Global optimization in generalized geometric programming

Costas D. Maranas, Christodoulos A. Floudas

Research output: Contribution to journalArticlepeer-review

193 Scopus citations


A deterministic global optimization algorithm is proposed for locating the global minimum of generalized geometric (signomial) problems (GGP). By utilizing an exponential variable transformation the initial nonconvex problem (GGP) is reduced to a (DC) programming problem where both the constraints and the objective are decomposed into the difference of two convex functions. A convex relaxation of problem (DC) is then obtained based on the linear lower bounding of the concave parts of the objective function and constraints inside some box region. The proposed branch and bound type algorithm attains finite ε-convergence to the global minimum through the successive refinement of a convex relaxation of the feasible region and/or of the objective function and the subsequent solution of a series of nonlinear convex optimization problems. The efficiency of the proposed approach is enhanced by eliminating variables through monotonicity analysis, by maintaining tightly bound variables through rescaling, by further improving the supplied variable bounds through convex minimization, and finally by transforming each inequality constraint so as the concave part lower bounding is as tight as possible. The proposed approach is illustrated with a large number of test examples and robust stability analysis problems.

Original languageEnglish (US)
Pages (from-to)351-369
Number of pages19
JournalComputers and Chemical Engineering
Issue number4
StatePublished - 1997

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications


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