An inner approximation method incorporating with a penalty function method for a reverse convex programming problem

[摘要] In this paper, we consider a reverse convex programming problem constrained by a convex set and a reverse convex set which is defined by the complement of the interior of a compact convex set X. When X is not necessarily a polytope, an inner approximation method has been proposed (J. Optim. Theory Appl. 107(2) (2000) 357). The algorithm utilizes inner approximation of X by a sequence of polytopes to generate relaxed problems. Then, every accumulation point of the sequence of optimal solutions of relaxed problems is an optimal solution of the original problem. In this paper, we improve the proposed algorithm. By underestimating the optimal value of the relaxed problem, the improved algorithms have the global convergence. (C) 2002 Elsevier Science B.V. All rights reserved.

[发布日期] 2002-09-01

[效力级别]  Proceedings Paper

[关键词] global optimization;reverse convex programming problem;dual problem;inner approximation method;penalty function method