# utilities¶

Utility routines for the game_theory submodule

class quantecon.game_theory.utilities.NashResult[source]

Bases: dict

Contain the information about the result of Nash equilibrium computation.

Notes

This is sourced from sicpy.optimize.OptimizeResult.

There may be additional attributes not listed above depending of the routine.

Attributes: NE : tuple(ndarray(float, ndim=1)) Computed Nash equilibrium. converged : bool Whether the routine has converged. num_iter : int Number of iterations. max_iter : int Maximum number of iterations. init : scalar or array_like Initial condition used.

Methods

 clear() copy() fromkeys(\$type, iterable[, value]) Returns a new dict with keys from iterable and values equal to value. get(k[,d]) items() keys() pop(k[,d]) If key is not found, d is returned if given, otherwise KeyError is raised popitem() 2-tuple; but raise KeyError if D is empty. setdefault(k[,d]) update([E, ]**F) If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] values()