n_scenarios = 5
np.random.seed(seed=985739465)
bs = [b + np.random.normal(scale=2.0, size=1) for i in range(n_scenarios)]
for i in range(n_scenarios):
instance = create_basic_dense_qp(G, A, bs[i], c)
nlp = PyomoNLP(instance)
models.append(instance)
scenario_name = "s{}".format(i)
scenarios[scenario_name] = nlp
coupling_vars[scenario_name] = [nlp.variable_idx(instance.x[0])]
nlp = TwoStageStochasticNLP(scenarios, coupling_vars)
x = nlp.x_init()
y = nlp.y_init()
jac_c = nlp.jacobian_c(x)
plt.spy(jac_c)
plt.title('Jacobian of the constraints\n')
plt.show()
hess_lag = nlp.hessian_lag(x, y)
plt.spy(hess_lag.tocoo())
plt.title('Hessian of the Lagrangian function\n')
plt.show()
kkt = BlockSymMatrix(2)
kkt[0, 0] = hess_lag
kkt[1, 0] = jac_c
model.x3 - 1 == 0)
model.cost = aml.Objective(expr=model.x1**2 + model.x2**2 + model.x3**2)
model.consteta1 = aml.Constraint(expr=model.eta1 == model.nominal_eta1)
model.consteta2 = aml.Constraint(expr=model.eta2 == model.nominal_eta2)
return model
#################################################################
m = create_model(4.5, 1.0)
opt = aml.SolverFactory('ipopt')
results = opt.solve(m, tee=True)
#################################################################
nlp = PyomoNLP(m)
x = nlp.x_init()
y = compute_init_lam(nlp, x=x)
J = nlp.jacobian_g(x)
H = nlp.hessian_lag(x, y)
M = BlockSymMatrix(2)
M[0, 0] = H
M[1, 0] = J
Np = BlockMatrix(2, 1)
Np[0, 0] = nlp.hessian_lag(x, y, subset_variables_col=[m.eta1, m.eta2])
Np[1, 0] = nlp.jacobian_g(x, subset_variables=[m.eta1, m.eta2])
ds = spsolve(M.tocsc(), Np.tocsc())
print(nlp.variable_order())
for i in range(n_scenarios):
instance = create_basic_dense_qp(G,
A,
bs[i],
c)
nlp = PyomoNLP(instance)
models.append(instance)
scenario_name = "s{}".format(i)
scenarios[scenario_name] = nlp
coupling_vars[scenario_name] = [nlp.variable_idx(instance.x[0])]
nlp = TwoStageStochasticNLP(scenarios, coupling_vars)
x = nlp.x_init()
y = nlp.y_init()
jac_c = nlp.jacobian_c(x)
plt.spy(jac_c)
plt.title('Jacobian of the constraints\n')
plt.show()
hess_lag = nlp.hessian_lag(x, y)
plt.spy(hess_lag.tocoo())
plt.title('Hessian of the Lagrangian function\n')
plt.show()
kkt = BlockSymMatrix(2)
kkt[0, 0] = hess_lag
kkt[1, 0] = jac_c
m.x[3].setlb(0.0)
m.x[2].setub(100.0)
m.obj = aml.Objective(expr=m.x[2]**2)
return m
model = create_basic_model()
solver = aml.SolverFactory('ipopt')
solver.solve(model, tee=True)
# build nlp initialized at the solution
nlp = PyomoNLP(model)
# get initial point
print(nlp.variable_order())
x0 = nlp.x_init()
# vectors of finite lower and upper bounds
xl = nlp.xl(condensed=True)
xu = nlp.xu(condensed=True)
# build expansion matrices
Pxl = nlp.expansion_matrix_xl()
Pxu = nlp.expansion_matrix_xu()
# lower and upper bounds residual
res_xl = Pxl.transpose() * x0 - xl
res_xu = xu - Pxu.transpose() * x0
print("Residuals lower bounds x-xl:", res_xl)
print("Residuals upper bounds xu-x:", res_xu)
m.x[2].setlb(0.0)
m.x[3].setlb(0.0)
m.x[2].setub(100.0)
m.obj = aml.Objective(expr=m.x[2]**2)
return m
model = create_basic_model()
solver = aml.SolverFactory('ipopt')
solver.solve(model, tee=True)
# build nlp initialized at the solution
nlp = PyomoNLP(model)
# get initial point
print(nlp.variable_order())
x0 = nlp.x_init()
# vectors of finite lower and upper bounds
xl = nlp.xl(condensed=True)
xu = nlp.xu(condensed=True)
# build expansion matrices
Pxl = nlp.expansion_matrix_xl()
Pxu = nlp.expansion_matrix_xu()
# lower and upper bounds residual
res_xl = Pxl.transpose() * x0 - xl
res_xu = xu - Pxu.transpose() * x0
print("Residuals lower bounds x-xl:", res_xl)
print("Residuals upper bounds xu-x:", res_xu)
