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Cubic regularization in symmetric rank-1 quasi-Newton methods

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Abstract

Quasi-Newton methods based on the symmetric rank-one (SR1) update have been known to be fast and provide better approximations of the true Hessian than popular rank-two approaches, but these properties are guaranteed under certain conditions which frequently do not hold. Additionally, SR1 is plagued by the lack of guarantee of positive definiteness for the Hessian estimate. In this paper, we propose cubic regularization as a remedy to relax the conditions on the proofs of convergence for both speed and accuracy and to provide a positive definite approximation at each step. We show that the n-step convergence property for strictly convex quadratic programs is retained by the proposed approach. Extensive numerical results on unconstrained problems from the CUTEr test set are provided to demonstrate the computational efficiency and robustness of the approach.

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Acknowledgements

We would like to thank Daniel Bienstock and Andreas Waechter for their handling of the paper as Editor and Associate Editor, respectively, for MPC. We would also like to thank the two anonymous referees whose feedback and suggestions have greatly improved the paper.

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Authors and Affiliations

  1. Drexel University, Philadelphia, PA, USA

    Hande Y. Benson

  2. Emeritus, Rutgers University, New Brunswick, NJ, USA

    David F. Shanno

Authors
  1. Hande Y. Benson
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  2. David F. Shanno
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Correspondence to Hande Y. Benson.

Appendix

Appendix

See Table 1.

Table 1 Iteration and runtime (in CPU seconds) results for the proposed modified SR1 approach and BFGS on unconstrained NLPs from the CUTEr test set
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Benson, H.Y., Shanno, D.F. Cubic regularization in symmetric rank-1 quasi-Newton methods. Math. Prog. Comp. 10, 457–486 (2018). https://doi.org/10.1007/s12532-018-0136-7

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