Image Space Accelerating Algorithm for Solving a Class of Multiplicative Programming Problems

This paper interprets an image space accelerating branch and bound algorithm for globally solving a class of multiplicative programming problems (MP). In this algorithm, in order to obtain the global optimal solution, problem (MP) is transformed into equivalent (P2) by introducing new variables. By utilizing linearizing relaxation technique, can be converted series linear problems, which provide reliable lower search. Meanwhile, method constructed improve computational performance deleting subintervals have no solution. Furthermore, convergence proved. The complexity analyzed, maximum iterations are estimated. Finally, numerical experimental results show that robust efficient.