def gen_state_output_vars(self, variables=None,
x_k=None, omega_tilde_k=None,
N_p=None, N_tilde=None,
mld_numeric_k: MldModel = None, mld_numeric_tilde=None,
mld_info_k: MldInfo = None):
state_output_vars = StructDict({var_name: EvoVariableStruct() for var_name in self._state_output_vars})
x_k = x_k if x_k is not None else self.x_k
omega_tilde_k = omega_tilde_k if omega_tilde_k is not None else self.omega_tilde_k
variables = variables or self
if mld_info_k.nx:
state_output_vars.x.var_N_tilde = (
matmul(self.mld_evo_matrices.state_input['Phi_x_N_tilde'], x_k) +
matmul(self.mld_evo_matrices.state_input['Gamma_v_N_tilde'], variables.v.var_N_tilde) +
matmul(self.mld_evo_matrices.state_input['Gamma_omega_N_tilde'], omega_tilde_k) +
self.mld_evo_matrices.state_input['Gamma_5_N_tilde']
)
else:
state_output_vars.x.var_N_tilde = np.empty((0, 1))
if mld_info_k.ny:
state_output_vars.y.var_N_tilde = (
matmul(self.mld_evo_matrices.output['L_x_N_tilde'], x_k) +
matmul(self.mld_evo_matrices.output['L_v_N_tilde'], variables['v']['var_N_tilde']) +
matmul(self.mld_evo_matrices.output['L_omega_N_tilde'], omega_tilde_k) +
self.mld_evo_matrices.output['L_5_N_tilde']
)
else:
state_output_vars.y.var_N_tilde = np.empty((0, 1))
for var_name in self._state_output_vars:
var_N_tilde = state_output_vars[var_name].var_N_tilde
self._set_var_using_var_N_tilde(variable=state_output_vars[var_name],
var_dim=mld_info_k.get_var_dim(var_name),
var_N_tilde=var_N_tilde,
N_p=N_p, N_tilde=N_tilde)
return state_output_vars
def apply_atom_with_matrix_weight(self, variable, weight, var_dim=None):
mat_weighted = matmul(weight, variable.var_N_tilde[:weight.shape[1]])
return cvx.sum(
cvx.norm1(cvx.reshape(mat_weighted,
(var_dim, mat_weighted.size / var_dim)),
axis=0))
def apply_atom_with_matrix_weight(self, variable, weight, var_dim=None):
return cvx.norm1(matmul(weight,
variable.var_N_tilde[:weight.shape[1]]))
def apply_atom_with_vector_weight(self, variable, weight, var_dim=None):
return matmul(weight.T, variable.var_N_tilde[:weight.shape[0]])
def gen_evo_constraints(self,
x_k=None,
omega_tilde_k=None,
omega_scenarios_k=ParNotSet,
N_p=ParNotSet,
N_tilde=ParNotSet,
mld_numeric_k=ParNotSet,
mld_numeric_tilde=ParNotSet,
mld_evo_matrices=ParNotSet):
# H_v @ v_tilde <= H_x @ x_0 + H_omega @ omega_tilde + H_5
x_k = x_k if x_k is not None else self.x_k
omega_tilde_k = omega_tilde_k if omega_tilde_k is not None else self.omega_tilde_k
if mld_evo_matrices is ParNotSet:
mld_numeric_k = mld_numeric_k if mld_numeric_k is not self.mld_numeric_k else ParNotSet
mld_numeric_tilde = mld_numeric_tilde if mld_numeric_tilde is not self.mld_numeric_tilde else ParNotSet
N_p = N_p if N_p is not ParNotSet else self.N_p
N_tilde = N_tilde if N_tilde is not ParNotSet else self.N_tilde
if not N_tilde <= self.N_tilde:
raise ValueError(
f"N_tilde: {N_tilde} must be less or equal to self.N_tilde: {self.N_tilde}"
)
if not N_p <= self.N_tilde:
raise ValueError(
f"N_p: {N_tilde} must be less or equal to self.N_tilde: {self.N_tilde}"
)
if not eq_all_val(mld_numeric_k, mld_numeric_tilde, val=ParNotSet):
mld_evo_matrices = MldEvoMatrices(
self,
N_p=N_p,
N_tilde=N_tilde,
mld_numeric_k=mld_numeric_k,
mld_numeric_tilde=mld_numeric_tilde)
elif N_tilde == self.N_tilde:
mld_evo_matrices = self._mld_evo_matrices
else:
mld_evo_matrices = self._mld_evo_matrices.get_evo_matrices_N_tilde(
N_tilde=N_tilde)
if mld_evo_matrices is not None and omega_scenarios_k is not None:
if mld_evo_matrices.mld_info_k.n_constraints:
if omega_scenarios_k is not ParNotSet:
H_omega_omega = np.min(matmul(
mld_evo_matrices.constraint['H_omega_N_tilde'],
omega_scenarios_k),
axis=1,
keepdims=True)
else:
H_omega_omega = matmul(
mld_evo_matrices.constraint['H_omega_N_tilde'],
omega_tilde_k)
LHS = (matmul(mld_evo_matrices.constraint['H_v_N_tilde'],
self._variables['v']['var_N_tilde']))
RHS = (
matmul(mld_evo_matrices.constraint['H_x_N_tilde'], x_k) +
H_omega_omega + mld_evo_matrices.constraint['H_5_N_tilde'])
return LHS <= RHS
else:
return cvx.Constant(0) <= cvx.Constant(0)
else:
return None