Feedback Vertex Set Problem Research Articles

Classifying subset feedback vertex set for H-free graphs

In the Feedback Vertex Set problem, we aim to find a small set S of vertices in a graph intersecting every cycle. The Subset Feedback Vertex Set problem requires S to intersect only those cycles that include a vertex of some specified set T. We also consider the Weighted Subset Feedback Vertex Set problem, where each vertex u has weight w(u)>0 and we ask that S has small weight. By combining known NP-hardness results with new polynomial-time results we prove full complexity dichotomies for Subset Feedback Vertex Set and Weighted Subset Feedback Vertex Set for H-free graphs, that is, graphs that do not contain a graph H as an induced subgraph.

Preprocessing to reduce the search space: Antler structures for feedback vertex set

The goal of this paper is to open up a new research direction aimed at understanding the power of preprocessing in speeding up algorithms that solve NP-hard problems exactly. We explore this direction for the classic Feedback Vertex Set problem on undirected graphs, leading to a new type of graph structure called antler decomposition, which identifies vertices that belong to an optimal solution. It is an analogue of the celebrated crown decomposition which has been used for Vertex Cover. We develop the graph structure theory around such decompositions and develop fixed-parameter tractable algorithms to find them, parameterized by the number of vertices for which they witness presence in an optimal solution. This reduces the search space of fixed-parameter tractable algorithms parameterized by the solution size that solve Feedback Vertex Set.

Threshold-Based Responsive Simulated Annealing for Directed Feedback Vertex Set Problem

As a classical NP-hard problem and the topic of the PACE 2022 competition, the directed feedback vertex set problem (DFVSP) aims to find a minimum subset of vertices such that, when vertices in the subset and all their adjacent edges are removed from the directed graph, the remainder graph is acyclic. In this paper, we propose a threshold-based responsive simulated annealing algorithm called TRSA for solving DFVSP. First, we simplify the problem instances with two new reduction rules proposed in this paper and eight reduction rules from the literature. Then, based on a new solution representation, TRSA solves DFVSP with a fast local search procedure featured by a swap-based neighborhood structure and three neighborhood acceleration strategies. Finally, all these strategies are incorporated into a threshold-based responsive simulated annealing framework. Computational experiments on 140 benchmark instances show that TRSA is highly competitive compared to the state-of-the-art methods. Specifically, TRSA can improve the best known results for 53 instances, while matching the best known results for 79 ones. Furthermore, some important features of TRSA are analyzed to identify its success factors.

Kernelization for feedback vertex set via elimination distance to a forest

We study efficient preprocessing for the undirected Feedback Vertex Set problem, a fundamental problem in graph theory which asks for a minimum-sized vertex set whose removal yields an acyclic graph. More precisely, we aim to determine for which parameterizations this problem admits a polynomial kernel. While a characterization is known for the related Vertex Cover problem based on the recently introduced notion of bridge-depth, it remained an open problem whether this could be generalized to Feedback Vertex Set. The answer turns out to be negative; the existence of polynomial kernels for structural parameterizations for Feedback Vertex Set is governed by the elimination distance to a forest. Under the standard assumption NP⁄⊆coNP/poly, we prove that for any minor-closed graph class G, Feedback Vertex Set parameterized by the size of a modulator to G has a polynomial kernel if and only if G has bounded elimination distance to a forest. This captures and generalizes all existing kernels for structural parameterizations of the Feedback Vertex Set problem. [Display omitted]

Exact algorithms for restricted subset feedback vertex set in chordal and split graphs

The Restricted Subset Feedback Vertex Set problem (R-SFVS) takes a graph G=(V,E), a terminal set T⊆V, and an integer k as the input. The task is to determine whether there exists a subset S⊆V∖T of at most k vertices, after deleting which no terminal in T is contained in a cycle in the remaining graph. R-SFVS is NP-complete even when the input graph is restricted to split graphs. In this paper, we mainly show that R-SFVS in chordal and split graphs can be solved in O(1.1550|V|) time and exponential space or in O(1.1605|V|) time and polynomial space, significantly improving all previous results. As a by-product, we show that the Maximum Independent Set problem parameterized by the edge clique cover number is fixed-parameter tractable. Furthermore, by using a simple reduction from R-SFVS to Vertex Cover, we obtain an O⁎(1.2738k)-time parameterized algorithm and a tight O(k2)-kernel for R-SFVS in chordal and split graphs.

