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 outgoing flow at all vertices except for certain designated terminals.^[1]

Specific types of network flow problems include:

• The maximum flow problem, in which the goal is to maximize the total amount of flow out of the source terminals and into the sink terminals^{[1]: 166–206}
• The minimum-cost flow problem, in which the edges have costs as well as capacities and the goal is to achieve a given amount of flow (or a maximum flow) that has the minimum possible cost^{[1]: 294–356}
• The multi-commodity flow problem, in which one must construct multiple flows for different commodities whose total flow amounts together respect the capacities^{[1]: 649–694}
• Nowhere-zero flow, a type of flow studied in combinatorics in which the flow amounts are restricted to a finite set of nonzero values

The max-flow min-cut theorem equates the value of a maximum flow to the value of a minimum cut, a partition of the vertices of the flow network that minimizes the total capacity of edges crossing from one side of the partition to the other. Approximate max-flow min-cut theorems provide an extension of this result to multi-commodity flow problems. The Gomory–Hu tree of an undirected flow network provides a concise representation of all minimum cuts between different pairs of terminal vertices.

Algorithms for constructing flows include

• Dinic's algorithm, a strongly polynomial algorithm for maximum flow^{[1]: 221–223}
• The Edmonds–Karp algorithm, a faster strongly polynomial algorithm for maximum flow
• The Ford–Fulkerson algorithm, a greedy algorithm for maximum flow that is not in general strongly polynomial
• The network simplex algorithm, a method based on linear programming but specialized for network flow^{[1]: 402–460}
• The out-of-kilter algorithm for minimum-cost flow^{[1]: 326–331}
• The push–relabel maximum flow algorithm, one of the most efficient known techniques for maximum flow

Otherwise the problem can be formulated as a more conventional linear program or similar and solved using a general purpose optimization solver.

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