A Framework for Globally Optimizing Mixed-Integer Signomial Programs

Article

Abstract

Mixed-integer signomial optimization problems have broad applicability in engineering. Extending the Global Mixed-Integer Quadratic Optimizer, GloMIQO (Misener, Floudas in J. Glob. Optim., 2012. doi:10.1007/s10898-012-9874-7), this manuscript documents a computational framework for deterministically addressing mixed-integer signomial optimization problems to ε-global optimality. This framework generalizes the GloMIQO strategies of (1) reformulating user input, (2) detecting special mathematical structure, and (3) globally optimizing the mixed-integer nonconvex program. Novel contributions of this paper include: flattening an expression tree towards term-based data structures; introducing additional nonconvex terms to interlink expressions; integrating a dynamic implementation of the reformulation-linearization technique into the branch-and-cut tree; designing term-based underestimators that specialize relaxation strategies according to variable bounds in the current tree node. Computational results are presented along with comparison of the computational framework to several state-of-the-art solvers.

Keywords

Mixed-integer signomial optimization Global optimization Numerical optimization software 

Supplementary material

10957_2013_396_MOESM1_ESM.pdf (195 kb)
(PDF 195 kB)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Chemical and Biological EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Centre for Process Systems EngineeringImperial College LondonLondonUK

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