GLOBAL OPTIMIZATION FOR MIXED 0-1 PROGRAMS WITH CONVEX OR SEPARABLE CONTINUOUS-FUNCTIONS

Abstract

This paper proposes a global approach for solving mixed 0-1 programming problems containing convex or separable continuous functions. Given a mixed 0-1 polynomial term z = x1x2...x(n)g(Y) where x1, x2,..., x(n) are 0-1 integer variables and g(Y) is a convex or a separable continuous function, we can transform z into a set of inequalities where x1, x2,..., x(n) and g(Y) are separated from each other. Based on this transformation, the original mixed 0-1 program can then be solved by a branch-and-bound method to obtain a global optimum.