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An Interior-Point Algorithm for Nonlinear Minimax Problems

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Abstract

We present a primal-dual interior-point method for constrained nonlinear, discrete minimax problems where the objective functions and constraints are not necessarily convex. The algorithm uses two merit functions to ensure progress toward the points satisfying the first-order optimality conditions of the original problem. Convergence properties are described and numerical results provided.

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Authors and Affiliations

  1. Equity Derivatives Group–Systematic Trading, Barclays Capital, London, UK

    E. Obasanjo

  2. Department of Computing, Imperial College London, London, SW7 2AZ, UK

    G. Tzallas-Regas & B. Rustem

Authors
  1. E. Obasanjo
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  2. G. Tzallas-Regas
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  3. B. Rustem
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Correspondence to G. Tzallas-Regas.

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Communicated by P.M. Pardalos.

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Obasanjo, E., Tzallas-Regas, G. & Rustem, B. An Interior-Point Algorithm for Nonlinear Minimax Problems. J Optim Theory Appl 144, 291–318 (2010). https://doi.org/10.1007/s10957-009-9599-z

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  • DOI: https://doi.org/10.1007/s10957-009-9599-z

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