Date of Original Version



Technical Report

Rights Management

All Rights Reserved

Abstract or Description

Abstract: "This paper presents a general overview of the global optimization algorithm by Quesada and Grossmann (1993a) for solving NLP problems involving linear fractional and bilinear terms, and it explores the use of alternative bounding approximations. These are applied in the global optimization of problems arising in different engineering areas and for which different relaxations are proposed depending on the mathematical structure of the models. These relaxations include linear and nonlinear underestimator problems. Reformulations that generate additional estimator functions are also employed. Examples from structural design, batch processes, portfolio investment and layout design are presented."