{"id":"W7132875402","doi":"","title":"Machine Learning for Cutting Planes in Mixed Integer Linear Programming","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Integer programming; Linear programming; Cutting-plane method; Artificial neural network; Feature selection; Graph; Constraint (computer-aided design); Constraint programming; Column generation","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":[0.001909152,0.001754204,0.001023199,0.0008466532,0.0004555267,0.001807632,0.001219709,0.001081566,0.00633434],"category_scores_gemma":[0.007317213,0.0008760639,0.001255738,0.001180501,0.001167749,0.001531964,0.001597,0.004240643,0.001816193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294194,"about_ca_system_score_gemma":0.001476913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044022,"about_ca_topic_score_gemma":0.003591786,"domain_scores_codex":[0.9987676,0.0005264864,0.00006757366,0.0001852736,0.0003662193,0.00008682301],"domain_scores_gemma":[0.996804,0.0025004,0.0001621324,0.0001841249,0.0002913203,0.0000580637],"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":[0.00006253074,0.00009478571,0.0005701177,0.0004272703,0.0000581324,0.00005779432,0.00009287972,0.6959503,0.001707592,0.09497102,0.006901627,0.1991059],"study_design_scores_gemma":[0.00001230448,0.00002197343,0.00004268212,0.0000441807,0.000004378547,0.00001088965,0.0000101258,0.9590302,0.0005964352,0.03700355,0.003218343,0.000004958644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002496434,0.0009207561,0.9913263,0.0003560674,0.00005321663,0.0000455701,0.0000525441,0.0004591979,0.004289893],"genre_scores_gemma":[0.08727794,0.001554307,0.9054464,0.0003681543,0.0001645311,0.0003346874,0.0003743055,0.000537904,0.003941854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00633434,"threshold_uncertainty_score":0.02119046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01808971292237653,"score_gpt":0.319695961561816,"score_spread":0.3016062486394394,"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."}}