def test_variables(self):
"""Test the variables method.
"""
p = Problem(Minimize(self.a), [self.a <= self.x, self.b <= self.A + 2])
vars_ = p.variables()
ref = [self.a, self.x, self.b, self.A]
if PY2:
self.assertItemsEqual(vars_, ref)
else:
self.assertCountEqual(vars_, ref)
def test_variables(self):
"""Test the variables method.
"""
p = Problem(Minimize(self.a), [self.a <= self.x, self.b <= self.A + 2])
vars_ = p.variables()
ref = [self.a, self.x, self.b, self.A]
if PY2:
self.assertItemsEqual(vars_, ref)
else:
self.assertCountEqual(vars_, ref)
def test_variables(self):
"""Test the variables method.
"""
p = Problem(Minimize(self.a), [self.a <= self.x, self.b <= self.A + 2])
vars_ = p.variables()
self.assertItemsEqual(vars_, [self.a, self.x, self.b, self.A])
def test_variables(self):
"""Test the variables method.
"""
p = Problem(Minimize(self.a), [self.a <= self.x, self.b <= self.A + 2])
vars_ = p.variables()
self.assertItemsEqual(vars_, [self.a, self.x, self.b, self.A])
def partial_optimize(prob: Problem,
opt_vars: Optional[List[Variable]] = None,
dont_opt_vars: Optional[List[Variable]] = None,
solver=None,
**kwargs) -> "PartialProblem":
"""Partially optimizes the given problem over the specified variables.
Either opt_vars or dont_opt_vars must be given.
If both are given, they must contain all the variables in the problem.
Partial optimize is useful for two-stage optimization and graph implementations.
For example, we can write
.. code :: python
x = Variable(n)
t = Variable(n)
abs_x = partial_optimize(Problem(Minimize(sum(t)),
[-t <= x, x <= t]), opt_vars=[t])
to define the entrywise absolute value of x.
Parameters
----------
prob : Problem
The problem to partially optimize.
opt_vars : list, optional
The variables to optimize over.
dont_opt_vars : list, optional
The variables to not optimize over.
solver : str, optional
The default solver to use for value and grad.
kwargs : keywords, optional
Additional solver specific keyword arguments.
Returns
-------
Expression
An expression representing the partial optimization.
Convex for minimization objectives and concave for maximization objectives.
"""
# One of the two arguments must be specified.
if opt_vars is None and dont_opt_vars is None:
raise ValueError(
"partial_optimize called with neither opt_vars nor dont_opt_vars.")
# If opt_vars is not specified, it's the complement of dont_opt_vars.
elif opt_vars is None:
ids = [id(var) for var in dont_opt_vars]
opt_vars = [var for var in prob.variables() if not id(var) in ids]
# If dont_opt_vars is not specified, it's the complement of opt_vars.
elif dont_opt_vars is None:
ids = [id(var) for var in opt_vars]
dont_opt_vars = [var for var in prob.variables() if not id(var) in ids]
elif opt_vars is not None and dont_opt_vars is not None:
ids = [id(var) for var in opt_vars + dont_opt_vars]
for var in prob.variables():
if id(var) not in ids:
raise ValueError(
("If opt_vars and new_opt_vars are both specified, "
"they must contain all variables in the problem."))
# Replace the opt_vars in prob with new variables.
id_to_new_var = {
id(var): Variable(var.shape, **var.attributes)
for var in opt_vars
}
new_obj = prob.objective.tree_copy(id_to_new_var)
new_constrs = [con.tree_copy(id_to_new_var) for con in prob.constraints]
new_var_prob = Problem(new_obj, new_constrs)
return PartialProblem(new_var_prob, opt_vars, dont_opt_vars, solver,
**kwargs)
