Overview
In this post I will explain with an example one way to code the Edmonds Karp max flow algorithm to solve a problem.
The Problem
The problem we study is Array and Operations http://codeforces.net/problemset/problem/498/C. The basic idea is you have a sequence of numbers and a bunch of special pairs taken from the sequence. For any of the pairs you can divide each number by a common factor greater than 1 and replace the elements in the pair with the result of this division. The question asks what is the maximum number of times you can do this.
Converting this to a max flow problem
This section is specific to the problem. You can skip it if you just want to know how to implement Edmonds Karp on a given flow network.
Before we convert this problem to a max flow problem we make some observations. First if we have a pair of numbers the maximum amount of times we can perform the operation described on this pair is equal to the number of prime divisors the pair have in common counting multiplicity (i.e if the same prime divides the pair multiple times we count it multiple times). Next we note this is the same as the number of prime divisors in the greatest common divisor of the pair counting multiplicity.
Next assume we have one number that is paired with multiple other numbers. Then to find the maximum number of times we can perform the operation on this number we first find the gcd between it and it first pairing. Then we count the number of prime divisors in this gcd. Next we replace the original number by the original number divided by the gcd found and repeat the process with the second pairing. We could instead find the gcd of the number and the product of all the numbers paired with it and then count the prime divisors of this gcd to get the same result. However this approach may lead to overflow issues.
Now for any given number we know how to count maximum the number of times we can perform the operation on a pair and the maximum number of times we can perform this operation on the number in total. If we think of the flow as the maximum number of times we can perform the operation we can construct a flow network as follows. We let each number that appears in special pair have two nodes associated with it. Call them the left node and the right node. The left node has a directed edge running from the source to it with a capacity equal to the maximum number of times the operation can be performed on this number in total. The right node has a directed edge running from it to the the sink with the same capacity. Next for each number in a special pair we connect its left node to the right node of the number with which it is paired with a directed edge from left to right with capacity equal to the number of times we can perform the operation on this pair. This gives us our flow network. We also mention it is different from the flow network given in the editorial but it also works.
Of course to do all this we need functions to calculate the number of prime divisors and greatest common divisors. For calculating the greatest common divisor the Euclidean algorithm is fairly easy to implement. Since the numbers given can go up to 109 calculating the number of prime divisor is harder. One is to calculate all primes up to 1000 by brute force. Then we can those as a sieve to get all primes up to 32000. Finally since 320002 > 109 with all these primes we can find the number of prime divisors for any number up to 109.
Set up three graphs
Now that we have our network and edge capacities we create three graphs. We store these in adjacency matrix form we can use a 2 dimensional array or a vector<vector> here. The first graph is the flow where all entries are set to 0. The second graph is the residual where all the edge values are set to the capacity. Finally the third graph is the network graph where we have a 1 if two edges are connected and 0 otherwise.
Find an augmenting path
Next we create a function to find an augmenting path that returns pair<int,vector> where the first term is the capacity and the second is the path. To do this we perform a breadth first search starting from the source. We only need to store the predecessor of each vertex and not the distance. With the predecessors it is easy to find a path by back tracking from the sink. If there is no path we just return an empty pair and deal with it later when we augment along the path. Next we find the capacity of the path by taking the minimum of all capacities of edges included in the path.
