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 …

We introduce the framework of cross-composition for proving kernelization lower bounds. A
classical problem L\and/or-cross-composes into a parameterized problem Q if it is possible …

Let F be a finite set of graphs. In the F-DELETION problem, we are given an n-vertex graph
G and an integer k as input, and asked whether at most k vertices can be deleted from G …

In this paper, we show that the problems Disjoint Cycles and Disjoint Paths do not have
polynomial kernels, unless NP⊆ coNP/poly. Thus, these problems do not allow polynomial …

Given an instance of a hard decision problem, a limited goal is to compress that instance
into a smaller, equivalent instance of a second problem. As one example, consider the …

Subset Sumand k-SAT are two of the most extensively studied problems in computer
science, and conjectures about their hardness are among the cornerstones of fine-grained …

In this paper we propose a new framework for analyzing the performance of preprocessing
algorithms. Our framework builds on the notion of kernelization from parameterized …