Our goal is to develop efficient algorithms for the fundamental set cover problem in the massive data model.

One of the classical problem in the area of combinatorial optimization is set cover problem in which we are given a ground set of elements and a collection of sets and the goal is the pick the smallest number of sets that cover the whole ground set. We have studied different variants of this problem in massive data models such as streaming model and sublinear model. Our goal is to extend the results for the more general cases such as the weighted variant and the linear programming relaxation of the problem.

Research Areas