Preflow-push Algorithms

As we talked about previously the idea of preflow-push is to start with the maximum flow in the network that is allowed. This is known as `flooding the network’. From there we try to send flow towards the sink.

An important concept in this case is the fact that during the algorithm we often violate mass-balance constraints. In other words we can have more flow going into a particular node than leaves it. The difference between the flow in and flow out is referred to as the `excess’. In order to find a feasible solution we need all the nodes apart from the source or sink to have a total excess of zero. We remove this excess by `pushing’ flow to adjacent nodes.


Flooding the network

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Augmenting Path Algorithms

In this post I will briefly describe one type of algorithm that has been used to solve the well-known max flow problem. This problem is essentially how to find the maximum flow that can be sent from a source node to a sink node in a network. Think of a graph connected with nodes and arcs each having a certain capacity or limit of flow that it can hold. This could be a very literal thing such as network of pipes or internet cables, but other stranger uses for network flow problems do exist such as the baseball elimination problem I talked about before.

Residual Capacities

The augmenting path algorithm utilises ideas in capacitated graphs called the residual capacity and the residual flow. This is the potential for flow to move along particular arcs. It can be summed up as Residual Flow = Capacity – Forwards Flow + Backwards Flow. See the diagram below to see how it works (the first number is the flow, the second the capacity).


Residual capacities

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Baseball Elimination Problem

Recently we had a masterclass from Pitu Mirchandani on the subject of network flows. These network flow problems have been developed since the 1950’s an example of which is my previous post on Dijkstra’s algorithm. The main thing I took away from the masterclass was that the idea of shortest paths, maximum flows and min cost max flows can be used to describe and solve a surprisingly wide variety of problems.

One interesting problem which can be solved using the ideas of network flows is that of the baseball-elimination problem. Baseball in the USA consists of two different leagues which are each split up into three divisions of about 5 teams. During regular season each team plays 162 games in total, most of which are against teams in their division (76 vs same division, 66 vs same league, 20 vs different league). The aim of this is try and qualify for the playoffs, which can be done by finishing first in the division.

All this adds up to a desire to be able to work out whether it is possible to see if a particular team can still potentially win the division and make it to the playoffs given the current league standings. Solving this problem means that we have to look at tables that might look something like the one below.


Who can make the playoffs?

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