A global optimization method for nonconvex separable programming problems

Conventional methods of solving nonconvex separable programming (NSP) problems by mixed integer programming methods requires adding numerous 0±1 variables. In this work, we present a new method of deriving the global optimum of a NSP program using less number of 0±1 variables. A separable function is initially expressed by a piecewise linear function with summation of absolute terms. Linearizing these absolute terms allows us to convert a NSP problem into a linearly mixed 0±1 program solvable for reaching a solution which is extremely close to the global optimum. Ó 1999 Elsevier Science B.V. All rights reserved.