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
Algorithm 896: LSA: Algorithms for large-scale optimization
ACM Transactions on Mathematical Software (TOMS)
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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.