def line_search_wolfe1(
state: OptimisationState,
old_state: Optional[OptimisationState] = None,
c1=1e-4,
c2=0.9,
amax=50,
amin=1e-8,
xtol=1e-14,
extra_condition=None,
**kwargs,
) -> Tuple[Optional[float], OptimisationState]:
"""
As `scalar_search_wolfe1` but do a line search to direction `pk`
Parameters
----------
f : callable
Function `f(x)`
fprime : callable
Gradient of `f`
xk : array_like
Current point
pk : array_like
Search direction
gfk : array_like, optional
Gradient of `f` at point `xk`
old_fval : float, optional
Value of `f` at point `xk`
old_old_fval : float, optional
Value of `f` at point preceding `xk`
The rest of the parameters are the same as for `scalar_search_wolfe1`.
Returns
-------
stp, f_count, g_count, fval, old_fval
As in `line_search_wolfe1`
gval : array
Gradient of `f` at the final point
"""
derphi0 = state.derphi(0)
old_fval = state.value
stepsize, _, _ = linesearch.scalar_search_wolfe1(
state.phi,
state.derphi,
-old_fval, # we are actually performing maximisation
old_state and -old_state.value,
derphi0,
c1=c1,
c2=c2,
amax=amax,
amin=amin,
xtol=xtol,
)
next_state = state.step(stepsize)
if stepsize is not None and extra_condition is not None:
if not extra_condition(stepsize, next_state):
stepsize = None
return stepsize, next_state
def test_scalar_search_wolfe1(self):
c = 0
for name, phi, derphi, old_phi0 in self.scalar_iter():
c += 1
s, phi1, phi0 = ls.scalar_search_wolfe1(phi, derphi, phi(0),
old_phi0, derphi(0))
assert_equal(phi0, phi(0), name)
assert_equal(phi1, phi(s), name)
assert_wolfe(s, phi, derphi, err_msg=name)
assert_(c > 3) # check that the iterator really works...
def test_scalar_search_wolfe1(self):
c = 0
for name, phi, derphi, old_phi0 in self.scalar_iter():
c += 1
s, phi1, phi0 = ls.scalar_search_wolfe1(phi, derphi, phi(0),
old_phi0, derphi(0))
assert_fp_equal(phi0, phi(0), name)
assert_fp_equal(phi1, phi(s), name)
assert_wolfe(s, phi, derphi, err_msg=name)
assert_(c > 3) # check that the iterator really works...