Global Optimization of Nonconvex MINLP’s by Interval Analysis

  • Ragavan Vaidyanathan
  • Mahmoud El-Halwagi
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 9)

Abstract

In this work, we introduce a global optimization algorithm based on interval analysis for solving nonconvex Mixed Integer Nonlinear Programs (MINLPs). The algorithm is a generalization of the procedure proposed by the authors (Vaidyanathan and ElHalwagi, 1994a) for solving nonconvex Nonlinear Programs (NLPs) globally. The algorithm features several tools for accelerating the convergence to the global solution. A new discretization procedure is proposed within the framework of interval analysis for partitioning the search space. Furthermore, infeasible search spaces are eliminated without directly checking the constraints. Illustrative examples are solved to demonstrate the applicability of the proposed algorithm to solve nonconvex MINLPs efficiently.

Keywords

Objective Function Search Space Global Optimization Global Solution Interval Analysis 
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 1996

Authors and Affiliations

  • Ragavan Vaidyanathan
    • 2
  • Mahmoud El-Halwagi
    • 1
  1. 1.Department of Chemical EngineeringAuburn UniversityAuburnUSA
  2. 2.The M. W. Kellogg CompanyHoustonUSA

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