How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Finite-budget bracketing and zoom for a strong Wolfe step
Definition
Let be open, let be ,
let , and let be arbitrary. Fix and finite
budgets for expansion and zoom trials. After the direction check passes, use the functions
and from
Wolfe and strong Wolfe line-search conditions. Output: either a positive
strong Wolfe step, or one of invalid-direction, domain-exit, and
evaluation-budget-exhausted.
- Evaluate and retain and , and set
and .
If , return
invalid-direction. Set and . For at most expansion trials, in order:- If , return
domain-exitwithout evaluating the trial. - Evaluate and . If or , call the zoom procedure below with the ordered endpoint pair and return its output.
- If , return as a strong Wolfe step.
- If , call zoom with the reversed endpoint pair and return its output.
- Otherwise set and .
- If , return
- If the expansion budget is exhausted, return
evaluation-budget-exhausted.
For zoom(a,b), preserve the endpoint order even when . For at most
zoom trials, in order:
- Set . If , return
domain-exitwithout evaluating the trial. - Evaluate and . If or , set . Otherwise, if , return as a strong Wolfe step. In the remaining branch, if , first set ; then set .
- If the zoom budget is exhausted, return
evaluation-budget-exhausted.
The procedure returns a step only after both strong Wolfe inequalities have been checked. Finite budgets and domain exits may return failure even when a strong Wolfe step exists; no convergence claim follows from the procedure alone.
Depends on
Used by
- Finite-budget inexact Newton–Krylov with a descent safeguard Algorithm
- Fletcher–Reeves and safeguarded Polak–Ribière nonlinear conjugate gradients Algorithm
- Full-memory Bfgs with Wolfe search, descent reset, and Powell damping Algorithm
- Limited-memory Bfgs two-loop recursion with curvature filtering Algorithm
- A scalar inexact Newton solve trades inner work for outer error Example
- Newton, Cauchy, and truncated-CG steps on one indefinite model Example
- One accepted Rosenbrock step gives different FR and PR+ coefficients Example
- The standard Rosenbrock start accepts a full Newton step Example
- Bracketing and zoom terminate with a checked step or a failure Lemma
- Finite-budget nonlinear conjugate gradient returns a status Theorem
- Full-memory Bfgs is locally superlinear when the Hessian is Lipschitz Theorem
Dependency tree · two levels
23 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Jorge Nocedal and Stephen J. Wright, Numerical Optimization, 2nd ed. (standard reference, not scraped)