Preprocessing, or data reduction, is a standard technique for simplifying and speeding up
computation. Written by a team of experts in the field, this book introduces a rapidly …

Parameterized complexity/multivariate complexity algorithmics is an exciting field of modern
algorithm design and analysis, with a broad range of theoretical and practical aspects that …

The goal of this textbook is twofold. First, the book serves as an introduction to the field of
parameterized algorithms and complexity accessible to graduate students and advanced …

Kernelization is a formalization of efficient preprocessing, aimed mainly at combinatorially
hard problems. Empirically, preprocessing is highly successful in practice, eg, in state-of-the …

Kernelization is a strong and widely-applied technique in parameterized complexity. A
kernelization algorithm, or simply a kernel, is a polynomial-time transformation that …

Possibly the most famous algorithmic meta-theorem is Courcelle's theorem, which states that
all MSO-expressible graph properties are decidable in linear time for graphs of bounded …

To solve NP-hard problems, polynomial-time preprocessing is a natural and promising
approach. Preprocessing is based on data reduction techniques that take a problem's input …

The Odd Cycle Transversal problem (OCT) asks whether a given undirected graph can be
made bipartite by deleting at most k of its vertices. In a breakthrough result, Reed, Smith, and …

The aim of this article is to motivate and describe the parameter ecology program, which
studies how different parameters contribute to the difficulty of classical problems. We call for …

In this survey, we look at kernelization: algorithms that transform in polynomial time an input
to a problem to an equivalent input, whose size is bounded by a function of a parameter …