feedback vertex set problem
Recently Published Documents


TOTAL DOCUMENTS

57
(FIVE YEARS 13)

H-INDEX

13
(FIVE YEARS 2)

2021 ◽  
Vol 867 ◽  
pp. 1-12
Author(s):  
Lawqueen Kanesh ◽  
Soumen Maity ◽  
Komal Muluk ◽  
Saket Saurabh

2021 ◽  
Vol 17 (2) ◽  
pp. 1-14
Author(s):  
Daniel Lokshtanov ◽  
Pranabendu Misra ◽  
Joydeep Mukherjee ◽  
Fahad Panolan ◽  
Geevarghese Philip ◽  
...  

A tournament is a directed graph T such that every pair of vertices is connected by an arc. A feedback vertex set is a set S of vertices in T such that T − S is acyclic. We consider the Feedback Vertex Set problem in tournaments. Here, the input is a tournament T and a weight function w : V ( T ) → N, and the task is to find a feedback vertex set S in T minimizing w ( S ) = ∑ v∈S w ( v ). Rounding optimal solutions to the natural LP-relaxation of this problem yields a simple 3-approximation algorithm. This has been improved to 2.5 by Cai et al. [SICOMP 2000], and subsequently to 7/3 by Mnich et al. [ESA 2016]. In this article, we give the first polynomial time factor 2-approximation algorithm for this problem. Assuming the Unique Games Conjecture, this is the best possible approximation ratio achievable in polynomial time.


2021 ◽  
Vol 26 ◽  
pp. 1-19
Author(s):  
Michael Hecht ◽  
Krzysztof Gonciarz ◽  
Szabolcs Horvát

The classical NP–hard feedback arc set problem (FASP) and feedback vertex set problem (FVSP) ask for a minimum set of arcs ε ⊆ E or vertices ν ⊆ V whose removal G ∖ ε, G ∖ ν makes a given multi–digraph G =( V , E ) acyclic, respectively. Though both problems are known to be APX–hard, constant ratio approximations or proofs of inapproximability are unknown. We propose a new universal O (| V || E | 4 )–heuristic for the directed FASP. While a ratio of r ≈ 1.3606 is known to be a lower bound for the APX–hardness, at least by empirical validation we achieve an approximation of r ≤ 2. Most of the relevant applications, such as circuit testing , ask for solving the FASP on large sparse graphs, which can be done efficiently within tight error bounds with our approach.


2020 ◽  
Vol 67 (12) ◽  
pp. 3492-3496
Author(s):  
Dawei Zhao ◽  
Lijuan Xu ◽  
Shao-Meng Qin ◽  
Guangqi Liu ◽  
Zhen Wang

Sign in / Sign up

Export Citation Format

Share Document