{"id":"W1821374938","doi":"10.1080/10556788.2015.1062891","title":"An efficient optimization approach for a cardinality-constrained index tracking problem","year":2015,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Key Research and Development Program of China; University of Otago","keywords":"Cardinality (data modeling); Mathematical optimization; Computer science; Tracking (education); Tracking error; Portfolio; Regularization (linguistics); Thresholding; Mathematics; Artificial intelligence; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002363547,0.001165672,0.001401968,0.0009865364,0.0006073655,0.001350193,0.001364569,0.001979844,0.004115816],"category_scores_gemma":[0.005928713,0.0008771259,0.00104914,0.001238221,0.0008632368,0.001758732,0.001881531,0.002173136,0.0007113799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009945253,"about_ca_system_score_gemma":0.001964298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003616579,"about_ca_topic_score_gemma":0.003207803,"domain_scores_codex":[0.9990683,0.0003383071,0.00005097142,0.0002020615,0.000257448,0.00008291196],"domain_scores_gemma":[0.9980032,0.001346224,0.0001682227,0.0001333752,0.000262455,0.00008659248],"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":[0.00005174601,0.00006926733,0.0004400355,0.0001108573,0.00003492166,0.0001001587,0.00005702929,0.9175522,0.001813506,0.02394473,0.002050478,0.05377506],"study_design_scores_gemma":[0.000007222796,0.00001563375,0.00004389988,0.000005266174,0.000003980067,0.00001651346,0.000004936561,0.9941122,0.0002203585,0.005189801,0.0003768951,0.000003343106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002767616,0.0001030511,0.9958107,0.0001453564,0.00001450673,0.00003785042,0.00002973858,0.00006705919,0.001024053],"genre_scores_gemma":[0.1699278,0.000386085,0.8245601,0.000199904,0.0001058829,0.000408774,0.0002844359,0.0001672202,0.003959757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004115816,"threshold_uncertainty_score":0.01376879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05089273698655643,"score_gpt":0.3335022468907675,"score_spread":0.2826095099042111,"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."}}