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The condition that for a given flow to be feasible, it must respect each edge's capacity constraints and the flow conservation principle at each vertex, excluding the source and sink.

An iterative approach to determine a feasible flow through a network by examining one path at a time from the source to the sink and pushing as much flow as possible along that path.

The capacity of the edge with the smallest capacity in an augmenting path. It limits the maximum amount of additional flow that can pass through the augmenting path.

After considering flow in a network, the residual network reflects the remaining capacity on the edges, which may permit additional flow.

A path from the source to the sink in the residual network where the flow can be increased. The bottleneck capacity of this path dictates by how much the overall flow can be augmented.

The problem of finding the greatest possible flow from a source to a sink in a network such that it does not exceed the capacity of any edge in the network.

A specific implementation of the Ford-Fulkerson algorithm that uses breadth-first search to find the shortest augmenting path, resulting in a complexity of $O(VE^2)$, where V is the number of vertices and E is the number of edges.

An algorithm that computes the maximum flow in a network by repeatedly finding augmenting paths. It uses the concept of residual graphs and augmenting paths to iteratively increase the flow.

A partition of the nodes of a graph into two disjoint sets, which separates the source from the sink, with edges crossing the partition. The capacity of the cut is the sum of capacities of these edges.