Maximum coverage problem with group budget constraints

We study the maximum coverage problem with group budget constraints (MCG). The input consists of a ground set X, a collection \(\psi \) of subsets of X each of which is associated with a combinatorial structure such that for every set \(S_j\in \psi \), a cost \(c(S_j)\) can be calculated based on the combinatorial structure associated with \(S_j\), a partition \(G_1,G_2,\ldots ,G_l\) of \(\psi \), and budgets \(B_1,B_2,\ldots ,B_l\), and B. A solution to the problem consists of a subset H of \(\psi \) such that \(\sum _{S_j\in H} c(S_j) \le B\) and for each \(i \in {1,2,\ldots ,l}\), \(\sum _{S_j \in H\cap G_i}c(S_j)\le B_i\). The objective is to maximize \(|\bigcup _{S_j\in H}S_j|\). In our work we use a new and improved analysis of the greedy algorithm to prove that it is a \((\frac{\alpha }{3+2\alpha })\)-approximation algorithm, where \(\alpha \) is the approximation ratio of a given oracle which takes as an input a subset \(X^{new}\subseteq X\) and a group \(G_i\) and returns a set \(S_j\in G_i\) which approximates the optimal solution for \(\max _{D\in G_i}\frac{|D\cap X^{new}|}{c(D)}\). This analysis that is shown here to be tight for the greedy algorithm, improves by a factor larger than 2 the analysis of the best known approximation algorithm for MCG.