{"id":"W3131144142","doi":"10.1016/j.orl.2021.09.006","title":"Performance guarantees of forward and reverse greedy algorithms for minimizing nonsupermodular nonsubmodular functions on a matroid","year":2021,"lang":"en","type":"preprint","venue":"Operations Research Letters","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Matroid; Automatic summarization; Heuristics; Greedy algorithm; Computer science; Set function; Set (abstract data type); Mathematical optimization; Submodular set function; Weighted matroid; Algorithm; Mathematics; Discrete mathematics; Artificial intelligence; Matroid partitioning; Graphic matroid","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001554086,0.000338341,0.0004969397,0.0009057279,0.001086334,0.0007706512,0.001248472,0.0001947111,0.00002229063],"category_scores_gemma":[0.0002866161,0.0003487289,0.0002683907,0.0007717191,0.0004559975,0.0005566284,0.001540747,0.001095769,0.000009147353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290567,"about_ca_system_score_gemma":0.0003533445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002132605,"about_ca_topic_score_gemma":0.00007842892,"domain_scores_codex":[0.996368,0.0003498237,0.0005607779,0.00112534,0.0009469335,0.0006491172],"domain_scores_gemma":[0.9970078,0.0003889815,0.00006469979,0.001548894,0.0008308684,0.0001587798],"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":[0.000445416,0.003268604,0.001703155,0.006964564,0.002691495,0.0003732133,0.02463851,0.3957073,0.1925328,0.03529096,0.03547708,0.3009069],"study_design_scores_gemma":[0.0006596509,0.0003209625,0.000672318,0.0006272516,0.00003058349,0.00003343869,0.0004382351,0.9915109,0.003015477,0.0001620831,0.002084338,0.0004447956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5828646,0.0003492898,0.4040476,0.01034925,0.0006436096,0.001400497,0.0001884135,0.00007251172,0.00008427716],"genre_scores_gemma":[0.5651955,0.0006946303,0.4311192,0.0007219163,0.0003572221,0.001253343,0.000271566,0.00005832867,0.0003282395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5958035,"threshold_uncertainty_score":0.9998965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0882885469965528,"score_gpt":0.3374389446826125,"score_spread":0.2491503976860597,"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."}}