A Limited-Memory Multipoint Symmetric Secant Method for Bound Constrained Optimization

A new algorithm for solving smooth large-scale minimization problems with bound constraints is introduced. The way of dealing with active constraints is similar to the one used in some recently introduced quadratic solvers. A limited-memory multipoint symmetric secant method for approximating the Hessian is presented. Positive-definiteness of the Hessian approximation is not enforced. A combination of trust-region and conjugategradient approaches is used to explore useful information. Global convergence is proved for a general model algorithm. Results of numerical experiments are presented.

• Type: Journal
• Year: 2002
• Where: ANOR
• Authors: Oleg P. Burdakov, José Mario Martínez, Elvio A. Pilotta
• Keywords: ANOR 2002, Hessian Approximation, Smooth Large-scale Minimization, Symmetric Secant Method