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Network flow problem. 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 ...
Minimum-cost flow problem. The minimum-cost flow problem (MCFP) is an optimization and decision problem to find the cheapest possible way of sending a certain amount of flow through a flow network. A typical application of this problem involves finding the best delivery route from a factory to a warehouse where the road network has some ...
The problem can be solved e.g. by minimizing . A common linearization of this problem is the minimization of the maximum utilization , where. In the minimum cost multi-commodity flow problem, there is a cost for sending a flow on . You then need to minimize. In the maximum multi-commodity flow problem, the demand of each commodity is not fixed ...
The Hardy Cross method is an application of continuity of flow and continuity of potential to iteratively solve for flows in a pipe network. [1] In the case of pipe flow, conservation of flow means that the flow in is equal to the flow out at each junction in the pipe. Conservation of potential means that the total directional head loss along ...
The maximum flow problem can be seen as a special case of more complex network flow problems, such as the circulation problem. The maximum value of an s-t flow (i.e., flow from source s to sink t) is equal to the minimum capacity of an s-t cut (i.e., cut severing s from t) in the network, as stated in the max-flow min-cut theorem .
Circulation problem. The circulation problem and its variants are a generalisation of network flow problems, with the added constraint of a lower bound on edge flows, and with flow conservation also being required for the source and sink (i.e. there are no special nodes). In variants of the problem, there are multiple commodities flowing ...
Edmonds–Karp algorithm. In computer science, the Edmonds–Karp algorithm is an implementation of the Ford–Fulkerson method for computing the maximum flow in a flow network in time. The algorithm was first published by Yefim Dinitz in 1970, [1][2] and independently published by Jack Edmonds and Richard Karp in 1972. [3]
Dinic's algorithm or Dinitz's algorithm is a strongly polynomial algorithm for computing the maximum flow in a flow network, conceived in 1970 by Israeli (formerly Soviet) computer scientist Yefim Dinitz. [1] The algorithm runs in time and is similar to the Edmonds–Karp algorithm, which runs in time, in that it uses shortest augmenting paths.