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

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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.

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Acknowledgments

We would like to acknowledge respective referee for his useful suggestions and comments on the previous versions of this paper, which improve this paper significantly

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Urmia University of Technology, Urmia, West Azarbayjan, Iran

    Fahimeh Biglari

  2. Department of Mathematics, Faculty of Science, Urmia University, Urmia, West Azarbayjan, Iran

    Ali Ebadian

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  1. Fahimeh Biglari
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  2. Ali Ebadian
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Correspondence to Fahimeh Biglari.

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Biglari, F., Ebadian, A. Limited memory BFGS method based on a high-order tensor model. Comput Optim Appl 60, 413–422 (2015). https://doi.org/10.1007/s10589-014-9678-4

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  • DOI: https://doi.org/10.1007/s10589-014-9678-4

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