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http://dx.doi.org/10.22771/nfaa.2021.26.03.11

MONOTONIC OPTIMIZATION TECHNIQUES FOR SOLVING KNAPSACK PROBLEMS  

Tran, Van Thang (Electric Power University)
Kim, Jong Kyu (Department of Mathematics Education, Kyungnam University)
Lim, Won Hee (Department of Mathematics Education, Kyungnam University)
Publication Information
Nonlinear Functional Analysis and Applications / v.26, no.3, 2021 , pp. 611-628 More about this Journal
Abstract
In this paper, we propose a new branch-reduction-and-bound algorithm to solve the nonlinear knapsack problems by using general discrete monotonic optimization techniques. The specific properties of the problem are exploited to increase the efficiency of the algorithm. Computational experiments of the algorithm on problems with up to 30 variables and 5 different constraints are reported.
Keywords
Lagrangian relaxation; discrete monotonic optimization; knapsack problem; quasi-conjugate duality;
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