# Wolfe conditions

In the unconstrained minimization problem, the Wolfe conditions are a set of inequalities for performing inexact line search, especially in quasi-Newton methods, first published by Philip Wolfe in 1969.[1][2]

In these methods the idea is to find

$\min_x f(\mathbf{x})$

for some smooth $f:\mathbb R^n\to\mathbb R$. Each step often involves approximately solving the subproblem

$\min_{\alpha} f(\mathbf{x}_k + \alpha \mathbf{p}_k)$

where $\mathbf{x}_k$ is the current best guess, $\mathbf{p}_k \in \mathbb R^n$ is a search direction, and $\alpha \in \mathbb R$ is the step length.

The inexact line searches provide an efficient way of computing an acceptable step length $\alpha$ that reduces the objective function 'sufficiently', rather than minimizing the objective function over $\alpha\in\mathbb R^+$ exactly. A line search algorithm can use Wolfe conditions as a requirement for any guessed $\alpha$, before finding a new search direction $\mathbf{p}_k$.

## Armijo rule and curvature

Denote a univariate function $\phi$ restricted to the direction $\mathbf{p}_k$ as $\phi(\alpha)=f(\mathbf{x}_k+\alpha\mathbf{p}_k)$. A step length $\alpha_k$ is said to satisfy the Wolfe conditions if the following two inequalities hold:

i) $f(\mathbf{x}_k+\alpha_k\mathbf{p}_k)\leq f(\mathbf{x}_k)+c_1\alpha_k\mathbf{p}_k^{\mathrm T}\nabla f(\mathbf{x}_k)$,
ii) $\mathbf{p}_k^{\mathrm T}\nabla f(\mathbf{x}_k+\alpha_k\mathbf{p}_k) \geq c_2\mathbf{p}_k^{\mathrm T}\nabla f(\mathbf{x}_k)$,

with $0. (In examining condition (ii), recall that to ensure that $\mathbf{p}_k$ is a descent direction, we have $\mathbf{p}_k^{\mathrm T}\nabla f(\mathbf{x}_k) < 0$.)

$c_1$ is usually chosen to be quite small while $c_2$ is much larger; Nocedal[3] gives example values of $c_1=10^{-4}$ and $c_2=0.9$ for Newton or quasi-Newton methods and $c_2=0.1$ for the nonlinear conjugate gradient method. Inequality i) is known as the Armijo rule[4] and ii) as the curvature condition; i) ensures that the step length $\alpha_k$ decreases $f$ 'sufficiently', and ii) ensures that the slope has been reduced sufficiently.

## Strong Wolfe condition on curvature

The Wolfe conditions, however, can result in a value for the step length that is not close to a minimizer of $\phi$. If we modify the curvature condition to the following,

iii) $\big|\mathbf{p}_k^{\mathrm T}\nabla f(\mathbf{x}_k+\alpha_k\mathbf{p}_k)\big|\leq c_2\big|\mathbf{p}_k^{\mathrm T}\nabla f(\mathbf{x}_k)\big|$

then i) and iii) together form the so-called strong Wolfe conditions, and force $\alpha_k$ to lie close to a critical point of $\phi$.

## Rationale

The principal reason for imposing the Wolfe conditions in an optimization algorithm where $\mathbf{x}_{k+1} = \mathbf{x}_k + \alpha \mathbf{p}_k$ is to ensure convergence of the gradient to zero. In particular, if the cosine of the angle between $\mathbf{p}_k$ and the gradient,

$\cos \theta_k = \frac {\nabla f(\mathbf{x}_k)^{\mathrm T}\mathbf{p}_k }{\| \nabla f(\mathbf{x}_k)\| \|\mathbf{p}_k\| }$

is bounded away from zero and the i) and ii) conditions hold, then $\nabla f(\mathbf{x}_k) \rightarrow 0$.

An additional motivation, in the case of a quasi-Newton method is that if $\mathbf{p}_k = -B_k^{-1} \nabla f(\mathbf{x}_k)$, where the matrix $B_k$ is updated by the BFGS or DFP formula, then if $B_k$ is positive definite ii) implies $B_{k+1}$ is also positive definite.

## References

1. ^ Wolfe, P. (1969). "Convergence Conditions for Ascent Methods". SIAM Review 11 (2): 226–000. doi:10.1137/1011036. JSTOR 2028111.
2. ^ Wolfe, P. (1971). "Convergence Conditions for Ascent Methods. II: Some Corrections". SIAM Review 13 (2): 185–000. doi:10.1137/1013035.
3. ^ Nocedal, Jorge; Wright, Stephen (1999). Numerical Optimization.
4. ^ Armijo, Larry (1966). "Minimization of functions having Lipschitz continuous first partial derivatives". Pacific J. Math. 16 (1): 1–3.