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Solving Minimax Problems: Local Smoothing Versus Global Smoothing

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Numerical Analysis and Optimization (NAO 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 235))

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Abstract

The aim of this chapter is to compare different smoothing techniques for solving finite minimax problems. We consider the local smoothing technique which approximates the function in some neighborhood of a point of nondifferentiability and also global smoothing techniques such as the exponential and hyperbolic smoothing which approximate the function in the whole domain. Computational results on the collection of academic test problems are used to compare different smoothing techniques. Results show the superiority of the local smoothing technique for convex problems and global smoothing techniques for nonconvex problems.

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Authors and Affiliations

  1. Faculty of Science and Technology, Federation University Australia, Ballarat, VIC, Australia

    A. M. Bagirov, N. Sultanova, A. Al Nuaimat & S. Taheri

Authors
  1. A. M. Bagirov
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  2. N. Sultanova
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  3. A. Al Nuaimat
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  4. S. Taheri
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Corresponding author

Correspondence to A. M. Bagirov .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, Sultan Qaboos University, Muscat, Oman

    Mehiddin Al-Baali

  2. Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, Calabria University, Arcavacata, Italy

    Lucio Grandinetti

  3. Department Mathematics and Statistics, Sultan Qaboos University, Muscat, Oman

    Anton Purnama

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Bagirov, A.M., Sultanova, N., Al Nuaimat, A., Taheri, S. (2018). Solving Minimax Problems: Local Smoothing Versus Global Smoothing. In: Al-Baali, M., Grandinetti, L., Purnama, A. (eds) Numerical Analysis and Optimization. NAO 2017. Springer Proceedings in Mathematics & Statistics, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-90026-1_2

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