Support enumeration

The support enumeration algorithm implemented in Nashpy is based on the one described in [Nisan2007].

The algorithm is as follows:

For a nondegenerate 2 player game \((A, B)\in{\mathbb{R}^{m\times n}}^2\) the following algorithm returns all nash equilibria:

  1. For all \(1\leq k\leq \min(m, n)\);

  2. For all pairs of support \((I, J)\) with \(|I|=|J|=k\)

  3. Solve the following equations (this ensures we have best responses):

    \[ \sum_{i\in I}{\sigma_{r}}_iB_{ij}=v\text{ for all }j\in J\]\[\sum_{j\in J}A_{ij}{\sigma_{c}}_j=u\text{ for all }i\in I\]
  4. Solve

    • \(\sum_{i=1}^{m}{\sigma_{r}}_i=1\) and \({\sigma_{r}}_i\geq 0\) for all \(i\)
    • \(\sum_{j=1}^{n}{\sigma_{c}}_i=1\) and \({\sigma_{c}}_j\geq 0\) for all \(j\)
  5. Check the best response condition.

Repeat steps 3,4 and 5 for all potential support pairs.


  1. Step 1 is a complete enumeration of all possible strategies that the equilibria could be.
  2. Step 2 is based on the definition of a non degenerate game which ensures that equilibria will be on supports of the same size.
  3. Step 3 are the linear equations that are to be solved, for a given pair of supports these ensure that neither player has an incentive to move to another strategy on that support.
  4. Step 4 is to ensure we have mixed strategies.
  5. Step 5 is a final check that there is no better utility outside of the supports.

In Nashpy this is all implemented algebraically using Numpy to solve the linear equations.