Global descent methods for unconstrained global optimization

  • Authors:

  • Z. Y. Wu;D. Li;L. S. Zhang

  • Affiliations:

  • School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Australia 3353;Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong;Department of Mathematics, Shanghai University, Baoshan, Shanghai, People's Republic of China 200436

  • Venue:
  • Journal of Global Optimization
  • Year:
  • 2011

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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.