def sd(fun, x0, search='inexact', eps=1e-8, maxiter=10000, **kwargs):
"""Steepest descent
Parameters
----------
fun: object
objective function, with callable method f, g and G
x0: ndarray
initial point
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for convergence criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations (f, g and G)
"""
x = x0
f0 = -np.inf
f1 = fun.f(x0)
g1 = fun.g(x0)
niter = 0
neval = 2
while (abs(f1 - f0) > eps) or (norm(g1) > eps):
d = -g1
if search == 'inexact':
alpha, v = ls.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = ls.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
x = x + alpha * d
f0 = f1
f1 = fun.f(x)
g1 = fun.g(x)
niter += 1
neval += (v + 2)
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval
def cg(fun, x0, method='prp+', search='inexact',
eps=1e-8, maxiter=10000, nu=0.2, a_high=.3, debug=False, **kwargs):
"""Non-linear conjugate gradient methods
Parameters
----------
fun: object
objective function, with callable method f and g
x0: ndarray
initial point
method: string, optional
options: 'fr' for FR, 'prp' for PRP, 'prp+' for PRP+, 'hs' for HS,
'cd' for conjugate descent, 'dy' for Dai-Yuan
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for stopping criterion
maxiter: int, optional
maximum number of iterations
nv: float, optional
parameter for restart by orthogonality test
debug: boolean, optional
output information for every iteration if set to True
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations (f and g)
flist: list
list of objective values along the iterations
xlist: list
list of points along the iterations
"""
x = x0
n = x.size
f0 = -np.inf
f1 = fun.f(x)
g0 = np.zeros(n)
g1 = fun.g(x)
d = -g1
niter = 0
neval = 2
flist = []
xlist = []
while (abs(f1 - f0) > eps) or (norm(g1) > eps):
if abs(np.dot(g1, g0)) > 0.2 * np.dot(g1, g1):
d = -g1
if search == 'inexact':
alpha, v = ls.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = ls.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
d = alpha * d
if norm(d, np.inf) > a_high:
d = d / norm(d, np.inf) * a_high
x = x + d
g0 = g1
g1 = fun.g(x)
y = g1 - g0
f0 = f1
f1 = fun.f(x)
neval += (v + 2)
flist.append(f1)
xlist.append(x)
if debug:
print('iter:', niter, alpha)
if method == 'fr':
beta = np.dot(g1, g1) / np.dot(g0, g0)
elif method == 'prp':
beta = np.dot(g1, y) / np.dot(g0, g0)
elif method == 'prp+':
beta = max(np.dot(g1, y) / np.dot(g0, g0), 0)
elif method == 'hs':
beta = np.dot(g1, y) / np.dot(d, y)
elif method == 'cd':
beta = -np.dot(g1, g1) / np.dot(g0, d)
elif method == 'dy':
beta = np.dot(g1, g1) / np.dot(d, y)
else:
raise ValueError('Invalid method name')
d = beta * d - g1
niter += 1
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval, flist, xlist
def momentum(fun,
x0,
search='inexact',
eps=1e-8,
maxiter=10000,
beta=.9,
a_high=.3,
**kwargs):
"""Momentum
Parameters
----------
fun: object
objective function, with callable method f, g
x0: ndarray
initial point
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for convergence criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations (f and g)
flist: list
list of objective values along the iterations
xlist: list
list of points along the iterations
"""
x = x0
f0 = -np.inf
f1 = fun.f(x0)
g1 = fun.g(x0)
niter = 0
neval = 2
d0 = np.zeros(x.size)
flist = []
xlist = []
while (abs(f1 - f0) > eps) or (norm(g1) > eps):
d = beta * d0 - g1
if search == 'inexact':
alpha, v = ls.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = ls.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
d = alpha * d
if norm(d, np.inf) > a_high:
d = d / norm(d, np.inf) * a_high
x = x + d
f0 = f1
f1 = fun.f(x)
g1 = fun.g(x)
d0 = alpha * d
flist.append(f1)
xlist.append(x)
niter += 1
neval += (v + 2)
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval, flist, xlist
def DennisGayWelsch(fun, x0, B0=None, method='ls', search='inexact',
eps=1e-8, maxiter=1000, **kwargs):
"""Dennis-Gay-Welsch method: line search or trust region
