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Global and local convergence of a new affine scaling trust region algorithm for linearly constrained optimization

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Abstract

Chen and Zhang [Sci. China, Ser. A, 45, 1390–1397 (2002)] introduced an affine scaling trust region algorithm for linearly constrained optimization and analyzed its global convergence. In this paper, we derive a new affine scaling trust region algorithm with dwindling filter for linearly constrained optimization. Different from Chen and Zhang’s work, the trial points generated by the new algorithm are accepted if they improve the objective function or improve the first order necessary optimality conditions. Under mild conditions, we discuss both the global and local convergence of the new algorithm. Preliminary numerical results are reported.

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Authors and Affiliations

  1. School of Mathematics and Information, Shanghai LiXin University of Commerce, Shanghai, 201620, P. R. China

    Chao Gu

  2. Department of Mathematics, Shanghai Normal University, Shanghai, 200234, P. R. China

    De Tong Zhu

Authors
  1. Chao Gu
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  2. De Tong Zhu
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Correspondence to Chao Gu.

Additional information

Supported by National Natural Science Foundation of China (Grant Nos. 11201304 and 11371253) and the Innovation Program of Shanghai Municipal Education Commission

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Gu, C., Zhu, D.T. Global and local convergence of a new affine scaling trust region algorithm for linearly constrained optimization. Acta. Math. Sin.-English Ser. 32, 1203–1213 (2016). https://doi.org/10.1007/s10114-016-4513-8

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  • DOI: https://doi.org/10.1007/s10114-016-4513-8

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