Edge Bipartization faster than $2^k$

TL;DR

This paper introduces a faster algorithm for the Edge Bipartization problem that improves the exponential dependence on the parameter k from 2^k to approximately 1.977^k, using advanced algorithmic techniques.

Contribution

It presents the first algorithm with a running time better than 2^k for Edge Bipartization, combining iterative compression, relaxation techniques, and Measure & Conquer analysis.

Findings

Achieves a running time of O(1.977^k nm) for Edge Bipartization.

First improvement over the classic 2^k exponential dependence for this problem.

Demonstrates the effectiveness of combining relaxation techniques with Measure & Conquer analysis.

Abstract

In the Edge Bipartization problem one is given an undirected graph $G$ and an integer $k$, and the question is whether $k$ edges can be deleted from $G$ so that it becomes bipartite. In 2006, Guo et al. [J. Comput. Syst. Sci., 72(8):1386-1396, 2006] proposed an algorithm solving this problem in time $O(2^k m^2)$; today, this algorithm is a textbook example of an application of the iterative compression technique. Despite extensive progress in the understanding of the parameterized complexity of graph separation problems in the recent years, no significant improvement upon this result has been yet reported. We present an algorithm for Edge Bipartization that works in time $O(1.977^k nm)$, which is the first algorithm with the running time dependence on the parameter better than $2^k$. To this end, we combine the general iterative compression strategy of Guo et al. [J. Comput. Syst. Sci., 72(8):1386-1396, 2006], the technique proposed by Wahlstrom [SODA 2014, 1762-1781] of using a polynomial-time solvable relaxation in the form of a Valued Constraint Satisfaction Problem to guide a bounded-depth branching algorithm, and an involved Measure & Conquer analysis of the recursion tree.