Deterministic Global Optimization in Design, Control, and Computational Chemistry

Abstract

This paper presents an overview of the deterministic global optimization approaches and their applications in the areas of Process Design, Control, and Computational Chemistry. The focus is on (i) decomposition-based primal dual methods, (ii) methods for generalized geometric programming problems, and (iii) global optimization methods for general nonlinear programming problems. The classes of mathematical problems that are addressed range from indefinite quadratic programming to concave programs, to quadratically constrained problems, to polynomials, to general twice continuously differentiable nonlinear optimization problems. For the majority of the presented methods nondistributed global optimization approaches are discussed with the exception of decomposition-based methods where a distributed global optimization approach is presented.KeywordsGlobal OptimizationDual ProblemMixed Integer Linear ProgramGlobal Optimization AlgorithmConvex RelaxationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.