def make_hessian(sol: Solution,
state: FrozenState) -> Tuple[Solution, Hessian]:
sol = Solution(
fval=sol.fval,
x=sol.x,
grad=sol.grad,
proj=sol.proj,
shifted_constr=sol.shifted_constr,
hess_up_to_date=True,
)
H = findiff(state.gradient, sol.x, state.constraints)
if state.opts.shaking == "x.shape[0]":
max_times = sol.x.shape[0]
else:
max_times = state.opts.shaking
return sol, Hessian(H, max_times=max_times)
def gradient_check(analytic: ndarray, x: ndarray, state: FrozenState,
check: GradientCheck) -> None:
iter: Final[int] = check.iter
gradient_infnorm: Final[float] = check.gradient_infnorm
initial_gradient_infnorm: Final[float] = check.initial_gradient_infnorm
opts: Final[Trust_Region_Options] = state.opts
need_rel_check = (
opts.check_iter is None or iter <= opts.check_iter
) and gradient_infnorm >= initial_gradient_infnorm * opts.check_rel
if need_rel_check or opts.check_abs is not None:
findiff_ = findiff(state.objective_np, x, state.constraints)
assert len(findiff_.shape) == 2 and findiff_.shape[0] == 1
findiff_.shape = (findiff_.shape[1], )
if need_rel_check:
relerr = difference.relative(analytic, findiff_)
if relerr > opts.check_rel:
raise Grad_Check_Failed(iter, relerr, analytic, findiff_)
if opts.check_abs is not None:
abserr = difference.absolute(analytic, findiff_)
if abserr > opts.check_abs:
raise Grad_Check_Failed(iter, abserr, analytic, findiff_)
def test_原点(self) -> None:
x = numpy.array([0, 0], dtype=numpy.float64)
hessian = findiff.findiff(grad, x, (A, b, lb, ub))
compare_hess(hessian, hessian_GT)