In the unconstrained minimization problem, the Wolfe conditions are a set of inequalities for performing inexact line search, especially in quasi-Newton methods.
In these methods the idea is to find
for some smooth . Each step often involves approximately solving the subproblem
where is the current best guess, is a search direction, and is the step length.
Then inexact line searches provide an efficient way of computing an acceptable step length that reduces the objective function 'sufficiently', rather than minimizing the objective function over exactly. A line search algorithm can use Wolfe conditions as a requirement for any guessed, before finding a new search direction .
Read more about Wolfe Conditions: Armijo Rule and Curvature, Strong Wolfe Condition On Curvature
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