def run_leastsq_bound(problem, ftol, xtol, gtol, jac,
scaling=None, **kwargs):
bounds, lb, ub = scipy_bounds(problem)
if scaling is None:
diag = np.ones_like(problem.x0)
else:
diag = None
if jac in ['2-point', '3-point']:
jac = None
else:
jac = problem.jac
x, cov_x, info, mesg, ier = leastsqbound(
problem.fun, problem.x0, bounds=bounds, full_output=True,
Dfun=jac, ftol=ftol, xtol=xtol, gtol=gtol, diag=diag, **kwargs)
x = make_strictly_feasible(x, lb, ub)
f = problem.fun(x)
g = problem.grad(x)
optimality = CL_optimality(x, g, lb, ub)
active = find_active_constraints(x, lb, ub)
return (info['nfev'], optimality, 0.5 * np.dot(f, f),
np.sum(active != 0), ier)
def run_l_bfgs_b(problem, ftol, gtol, xtol, jac):
bounds, l, u = scipy_bounds(problem)
factr = ftol / np.finfo(float).eps
if jac in ['2-point', '3-point']:
grad = None
approx_grad = True
else:
grad = problem.grad
approx_grad = False
x, cost, info = fmin_l_bfgs_b(
problem.obj_value, problem.x0, fprime=grad, bounds=bounds,
approx_grad=approx_grad, m=100, factr=factr, pgtol=gtol, iprint=-1)
g = problem.grad(x)
optimality = CL_optimality(x, g, l, u)
active = find_active_constraints(x, l, u)
return (info['funcalls'], optimality, cost, np.sum(active != 0),
info['warnflag'])
