{"id":"W3183136893","doi":"10.1007/s11081-021-09659-3","title":"Norm induced polyhedral uncertainty sets for robust linear optimization","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robust optimization; Intersection (aeronautics); Uncertain data; Mathematical optimization; Uncertainty theory; Constraint (computer-aided design); Mathematics; Set (abstract data type); Norm (philosophy); Uncertainty quantification; Uncertainty analysis; Computer science; Data mining","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":[0.005232578,0.002288396,0.002847937,0.002242834,0.0006813893,0.003350342,0.002233708,0.002172841,0.003115348],"category_scores_gemma":[0.01543871,0.001398048,0.001947087,0.001892578,0.003363539,0.003543393,0.004591233,0.006252543,0.0006715988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00228477,"about_ca_system_score_gemma":0.001243994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002347175,"about_ca_topic_score_gemma":0.001344602,"domain_scores_codex":[0.9961857,0.001792425,0.0001781764,0.0004580188,0.001238147,0.0001475322],"domain_scores_gemma":[0.9908186,0.006498299,0.000701178,0.0004813862,0.001152106,0.0003485543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007038088,0.00007217054,0.000183677,0.000264007,0.00009056427,0.00004842437,0.00009975846,0.4508514,0.001195434,0.5213144,0.00215923,0.02365067],"study_design_scores_gemma":[0.000006868187,0.00004254653,0.00006774741,0.00003865281,0.00001037801,0.00001456182,0.00001592467,0.6737315,0.0003197,0.3241686,0.001563007,0.00002053575],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003752362,0.0006164342,0.9905277,0.0002704572,0.0001054054,0.00003917655,0.0001287597,0.00004732756,0.004512351],"genre_scores_gemma":[0.5221401,0.004319724,0.4429086,0.0008559594,0.0008566665,0.001208554,0.001693116,0.0006707542,0.02534646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005232578,"threshold_uncertainty_score":0.02767283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06890294975717556,"score_gpt":0.3245371272210113,"score_spread":0.2556341774638358,"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."}}