Parameters
----------
fun: object
objective function, with callable method eval (J, r, f, g)
x0: ndarray
initial point
B0: ndarray, optional
initial approximation of matrix S, half the identity by default
method: string, optional
'ls' for DGW with line search, 'tr' for DGW with trust region
search: string, optional
used only when method == 'ls'
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for stopping criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations
"""
def dogleg(G, g1):
d_SD = -g1
d_GN = np.linalg.solve(G, -g1)
alpha = norm(d_SD) ** 2 / np.dot(d_SD, G @ d_SD)
if norm(d_GN) < delta:
d = d_GN
elif alpha * norm(d_SD) > delta:
d = delta * (d_SD / norm(d_SD))
else:
D = norm(d_GN - alpha * d_SD) ** 2
E = 2 * alpha * np.dot(d_SD, d_GN - alpha * d_SD)
F = (alpha * norm(d_SD)) ** 2 - delta ** 2
det = E ** 2 - 4 * D * F
beta = (-E + sqrt(det)) / (2 * D)
d = (1 - beta) * alpha * d_SD + beta * d_GN
return d
x = x0
f0 = -np.inf
J, r, f1, g1 = fun.eval(x)
B = .5 * np.identity(x.size) if B0 is None else B0
delta = 1 # radius of trust-region
niter = 0
neval = 1
uflag = True
while (abs(f1 - f0) >= eps * abs(f0)):
# DGW with line search routine
if method == 'ls':
d = np.linalg.solve(np.dot(J.T, J) + B, -g1)
if np.dot(g1, d) > 0:
d = np.linalg.solve(np.dot(J.T, J), -g1)
if search == 'inexact':
alpha, v = linesearch.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = linesearch.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
s = alpha * d
# DGW with trust region (dogleg) routine
elif method == 'tr':
G = np.dot(J.T, J) + B
d = dogleg(G, g1)
delta_f = f1 - fun.f(x + d)
if delta_f < 0:
G = np.dot(J.T, J)
d = dogleg(G, g1)
delta_f = f1 - fun.f(x + d)
delta_q = -np.dot(d, g1) - np.dot(d, G @ d) / 2
gamma = delta_f / delta_q
uflag = False
if gamma < .25:
delta = delta / 4
if gamma > .75 and abs(norm(d) - delta) < 1e-8 * delta:
delta = delta * 2
if gamma > 0:
s = d
uflag = True
else:
raise ValueError('Invalid method name')
# Update of point x and matrix approximation B
if uflag == True:
x = x + s
g0 = g1
f0 = f1
J0 = J
J, r, f1, g1 = fun.eval(x)
neval += 1
y = g1 - g0
y_hat = g1 - np.dot(J0.T, r)
# Scaling for faster convergence
tau = min(1, abs(np.dot(s, y_hat) / np.dot(s, B @ s)))
B = tau * B
z = y_hat - B @ s
t = np.dot(y, s)
B = B + np.outer(z, y) / t + np.outer(y, z) / t \
- np.outer(y * np.dot(z, s) / t, y / t)
niter += 1
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval
def GaussNewton(fun, x0, method=None, search='inexact',
eps=1e-8, maxiter=1000, **kwargs):
"""Gauss-Newton (GN) method
Parameters
----------
fun: object
objective function, with callable method eval (J, r, f, g)
x0: ndarray
initial point
method: string, optional
ignored
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for stopping criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations
"""
x = x0
f0 = -np.inf
J, r, f1, g1 = fun.eval(x)
niter = 0
neval = 1
while (abs(f1 - f0) >= eps * abs(f0)):
d = np.linalg.lstsq(J, -r, rcond=None)[0]
if search == 'inexact':
alpha, v = linesearch.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = linesearch.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
x = x + alpha * d
f0 = f1
J, r, f1, g1 = fun.eval(x)
niter += 1
neval += (v + 1)
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval
def quasiNewton(fun,
x0,
H0=None,
method='bfgs',
search='inexact',
eps=1e-8,
maxiter=1000,
**kwargs):
"""Quasi-Newton methods: SR1 / DFP / BFGS
Parameters
----------
fun: object
objective function, with callable method f and g
x0: ndarray
initial point
H0: ndarray, optional
initial Hessian inverse, identity by default
method: string, optional
'sr1' for SR1, 'dfp' for DFP, 'bfgs' for BFGS
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for convergence criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations (f and g)
"""
x = x0
if H0 is not None:
H = H0
else:
H = np.eye(x.size)
f0 = -np.inf
f1 = fun.f(x)
g0 = np.zeros(x.size)
g1 = fun.g(x)
niter = 0
neval = 2
