A global optimization method for nonconvex separable programming problems

Conventional methods of solving nonconvex separable programming (NSP) problems by mixed integer programming methods require adding numerous 0–1 variables. In this work, we present a new method of deriving the global optimum of an 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 an NSP problem into a linearly mixed 0–1 program solvable for reaching a solution which is extremely close to the global optimum.