Parameterized Approximation Scheme for Feedback Vertex Set

Authors Satyabrata Jana , Daniel Lokshtanov, Soumen Mandal , Ashutosh Rai, Saket Saurabh

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Author Details

Satyabrata Jana
  • Institute of Mathematical Sciences, HBNI, Chennai, India
Daniel Lokshtanov
  • Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA, USA
Soumen Mandal
  • Department of Mathematics, IIT Delhi, New Delhi, India
Ashutosh Rai
  • Department of Mathematics, IIT Delhi, New Delhi, India
Saket Saurabh
  • Institute of Mathematical Sciences, HBNI, Chennai, India
  • University of Bergen, Bergen, Norway

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Satyabrata Jana, Daniel Lokshtanov, Soumen Mandal, Ashutosh Rai, and Saket Saurabh. Parameterized Approximation Scheme for Feedback Vertex Set. In 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 272, pp. 56:1-56:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Feedback Vertex Set (FVS) is one of the most studied vertex deletion problems in the field of graph algorithms. In the decision version of the problem, given a graph G and an integer k, the question is whether there exists a set S of at most k vertices in G such that G-S is acyclic. It is one of the first few problems which were shown to be NP-complete, and has been extensively studied from the viewpoint of approximation and parameterized algorithms. The best-known polynomial time approximation algorithm for FVS is a 2-factor approximation, while the best known deterministic and randomized FPT algorithms run in time 𝒪^*(3.460^k) and 𝒪^*(2.7^k) respectively. In this paper, we contribute to the newly established area of parameterized approximation, by studying FVS in this paradigm. In particular, we combine the approaches of parameterized and approximation algorithms for the study of FVS, and achieve an approximation guarantee with a factor better than 2 in randomized FPT running time, that improves over the best known parameterized algorithm for FVS. We give three simple randomized (1+ε) approximation algorithms for FVS, running in times 𝒪^*(2^{εk}⋅ 2.7^{(1-ε)k}), 𝒪^*(({(4/(1+ε))^{(1+ε)}}⋅{(ε/3)^ε})^k), and 𝒪^*(4^{(1-ε)k}) respectively for every ε ∈ (0,1). Combining these three algorithms, we obtain a factor (1+ε) approximation algorithm for FVS, which has better running time than the best-known (randomized) FPT algorithm for every ε ∈ (0, 1). This is the first attempt to look at a parameterized approximation of FVS to the best of our knowledge. Our algorithms are very simple, and they rely on some well-known reduction rules used for arriving at FPT algorithms for FVS.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Feedback Vertex Set
  • Parameterized Approximation


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