In graph theory, approximate max-flow min-cut theorems concern the relationship between the maximum flow rate (max-flow) and the minimum cut (min-cut) in multi-commodity flow problems. The classic max-flow min-cut theorem states that for networks with a single type of flow (single-commodity flows), the maximum possible flow from source to sink is precisely equal to the capacity of the smallest cut. However, this equality doesn't generally hold when multiple types of flow exist in the network (multi-commodity flows). In these more complex scenarios, the maximum flow and the minimum cut are not necessarily equal. Instead, approximate max-flow min-cut theorems provide bounds on how close the maximum flow can get to the minimum cut, with the max-flow always being lower or equal to the min-cut.
For example, imagine two factories (the sources) producing different goods (the commodities) that need to be shipped to two warehouses (the sinks). Each road has a capacity limit for all goods combined. The min-cut is the smallest total road capacity that, if closed, would prevent goods from both factories from reaching their respective warehouses. The max-flow is the maximum total amount of goods that can be shipped. Because both types of goods compete for the same roads, the max-flow may be lower than the min-cut. The approximate max-flow min-cut theorem tells us how close the maximum amount of shipped goods can get to that minimum road capacity.
The theorems have enabled the development of approximation algorithms for use in graph partition and related problems, where finding the absolute best solution is computationally prohibitive.