Mathematical Programming

, Volume 53, Issue 1–3, pp 323–338 | Cite as

Pure adaptive search in global optimization

  • Zelda B. Zabinsky
  • Robert L. Smith


Pure adaptive seach iteratively constructs a sequence of interior points uniformly distributed within the corresponding sequence of nested improving regions of the feasible space. That is, at any iteration, the next point in the sequence is uniformly distributed over the region of feasible space containing all points that are strictly superior in value to the previous points in the sequence. The complexity of this algorithm is measured by the expected number of iterations required to achieve a given accuracy of solution. We show that for global mathematical programs satisfying the Lipschitz condition, its complexity increases at mostlinearly in the dimension of the problem.

Key words

Random search Monte Carlo optimization global optimization complexity 


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Copyright information

© The Mathematical Programming Society, Inc. 1992

Authors and Affiliations

  • Zelda B. Zabinsky
    • 1
  • Robert L. Smith
    • 2
  1. 1.Industrial Engineering Program, FU-20University of WashingtonSeattleUSA
  2. 2.Department of Industrial & Operations EngineeringThe University of MichiganAnn ArborUSA

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