A least-squares minimum-cost network flow algorithm

Node-arc incidence matrices in network flow problems exhibit several special least-squares properties. We show how these properties can be leveraged in a least-squares primal-dual algorithm for solving minimum-cost network flow problems quickly. Computational results show that the performance of an upper-bounded version of the least-squares minimum-cost network flow algorithm with a special dual update operation is comparable to CPLEX Network and Dual Optimizers for solving a wide range of minimum-cost network flow problems.