`scipy.optimize._linesearch:scalar_search_wolfe2`

source: /scipy/optimize/_linesearch.py :329

Signature

   def     scalar_search_wolfe2 (    phi    ,    derphi    ,    phi0    =  None   ,    old_phi0    =  None   ,    derphi0    =  None   ,    c1    =  0.0001   ,    c2    =  0.9   ,    amax    =  None   ,    extra_condition    =  None   ,    maxiter    =  10     )

Summary

Find alpha that satisfies strong Wolfe conditions.

Extended Summary

alpha > 0 is assumed to be a descent direction.

Parameters

phi : callable phi(alpha): Objective scalar function.
derphi : callable phi'(alpha): Objective function derivative. Returns a scalar.
phi0 : float, optional: Value of phi at 0.
old_phi0 : float, optional: Value of phi at previous point.
derphi0 : float, optional: Value of derphi at 0
c1 : float, optional: Parameter for Armijo condition rule.
c2 : float, optional: Parameter for curvature condition rule.
amax : float, optional: Maximum step size.
extra_condition : callable, optional: A callable of the form extra_condition(alpha, phi_value) returning a boolean. The line search accepts the value of alpha only if this callable returns True. If the callable returns False for the step length, the algorithm will continue with new iterates. The callable is only called for iterates satisfying the strong Wolfe conditions.
maxiter : int, optional: Maximum number of iterations to perform.

Returns

alpha_star : float or None: Best alpha, or None if the line search algorithm did not converge.
phi_star : float: phi at alpha_star.
phi0 : float: phi at 0.
derphi_star : float or None: derphi at alpha_star, or None if the line search algorithm did not converge.

Notes

Uses the line search algorithm to enforce strong Wolfe conditions. See Wright and Nocedal, 'Numerical Optimization', 1999, pp. 59-61.

Aliases

scipy.optimize._linesearch.scalar_search_wolfe2