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Scaling Damped Limited-Memory Updates for Unconstrained Optimization

Author

Listed:
  • Fahimeh Biglari

    (Urmia University of Technology)

  • Farideh Mahmoodpur

    (Urmia University of Technology)

Abstract

This paper investigates scaling a modified limited-memory algorithm to solve unconstrained optimization problems. The basic idea was to combine the damped techniques for the limited-memory update and the technique of equilibrating the inverse Hessian matrix. Enhanced curvature information about the objective function is stored in the form of a diagonal matrix and plays the dual roles of providing an initial matrix and equilibrating for damped limited-memory iterations. Numerical experiments indicated that the new algorithm is very effective.

Suggested Citation

  • Fahimeh Biglari & Farideh Mahmoodpur, 2016. "Scaling Damped Limited-Memory Updates for Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 177-188, July.
    • Handle: RePEc:spr:joptap:v:170:y:2016:i:1:d:10.1007_s10957-016-0940-z
    DOI: 10.1007/s10957-016-0940-z
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    References listed on IDEAS

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    4. Chengxian Xu & Jianzhong Zhang, 2001. "A Survey of Quasi-Newton Equations and Quasi-Newton Methods for Optimization," Annals of Operations Research, Springer, vol. 103(1), pages 213-234, March.
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    7. Biglari, Fahimeh, 2015. "Dynamic scaling on the limited memory BFGS method," European Journal of Operational Research, Elsevier, vol. 243(3), pages 697-702.
    8. Fahimeh Biglari & Ali Ebadian, 2015. "Limited memory BFGS method based on a high-order tensor model," Computational Optimization and Applications, Springer, vol. 60(2), pages 413-422, March.
    9. Shmuel S. Oren, 1974. "Self-Scaling Variable Metric (SSVM) Algorithms," Management Science, INFORMS, vol. 20(5), pages 863-874, January.
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    Cited by:

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