The global convergence of self-scaling BFGS algorithm with non-monotone line search for unconstrained nonconvex optimization problems (Q2644350)
From MaRDI portal
 | This is the item page for this Wikibase entity, intended for internal use and editing purposes. Please use this page instead for the normal view: The global convergence of self-scaling BFGS algorithm with non-monotone line search for unconstrained nonconvex optimization problems |
scientific article
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | The global convergence of self-scaling BFGS algorithm with non-monotone line search for unconstrained nonconvex optimization problems |
scientific article |
Statements
The global convergence of self-scaling BFGS algorithm with non-monotone line search for unconstrained nonconvex optimization problems (English)
0 references
31 August 2007
0 references
The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method for continuous multivariate optimisation is a quasi-Newton method using an estimate of the inverse Hessian which is updated each iteration. Self-scaling versions modify the inverse Hessian estimate's update rule to avoid very large eigenvalues. The algorithm proposed here chooses search directions according to a self-scaling BFGS method, and step lengths along those directions according to a nonmonotone line search technique. Global convergence is proved.
0 references
nonmonotone line search
0 references
self-scaling BFGS
0 references
global convergence
0 references
0 references
0 references
