Minimum-cost flow problem

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The minimum-cost flow problem is to find the cheapest possible way of sending a certain amount of flow through a flow network. Solving this problem is useful for real-life situations involving networks with costs associated (e.g. telecommunications networks), as well as in other situations where the analogy is not so obvious, such as where to locate warehouses.

Definition[edit]

Given a flow network, that is, a directed graph G=(V,E) with source s ∈ V and sink t ∈ V, where edge (u,v) ∈ E has capacity c(u,v) > 0, flow f(u,v) \ge 0 and cost a(u,v) (most minimum-cost flow algorithms support edges with negative costs). The cost of sending this flow is f(u,v)\cdot a(u,v). You are required to send an amount of flow d from s to t.

The definition of the problem is to minimize the total cost of the flow:

\sum_{(u,v) \in E} a(u,v) \cdot f(u,v)

with the constraints

Capacity constraints: \,f(u,v) \le c(u,v)
Skew symmetry: \,f(u,v) = - f(v,u)
Flow conservation: \,\sum_{w \in V} f(u,w) = 0 \text{ for all } u \neq s, t
Required flow: \,\sum_{w \in V} f(s,w) = d \text{ and } \sum_{w \in V} f(w,t) = d

Relation to other problems[edit]

A variation of this problem is to find a flow which is maximum, but has the lowest cost among the maximums. This could be called a minimum-cost maximum-flow problem. This is useful for finding minimum cost maximum matchings.

With some solutions, finding the minimum cost maximum flow instead is straightforward. If not, you can do a binary search on d.

A related problem is the minimum cost circulation problem, which can be used for solving minimum cost flow. You do this by setting the lower bound on all edges to zero, and then make an extra edge from the sink t to the source s, with capacity c(t,s)=d and lower bound l(t,s)=d, forcing the total flow from s to t to also be d.

The problem can be specialized into two other problems:

Solutions[edit]

The minimum cost flow problem can be solved by linear programming, since we optimize a linear function, and all constraints are linear.

Apart from that, many combinatorial algorithms exist, for a comprehensive survey, see [1]. Some of them are generalizations of maximum flow algorithms, others use entirely different approaches.

Well-known fundamental algorithms (they have many variations):

Application[edit]

Minimum weight bipartite matching[edit]

Reducing Minimum weight bipartite matching to minimum cost max flow problem

Given an bipartite graph G = (AB, E), we like to find the maximum cardinality matching in G that has minimum cost. Let w: ER be a weight function on the edges of E. The minimum weight bipartite matching problem or assignment problem is to find a perfect matching ME whose total weight is minimized. The idea is to reduce this problem to a network flow problem.

Let G’ = (V’=AB, E’=E). Assign the capacity of all the edges in E’ to 1. Add a source vertex s and connect it to all the vertices in A’ and add a sink vertex t and connect all vertices inside group B’ to this vertex. The capacity of all the new edges is 1 and their costs is 0. It is proved that there is minimum weight perfect bipartite matching in G if and only if there a minimum cost flow in G’. [7]

See also[edit]

References[edit]

  1. ^ Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin (1993). Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Inc. ISBN 0-13-617549-X. 
  2. ^ Morton Klein (1967). "A primal method for minimal cost flows with applications to the assignment and transportation problems". Management Science 14: 205–220. doi:10.1287/mnsc.14.3.205. 
  3. ^ Andrew V. Goldberg and Robert E. Tarjan (1989). "Finding minimum-cost circulations by canceling negative cycles". Journal of the ACM 36 (4): 873–886. doi:10.1145/76359.76368. 
  4. ^ Jack Edmonds and Richard M. Karp (1972). "Theoretical improvements in algorithmic efficiency for network flow problems". Journal of the ACM 19 (2): 248–264. doi:10.1145/321694.321699. 
  5. ^ Andrew V. Goldberg and Robert E. Tarjan (1990). "Finding minimum-cost circulations by successive approximation". Math. Oper. Res. 15 (3): 430–466. doi:10.1287/moor.15.3.430. 
  6. ^ James B. Orlin (1997). "A polynomial time primal network simplex algorithm for minimum cost flows". Mathematical Programming 78: 109–129. doi:10.1007/bf02614365. 

External links[edit]