{"id":"W3175250543","doi":"10.1016/j.dam.2023.02.003","title":"A lifted-space dynamic programming algorithm for the Quadratic Knapsack Problem","year":2023,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal","keywords":"Knapsack problem; Mathematics; Mathematical optimization; Change-making problem; Continuous knapsack problem; Quadratic programming; Heuristic; Algorithm; Cutting stock problem; Dynamic programming; Binary number; Constraint (computer-aided design); Function (biology); Integer programming; Optimization problem","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000305592,0.000228378,0.0002272932,0.00008218716,0.0001818347,0.0001543615,0.0002411863,0.00008993749,0.00002020073],"category_scores_gemma":[0.00001802073,0.0001689901,0.00008691548,0.0005446077,0.00004662265,0.00005354624,0.00004416625,0.0001467019,0.0001619188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003402369,"about_ca_system_score_gemma":0.00001614048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.601517e-7,"about_ca_topic_score_gemma":0.000006003462,"domain_scores_codex":[0.9988853,0.000005217135,0.000323376,0.0001781087,0.0001991132,0.0004088827],"domain_scores_gemma":[0.9991967,0.0002833644,0.0000706986,0.0003492411,0.00003547817,0.00006449874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008555828,0.0001108843,0.00000532903,0.004218897,0.0005828455,0.000004713152,0.01800043,0.5459022,0.001763739,0.1035942,0.01130378,0.3145045],"study_design_scores_gemma":[0.0002963661,0.00001639423,0.000003033576,0.00006425515,0.0000587582,0.000002798971,0.001145781,0.9814823,0.0001794697,0.006854882,0.00965368,0.0002422801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000219156,0.00009841338,0.9920965,0.0002583586,0.0001459351,0.001856639,0.00003036689,0.001428583,0.003866091],"genre_scores_gemma":[0.03350947,0.000196916,0.9620299,0.00005909833,0.00009249839,0.00217533,0.0002207215,0.0002427053,0.001473329],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4355801,"threshold_uncertainty_score":0.6891218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124201419681223,"score_gpt":0.2436527707356479,"score_spread":0.2312326287675256,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}