An approximate dynamic programming approach to convex quadratic knapsack problems

Authors:
Zhongsheng Hua;Bin Zhang;Liang Liang
Affiliations:
School of Business, University of Science & Technology of China, Hefei, Anhui 230026, People's Republic of China;School of Business, University of Science & Technology of China, Hefei, Anhui 230026, People's Republic of China;School of Business, University of Science & Technology of China, Hefei, Anhui 230026, People's Republic of China
Venue:
Computers and Operations Research
Year:
2006

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Abstract

Quadratic knapsack problem (QKP) has a central role in integer and combinatorial optimization, while efficient algorithms to general QKPs are currently very limited. We present an approximate dynamic programming (ADP) approach for solving convex QKPs where variables may take any integer value and all coefficients are real numbers. We approximate the function value using (a) continuous quadratic programming relaxation (CQPR), and (b) the integral parts of the solutions to CQPR. We propose a new heuristic which adaptively fixes the variables according to the solution of CQPR. We report computational results for QKPs with up to 200 integer variables. Our numerical results illustrate that the new heuristic produces high-quality solutions to large-scale QKPs fast and robustly.