Recently, Khuller, Moss and Naor presented a greedy algorithm for the budgeted maximum coverage problem. In this note, we observe that this algorithm also approximates a special case of set-union knapsack problem within a constant factor. In the special case, an element is a member of less than a constant number of subsets. This guarantee naturally extends to densest k-subgraph problem on graphs of bounded degree.
A Branch-Cut-And-Price Algorithm for a Combined Buy-at-Bulk Network Design Facility Location Problem
(2014)
In the combined buy-at-bulk network design facility location
problem we are given an undirected network with a set of potential facilities and a set of clients with demands. In addition, we are also provided a set of cable types with each cable type having a capacity and cost per unit length. The cost to capacity ratio decreases from large to small
cables following economies of scale. A network planner is expected to determine a set of facilities to open and install cables along the edges of the network in order to route the demands of the clients to some open facility. The capacities of the installed cable must be able to support the demands of the clients routed along that edge.The objective is to minimize the cost that is paid for opening facilities and installing cables. We model the problem as an integer program and propose a branch-cut-and-price algorithm for solving it. We study the effect of two family of valid inequalities that naturally emerge from the model. We present the results of our implementation that were tested on a set of large real world instances.
We study the incremental facility location problem, wherein we are given an instance of the uncapacitated facility location problem. We seek an incremental sequence of opening facilities and an incremental sequence of serving customers along with their fixed assignments to facilities open in the partial sequence. Our aim is to have the solution obtained for serving the first l customers in the sequence be competitive with the optimal solution to serve any l customers. We provide an incremental framework that provides an overall competitive factor of 8 and a worst case instance that provides the lower bound of 3. The problem has applications in multi-stage network planning.