def fmin_bfgs_f(f_g,
x0,
B0=None,
M=2,
gtol=1e-5,
Delta=10.0,
maxiter=None,
callback=None,
norm_ord=np.Inf,
**_kwargs):
"""test BFGS with nonmonote line search"""
fk, gk = f_g(x0)
if B0 is None:
Bk = np.eye(len(x0))
else:
Bk = B0
Hk = np.linalg.inv(Bk)
maxiter = 200 * len(x0) if maxiter is None else maxiter
xk = x0
norm = lambda x: np.linalg.norm(x, ord=norm_ord)
theta = 0.9
C = 0.5
k = 0
old_old_fval = fk + np.linalg.norm(gk) / 2
old_fval = fk
f_s = Seq(M)
f_s.add(fk)
flag = 0
re_search = 0
for k in range(maxiter):
if norm(gk) <= gtol:
break
dki = -np.dot(Hk, gk)
try:
pk = dki
f = f_g.fun
myfprime = f_g.grad
gfk = gk
old_fval = fk
(
alpha_k,
fc,
gc,
old_fval,
old_old_fval,
gfkp1,
) = line_search_wolfe2(f, myfprime, xk, pk, gfk, f_s.get_max(),
old_fval, old_old_fval)
except Exception as e:
print(e)
re_search += 1
xk = xk + dki
fk, gk = f_g(xk)
old_fval, old_old_fval = fk, old_fval
f_s.add(fk)
if re_search > 2:
flag = 1
break
continue
if alpha_k is None:
print("alpha is None")
xk = xk + dki
fk, gk = f_g(xk)
old_fval, old_old_fval = fk, old_fval
f_s.add(fk)
re_search += 1
if re_search > 2:
flag = 1
break
continue
dki = alpha_k * pk
# fki, gki = f_g(xk + dki)
fki, gki = old_fval, gfkp1
Aredk = fk - fki
Predk = -(np.dot(gk, dki) + 0.5 * np.dot(np.dot(Bk, dki), dki))
rk = Aredk / Predk
xk = xk + dki
fk = fki
yk = gki - gk
tk = C + max(0, -np.dot(yk, dki) / norm(dki)**2) / norm(gk)
ystark = (1 - theta) * yk + theta * tk * norm(gk) * dki
gk = gki
bs = np.dot(Bk, dki)
Bk = (Bk + np.outer(yk, yk) / np.dot(yk, dki) -
np.outer(bs, bs) / np.dot(bs, dki))
# sk = dki
# rhok = 1.0 / (np.dot(yk, sk))
# A1 = 1 - np.outer(sk, yk) * rhok
# A2 = 1 - np.outer(yk, sk) * rhok
# Hk = np.dot(A2, np.dot(Hk, A1)) - (rhok * np.outer(sk, sk))
# Bk = Bk + np.outer(ystark, ystark)/np.dot(ystark, dki) - \
# np.outer(bs, bs)/np.dot(bs, dki) # MBFGS
# print(np.dot(Hk, Bk))
try:
Hk = np.linalg.inv(Bk)
except Exception:
pass
f_s.add(fk)
if callback is not None:
callback(xk)
else:
flag = 2
# print("fit final: ", k, p, f_g.ncall)
s = OptimizeResult()
s.messgae = message_dict[flag]
s.fun = float(fk)
s.nit = k
s.nfev = f_g.ncall
s.njev = f_g.ncall
s.status = flag
s.x = np.array(xk)
s.jac = np.array(gk)
s.hess = np.array(Bk)
s.success = flag == 0
return s
