Exact Algorithms for Global Optimization of Mixed-Integer Nonlinear Programs

  • Mohit Tawarmalani
  • Nikolaos V. Sahinidis
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 62)


This chapter presents recent advances in the development and application of global optimization algorithms for solving mixed-integer nonlinear programs (MINLPs). It is demonstrated that practically relevant nonlinear programs can be solved to global optimality in a completely automated fashion when carefully chosen relaxation schemes, branching strategies, and domain reduction techniques are are used in conjunction with branch and bound to enhance its performance. In particular, this chapter presents a) applications of the convex extensions theory for constructing tight relaxations, b) unifying ideas behind domain reduction schemes, c) linear outer-approximation schemes with proven convergence guarantees, and d) branching schemes for factorable nonlinear programs. The chapter concludes with computational results on some benchmark mixed-integer nonlinear problems. New solutions are reported for four of these problems.


Global Optimization Exact Algorithm Nonlinear Program Global Optimization Algorithm Convex Envelope 
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 2002

Authors and Affiliations

  • Mohit Tawarmalani
    • 1
  • Nikolaos V. Sahinidis
    • 2
  1. 1.Krannert School of ManagementPurdue UniversityWest LafayetteUSA
  2. 2.Department of Chemical EngineeringUniversity of Illinois at Urbana-ChampaignUSA

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