{"id":"W2636499139","doi":"10.1287/mnsc.2017.2992","title":"Inverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit","year":2018,"lang":"en","type":"preprint","venue":"Management Science","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mathematical optimization; Goodness of fit; Inverse; Applied mathematics; Metric (unit); Geometric programming; Inverse problem; Optimization problem; Mathematical analysis; Geometry; Statistics","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":[0.00564848,0.002071458,0.002375063,0.001944878,0.0006943524,0.003220734,0.001504123,0.003365694,0.002749546],"category_scores_gemma":[0.03321022,0.0009804982,0.001152533,0.001897091,0.004814736,0.005111428,0.003976243,0.00380015,0.0005648338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001809322,"about_ca_system_score_gemma":0.001447472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694771,"about_ca_topic_score_gemma":0.00112646,"domain_scores_codex":[0.996294,0.002046166,0.0001349597,0.0005302114,0.0008236538,0.0001710309],"domain_scores_gemma":[0.987153,0.01011434,0.0008730235,0.0007383663,0.0009227186,0.0001985662],"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":[0.00004667942,0.00005027839,0.0006949726,0.0002453995,0.000054509,0.0001024699,0.0001360138,0.5477222,0.001171127,0.4130089,0.002843466,0.03392401],"study_design_scores_gemma":[0.000008284528,0.00004170729,0.0001580489,0.00004379566,0.000008481168,0.00007311729,0.00003595376,0.7382147,0.0004675111,0.2594082,0.001517441,0.00002274588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003310286,0.0004913719,0.9939538,0.0005039535,0.00002326356,0.00001998537,0.00004064456,0.00007002788,0.001586742],"genre_scores_gemma":[0.4829835,0.003507584,0.5067407,0.0006120448,0.0003968269,0.0005006118,0.0005470081,0.0004806778,0.004230953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00564848,"threshold_uncertainty_score":0.02987236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03658550155032685,"score_gpt":0.2556119515544764,"score_spread":0.2190264500041495,"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."}}