Abstract:
Preprocessing or data reductions 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. In spite of wide practical applications of preprocessing, a systematic theoretical study of such algorithms remains elusive. The framework of parameterized complexity can be used as an approach to analyse preprocessing algorithms. Input to parameterized algorithms include a parameter (in addition to the input) which is likely to be small, and this resulted in a study of preprocessing algorithms that reduce the size of the input to a pure function of the parameter(independent ofthe input size). Such type of preprocessing algorithms are called kernelization algorithms. In the talk we discuss some classical and new trends in the design of kernelization algorithms.