Solving the Convex Cost Integer Dual Network Flow Problem

In this paper, we consider an integer convex optimization problem where the objective function is the sum of separable convex functions (that is, of the form Σ(i,j)∈Q ij ij F (w ) + Σi∈P i i B ( ) μ ), the constraints are similar to those arising in the dual of a minimum cost flow problem (that is, of the form μi μj ≤ wij, (i, j) ∈ Q), with lower and upper bounds on variables. Let n = |P|, m = |Q|, and U be the largest magnitude in the lower and upper bounds of variables. We call this problem the convex cost integer dual network flow problem. In this paper, we describe several applications of the convex cost integer dual network flow problem arising in dial-a-ride transit problems, inverse spanning tree problem, project management, and regression analysis. We develop network flow based algorithms to solve the convex cost integer dual network flow problem. We show that using the Lagrangian relaxation technique, the convex cost integer dual network flow problem can be transformed to a convex cost primal network flow problem where each cost function is a piecewise linear convex function with integer slopes. Its special structure allows the convex cost primal network flow problem to be solved in O(nm log n log(nU)) time using a cost-scaling algorithm, which is the best available time bound to solve the convex cost integer dual network flow problem. 1 Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA. 2 Department of IE and OR and Haas School of Management, University of California, Berkeley, CA 94720, USA. 3 Sloan School of Management, MIT, Cambridge, MA 02139, USA.