Optimization Letters

, Volume 10, Issue 2, pp 283–308

Performance of convex underestimators in a branch-and-bound framework

  • Yannis A. Guzman
  • M. M. Faruque Hasan
  • Christodoulos A. Floudas
Original Paper

DOI: 10.1007/s11590-014-0799-6

Cite this article as:
Guzman, Y.A., Hasan, M.M.F. & Floudas, C.A. Optim Lett (2016) 10: 283. doi:10.1007/s11590-014-0799-6

Abstract

The efficient determination of tight lower bounds in a branch-and-bound algorithm is crucial for the global optimization of models spanning numerous applications and fields. The global optimization method \(\alpha \)-branch-and-bound (\(\alpha \)BB, Adjiman et al. in Comput Chem Eng 22(9):1159–1179, 1998b, Comput Chem Eng 22(9):1137–1158, 1998a; Adjiman and Floudas in J Global Optim 9(1):23–40, 1996; Androulakis et al. J Global Optim 7(4):337–363, 1995; Floudas in Deterministic Global Optimization: Theory, Methods and Applications, vol. 37. Springer, Berlin, 2000; Maranas and Floudas in J Chem Phys 97(10):7667–7678, 1992, J Chem Phys 100(2):1247–1261, 1994a, J Global Optim 4(2):135–170, 1994), guarantees a global optimum with \(\epsilon \)-convergence for any \(\mathcal {C}^2\)-continuous function within a finite number of iterations via fathoming nodes of a branch-and-bound tree. We explored the performance of the \(\alpha \)BB method and a number of competing methods designed to provide tight, convex underestimators, including the piecewise (Meyer and Floudas in J Global Optim 32(2):221–258, 2005), generalized (Akrotirianakis and Floudas in J Global Optim 30(4):367–390, 2004a, J Global Optim 29(3):249–264, 2004b), and nondiagonal (Skjäl et al. in J Optim Theory Appl 154(2):462–490, 2012) \(\alpha \)BB methods, the Brauer and Rohn+E (Skjäl et al. in J Global Optim 58(3):411–427, 2014) \(\alpha \)BB methods, and the moment method (Lasserre and Thanh in J Global Optim 56(1):1–25, 2013). Using a test suite of 40 multivariate, box-constrained, nonconvex functions, the methods were compared based on the tightness of generated underestimators and the efficiency of convergence of a branch-and-bound global optimization algorithm.

Keywords

Global optimization Convex underestimators Branch-and-bound 

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yannis A. Guzman
    • 1
  • M. M. Faruque Hasan
    • 1
    • 2
  • Christodoulos A. Floudas
    • 1
  1. 1.Department of Chemical and Biological EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Artie McFerrin Department of Chemical EngineeringTexas A&M UniversityCollege StationUSA

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