{"id":"W4385763708","doi":"10.24963/ijcai.2023/739","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","year":2023,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Integer programming; Computer science; Linear programming; Linear programming relaxation; Set (abstract data type); Node (physics); Heuristic; Task (project management); Selection (genetic algorithm); Mathematical optimization; Tree (set theory); Machine learning; Process (computing); Integer (computer science); Decision tree; Branch and bound; Branch and price; Artificial intelligence; Algorithm; Mathematics; Programming language; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006168168,0.002669994,0.002593843,0.003348256,0.0006986554,0.003864455,0.003464309,0.002038244,0.005617852],"category_scores_gemma":[0.01757246,0.001249358,0.002025991,0.008605571,0.001394707,0.004858337,0.002015937,0.004681943,0.003233514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325438,"about_ca_system_score_gemma":0.002063797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002230776,"about_ca_topic_score_gemma":0.002147122,"domain_scores_codex":[0.995905,0.001401501,0.0003758885,0.0006778232,0.001477274,0.0001625072],"domain_scores_gemma":[0.9838074,0.01306125,0.0006448671,0.0008702186,0.001436108,0.0001800756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000954311,0.0003296067,0.002522325,0.00391432,0.0001589899,0.00007409004,0.0001453275,0.1156574,0.000529135,0.06774556,0.01655569,0.7922722],"study_design_scores_gemma":[0.00005899072,0.0002721088,0.001431582,0.002789347,0.0001002467,0.0003375518,0.0002382183,0.6353394,0.001853798,0.2109751,0.1465024,0.0001013648],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003905403,0.1540213,0.8250581,0.00304531,0.0004747165,0.0001762768,0.0004639681,0.0007854463,0.01206951],"genre_scores_gemma":[0.07628538,0.2615136,0.6497504,0.001798816,0.002975998,0.0006806452,0.00250799,0.0006493065,0.003837876],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006168168,"threshold_uncertainty_score":0.03262079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567181972411723,"score_gpt":0.3069942294338229,"score_spread":0.2613224097097057,"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."}}