Updating the Self-Scaling Symmetric Rank One Algorithm with Limited Memory for Large-Scale Unconstrained Optimization

Authors:
Sun Linping
Affiliations:
Department of Mathematics, Nanjing University, Nanjing 210093, People's Republic of China. lpsun@nju.edu.cn
Venue:
Computational Optimization and Applications
Year:
2004

Citing 5
Cited 1

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Abstract

A new limited memory quasi-Newton algorithm is developed, in which the self-scaling symmetric rank one update with Davidon's optimal condition is applied. Preliminary numerical tests show that the new algorithm is very efficient for large-scale problems as well as general nonlinear optimization.