choices of the parameter. [ABSTRACT FROM AUTHOR]

The k-restricted edge-connectivity of a graph G , denoted by λ k ( G ) , is defined as the minimum size of an edge set whose removal leaves exactly two connected components each containing at least k vertices. This graph invariant, which can be seen as a generalization of a minimum edge-cut, has been extensively studied from a combinatorial point of view. However, very little is known about the complexity of computing λ k ( G ) . Very recently, in the parameterized complexity community the notion of good edge separation of a graph has been defined, which happens to be essentially the same as the k -restricted edge-connectivity. Motivated by the relevance of this invariant from both combinatorial and algorithmic points of view, in this article we initiate a systematic study of its computational complexity, with special emphasis on its parameterized complexity for several choices of the parameters. We provide a number of NP -hardness and W [1]-hardness results, as well as FPT -algorithms. [ABSTRACT FROM AUTHOR]

In the Red–Blue Dominating Set problem, we are given a bipartite graph G = ( V B ∪ V R , E ) and an integer k , and asked whether G has a subset D ⊆ V B of at most k “blue” vertices such that each “red” vertex from V R is adjacent to a vertex in D . We provide the first explicit linear kernel for this problem on planar graphs, of size at most 43 k . [ABSTRACT FROM AUTHOR]

Several algorithmic meta-theorems on kernelization have appeared in the last years, starting with the result of Bodlaender et al. [(Meta) kernelization, in Proceedings of the 50th IEEE Symposium on Foundations of Computer Science (FOCS), IEEE Computer Society, 2009, pp. 629– 638] on graphs of bounded genus, then generalized by Fomin et al. [Bidimensionality and kernels, in Proceedings of the 21st ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, Philadephia, 2010, pp. 503–510] to graphs excluding a fixed minor, and by Kim et al. [Linear kernels and single-exponential algorithms via protrusion decompositions, in Proceedings of the 40th International Colloquium on Automata, Languages and Programming (ICALP), Lecture Notes in Comput. Sci., 7965 (2013), pp. 613–624] to graphs excluding a fixed topological minor. Typically, these results guarantee the existence of linear or polynomial kernels on sparse graph classes for problems satisfying some generic conditions, but, mainly due to their generality, it is not clear how to derive from them constructive kernels with explicit constants. In this paper, we make a step toward a fully constructive meta-kernelization theory on sparse graphs. Our approach is based on a more explicit protrusion replacement machinery that, instead of expressibility in counting monadic second order logic, uses dynamic programming, which allows us to find an explicit upper bound on the size of the derived kernels. We demonstrate the usefulness of our techniques by providing the first explicit linear kernels for r-Dominating Set and r-Scattered Set on apex-minor-free graphs, and for PLANAR-F-DELETION on graphs excluding a fixed (topological) minor in the case where all the graphs in F are connected. [ABSTRACT FROM AUTHOR]

For some years it was believed that for “connectivity” problems such as Hamiltonian Cycle , algorithms running in time 2 O ( tw ) ⋅ n O ( 1 ) – called single-exponential – existed only on planar and other topologically constrained graph classes, where tw stands for the treewidth of the n -vertex input graph. This was recently disproved by Cygan et al. [ 3 , FOCS 2011], Bodlaender et al. [ 1 , ICALP 2013], and Fomin et al. [ 11 , SODA 2014], who provided single-exponential algorithms on general graphs for most connectivity problems that were known to be solvable in single-exponential time on topologically constrained graphs. In this article we further investigate the role of planarity in connectivity problems parameterized by treewidth, and convey that several problems can indeed be distinguished according to their behavior on planar graphs. Known results from the literature imply that there exist problems, like Cycle Packing , that cannot be solved in time 2 o ( tw log ⁡ tw ) ⋅ n O ( 1 ) on general graphs but that can be solved in time 2 O ( tw ) ⋅ n O ( 1 ) when restricted to planar graphs. Our main contribution is to show that there exist natural problems that can be solved in time 2 O ( tw log ⁡ tw ) ⋅ n O ( 1 ) on general graphs but that cannot be solved in time 2 o ( tw log ⁡ tw ) ⋅ n O ( 1 ) even when restricted to planar graphs. Furthermore, we prove that Planar Cycle Packing and Planar Disjoint Paths cannot be solved in time 2 o ( tw ) ⋅ n O ( 1 ) . The mentioned negative results hold unless the ETH fails. We feel that our results constitute a first step in a subject that can be further exploited. [ABSTRACT FROM AUTHOR]