A 7/3-approximation algorithm for feedback vertex set in tournaments via Sherali–Adams

We study the feedback vertex set problem in tournaments from the polyhedral point of view, and in particular we show that performing just one round of the Sherali–Adams hierarchy gives a relaxation with integrality gap 7/3. This allows us to derive a 7/3-approximation algorithm for the feedback vertex set problem in tournaments that matches the best deterministic approximation guarantee due to Mnich, Williams, and Végh, and is a simplification and runtime improvement of their approach.

A parameterized algorithm for subset feedback vertex set in tournaments

The Subset Feedback Vertex Set problem (SFVS) takes an n-vertex graph G=(V,E), a terminal set T⊆V, and an integer k as the input. The goal is to determine whether there exists a subset S⊆V of at most k vertices whose removal makes no terminal in T contained in a cycle in the remaining graph. When T=V, SFVS degenerates to the classical Feedback Vertex Set problem (FVS). Both SFVS and FVS have been extensively studied in parameterized algorithms. In this paper, we study parameterized algorithms for Subset Feedback Vertex Set in Tournaments (SFVST), i.e., SFVS with the restriction that the input graph is always a tournament. By using the iterative compression method and a novel dynamic programming, we show that SFVST can be solved in 2k+o(k)nO(1) time, improving the bound obtained from 3-Hitting Set.

First-order Logic with Connectivity Operators

First-order logic (FO) can express many algorithmic problems on graphs, such as the independent set and dominating set problem parameterized by solution size. However, FO cannot express the very simple algorithmic question whether two vertices are connected. We enrich FO with connectivity predicates that are tailored to express algorithmic graph problems that are commonly studied in parameterized algorithmics. By adding the atomic predicates conn k ( x,y,z_1,..., z k ) that hold true in a graph if there exists a path between (the valuations of) x and y after (the valuations of) z 1 ,..., z k have been deleted, we obtain separator logic FO + conn. We show that separator logic can express many interesting problems, such as the feedback vertex set problem and elimination distance problems to first-order definable classes. Denote by FO + conn k the fragment of separator logic that is restricted to connectivity predicates with at most k + 2 variables (that is, at most k deletions), we show that FO + conn k + 1 is strictly more expressive than FO + conn k for all k ≥ 0 . We then study the limitations of separator logic and prove that it cannot express planarity, and, in particular, not the disjoint paths problem. We obtain the stronger disjoint-paths logic FO + DP by adding the atomic predicates disjoint-paths k [( x 1 , y 1 ),..., ( x k , y k ) that evaluate to true if there are internally vertex-disjoint paths between (the valuations of) x i and y i for all 1 ≤ i ≤ k . Disjoint-paths logic can express the disjoint paths problem, the problem of (topological) minor containment, the problem of hitting (topological) minors, and many more. Again, we show that the fragments FO + DP k that use predicates for at most k disjoint paths form a strict hierarchy of expressiveness. Finally, we compare the expressive power of the new logics with that of transitive-closure logics and monadic second-order logic.

Searching for a Feedback Vertex Set with the link-cut tree

In this paper we study the Feedback Vertex Set problem of an undirected graph. This problem has several practical applications and is know to be NP-Complete. We consider this problem from its dual formulation which consists in finding a largest subset of vertexes whose induced subgraph is a forest, i.e., contains no cycles. Our main goal is to explore how the link-cut tree data structure can be used in this context. This data structure makes it possible to identify and resolve cycles quickly. This property is useful for this problem and allows us to support a simple simulated annealing algorithm. We used the PACE 2016 dataset to test the performance of the resulting algorithm. The worst approximation ratio obtained was about 1.21. The algorithm is very fast. The longest execution time was 103 seconds. To justify the importance of escaping local minimums we also present a simple greedy algorithm.

