def lemke_optimizer(eco, payoff_matrix=None, dirac_mode=True):
A = np.zeros((eco.populations.size, eco.populations.size * eco.layers))
for k in range(eco.populations.size):
A[k, k * eco.layers:(k + 1) * eco.layers] = -1
q = np.zeros(eco.populations.size + eco.populations.size * eco.layers)
q[eco.populations.size * eco.layers:] = -1
q = q.reshape(-1, 1)
if payoff_matrix is None:
payoff_matrix = total_payoff_matrix_builder(eco)
H = np.block([[-payoff_matrix, A.T],
[-A, np.zeros((A.shape[0], eco.populations.size))]])
lcp = sn.LCP(H, q)
ztol = 1e-8
#solvers = [sn.SICONOS_LCP_PGS, sn.SICONOS_LCP_QP,
# sn.SICONOS_LCP_LEMKE, sn.SICONOS_LCP_ENUM]
z = np.zeros((eco.layers * 2 + eco.populations.size, ), np.float64)
w = np.zeros_like(z)
options = sn.SolverOptions(sn.SICONOS_LCP_PIVOT)
#sn.SICONOS_IPARAM_MAX_ITER = 10000000
#options.iparam[sn.SICONOS_IPARAM_MAX_ITER] = 1000000
options.dparam[sn.SICONOS_DPARAM_TOL] = 10**(-5)
info = sn.linearComplementarity_driver(lcp, z, w, options)
if sn.lcp_compute_error(lcp, z, w, ztol) > 10**(-5):
print(sn.lcp_compute_error(lcp, z, w, ztol), "Error")
#print(info, "Info")
return z
def lcp_generic(id, z, w):
options = sn.SolverOptions(id)
info = sn.linearComplementarity_driver(lcp, z, w, options)
print(' iter =', options.iparam[sn.SICONOS_IPARAM_ITER_DONE])
print(' error=', options.dparam[sn.SICONOS_DPARAM_RESIDU])
return info