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Limited memory BFGS method based on a high-order tensor model

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  • Fahimeh Biglari
  • Ali Ebadian

Abstract

This paper is aimed to employ a modified quasi-Newton equation in the framework of the limited memory BFGS method to solve large-scale unconstrained optimization problems. The modified secant equation is derived by means of a forth order tensor model to improve the curvature information of the objective function. The global and local convergence properties of the modified LBFGS method, on uniformly convex problems are also studied. The numerical results indicate that the proposed limited memory method is superior to the standard LBFGS method. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:coopap:v:60:y:2015:i:2:p:413-422
    DOI: 10.1007/s10589-014-9678-4
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    References listed on IDEAS

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    1. Wenyu Sun & Ya-Xiang Yuan, 2006. "Optimization Theory and Methods," Springer Optimization and Its Applications, Springer, number 978-0-387-24976-6, June.
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    Cited by:

    1. 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.

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