Python prototype for Sherman's outlined procedure

Sherman's with naiive conductance congestion approximators.

I refactored the code involved with computing matrix products of B, C, R (from the paper) and the almost_route routine into a class, along with avoiding the matrix operations explicitly. I'm envisioning overriding the compute_R / compute_RT methods for smarter congestion approximators.

I added some tests to verify that the flow returned is actually within the epsilon bounds of the actual max flow as given by networkx ford-fulkerson for some small randomly generated dense graphs.

FYI: I was able to get much better memory usage with these changes. I tested complete graphs up to N=2048 which took a few hours to complete.

![image](https://cloud.githubusercontent.com/assets/6294260/4556060/0b8dbd1e-4ec5-11e4-8d86-5bf349bd32f7.png)

![image 1](https://cloud.githubusercontent.com/assets/6294260/4556062/11cd2e94-4ec5-11e4-9018-77a82a40c55e.png)