Given a graph G on n vertices and two integers k and d , the Contraction(vc) problem asks whether one can contract at most k edges to reduce the vertex cover number of G by at least d. Recently, Lima et al. [JCSS 2021] proved that Contraction(vc) admits an XP algorithm running in time f (d) ⋅ n O (d). They asked whether this problem is FPT under this parameterization. In this article, we prove that: (i) Contraction(vc) is W [1]- hard parameterized by k + d. Moreover, unless the ETH fails, the problem does not admit an algorithm running in time f (k + d) ⋅ n o (k + d) for any function f. This answers negatively the open question stated in Lima et al. [JCSS 2021]. (ii) Contraction(vc) is NP - hard even when k = d. (iii) Contraction(vc) can be solved in time 2 O (d) ⋅ n k − d + O (1). This improves the algorithm of Lima et al. [JCSS 2021], and shows that when k = d , Contraction(vc) is FPT parameterized by d (or by k). [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]

Minor Containment is a fundamental problem in Algorithmic Graph Theory used as a subroutine in numerous graph algorithms. A model of a graph H in a graph G is a set of disjoint connected subgraphs of G indexed by the vertices of H, such that if { u, v} is an edge of H, then there is an edge of G between components C and C. A graph H is a minor of G if G contains a model of H as a subgraph. We give an algorithm that, given a planar n-vertex graph G and an h-vertex graph H, either finds in time $\mathcal{O}(2^{\mathcal{O}(h)} \cdot n +n^{2}\cdot\log n)$ a model of H in G, or correctly concludes that G does not contain H as a minor. Our algorithm is the first single-exponential algorithm for this problem and improves all previous minor testing algorithms in planar graphs. Our technique is based on a novel approach called partially embedded dynamic programming. [ABSTRACT FROM AUTHOR]

Abstract: In this article we study the parameterized complexity of problems consisting in finding degree-constrained subgraphs, taking as the parameter the number of vertices of the desired subgraph. Namely, given two positive integers d and k, we study the problem of finding a d-regular (induced or not) subgraph with at most k vertices and the problem of finding a subgraph with at most k vertices and of minimum degree at least d. The latter problem is a natural parameterization of the d-girth of a graph (the minimum order of an induced subgraph of minimum degree at least d). We first show that both problems are fixed-parameter intractable in general graphs. More precisely, we prove that the first problem is -hard using a reduction from Multi-Color Clique. The hardness of the second problem (for the non-induced case) follows from an easy extension of an already known result. We then provide explicit fixed-parameter tractable (FPT) algorithms to solve these problems in graphs with bounded local treewidth and graphs with excluded minors, using a dynamic programming approach. Although these problems can be easily defined in first-order logic, hence by the results of Frick and Grohe (2001) are FPT in graphs with bounded local treewidth and graphs with excluded minors, the dependence on k of our algorithms is considerably better than the one following from Frick and Grohe (2001) . [Copyright &y& Elsevier]

Abstract: We present subexponential parameterized algorithms on planar graphs for a family of problems of the following shape: given a graph, find a connected (induced) subgraph with bounded maximum degree and with maximum number of edges (or vertices). These problems are natural generalisations of the Longest Path problem. Our approach uses bidimensionality theory combined with novel dynamic programming techniques over branch decompositions of the input graph. These techniques can be applied to a more general family of problems that deal with finding connected subgraphs under certain degree constraints. [Copyright &y& Elsevier]

Abstract: We present subexponential parameterized algorithms on planar graphs for a family of problems that consist in, given a graph G, finding a connected (induced) subgraph H with bounded maximum degree, while maximising the number of edges (or vertices) of H. These problems are natural generalisations of Longest Path. Our approach uses bidimensionality theory combined with novel dynamic programming techniques over branch decompositions of the input graph. These techniques can be applied to a more general family of problems that deal with finding connected subgraphs under certain degree constraints. [Copyright &y& Elsevier]

For a fixed graph H , the H -IS-Deletion problem asks, given a graph G , for the minimum size of a set S ⊆ V (G) such that G ∖ S excludes H as an induced subgraph. We are interested in determining, for a fixed H , the smallest function f H (t) such that H -IS-Deletion can be solved in time f H (t) ⋅ n O (1) assuming the Exponential Time Hypothesis, where t and n denote the treewidth and the number of vertices of G , respectively. We show that f H (t) = 2 O (t h − 2) for every H on h ≥ 3 vertices, and that f H (t) = 2 O (t) if H is a clique or an independent set. When H deviates slightly from a clique, the function f H (t) suffers a sharp jump: if H is obtained from a clique of size h by removing one edge, then f H (t) = 2 Θ (t h − 2). Moreover, f H (t) = 2 Ω (t h) when H = K h , h , answering a question of Pilipczuk [MFCS 2011]. [ABSTRACT FROM AUTHOR]