{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009395871,0.0007919076,0.0009107913,0.0002707916,0.0003861759,0.001096632,0.00101521,0.001107534,0.004947396],"category_scores_gemma":[0.002662408,0.0002701428,0.0004949867,0.0005820165,0.0007842525,0.0009480534,0.0005943757,0.001797323,0.001813142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008049107,"about_ca_system_score_gemma":0.0007954994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001417871,"about_ca_topic_score_gemma":0.002163613,"domain_scores_codex":[0.9994354,0.0001894389,0.00003229022,0.0001474497,0.0001569028,0.0000385382],"domain_scores_gemma":[0.9992009,0.0004090681,0.0000611586,0.00005032775,0.0002326728,0.00004597241],"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":[0.0003724816,0.00009058695,0.0006448567,0.0005129098,0.0001384695,0.000832976,0.0001529372,0.3523965,0.004755741,0.194497,0.2156281,0.2299775],"study_design_scores_gemma":[0.00004443803,0.00005285951,0.0002153251,0.00002745351,0.00002534535,0.00018176,0.00001527706,0.9154305,0.001598096,0.03457019,0.0478149,0.0000237994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007781782,0.001692719,0.9617365,0.01329091,0.006402725,0.00004609043,0.0001804981,0.0002755745,0.00859332],"genre_scores_gemma":[0.5079919,0.005249604,0.3860035,0.005070006,0.02405379,0.0003437387,0.0005646351,0.0009669634,0.06975582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004947396,"threshold_uncertainty_score":0.01655066,"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."}}