A linear input dependence model for interdependent networks

This repository contains raw data generated for use in Kaul and Rumpf 2021 (referenced below), and includes the results from a series of computational trials that compare the behaviors of binary and linear input dependence models for interdependent flow networks. A large number of artificial test networks was generated, solved exactly using the binary input dependence model, solved exactly using the linear relaxation, and then solved approximately using a randomized rounding algorithm based on the solution of the linear relaxation. The results from these trials suggest that the linear relaxation typically produces objective values extremely similar to the binary model while being significantly less computationally expensive, and that the linear relaxation can be used as part of a randomized rounding scheme to produce near-optimal feasible solutions to the binary model usually within only a few attempts, although these results can vary significantly by network. Moreover, the linear relaxation and randomized rounding schemes tend to perform better and to produce results closer to the binary model when the density of interdependent arcs is relatively low. See the README included in the data set for a full description.