Convergent Lagrangian and domain cut method for nonlinear knapsack problems

Authors:
D. Li;X. L. Sun;J. Wang;K. I. Mckinnon
Affiliations:
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong;Department of Management Science, School of Management, Fudan University, Shanghai, China 200433;Department of Management Science and Engineering, International Business College, Qingdao University, Qingdao, China 266071;School of Mathematics, The University of Edinburgh, Edinburgh, UK EH9 3JZ
Venue:
Computational Optimization and Applications
Year:
2009

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Abstract

The nonlinear knapsack problem, which has been widely studied in the OR literature, is a bounded nonlinear integer programming problem that maximizes a separable nondecreasing function subject to separable nondecreasing constraints. In this paper we develop a convergent Lagrangian and domain cut method for solving this kind of problems. The proposed method exploits the special structure of the problem by Lagrangian decomposition and dual search. The domain cut is used to eliminate the duality gap and thus to guarantee the finding of an optimal exact solution to the primal problem. The algorithm is first motivated and developed for singly constrained nonlinear knapsack problems and is then extended to multiply constrained nonlinear knapsack problems. Computational results are presented for a variety of medium- or large-size nonlinear knapsack problems. Comparison results with other existing methods are also reported.