Module nocedal_wright_wolfe

Functions

nocedal_wright_wolfe(func, func_prime, args, x, f, g, p, a_init=1.0, max_a=100000.0, mu=0.001, eta=0.9, tol=1e-10, print_flag=0)
A line search algorithm implemented using the strong Wolfe conditions.

print_data(text, k, a)
Temp func for debugging.

zoom(func, func_prime, args, f_count, g_count, x, f, g, p, mu, eta, i, a0, a_lo, a_hi, tol, print_flag=0)
Find the minimum function value in the open interval (a_lo, a_hi)

Imports: copy, dot, sqrt, cubic_ext, quadratic_fafbga, quadratic

Function Details

nocedal_wright_wolfe(func, func_prime, args, x, f, g, p, a_init=1.0, max_a=100000.0, mu=0.001, eta=0.9, tol=1e-10, print_flag=0)

A line search algorithm implemented using the strong Wolfe conditions.

Algorithm 3.2, page 59, from 'Numerical Optimization' by Jorge Nocedal and Stephen J. Wright,
1999, 2nd ed.

Requires the gradient function.

#######################################################################################
These functions require serious debugging and recoding to work properly (especially the
safeguarding).
#######################################################################################

Function options
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func       - The function to minimise.
func_prime - The function which returns the gradient vector.
args       - The tuple of arguments to supply to the functions func and dfunc.
x          - The parameter vector at minimisation step k.
f          - The function value at the point x.
g          - The function gradient vector at the point x.
p          - The descent direction.
a_init     - Initial step length.
a_max      - The maximum value for the step length.
mu         - Constant determining the slope for the sufficient decrease condition
    (0 < mu < eta < 1).
eta        - Constant used for the Wolfe curvature condition (0 < mu < eta < 1).

zoom(func, func_prime, args, f_count, g_count, x, f, g, p, mu, eta, i, a0, a_lo, a_hi, tol, print_flag=0)

Find the minimum function value in the open interval (a_lo, a_hi)

Algorithm 3.3, page 60, from 'Numerical Optimization' by Jorge Nocedal and Stephen J. Wright, 1999, 2nd ed.