Expansion Lemma—Variations and Applications to Polynomial-Time Preprocessing

In parameterized complexity, it is well-known that a parameterized problem is fixed-parameter tractable if and only if it has a kernel—an instance equivalent to the input instance, whose size is just a function of the parameter. The size of the kernel can be exponential or worse, resulting in a quest for fixed-parameter tractable problems with polynomial-sized kernels. The developments in machinery (showing lower bounds for the sizes of the kernels) have led researchers to question what are the asymptotically optimum sizes for the kernels of fixed-parameter tractable problems. In this article, we surveyed a tool called expansion lemma that helps in reducing the size of the kernel. Its early origin was in the form of crown decomposition, i.e., to obtain the linear kernel for the Vertex Cover problem; the specific lemma was identified as the tool behind the optimal O(k2) kernel for the undirected feedback vertex set problem. Since then, several variations and extensions of the tool have been discovered. We surveyed them along with their applications in this article.

Fixed parameterized algorithms for generalized feedback vertex set problems

A graph is an r-pseudoforest if every connected component of it has a feedback edge set of size at most r. A graph is a d-quasi-forest if every connected component of it has a feedback vertex set of size at most d. The r-Pseudoforest Deletion problem (d-Quasi-Forest Deletion problem) asks to delete a minimum number of vertices to get an r-pseudoforest (a d-quasi-forest, respectively). The well-studied feedback vertex set problem is the special case of r-Pseudoforest Deletion (d-Quasi-Forest Deletion, respectively) in which r=0 (d=0, respectively).We provide an improved FPT algorithm and a smaller kernel for r-Pseudoforest Deletion when parameterized by the solution size (when r is fixed). For d-Quasi-Forest Deletion, we show that it is FPT as well when parameterized by d and the solution size.

Dynamic thresholding search for the feedback vertex set problem.

Given a directed graph G = (V, E), a feedback vertex set is a vertex subset C whose removal makes the graph G acyclic. The feedback vertex set problem is to find the subset C* whose cardinality is the minimum. As a general model, this problem has a variety of applications. However, the problem is known to be NP-hard, and thus computationally challenging. To solve this difficult problem, this article develops an iterated dynamic thresholding search algorithm, which features a combination of local optimization, dynamic thresholding search, and perturbation. Computational experiments on 101 benchmark graphs from various sources demonstrate the advantage of the algorithm compared with the state-of-the-art algorithms, by reporting record-breaking best solutions for 24 graphs, equally best results for 75 graphs, and worse best results for only two graphs. We also study how the key components of the algorithm affect its performance of the algorithm.

Constant factor approximation for tracking paths and fault tolerant feedback vertex set

Consider a vertex-weighted graph G with a source s and a target t. Tracking Paths requires finding a minimum weight set of vertices (trackers) such that the sequence of trackers in each path from s to t is unique. In this work, we derive a factor 6-approximation algorithm for Tracking Paths in weighted graphs and a factor 4-approximation algorithm if the input is unweighted. This is the first constant factor approximation for this problem. While doing so, we also study approximation of the closely related r-Fault Tolerant Feedback Vertex Set problem. There, for a fixed integer r and a given vertex-weighted graph G, the task is to find a minimum weight set of vertices intersecting every cycle of G in at least r+1 vertices. We give a factor O(r) approximation algorithm for r-Fault Tolerant Feedback Vertex Set if r is a constant.

Detecting Feedback Vertex Sets of Size k in O ⋆ (2.7 k ) Time

In the Feedback Vertex Set (FVS) problem, one is given an undirected graph G and an integer k , and one needs to determine whether there exists a set of k vertices that intersects all cycles of G (a so-called feedback vertex set). Feedback Vertex Set is one of the most central problems in parameterized complexity: It served as an excellent testbed for many important algorithmic techniques in the field such as Iterative Compression [Guo et al. (JCSS’06)], Randomized Branching [Becker et al. (J. Artif. Intell. Res’00)] and Cut&Count [Cygan et al. (FOCS’11)]. In particular, there has been a long race for the smallest dependence f(k) in run times of the type O ⋆ (f(k)) , where the O ⋆ notation omits factors polynomial in n . This race seemed to have reached a conclusion in 2011, when a randomized O ⋆ (3 k ) time algorithm based on Cut&Count was introduced. In this work, we show the contrary and give a O ⋆ (2.7 k ) time randomized algorithm. Our algorithm combines all mentioned techniques with substantial new ideas: First, we show that, given a feedback vertex set of size k of bounded average degree, a tree decomposition of width (1-Ω (1))k can be found in polynomial time. Second, we give a randomized branching strategy inspired by the one from [Becker et al. (J. Artif. Intell. Res’00)] to reduce to the aforementioned bounded average degree setting. Third, we obtain significant run time improvements by employing fast matrix multiplication.

