A Conical Branch-and-Bound Algorithm for a Class of Reverse Convex Programs
نویسندگان
چکیده
The purpose of this paper is to construct a conical branch-and-bound algorithm for solving linear programming problems with an additional reverse convex constraint. We propose an inexpensive bound-tightening procedure, which is based on the surrogate relaxation. We show that this procedure considerably tightens lower bounds yielded by the usual linear programming relaxation. We also report numerical results, which indicate that the proposed algorithm is much promising, compared with existing ones.
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تاریخ انتشار 2005