Successive Convex Relaxation Approach to Bilevel Quadratic Optimization Problems
نویسندگان
چکیده
The quadratic bilevel programming problem is an instance of a quadratic hierarchical decision process where the lower level constraint set is dependent on decisions taken at the upper level. By replacing the inner problem by its corresponding KKT optimality conditions, the problem is transformed to a single yet non-convex quadratic program, due to the complementarity condition. In this paper we adopt the successive convex relaxation approach proposed by Kojima and Tun cel for computing a convex relaxation of a nonconvex feasible region. By further exploiting the special structure of the bilevel problem, we establish new techniques which enable the e cient implementation of the proposed algorithm. The performance of these techniques is tested in a comparison with other procedures using a number of test problems of quadratic bilevel programming.
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