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A strong Wolfe step exists on a bounded-below descent ray
Statement
Let be open, let be , and let and satisfy for every . Let and suppose and is bounded below on . For the constants of Wolfe and strong Wolfe line-search conditions, the set of positive strong Wolfe steps contains a nonempty open interval.
Facts & Assumptions
Given: as in the Statement.
Wolfe and strong Wolfe line-search conditions: ; a strong Wolfe step satisfies and , with .
The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with : if a real function is continuous on and differentiable on , then for some its endpoint increment equals .
The derivative of at a point that is a limit point of , and differentiability on a set: when the derivative exists at .
Every nonempty set bounded below has an infimum: every nonempty subset of that is bounded below has an infimum, characterized as its greatest lower bound.
Proof
Given: The hypotheses and notation in the Statement.
The chain rule in [F1] makes continuously differentiable on a neighborhood of each finite point of . Define . Then and . By [F3], as . Choose so that for ; hence there.
Since is bounded below and , as . Thus is nonempty and bounded below by , so [F4] gives . Step 1.1 shows that has no points near , hence . Every has , while the defining approximation property of the infimum gives points of arbitrarily close above . Continuity of therefore forces .
Every has , so Armijo holds strictly throughout that interval. Apply [F2] to on to get with . Hence , so the strong curvature inequality holds strictly at .
The strict Armijo inequality holds at every point of , including , and both and are continuous there. Therefore both strict inequalities persist on some open interval about contained in . Every point of that interval is a strong Wolfe step, proving the asserted nonemptiness.
Depends on
- Wolfe and strong Wolfe line-search conditions
- Continuously differentiable maps, local inverses, and local diffeomorphisms
- The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- The chain rule for total derivatives: $D(g\circ f)(a)=Dg(f(a))\circ Df(a)$
- The derivative $f'(c) = \lim_{x \to c} \frac{f(x) - f(c)}{x - c}$ of $f : A \to \mathbb{R}$ at a point $c \in A$ that is a limit point of $A$, and differentiability on a set
- The mean value theorem, as the case $g(x) = x$ of Cauchy's: for $f$ continuous on $[a,b]$ with $a < b$ and differentiable on $(a,b)$ there is $c \in (a,b)$ with $f(b) - f(a) = f'(c)(b-a)$
- Every nonempty set bounded below has an infimum
Used by
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Sources
- Jorge Nocedal and Stephen J. Wright, Numerical Optimization, 2nd ed. (standard reference, not scraped)