The Set Covering Problem is a classic optimization problem in computer science and operations research. It involves selecting a minimum number of subsets from a given collection so that their union covers all elements in a universal set. This problem is often represented using sets and can be applied in various fields, such as network design and resource allocation. In mathematical terms, the goal is to minimize the total cost associated with the selected subsets while ensuring that every element in the universal set is included at least once. The greedy algorithm is a common heuristic used to find approximate solutions, as the problem is known to be NP-hard, meaning that finding an exact solution efficiently is challenging.