{"id":"W2693076760","doi":"10.1016/j.ejor.2018.02.045","title":"Trade-off preservation in inverse multi-objective convex optimization","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Inverse; Linear programming; Computer science; Pareto principle; Mathematics; Optimization problem; Convex optimization; Regular polygon","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":[0.005446063,0.001599834,0.001863724,0.0009525011,0.0006038916,0.002528219,0.002098561,0.001799711,0.003074924],"category_scores_gemma":[0.01899751,0.001150867,0.001250378,0.001272182,0.002479535,0.003896769,0.003900414,0.00318627,0.000492654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008440422,"about_ca_system_score_gemma":0.0008852358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001090579,"about_ca_topic_score_gemma":0.000835485,"domain_scores_codex":[0.9980788,0.0009951621,0.00008246349,0.0002046251,0.000511142,0.0001277483],"domain_scores_gemma":[0.9944088,0.003999041,0.0003116447,0.0006368416,0.0004830172,0.0001606323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000406639,0.0001559225,0.000851289,0.0005034263,0.0001313,0.0001904,0.0002708055,0.7777855,0.003394738,0.1260301,0.001956569,0.08832326],"study_design_scores_gemma":[0.00001613597,0.0001078423,0.0001340028,0.00002920721,0.00001832039,0.0000692945,0.0000231269,0.9506938,0.001131584,0.04707778,0.0006870811,0.00001168973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01555587,0.0004439245,0.9792437,0.0002575433,0.00004894124,0.00003224998,0.00003782524,0.0001085584,0.004271468],"genre_scores_gemma":[0.6306629,0.0008113917,0.3584372,0.0002512129,0.0001359297,0.0002186405,0.0001874816,0.0006333415,0.008661964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005446063,"threshold_uncertainty_score":0.02880186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08503374081602867,"score_gpt":0.3640516131639794,"score_spread":0.2790178723479507,"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."}}