A Branch and Bound Algorithm for Low Rank Multiplicative Nonconvex Minimization Problem
Tingsong Du, Pusheng Fei, Jigui Jian · 2009
In this paper, we discuss a practical branch and bound algorithm for solving rank-p linear multiplicative programming problem (LMP). We will show that the programming problem can be solved in an efficient manner by adapting a branch and bound algorithm proposed by Androulakis-Maranas-Floudas for nonconvex problem containing products of two variables. The algorithm is coded in MATLAB, and is tested through series of stochastic optimization problem instances. The experiment indicates that the improved algorithm performs much better than other reported algorithms for the kind of LMP.