# 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(iterable[, value]) Create a new dictionary with keys from iterable and values set to value. get(key[, default]) Return the value for key if key is in the dictionary, else default. items() keys() pop(key[, default]) If key is not found, default is returned if given, otherwise KeyError is raised popitem(/) Remove and return a (key, value) pair as a 2-tuple. setdefault(key[, default]) Insert key with a value of default if key is not in the dictionary. 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()