Algorithm of Global Optimization for Generalized Geometric Programming
广义几何规划的全局优化算法

A deterministic global optimization algorithm is proposed for locating global minimum of generalized geometric programming (GGP), which can be applied to engineering designs. By utilizing the linear underestimates of the objective and constraint functions, the relaxation linear programming (RLP) about GGP is established, thus the initial non-convex problem (GGP) is reduced to a series of linear programming (RLP).The proposed branch and bound algorithm is convergent to the global minimum of GGP through the successive refinement of the feasible region and the solutions of a series of RLP. And finally the numerical example is given to illustrate the feasibility of the present algorithm.