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Global descent methods for unconstrained global optimization

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Abstract

We propose in this paper novel global descent methods for unconstrained global optimization problems to attain the global optimality by carrying out a series of local minimization. More specifically, the solution framework consists of a two-phase cycle of local minimization: the first phase implements local search of the original objective function, while the second phase assures a global descent of the original objective function in the steepest descent direction of a (quasi) global descent function. The key element of global descent methods is the construction of the (quasi) global descent functions which possess prominent features in guaranteeing a global descent.

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Authors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, VIC, 3353, Australia

    Z. Y. Wu

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    D. Li

  3. Department of Mathematics, Shanghai University, Baoshan, Shanghai, 200436, People’s Republic of China

    L. S. Zhang

Authors
  1. Z. Y. Wu
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  2. D. Li
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  3. L. S. Zhang
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Correspondence to D. Li.

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Wu, Z.Y., Li, D. & Zhang, L.S. Global descent methods for unconstrained global optimization. J Glob Optim 50, 379–396 (2011). https://doi.org/10.1007/s10898-010-9587-8

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  • DOI: https://doi.org/10.1007/s10898-010-9587-8

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