{"id":"W4399411102","doi":"10.1109/jas.2024.124356","title":"Semi-Decentralized Convex Optimization on $\\mathcal{SO}(3)$","year":2024,"lang":"en","type":"article","venue":"IEEE/CAA Journal of Automatica Sinica","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Conic optimization; Regular polygon; Convex optimization; Mathematical optimization; Convex analysis; Mathematics; Computer science; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123033,0.0002169782,0.0004229865,0.0004050036,0.0001446563,0.001031019,0.0009609839,0.0001294021,0.0003732741],"category_scores_gemma":[0.0003109839,0.0001697813,0.0002465814,0.0007758334,0.0001112988,0.0009478994,0.00007334154,0.0004753059,0.0002206245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160424,"about_ca_system_score_gemma":0.0005737553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001554008,"about_ca_topic_score_gemma":2.918854e-7,"domain_scores_codex":[0.9970527,0.0002895636,0.001047692,0.0002976998,0.0009252102,0.0003871531],"domain_scores_gemma":[0.9978953,0.0006396456,0.0003278681,0.0004541201,0.0003575795,0.0003255],"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":[0.0001062875,0.0008855184,0.00004325791,0.0004083949,0.0005377785,0.000745321,0.004644176,0.7529163,0.001765767,0.1013017,0.08902424,0.04762118],"study_design_scores_gemma":[0.0006899096,0.0004064447,0.00004161322,0.0005376848,0.000027023,0.0002122554,0.00001573195,0.9763178,0.0007550652,0.001784029,0.01901472,0.0001977385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001739857,0.0005938624,0.9803931,0.01150886,0.002114398,0.0002490015,0.000003970352,0.0003740009,0.003023011],"genre_scores_gemma":[0.5586426,0.001466731,0.4358808,0.002527136,0.000385254,0.00001082712,0.000004765584,0.00007152059,0.001010471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5569027,"threshold_uncertainty_score":0.9942141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0209859215172364,"score_gpt":0.3026453421870993,"score_spread":0.2816594206698629,"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."}}