αBB: A global optimization method for general constrained nonconvex problems

I. P. Androulakis, C. D. Maranas, C. A. Floudas

Research output: Contribution to journalArticle

267 Scopus citations

Abstract

A branch and bound global optimization method, αBB, for general continuous optimization problems involving nonconvexities in the objective function and/or constraints is presented. The nonconvexities are categorized as being either of special structure or generic. A convex relaxation of the original nonconvex problem is obtained by (i) replacing all nonconvex terms of special structure (i.e. bilinear, fractional, signomial) with customized tight convex lower bounding functions and (ii) by utilizing the α parameter as defined in [17] to underestimate nonconvex terms of generic structure. The proposed branch and bound type algorithm attains finite ε-convergence to the global minimum through the successive subdivision of the original region and the subsequent solution of a series of nonlinear convex minimization problems. The global optimization method, αBB, is implemented in C and tested on a variety of example problems.

Original languageEnglish (US)
Pages (from-to)337-363
Number of pages27
JournalJournal of Global Optimization
Volume7
Issue number4
DOIs
StatePublished - Dec 1 1995

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

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