{"id":"W2105318109","doi":"10.1287/ijoc.1080.0270","title":"Computing Globally Optimal Solutions for Single-Row Layout Problems Using Semidefinite Programming and Cutting Planes","year":2008,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semidefinite programming; Mathematical optimization; Cutting-plane method; Relaxation (psychology); Linear programming; Semidefinite embedding; Mathematics; Computer science; Integer programming; Algorithm; Quadratically constrained quadratic program; Quadratic programming","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":[0.001211229,0.001930729,0.001749957,0.0009539047,0.0003898041,0.001213462,0.0008770374,0.001110329,0.003401838],"category_scores_gemma":[0.004260864,0.001004537,0.001082984,0.001306779,0.0009448634,0.001605657,0.001235578,0.001601467,0.0006900373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008233701,"about_ca_system_score_gemma":0.001549029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002156001,"about_ca_topic_score_gemma":0.003473104,"domain_scores_codex":[0.9993231,0.0003022233,0.00003088533,0.0001402597,0.0001288235,0.00007481172],"domain_scores_gemma":[0.9979202,0.001435667,0.0002408275,0.0001393468,0.0001838981,0.00008013398],"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":[0.00003576756,0.00003233716,0.0002112113,0.0000705237,0.00002591771,0.00003538482,0.000026056,0.9731891,0.001071827,0.006245063,0.000791449,0.01826532],"study_design_scores_gemma":[0.00001433325,0.00004225401,0.00004564754,0.000008111114,0.000005455166,0.0000111608,0.0000252157,0.9897357,0.0006887717,0.009116663,0.0003006796,0.000005951243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03301354,0.0001770545,0.9625115,0.0001560163,0.00002144736,0.00004830146,0.0001895987,0.000533717,0.003348914],"genre_scores_gemma":[0.2608766,0.0002869544,0.73575,0.00008600163,0.00002924822,0.0003077554,0.0006236006,0.0002659048,0.001773967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003401838,"threshold_uncertainty_score":0.01138031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05195044819780355,"score_gpt":0.2483886363366176,"score_spread":0.196438188138814,"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."}}