while (abs(f1 - f0) > eps) or (norm(g1) > eps):
d = -(H @ g1)
if search == 'inexact':
alpha, v = ls.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = ls.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
s = alpha * d
x = x + s
g0 = g1
g1 = fun.g(x)
y = g1 - g0
if f0 == 0 and H0 is None: # initial scaling
H = (np.dot(y, s) / np.dot(y, y)) * H
f0 = f1
f1 = fun.f(x)
neval += (v + 2)
if method == 'sr1':
z = s - H @ y
if abs(np.dot(z, y)) >= eps * norm(z) * norm(y):
H = H + np.outer(z, z / np.dot(z, y))
elif method == 'dfp':
z = H @ y
H = H + np.outer(s, s / np.dot(s, y)) - np.outer(
z, z / np.dot(y, z))
elif method == 'bfgs':
r = 1 / np.dot(s, y)
z = r * (H @ y)
H = H + r * (1 + np.dot(y, z)) * np.outer(s, s) \
- np.outer(s, z) - np.outer(z, s)
else:
raise ValueError('Invalid method name')
niter += 1
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval
def Newton(fun,
x0,
method='damped',
search='inexact',
eps=1e-8,
maxiter=1000,
**kwargs):
"""Newton's method: normal or damped
Parameters
----------
fun: object
objective function, with callable method f, g and G
x0: ndarray
initial point
method: string, optional
'normal' for Normal Newton, 'damped' for damped Newton
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for convergence criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations (f, g and G)
"""
x = x0
f0 = -np.inf
f1 = fun.f(x0)
g1 = fun.g(x0)
niter = 0
neval = 2
errflag = 0
while (abs(f1 - f0) > eps) or (norm(g1) > eps):
G = fun.G(x)
try: # test if positive definite
L = np.linalg.cholesky(G)
except np.linalg.LinAlgError:
errflag = 1
d = np.linalg.solve(G, -g1)
if method == 'normal':
alpha, v = 1, 0
elif method == 'damped':
if search == 'inexact':
alpha, v = ls.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = ls.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
else:
raise ValueError('Invalid method name')
x = x + alpha * d
f0 = f1
f1 = fun.f(x)
g1 = fun.g(x)
niter += 1
neval += (v + 3)
if niter == maxiter:
break
if errflag == 1:
print('Warning: Non-positive-definite Hessian encountered.')
return x, f1, norm(g1), niter, neval
def modifiedNewton(fun,
x0,
method='mix',
search='inexact',
eps=1e-8,
maxiter=1000,
**kwargs):
"""Modified Newton's method: mixed direction or LM method
Parameters
----------
fun: object
objective function, with callable method f, g and G
x0: ndarray
initial point
method: string, optional
'mix' for mixed direction method, 'lm' for Levenberg-Marquardt method
search: string, optional
'exact' for exact line search, 'inexact' for inexact line search
eps: float, optional
tolerance, used for convergence criterion
maxiter: int, optional
maximum number of iterations
kwargs: dict, optional
other arguments to pass down
Returns
-------
x: ndarray
optimal point
f: float
optimal function value
gnorm: float
norm of gradient at optimal point
niter: int
number of iterations
neval: int
number of function evaluations (f, g and G)
"""
x = x0
f0 = -np.inf
f1 = fun.f(x0)
g1 = fun.g(x0)
niter = 0
neval = 2
while (abs(f1 - f0) > eps) or (norm(g1) > eps):
if method == 'mix':
try: # test if singular
d = np.linalg.solve(fun.G(x), -g1)
if abs(np.dot(g1, d)) < eps * norm(g1) * norm(d): # orthogonal
d = -g1
if np.dot(g1, d) > eps * norm(g1) * norm(d): # non-descent
d = -d
except np.linalg.LinAlgError:
d = -g1
elif method == 'lm':
G = fun.G(x)
v = 0
while True:
try: # test if positive definite
L = np.linalg.cholesky(G + v * np.eye(x.size))
break
except np.linalg.LinAlgError:
if v == 0:
v = norm(G) / 2 # Frobenius norm
else:
v *= 2
y = np.linalg.solve(L, -g1)
d = np.linalg.solve(L.T, y)
else:
raise ValueError('Invalid method name')
if search == 'inexact':
alpha, v = ls.inexact(fun, x, d, fx=f1, gx=g1, **kwargs)
elif search == 'exact':
alpha, v = ls.exact(fun, x, d, **kwargs)
else:
raise ValueError('Invalid search type')
x = x + alpha * d
f0 = f1
f1 = fun.f(x)
g1 = fun.g(x)
niter += 1
neval += (v + 3)
if niter == maxiter:
break
return x, f1, norm(g1), niter, neval
