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A globally convergent Newton method for convex SC1 minimization problems

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Abstract

This paper presents a globally convergent and locally superlinearly convergent method for solving a convex minimization problem whose objective function has a semismooth but nondifferentiable gradient. Applications to nonlinear minimax problems, stochastic programs with recourse, and their extensions are discussed.

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Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, The Johns Hopkins University, Baltimore, Maryland

    J. S. Pang (Professor)

  2. Department of Applied Mathematics, University of New South Wales, Sydney, New South Wales, Australia

    L. Qi (Associate Professor)

Authors
  1. J. S. Pang
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  2. L. Qi
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Additional information

Communicated by Z. Q. Luo

The research of the first author is based on work supported by the National Science Foundation under Grants DDM-9104078 and CCR-9213739. This research was carried out while he was visiting the University of New South Wales. The research of the second author is based on work supported by the Australian Research Council.

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Pang, J.S., Qi, L. A globally convergent Newton method for convex SC1 minimization problems. J Optim Theory Appl 85, 633–648 (1995). https://doi.org/10.1007/BF02193060

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