A Controlled Random Search Algorithm with Local Newton-type Search for Global Optimization

  • Gianni Di Pillo
  • Stefano Lucidi
  • Laura Palagi
  • Massimo Roma
Part of the Applied Optimization book series (APOP, volume 24)


In this work we deal with the problem of finding an unconstrained global minimizer of a multivariate twice continuously differentiable function. In particular we propose an algorithm which combines a controlled random search procedure based on the modified Price algorithm described in [2] with a Newton-type unconstrained minimization algorithm proposed in [7]. More in particular, we exploit the skill of the Price strategy to examine the whole region of interest in order to locate the subregions “more promising” to contain a global minimizer. Then starting from a point in these regions, we use an effective Newton-type algorithm to compute very quickly the closest local minimizer. In this way we succeed in improving the efficiency of the Price approach. Numerical results on as set of standard test problems are reported with the aim to put in evidence the improvement in efficiency when dealing with large scale problems.


Global Optimization Local Algorithm Large Scale Problem Price Approach Price Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers, Boston 1998

Authors and Affiliations

  • Gianni Di Pillo
    • 1
  • Stefano Lucidi
    • 1
  • Laura Palagi
    • 1
  • Massimo Roma
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Roma “La Sapienza”RomaItaly

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