Economical, managerial, engineering, and natural systems are often represented by nonlinear equations and inequalities, using discrete and continuous variables. Global Optimization provides methodologies to find the global solutions for the prescriptive models that attempt to describe, predict, and optimize their behavior. OQGRG, the algorithm presented in this book, was developed to solve problems in this large target class of mixed integer, nonlinear, constrained optimization models that often have multiple local optima. OQGRG is a multi-start, 2-stage, global optimization algorithm that combines the efficiency of a broad meta-heuristic search and the power of a reduced gradient nonlinear solver. The effectiveness of the algorithm is demonstrated by solving a large set of test problems.

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