Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Representations of quasi-Newton matrices and their use in limited memory methods
Mathematical Programming: Series A and B
Variable metric methods for unconstrainted optimization and nonlinear least squares
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Limited-Memory Reduced-Hessian Methods for Large-Scale Unconstrained Optimization
SIAM Journal on Optimization
Generalizations of the limited-memory BFGS method based on the quasi-product form of update
Journal of Computational and Applied Mathematics
| Hi-index | 7.29 |
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.