{"id":"W4401813859","doi":"10.1007/s10107-024-02130-y","title":"Machine learning augmented branch and bound for mixed integer linear programming","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada; Horizon 2020 Framework Programme; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Government of Canada; Polytechnique Montréal","keywords":"Integer programming; Mathematics; Branch and bound; Branch and cut; Branch and price; Linear programming; Integer (computer science); Mathematical optimization; Numerical analysis; Computer science; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004588252,0.001763455,0.002242981,0.001265042,0.0006747115,0.002948124,0.001640767,0.001504941,0.006743271],"category_scores_gemma":[0.01280099,0.0007227475,0.0009157098,0.001930241,0.001483119,0.001887531,0.00196772,0.003954258,0.001737263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539769,"about_ca_system_score_gemma":0.002186027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00288971,"about_ca_topic_score_gemma":0.002548387,"domain_scores_codex":[0.9976676,0.001193071,0.00009452402,0.0002445979,0.0005976256,0.0002024679],"domain_scores_gemma":[0.991663,0.006512837,0.0005458965,0.0003702159,0.0007569789,0.0001511025],"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":[0.00008518792,0.00007659952,0.0003540028,0.0001640114,0.00004355033,0.0000364636,0.0000308177,0.8793586,0.0002903448,0.0541741,0.003424658,0.06196168],"study_design_scores_gemma":[0.000005138494,0.0000108435,0.00001708731,0.00001622063,0.000002610674,0.000004120953,0.000002055279,0.9841769,0.00009805117,0.01502062,0.0006442774,0.000002187207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003962457,0.0009511942,0.9878983,0.0005192594,0.0001236193,0.00005465085,0.00007269494,0.0005459663,0.005871883],"genre_scores_gemma":[0.342135,0.001590509,0.6474007,0.0006242528,0.0004580402,0.0006221927,0.0005471859,0.0004134869,0.006208766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006743271,"threshold_uncertainty_score":0.02426523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09935322029303427,"score_gpt":0.4338918460942113,"score_spread":0.334538625801177,"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."}}