def _generate_model(self):
x = self._generate_base_model()
model = self.model
A, lb, ub, eq_index = self._get_dense_data()
model.Amatrix = pmo.matrix_constraint(
A, lb=lb, ub=ub, x=x, sparse=True)
for i in eq_index:
assert model.Amatrix[i].lb == \
model.Amatrix[i].ub
model.Amatrix[i].rhs = \
model.Amatrix[i].lb
def _generate_model(self):
x = self._generate_base_model()
model = self.model
A, lb, ub, eq_index = self._get_dense_data()
model.Amatrix = pmo.matrix_constraint(
A, lb=lb, ub=ub, x=x, sparse=True)
for i in eq_index:
assert model.Amatrix[i].lb == \
model.Amatrix[i].ub
model.Amatrix[i].rhs = \
model.Amatrix[i].lb
def _generate_model(self):
variable_order = self._generate_base_model()
model = self.model
A, lb, ub, eq_index = self._get_dense_data()
model.Amatrix = pmo.matrix_constraint(
A, lb=lb, ub=ub,
variable_order=variable_order,
sparse=False)
for i in eq_index:
assert model.Amatrix[i].lb == \
model.Amatrix[i].ub
model.Amatrix[i].rhs = \
model.Amatrix[i].lb
def bounding_box(A, b, solver_name="gurobi"):
"""
Find upper and lower bounds for each variable.
Parameters
----------
A : np.ndarray
e.g. lhs of polytope for N-X secured network
b : np.ndarray
e.g. rhs of polytope for N-X secured network
solver_name : str, default "gurobi"
Solver
Returns
-------
np.ndarray
lower bounds
np.ndarray
upper bounds
"""
dim = A.shape[1]
model = pmo.block()
variables = []
for i in range(dim):
setattr(model, f"x{i}", pmo.variable())
variables.append(getattr(model, f"x{i}"))
model.A = pmo.matrix_constraint(A, ub=b, x=variables, sparse=True)
opt = pmo.SolverFactory(solver_name)
lower_upper_bounds = []
for sense in [pmo.minimize, pmo.maximize]:
bounds = []
for i in range(dim):
model.objective = pmo.objective(getattr(model, f"x{i}"),
sense=sense)
result = opt.solve(model)
assert str(result.solver.termination_condition) == "optimal"
bounds.append(result["Problem"][0]["Lower bound"])
del model.objective
bounds = np.array(bounds).reshape(len(bounds), 1)
lower_upper_bounds.append(bounds)
return tuple(lower_upper_bounds)
def build_matrix_constraint():
"""Build a constraint_list with no references to external
objects so its size can be computed."""
return matrix_constraint(A, rhs=b, x=X_kernel)
def build_matrix_constraint():
"""Build a constraint_list with no references to external
objects so its size can be computed."""
return matrix_constraint(A, rhs=b, x=X_kernel)