Neighborhood-based Strategies for Widening of the Greedy Algorithm of the Set Cover Problem

Abstract

We live in the age of ever-increasing parallel computing resources. For a decade there has been intense research into parallel data mining algorithms. Most of this research is focused on improving the running time of existing algorithms. In contrast our focus is the improvement of the solution quality, or model accuracy. We are looking for "smart" strategies to invest parallel compute resources in order to achieve a better exploration of the search space by exploring several solutions in parallel, referred to as Widening. In this paper, we demonstrate the effect of neighborhood-based Widening with different types of neighborhoods for the greedy algorithm of the set cover problem.