Is there a way to represent a Max Flow problem as a dynamic programming task?

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I've recently started practising some graph theory problems, and I wanted to know if there is a method which would allow us to approach the Max Flow problem through dynamic programming. I cannot seem to find any resources where they outline a similar approach, and in most places, they seem to utilise either Linear Programming or algorithms, such as the Ford–Fulkerson algorithm. I would be extremely grateful for any suggestions about books/videos where they consider it, or for any ideas that would help me to derive the method by myself. Thanks in advance!

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Hi: This is not an answer but I'm putting it here because it's easier to type and there's more room. The link below says that a greedy algorithm can be used to get an approximation but it does not result in optimality. To me, that's a hint that dynamic programming will not lead to an optimal solution because dynamic programming is really only applicable when greedy-ness works. So, I'm waving my hands here for sure but my guess is that you'll be going into a dark cave if you try to solve maximum flow using dynamic programming.

https://www.geeksforgeeks.org/max-flow-problem-introduction/

Also, just from a practical standoint, really smart people have been working on this problem for a long time. So, if a dynamic programming approach has not been used, chances are that it's not the way to proceed. Although, at the same time, if you did come up with such a result, it would most likely be viewed as an amazing achievement.