scipy.optimize.line_search

scipy.optimize.line_search(f, myfprime, xk, pk, gfk, old_fval, old_old_fval, args=(), c1=0.0001, c2=0.90000000000000002, amax=50)

Find alpha that satisfies strong Wolfe conditions.

Parameters:

f : callable f(x,*args)

Objective function.

myfprime : callable f’(x,*args)

Objective function gradient (can be None).

xk : ndarray

Starting point.

pk : ndarray

Search direction.

gfk : ndarray

Gradient value for x=xk (xk being the current parameter estimate).

args : tuple

Additional arguments passed to objective function.

c1 : float

Parameter for Armijo condition rule.

c2 : float

Parameter for curvature condition rule.

Returns:

alpha0 : float

Alpha for which x_new = x0 + alpha * pk.

fc : int

Number of function evaluations made.

gc : int

Number of gradient evaluations made.

Notes:

Uses the line search algorithm to enforce strong Wolfe conditions. See Wright and Nocedal, ‘Numerical Optimization’, 1999, pg. 59-60.

For the zoom phase it uses an algorithm by [...].