def test_mcp_newton_minFBLSA():
mcp = SN.MixedComplementarityProblem2(0, 2, mcp_function, mcp_Nablafunction)
z = array([0., 0.])
w = array([0., 0.])
SO = SN.SolverOptions(mcp, SN.SICONOS_MCP_NEWTON_MINFBLSA)
info = SN.mcp_newton_minFBLSA(mcp, z, w, SO)
print("z = ", z)
print("w = ", w)
assert (linalg.norm(z-zsol) <= ztol)
assert not info
SO.iparam[0] = 100
SO.iparam[3] = 2
SO.iparam[4] = 10
lambdaPM = np.empty((N, 4))
signs = np.empty((N, 2))
sol = np.empty((N, 2))
sol[0, :] = xk
z[4:6] = xk
z[6] = hh
k = 0
while t <= T:
k += 1
info = SN.mcp_newton_minFBLSA(mcp, z, w, SO)
#info = SN.mcp_newton_FBLSA(mcp, z, w, SO)
# print('iter {:} ; solver iter = {:} ; prec = {:}'.format(k, SO.iparam[1], SO.dparam[1]))
if info > 0:
mcp_function(0, 4, z, w)
sol[k, 0] = w[0] - z[1]
sol[k, 1] = w[2] - z[3]
if sol[k, 0] < -1e-7 and np.abs(z[1]) < 1e-10:
z[1] = -sol[k, 0]
z[0] = 1.0
if z[4] < -1e-7 and np.abs(z[3]) < 1e-10:
z[3] = -sol[k, 1]
z[2] = 1.0
if z[1] < -1e-7:
z[1] = 0.0
z[0] = 0.0
