def find_root(self):
L_alpha = [1.0, 10.0]
L_lam = [[1.0, 2.0, 3.0],
[1.0, 1.0],
[0.0],
[-1.0, 1.0],
[-1.0, 0.0, 1.0],
[1.0, -2.0, -3.0],
[-3.0, -4.0, -5.0, -6.0]]
L_rho = [1e-2, 1e-1, 1e0, 1e1]
L_tol = [1e-3, 1e-5, 1e-10, 1e-14]
for alpha_r, lam, rho, tol in product(L_alpha, L_lam, L_rho, L_tol):
alpha = ones(len(lam)) * alpha_r
lam = array(lam)
tol = rho * tol
if min(lam) > 0:
if calculate_distance(alpha, lam, 0.0) < rho:
continue
print 'alpha', alpha, 'lam', lam, 'rho', rho, 'tol', tol
nu = find_step_size(alpha, lam, rho, tol)
f_nu = calculate_distance(alpha, lam, nu)
print 'nu', nu, 'f(nu)', f_nu
abs(rho - calculate_distance(alpha, lam, nu)) < Assert(tol)
def main():
parser = argparse.ArgumentParser(
description="Test root-finding method to find step size in trust region optimization methods."
)
parser.add_argument("--alpha", type=float)
parser.add_argument("--lam", type=float, nargs="+")
parser.add_argument("--rho", type=float)
args = parser.parse_args()
alpha = args.alpha
lam = array(args.lam)
rho = args.rho
print "α:", alpha
print "λ:", lam
print "ρ:", rho
print "f(0)=", calculate_distance(alpha, lam, 0)
nu = find_step_size(alpha, lam, rho, 1e-14)
print "nu=", nu
print "f(nu)-rho=", calculate_distance(alpha, lam, nu) - rho
rez = 0.01
xx = mgrid[nu / 100.0 : nu * 100 : rez]
yy = array([calculate_distance(alpha, lam, x) for x in xx])
ion()
plot(xx, yy)
plot([nu / 10.0, nu * 10.0], [rho, rho], "r-")
plot(nu, calculate_distance(alpha, lam, nu), "rd")
grid()
code.interact()
