Image space branch-reduction-bound algorithm for globally minimizing a class of multiplicative problems

This paper presents an image space branch-reduction-bound algorithm for solving a class of multiplicative problems (MP). First all, by introducing auxiliary variables and taking the logarithm objective function, equivalent problem (EP) (MP) is obtained. Next, using new linear relaxation technique, parametric programming (PLRP) equivalence can be established acquiring lower bound optimal value to (EP). Based on characteristics function structure branch-and-bound algorithm, some region reduction techniques are constructed improving convergence speed algorithm. Finally, global proved its computational complexity estimated, numerical experiments reported indicate higher performance