pygambit.nash.simpdiv_solve#
- pygambit.nash.simpdiv_solve(start: MixedStrategyProfileRational, maxregret: Rational | None = None, refine: int = 2, leash: int | None = None) NashComputationResult#
Compute Nash equilibria of a game using simplicial subdivision.
Changed in version 16.2.0: Method now takes a starting point, as a mixed strategy profile, instead of a game.
- Parameters:
start (MixedStrategyProfileRational) – The starting profile for the algorithm. Up to one equilibrium will be found from any starting profile, and the equilibrium found may (and generally will) depend on the initial profile chosen.
maxregret (Rational, default 1e-8) –
The acceptance criterion for approximate Nash equilibrium; the maximum regret of any player must be no more than maxregret times the difference of the maximum and minimum payoffs of the game
refine (int, default 2) – This controls the rate at which the triangulation of the space of mixed strategy profiles is made more fine at each iteration.
leash (int, optional) – Simplicial subdivision is guaranteed to converge to an (approximate) Nash equilibrium. The method may take arbitrarily long paths through the space of mixed strategies in doing so. If specified, leash sets a maximum number of grid steps the method may explore. This trades off the possibility of finding an equilibrium more quickly by giving up the guarantee than an equilibrium will necessarily be found.
- Returns:
res – The result represented as a
NashComputationResultobject.- Return type: