Numerical Experience with a Class of Self-Scaling Quasi-Newton Algorithms-Reference-Cited by-同舟云学术

Numerical Experience with a Class of Self-Scaling Quasi-Newton Algorithms

Published:1998-03 Issue:3 Volume:96 Page:533-553
ISSN:0022-3239
Container-title:Journal of Optimization Theory and Applications
language:en
Short-container-title:Journal of Optimization Theory and Applications

Author:

Al-Baali M.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Management Science and Operations Research,Control and Optimization

Link

http://link.springer.com/content/pdf/10.1023/A:1022608410710.pdf

Reference23 articles.

1. Oren, S. S., and Luenberger, D. G., Self-Scaling Variable Metric (SSVM) Algorithms, I: Criteria and Sufficient Conditions for Scaling a Class of Algorithms, Management Science, Vol. 20, pp. 845–862, 1974.

2. Fletcher, R., Practical Methods of Optimization, 2nd Edition, Wiley, Chichester, England, 1987.

3. Powell, M. J. D., Some Global Convergence Properties of a Variable Metric Algorithm for Minimization without Exact Line Searches, Nonlinear Programming, Edited by R. W. Cottle and C. E. Lemke, SIAM Publications, Philadelphia, Pennsylvania, Vol. 9, pp. 53–72, 1976.

4. Zhang, Y., and Tewarson, R. P., Quasi-Newton Algorithms with Updates from the Preconvex Part of Broyden's Family, IMA Journal of Numerical Analysis, Vol. 8, pp. 487–509, 1988.

5. Byrd, R., Nocedal, J., and Juan, Y., Global Convergence of a Class of Quasi-Newton Methods on Convex Problems, SIAM Journal on Numerical Analysis, Vol. 24, pp. 1171–1190, 1987.

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