Global Optimization under Non-Convex Constraints — The Index Approach

  • Roman G. Strongin
  • Yaroslav D. Sergeyev
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 45)


Consider the constrained global minimization problem
where the objective function φ, henceforth denoted g m +1, i.e., g m +1(x) = φ(x), and left-hand sides g i , 1 ≤ im, of the constraints are assumed to be Lipschitzian respectively with constants L i , 1 ≤ im + 1, and, in general, are multi-extremal.


Objective Function Global Optimization Limit Point Convergence Condition Trial Point 
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

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Roman G. Strongin
    • 1
  • Yaroslav D. Sergeyev
    • 1
    • 2
  1. 1.Nizhni Novgorod State UniversityNizhni NovgorodRussia
  2. 2.Institute of Systems Analysis and Information TechnologyUniversity of CalabriaRendeItaly

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