Princeton University Princteon, New Jersey Global optimization addresses the computation and characterization of
global optima (i.e., minima and maxima) of nonconvex functions constrained
in a specified domain. Given an objective function - Determine a global minimum of the objective function
*f*(i.e.,*f*has the lowest possible value in*S*) subject to the set of constraints*S*; - Determine lower and upper bounds on the global minimum of the
objective function
*f*on*S*that are valid for the whole feasible region*S*; - Enclose all solutions of the set of equality and inequality
constraints
*S*.
In the last decade, we have experienced an explosive growth in the area of Global Optimization. This is complimented by the ubiquitous applications that include important problems such as the structure prediction in protein folding and peptide docking in computational chemistry and molecular biology, phase and chemical equilibrium in thermodynamics, pooling and blending in chemical refineries, robust stability analysis in control, parameter estimation and data reconciliation, energy recovery systems in process synthesis, and multiple steady states in reactors. In this plenary talk, we will discuss recent advances in the area of
deterministic global optimization. We will focus on (i) constrained
optimization problems that are twice-continuously differentiable, (ii)
nonlinear and mixed integer optimization models, and (iii) systems with
differential and algebraic constraints. The difference of convex
functions method, denoted as α |