def get_sparsity_in(self, i):
n = nlpsol_out(i)
if n == "f":
return Sparsity.scalar()
elif n in ("x", "lam_x"):
return Sparsity.dense(self.nx)
elif n in ("g", "lam_g"):
return Sparsity.dense(self.ng)
else:
return Sparsity(0, 0)
def get_sparsity_in(self, i: int) -> tuple:
"""
Get the sparsity of a specific variable
Parameters
----------
i: int
The index of the variable
Returns
-------
The sparsity of the variable
"""
n = nlpsol_out(i)
if n == "f":
return Sparsity.scalar()
elif n in ("x", "lam_x"):
return Sparsity.dense(self.nx)
elif n in ("g", "lam_g"):
return Sparsity.dense(self.ng)
else:
return Sparsity(0, 0)
ocp.cost.cost_type_e = 'EXTERNAL'
W_u = 1e-3
theta = model.x[1]
ocp.model.cost_expr_ext_cost = tanh(theta)**2 + .5 * (model.x[0]**2 +
W_u * model.u**2)
ocp.model.cost_expr_ext_cost_e = tanh(theta)**2 + .5 * model.x[0]**2
custom_hess_u = W_u
J = horzcat(SX.eye(2), SX(2, 2))
print(DM(J.sparsity()))
# diagonal matrix with second order terms of outer loss function.
D = SX.sym('D', Sparsity.diag(2))
D[0, 0] = 1
[hess_tan, grad_tan] = hessian(tanh(theta)**2, theta)
D[1, 1] = if_else(theta == 0, hess_tan, grad_tan / theta)
custom_hess_x = J.T @ D @ J
zeros = SX(1, nx)
cost_expr_ext_cost_custom_hess = blockcat(custom_hess_u, zeros, zeros.T,
custom_hess_x)
cost_expr_ext_cost_custom_hess_e = custom_hess_x
ocp.model.cost_expr_ext_cost_custom_hess = cost_expr_ext_cost_custom_hess
ocp.model.cost_expr_ext_cost_custom_hess_e = cost_expr_ext_cost_custom_hess_e
# set constraints
