def power_matrix(self, solver):
p = Problem(Minimize(sum(power(self.A - 3., 2))), [])
self.solve_QP(p, solver)
for var in p.variables():
self.assertItemsAlmostEqual([3., 3., 3., 3.], var.value, places=4)
def power_inv(t):
if expr.p.value == 1:
return t
return atoms.power(t, 1 /
expr.p.value) if t.is_nonneg() else np.inf
def power_matrix(self, solver):
p = Problem(Minimize(sum(power(self.A - 3., 2))), [])
s = self.solve_QP(p, solver)
for var in p.variables():
self.assertItemsAlmostEqual([3., 3., 3., 3.],
s.primal_vars[var.id])
def power(self, solver):
p = Problem(Minimize(sum(power(self.x, 2))), [])
s = self.solve_QP(p, solver)
for var in p.variables():
self.assertItemsAlmostEqual([0., 0.], var.value, places=4)
def power_inv(t):
if expr.p == 1:
return t
return atoms.power(t, 1 / expr.p) if t.value > 0 else np.inf
