{"id":"W2997995007","doi":"10.48550/arxiv.1912.09232","title":"Improving Clique Decompositions of Semidefinite Relaxations for Optimal\\n Power Flow Problems","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Semidefinite programming; Clique; Mathematics; Chordal graph; Mathematical optimization; Decomposition; Power flow; Clique problem; Extension (predicate logic); Flow (mathematics); Power (physics); Computer science; Combinatorics; Electric power system; Graph","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":[0.002981164,0.001593679,0.0009626082,0.001030455,0.0006040985,0.001537535,0.001182626,0.00103573,0.006180685],"category_scores_gemma":[0.008140934,0.0006309592,0.001245718,0.00114403,0.001012456,0.001991642,0.0017071,0.002769942,0.0007656062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145005,"about_ca_system_score_gemma":0.001434574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002707683,"about_ca_topic_score_gemma":0.005290282,"domain_scores_codex":[0.9984387,0.0008082791,0.00004884398,0.0001955782,0.0003097078,0.0001990254],"domain_scores_gemma":[0.9955128,0.003144249,0.0002469085,0.0005190725,0.0003671786,0.0002097235],"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":[0.0002395025,0.000389769,0.000433364,0.0001692188,0.00005407774,0.00005976854,0.0001270882,0.8834963,0.003919081,0.04804062,0.006521961,0.05654925],"study_design_scores_gemma":[0.00004191919,0.0000503588,0.00005962221,0.00001034628,0.000006864847,0.00001139901,0.00002396204,0.9835941,0.0009879066,0.01435446,0.0008536375,0.000005396024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06652852,0.0003422272,0.9172063,0.0005813023,0.00009748415,0.0001596011,0.0003230574,0.0008943819,0.01386702],"genre_scores_gemma":[0.4087088,0.0003687048,0.584304,0.0003426944,0.0000959594,0.0002904775,0.0009964519,0.000577815,0.004315024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006180685,"threshold_uncertainty_score":0.02067643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04127062701432777,"score_gpt":0.1787413784370388,"score_spread":0.137470751422711,"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."}}