In this talk, we address continuous, integer and combinatorial k-sum optimization problems. We analyse different formulations of this problem that allow to solve it through the minimization of a relatively small number of minisum optimization problems.
This approach provides a general tool for solving a variety of k-sum optimization problems and, at the same time, improves the complexity bounds of many ad-hoc algorithms previously reported in the literature for particular versions of this problem. Moreover, the results developed for k-sum optimization have been extended to the more general case of the convex ordered median problem, improving upon existing solution approaches.
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