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A Modified Quasi-Newton Method for Structured Optimization with Partial Information on the Hessian

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Abstract

This paper develops a modified quasi-Newton method for structured unconstrained optimization with partial information on the Hessian, based on a better approximation to the Hessian in current search direction. The new approximation is decided by both function values and gradients at the last two iterations unlike the original one which only uses the gradients at the last two iterations. The modified method owns local and superlinear convergence. Numerical experiments show that the proposed method is encouraging comparing with the methods proposed in [4] for structured unconstrained optimization

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Authors and Affiliations

  1. Guanghua School of Management, Peking University, Beijing, 100080, China

    L. H. Chen

  2. Division of Basic Science, Agricultural University of China, China

    N. Y. Deng

  3. Department of Mathematics, City University of Hong Kong, Hong Kong

    J. Z. Zhang

Authors
  1. L. H. Chen
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  2. N. Y. Deng
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  3. J. Z. Zhang
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Communicated by Masao Fukushima and Liqun Qi

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Chen, L.H., Deng, N.Y. & Zhang, J.Z. A Modified Quasi-Newton Method for Structured Optimization with Partial Information on the Hessian. Comput Optim Applic 35, 5–18 (2006). https://doi.org/10.1007/s10589-006-6440-6

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  • DOI: https://doi.org/10.1007/s10589-006-6440-6

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