Rights statement: This is the author’s version of a work that was accepted for publication in Discrete Optimization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Discrete Optimization, 44, 2, 2022 DOI: 10.1016/j.disopt.2020.100579
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A cut-and-branch algorithm for the quadratic knapsack problem
AU - Djeumou Fomeni, Franklin
AU - Kaparis, Konstantinos
AU - Letchford, Adam
N1 - This is the author’s version of a work that was accepted for publication in Discrete Optimization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Discrete Optimization, 44, 2, 2022 DOI: 10.1016/j.disopt.2020.100579
PY - 2022/5/31
Y1 - 2022/5/31
N2 - The Quadratic Knapsack Problem (QKP) is a well-known NP-hard combinatorial optimisation problem, with many practical applications. We present a ‘cut-and-branch’ algorithm for the QKP, in which a cutting-plane phase is followed by a branch-and-bound phase. The cutting-plane phase is more sophisticated than the existing ones in the literature, incorporating several classes of cutting planes, two primal heuristics, and several rules for eliminating variables and constraints. Computational results show that the algorithm is competitive.
AB - The Quadratic Knapsack Problem (QKP) is a well-known NP-hard combinatorial optimisation problem, with many practical applications. We present a ‘cut-and-branch’ algorithm for the QKP, in which a cutting-plane phase is followed by a branch-and-bound phase. The cutting-plane phase is more sophisticated than the existing ones in the literature, incorporating several classes of cutting planes, two primal heuristics, and several rules for eliminating variables and constraints. Computational results show that the algorithm is competitive.
KW - integer programming
KW - combinatorial optimisation
KW - knapsack problems
U2 - 10.1016/j.disopt.2020.100579
DO - 10.1016/j.disopt.2020.100579
M3 - Journal article
VL - 44
JO - Discrete Optimization
JF - Discrete Optimization
SN - 1572-5286
IS - 2
M1 - 100579
ER -