{"id":"W4312940312","doi":"10.23952/jnva.6.2022.6.07","title":"A nonmonotone gradient method for constrained multiobjective optimization problems","year":2022,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Multi-objective optimization; Computer science; Gradient method; Proximal Gradient Methods; Constrained optimization; Mathematics; Gradient descent; Artificial intelligence; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009628369,0.000117873,0.0003241625,0.0006491675,0.0003480487,0.00006809693,0.0002459517,0.00002853781,0.0000509993],"category_scores_gemma":[0.0001901765,0.0001090006,0.0002477375,0.001351197,0.00002299965,0.0004213378,0.0001063994,0.0001553988,2.110671e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001440893,"about_ca_system_score_gemma":0.0001615192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001237797,"about_ca_topic_score_gemma":0.000003280531,"domain_scores_codex":[0.9985351,0.0001712151,0.0005099568,0.0002493489,0.000398149,0.0001362267],"domain_scores_gemma":[0.9978441,0.0003883194,0.0006974593,0.000120049,0.0008604259,0.00008967218],"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":[0.0000385572,0.0001889386,0.0001452471,0.000003661071,0.0007409441,0.000002555918,0.0006227721,0.9918345,0.00007539658,0.003618702,0.00000897488,0.002719783],"study_design_scores_gemma":[0.001274019,0.0002296119,0.0006856211,0.000002270441,0.0003357022,0.0000634645,0.0001251752,0.9954556,0.00003169501,0.001299076,0.0003765294,0.0001211888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001066059,0.00006426451,0.998538,0.0007987233,0.0001332879,0.0002305297,0.00008145803,0.00001503656,0.00003212319],"genre_scores_gemma":[0.01058476,0.00002102881,0.9889558,0.000173889,0.00009376769,0.00003764491,0.00004821157,0.000007915212,0.00007700235],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01047815,"threshold_uncertainty_score":0.4444914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327828570408893,"score_gpt":0.2868655736015706,"score_spread":0.2735872878974817,"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."}}