Parameterized algorithms for generalizations of Directed Feedback Vertex Set

The Directed Feedback Vertex Set (DFVS) problem takes as input a directed graph G and seeks a smallest vertex set S that hits all cycles in G. This is one of Karp’s 21 NP-complete problems. Resolving the parameterized complexity status of DFVS was a long-standing open problem until Chen et al. (2008) showed its fixed-parameter tractability via a 4kk!nO(1)-time algorithm, where k=|S|.Here we show fixed-parameter tractability of two generalizations of DFVS: •Find a smallest vertex set S such that every strong component of G−S has size at most s: we give an algorithm solving this problem in time 4k(ks+k+s)!⋅nO(1). This generalizes an algorithm by Xiao (2017) for the undirected version of the problem.•Find a smallest vertex set S such that every non-trivial strong component of G−S is 1-out-regular: we give an algorithm solving this problem in time 2O(k3)⋅nO(1). We also solve the corresponding arc versions of these problems by fixed-parameter algorithms.

Stochastic simulated annealing for directed feedback vertex set

The minimum feedback vertex set problem consists in finding the minimum set of vertices that should be removed in order to make the graph acyclic. This is a well known NP-hard optimization problem with applications in various fields, such as VLSI chip design, bioinformatics and transaction processing. In this paper, we explore the complementary problem in directed graphs, i.e., how to construct the maximum directed acyclic graph (max-DAG). We show that the max-DAG problem is Poly-APX complete, which implies that even trying to obtain approximation algorithms for this problems is likely to be unfeasible. In light of these considerations, we introduce a new algorithmic solution, based on Simulated Annealing (SA), which combines techniques such as kernelization, efficient data-structures, novel heuristics to initialize the search process, a global re-structuring procedure, and a neighbor re-ordering technique to speed-up the local search step. We present an extensive experimental study that validates the key design and implementation choices undertaken in our proposal and compares it to state of the art SA-based solutions Galinier et al. (2013) and Tang et al. (2017). The proposed algorithm provides significant performance gains by obtaining feedback vertex sets up to 13.3× closer to the optimal solution in a wide variety of synthetic and real-world graphs.

On the complexity of singly connected vertex deletion

A digraph D is singly connected if for all ordered pairs of vertices u,v∈V(D), there is at most one path in D from u to v. In this paper, we study the Singly Connected Vertex Deletion (SCVD) problem: Given an n-vertex digraph D and a positive integer k, does there exist a set S⊆V(D) such that |S|≤k and D−S is singly connected? This problem may be seen as a directed counterpart of the (Undirected) Feedback Vertex Set problem, as an undirected graph is singly connected if and only if it is acyclic. SCVD is known to be NP-hard on general digraphs. We study the complexity of SCVD on various classes of digraphs such as tournaments, and various generalisations of tournaments such as digraphs of bounded independence number, in- and out-tournaments and local tournaments. We show that unlike the Feedback Vertex Set on Tournaments (FVST) problem, SCVD is polynomial-time solvable on tournaments. In addition, we show that SCVD is polynomial-time solvable on digraphs of bounded independence number, and on the class of acyclic local tournaments. We also study the parameterized complexity of SCVD, with k as the parameter, on the class of in-tournaments. And we show that on in-tournaments, SCVD admits a fixed-parameter tractable algorithm and a quadratic vertex kernel. We also show that on the class of local tournaments, which is a sub-class of in-tournaments, SCVD admits a linear vertex kernel.

