I can't understand how to find circulation flow in the network with lower bounds(not demands). I found next documents with problem description and solving strategies:
Lets consider a network with following edges(l - lower bound, c - capacity):
1 -> 2 : l = 1 c = 3
2 -> 3 : l = 2 c = 4
3 -> 1 : l = 1 c = 2
As I understand to solve the problem we should make next steps:
After performing these steps new network will be:
S -> 2 : c = 1
2 -> 3 : c = 2
3 -> T : c = 1
1 -> 2 : c = 2
3 -> 1 : c = 1
demands for vertices:
d1 = 0
d2 = -1
d3 = 1
We see that maximum flow equals to 1, and equals to the sum of edges to T which is also 1. And it cover edges A->2->3->T.
The question is how get circulation flow in the original network with original bounds?
Circulation flow in the original network exists - we need just assign flow equals to 2 to all edges.
There is a feasible circulation with demands {dv} in G if and only if the maximum s-t flow in H has value D. If all capacities and demands in G are integers, and there is a feasible circulation, then there is a feasible circulation that is integer valued.
The max-flow min-cut theorem states that the maximum flow through any network from a given source to a given sink is exactly equal to the minimum sum of a cut. This theorem can be verified using the Ford-Fulkerson algorithm. This algorithm finds the maximum flow of a network or graph.
In computer science and optimization theory, the max-flow min-cut theorem states that in a flow network, the maximum amount of flow passing from the source to the sink is equal to the total weight of the edges in a minimum cut, i.e., the smallest total weight of the edges which if removed would disconnect the source ...
In combinatorial optimization, network flow problems are a class of computational problems in which the input is a flow network (a graph with numerical capacities on its edges), and the goal is to construct a flow, numerical values on each edge that respect the capacity constraints and that have incoming flow equal to ...
It is a bit late, but I stumbled upon this question when working on a solution for this problem.
If you do it the other way, which is:
After this, the solution found by any max flow algorithm will be:
What you need to do now is just add the values of lower bounds to the result of previous steps. In this case:
And you have the answer you were looking for. I hope this helps somebody.
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