𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A limited memory BFGS-type method for large-scale unconstrained optimization

✍ Scribed by Yunhai Xiao; Zengxin Wei; Zhiguo Wang


Publisher
Elsevier Science
Year
2008
Tongue
English
Weight
408 KB
Volume
56
Category
Article
ISSN
0898-1221

No coin nor oath required. For personal study only.

✦ Synopsis


In this paper, a new numerical method for solving large-scale unconstrained optimization problems is presented. It is derived from a modified BFGS-type update formula by Wei, Li, and Qi. It is observed that the update formula can be extended to the framework of limited memory scheme with hardly more storage or arithmetic operations. Under some suitable conditions, the global convergence property is established. The implementations of the method on a set of CUTE problems indicate that this extension is beneficial to the performance of the algorithm.


πŸ“œ SIMILAR VOLUMES


Modified subspace limited memory BFGS al
✍ Yunhai Xiao; Hongchuan Zhang πŸ“‚ Article πŸ“… 2008 πŸ› Elsevier Science 🌐 English βš– 516 KB

In this paper, a subspace limited memory BFGS algorithm for solving large-scale bound constrained optimization problems is developed. It is modifications of the subspace limited memory quasi-Newton method proposed by Ni and Yuan [Q. Ni, Y.X. Yuan, A subspace limited memory quasi-Newton algorithm for

Performance of hybrid methods for large-
✍ B. Das; H. Meirovitch; I. M. Navon πŸ“‚ Article πŸ“… 2003 πŸ› John Wiley and Sons 🌐 English βš– 142 KB

## Abstract Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large‐scale uncons

A Modified Augmented Lagrange Multiplier
✍ Liang, Ximing ;Hu, Jianbo ;Zhong, Weitao ;Qian, Jixin πŸ“‚ Article πŸ“… 2008 πŸ› Curtin University of Technology 🌐 English βš– 486 KB πŸ‘ 2 views

## Abstract A modified augmented Lagrange multiplier method for large‐scale nonlinear optimization problem is studied in this paper. The basic steps of the proposed algorithm comprise an outer iteration, in which the Lagrange multipliers and various penalty parameters are updated, and an inner iter