Global optimization for special reverse convex programming

Authors:
Yanjun Wang;Ying Lan
Affiliations:
Department of Applied Mathematics, Shanghai University of Finance and Economics, Shanghai, 200433, PR China;School of Economics, Shanghai University of Finance and Economics, Shanghai, 200433, PR China
Venue:
Computers & Mathematics with Applications
Year:
2008

Citing 2
Cited 2

Convex programs with an additional reverse convex constraint

Journal of Optimization Theory and Applications
A Finite Algorithm for Globally Optimizing a Class of Rank-Two Reverse Convex Programs

Journal of Global Optimization

A nonisolated optimal solution for special reverse convex programming problems

Journal of Computational and Applied Mathematics
Global Optimization of Robot Control System Via Generalized Geometric Programming

ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I

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Abstract

A global optimization algorithm is proposed in order to locate the global minimum of the special reverse convex programming which is both nonconvex and nonlinear. Three new strategies are adopted in this paper. Some of them can be used to solve general reverse convex programming. Global solution locating is to identify the location of the solution. The linear relaxation method is used to obtain the lower bound of the optimum of the primal programming, and in this paper the relaxed programming is a kind of linear programming, which can be solved by standard simplex algorithm. The final strategy is upper bound updating method, which provides a better upper bound than the standard branch and bound method. According to the strategies, a global optimization algorithm is derived based on branch and bound theory. It is proved that the algorithm possesses global convergence. Finally, a numerical experiment is given to illustrate the feasibility and the smaller computational effort.