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)
alpha > 0 is assumed to be a descent direction.
Uses the line search algorithm to enforce strong Wolfe conditions. See Wright and Nocedal, 'Numerical Optimization', 1999, pp. 59-61.
Objective scalar function.
Objective function derivative. Returns a scalar.
Value of phi at 0.
Value of phi at previous point.
Value of derphi at 0
Parameter for Armijo condition rule.
Parameter for curvature condition rule.
Maximum step size.
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.
Maximum number of iterations to perform.
Best alpha, or None if the line search algorithm did not converge.
phi at alpha_star.
phi at 0.
derphi at alpha_star, or None if the line search algorithm did not converge.
Find alpha that satisfies strong Wolfe conditions.
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scipy.optimize._linesearch._zoom
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