Final Report Summary - PREPROCESSING (RIGOROUS THEORY OF PREPROCESSING)
The main research goal of this project was the quest for rigorous mathematical theory explaining the power and failure of heuristics. A large family of heuristics consists of preprocessing techniques, also known as data reduction or kernelization. Preprocessing means reducing the input to something simpler by solving an easy part of the input and this is the type of algorithms used in almost every application. The ubiquity of preprocessing makes the theory of compressibility extremely important.
Within this project we have obtained several breakthroughs in the study of kernelization. The highlights of our work are
- novel kernelization techniques allowing to show kernelization techniques for a wide class of problems;
- new algorithmic tools for analyzing preprocessing algorithms on sparse graphs;
- discovery of strong connection between kernelization, subexponential parameterized algorithms and approximation algorithms;
- developing of new algorithmic techniques for solving parameterized problems on directed graphs (tournaments and directed planar graphs)
Within this project we have obtained several breakthroughs in the study of kernelization. The highlights of our work are
- novel kernelization techniques allowing to show kernelization techniques for a wide class of problems;
- new algorithmic tools for analyzing preprocessing algorithms on sparse graphs;
- discovery of strong connection between kernelization, subexponential parameterized algorithms and approximation algorithms;
- developing of new algorithmic techniques for solving parameterized problems on directed graphs (tournaments and directed planar graphs)