Compression via Matroids: A Randomized Polynomial Kernel for Odd Cycle Transversal

TL;DR

This paper introduces a novel randomized polynomial kernelization for the Odd Cycle Transversal problem using matroid theory, enabling significant instance size reduction and advancing kernelization research.

Contribution

It presents the first randomized polynomial kernel for OCT, leveraging matroid representations to simulate iterative compression efficiently.

Findings

Achieves a cubic size bound in the approximate solution

Reduces problem instances to size O(k^{4.5})

Provides a one-sided error randomized kernelization

Abstract

The Odd Cycle Transversal problem (OCT) asks whether a given graph can be made bipartite by deleting at most $k$ of its vertices. In a breakthrough result Reed, Smith, and Vetta (Operations Research Letters, 2004) gave a $\BigOh(4^kkmn)$ time algorithm for it, the first algorithm with polynomial runtime of uniform degree for every fixed $k$. It is known that this implies a polynomial-time compression algorithm that turns OCT instances into equivalent instances of size at most $\BigOh(4^k)$, a so-called kernelization. Since then the existence of a polynomial kernel for OCT, i.e., a kernelization with size bounded polynomially in $k$, has turned into one of the main open questions in the study of kernelization. This work provides the first (randomized) polynomial kernelization for OCT. We introduce a novel kernelization approach based on matroid theory, where we encode all relevant information about a problem instance into a matroid with a representation of size polynomial in $k$. For OCT, the matroid is built to allow us to simulate the computation of the iterative compression step of the algorithm of Reed, Smith, and Vetta, applied (for only one round) to an approximate odd cycle transversal which it is aiming to shrink to size $k$. The process is randomized with one-sided error exponentially small in $k$, where the result can contain false positives but no false negatives, and the size guarantee is cubic in the size of the approximate solution. Combined with an $\BigOh(\sqrt{\log n})$-approximation (Agarwal et al., STOC 2005), we get a reduction of the instance to size $\BigOh(k^{4.5})$, implying a randomized polynomial kernelization.