nocedal_wright_interpol(func,
args,
x,
f,
g,
p,
a_init=1.0,
mu=0.001,
print_flag=0)
| source code
|
A line search algorithm based on interpolation.
Pages 56-57, from 'Numerical Optimization' by Jorge Nocedal and
Stephen J. Wright, 1999, 2nd ed.
Requires the gradient function.
- Parameters:
func (func) - The function to minimise.args (tuple) - The tuple of arguments to supply to the functions func.x (numpy array) - The parameter vector at minimisation step k.f (float) - The function value at the point x.g (numpy array) - The function gradient vector at the point x.p (numpy array) - The descent direction.a_init (flaot) - Initial step length.mu (float) - Constant determining the slope for the sufficient decrease
condition (0 < mu < 1).print_flag (int) - The higher the value, the greater the amount of info printed out.
|