Faster Exponential-Time Approximation Algorithms Using Approximate Monotone Local Search

TL;DR

This paper introduces a generalized approach to approximate monotone local search, enabling faster exponential-time algorithms for various subset minimization problems, improving existing approximation ratios and running times.

Contribution

It establishes a connection between parameterized and exponential-time approximation algorithms for monotone subset minimization problems, and demonstrates new faster algorithms for several problems.

Findings

Derived a faster 1.1-approximation for Vertex Cover with 1.114^n time

Generalized monotone local search to improve exponential-time approximations

Achieved new algorithms for problems like Hitting Set and Feedback Vertex Set

Abstract

We generalize the monotone local search approach of Fomin, Gaspers, Lokshtanov and Saurabh [J. ACM 2019], by establishing a connection between parameterized approximation and exponential-time approximation algorithms for monotone subset minimization problems. In a monotone subset minimization problem the input implicitly describes a non-empty set family over a universe of size $n$ which is closed under taking supersets. The task is to find a minimum cardinality set in this family. Broadly speaking, we use approximate monotone local search to show that a parameterized $\alpha$-approximation algorithm that runs in $c^k \cdot n^{O(1)}$ time, where $k$ is the solution size, can be used to derive an $\alpha$-approximation randomized algorithm that runs in $d^n \cdot n^{O(1)}$ time, where $d$ is the unique value in $d \in (1,1+\frac{c-1}{\alpha})$ such that $\mathcal{D}(\frac{1}{\alpha}\|\frac{d-1}{c-1})=\frac{\ln c}{\alpha}$ and $\mathcal{D}(a \|b)$ is the Kullback-Leibler divergence. This running time matches that of Fomin et al. for $\alpha=1$, and is strictly better when $\alpha >1$, for any $c > 1$. Furthermore, we also show that this result can be derandomized at the expense of a sub-exponential multiplicative factor in the running time. We demonstrate the potential of approximate monotone local search by deriving new and faster exponential approximation algorithms for Vertex Cover, $3$-Hitting Set, Directed Feedback Vertex Set, Directed Subset Feedback Vertex Set, Directed Odd Cycle Transversal and Undirected Multicut. For instance, we get a $1.1$-approximation algorithm for Vertex Cover with running time $1.114^n \cdot n^{O(1)}$, improving upon the previously best known $1.1$-approximation running in time $1.127^n \cdot n^{O(1)}$ by Bourgeois et al. [DAM 2011].