{"id":"W4313349371","doi":"10.1007/978-3-031-20350-3_13","title":"Two-Stage Submodular Maximization Under Knapsack and Matroid Constraints","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Submodular set function; Matroid; Knapsack problem; Combinatorics; Constraint (computer-aided design); Maximization; Mathematics; Monotone polygon; Similarity (geometry); Discrete mathematics; Mathematical optimization; Computer science; Artificial intelligence","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":[0.00111936,0.001467326,0.001359304,0.0005240137,0.0004699167,0.00182653,0.002187712,0.001481335,0.009355797],"category_scores_gemma":[0.002631026,0.001051605,0.001228506,0.001767813,0.0007722001,0.003344769,0.001950191,0.002589265,0.001966271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001272,"about_ca_system_score_gemma":0.001177566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001514932,"about_ca_topic_score_gemma":0.002003643,"domain_scores_codex":[0.9992452,0.0002204649,0.00003044632,0.00015482,0.0002020792,0.0001470743],"domain_scores_gemma":[0.9992612,0.000414987,0.0000641549,0.000101375,0.00009817725,0.00006011262],"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":[0.0003999436,0.0004062249,0.0003820682,0.0009686984,0.00009474572,0.0003261436,0.0002126206,0.4305078,0.01625917,0.3354657,0.02508674,0.1898901],"study_design_scores_gemma":[0.00004659387,0.0001544604,0.0003481049,0.00004823705,0.00002273174,0.0001539177,0.00004957129,0.8083967,0.004841467,0.1784952,0.007406592,0.00003648294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01441692,0.0005733435,0.9548749,0.0003207265,0.00008777076,0.0001244768,0.0003779272,0.0003058436,0.02891801],"genre_scores_gemma":[0.3192197,0.001563165,0.6069951,0.0002816218,0.000255946,0.0005230134,0.001009264,0.0005923323,0.06955983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009355797,"threshold_uncertainty_score":0.03129822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104701131332854,"score_gpt":0.2545204206725667,"score_spread":0.2334734093592382,"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."}}