pygambit.nash.liap_solve

pygambit.nash.liap_solve(start: MixedStrategyProfileDouble | MixedBehaviorProfileDouble, maxregret: float = 0.0001, maxiter: int = 1000) → NashComputationResult

Compute approximate Nash equilibria of a game using Lyapunov function minimization.

Changed in version 16.2.0: Method now takes a starting point (as a mixed strategy or mixed behavior profile) instead of a game. Implemented maxregret to specify acceptance criterion for approximation.

Parameters:

• start (MixedStrategyProfileDouble or MixedBehaviorProfileDouble) – The starting profile for function minimization. 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 (float, default 1e-4) –

  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

• maxiter (int, default 1000) –

  Maximum number of iterations in function minimization.

Returns:

res – The result represented as a NashComputationResult object.

Return type:

NashComputationResult