Component Order Connectivity in Directed Graphs

A directed graph D is semicomplete if for every pair x, y of vertices of D, there is at least one arc between x and y. Thus, a tournament is a semicomplete digraph. In the Directed Component Order Connectivity (DCOC) problem, given a digraph D=(V,A) and a pair of natural numbers k and ell , we are to decide whether there is a subset X of V of size k such that the largest strongly connected component in D-X has at most ell vertices. Note that DCOC reduces to the Directed Feedback Vertex Set problem for ell =1. We study the parameterized complexity of DCOC for general and semicomplete digraphs with the following parameters: k, ell ,ell +k and n-ell . In particular, we prove that DCOC with parameter k on semicomplete digraphs can be solved in time O^*(2^{16k}) but not in time O^*(2^{o(k)}) unless the Exponential Time Hypothesis (ETH) fails. The upper bound O^*(2^{16k}) implies the upper bound O^*(2^{16(n-ell )}) for the parameter n-ell . We complement the latter by showing that there is no algorithm of time complexity O^*(2^{o({n-ell })}) unless ETH fails. Finally, we improve (in dependency on ell ) the upper bound of Göke, Marx and Mnich (2019) for the time complexity of DCOC with parameter ell +k on general digraphs from O^*(2^{O(kell log (kell ))}) to O^*(2^{O(klog (kell ))}). Note that Drange, Dregi and van ’t Hof (2016) proved that even for the undirected version of DCOC on split graphs there is no algorithm of running time O^*(2^{o(klog ell )}) unless ETH fails and it is a long-standing problem to decide whether Directed Feedback Vertex Set admits an algorithm of time complexity O^*(2^{o(klog k)}).

Maximum weighted induced forests and trees: new formulations and a computational comparative review

AbstractGiven a graph with a weight associated with each vertex , the maximum weighted induced forest problem (MWIF) consists of encountering a maximum weighted subset of the vertices such that induces a forest. This NP‐hard problem is known to be equivalent to the minimum weighted feedback vertex set problem, which has applicability in a variety of domains. The closely related maximum weighted induced tree problem (MWIT), on the other hand, requires that the subset induces a tree. We propose two new integer programming formulations with an exponential number of constraints and branch‐and‐cut procedures for MWIF. Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when solved by a standard commercial mixed integer programming solver. More specifically, five formulations are compared, two compact ones (i.e., with a polynomial number of variables and constraints) and the three others with an exponential number of constraints. The experiments show that a new formulation for the problem based on directed cutset inequalities for eliminating cycles (DCUT) offers stronger linear relaxation bounds earlier in the search process. The results also indicate that the other new formulation, denoted tree with cycle elimination (TCYC), outperforms those available in the literature when it comes to the average times for proving optimality for the benchmark instances with up to 81 vertices. Additionally, this formulation can achieve much lower average times for solving the larger random instances that can be optimally solved. Furthermore, we show how the formulations for MWIF can be easily extended for MWIT. Such extension allowed us to analyze the difference between the optimal solution values of the two problems when considering different classes of graphs.

Network dismantling on factor graphs: break long loops and spare local structures

A new solution framework for the task of network dismantling is recently developed, based on a two-scale bipartite factor-graph representation of the original graph where local structures are abstracted as factor nodes. This technique leads to advancement of extant dismantling algorithms, among which the belief-propagation decimation (BPD) algorithm has an efficient counterpart (factor BPD, i.e., FBPD) on the factor graph, building upon a mean-field spin-glass theory developed for the underlying long-loop feedback vertex set (FVS) problem. In this paper, I (1) demonstrate the advantage as well as disadvantage of the new factor-graph approach, and investigate the varying choice of factors, (2) show that the method can be supported by an alternative microscopic picture, and the two distinct spin-glass theories derive equivalent outcomes, whose analytical results serve as lower bounds for the FVS size on random regular factor graphs, besides (3) an extra mathematical lower bound from the result on random regular (original) graphs. Performances of graph/factor-graph algorithms are compared on various real networks. It shows empirically and analytically that the factor-graph approach does not interfere with what we could achieve without applying this technique; the new approach does a good job where traditional algorithms may perform poorly.