Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4643492 | Journal of Computational and Applied Mathematics | 2006 | 26 Pages |
Abstract
A new family of numerically efficient full-memory variable metric or quasi-Newton methods for unconstrained minimization is given, which give simple possibility to derive related limited-memory methods. Global convergence of the methods can be established for convex sufficiently smooth functions. Numerical experience by comparison with standard methods is encouraging.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jan Vlček, Ladislav Lukšan,