{"id":"W4412767593","doi":"10.23952/jano.7.2025.2.02","title":"Solving an uncertain quadratic multiobjective optimization problem using Newton’s descent method via a robust optimization approach","year":2025,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Descent (aeronautics); Mathematical optimization; Multi-objective optimization; Robust optimization; Quadratic equation; Descent direction; Optimization problem; Newton's method; Quadratic programming; Computer science; Mathematics; Newton's method in optimization; Gradient descent; Iterative method; Engineering; Nonlinear system; Artificial intelligence; Local convergence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001255774,0.0009856161,0.001018332,0.0005931208,0.0004008951,0.000773641,0.0008598882,0.001167052,0.001628996],"category_scores_gemma":[0.001607823,0.000439205,0.001028279,0.0004790789,0.0005435779,0.0006566945,0.0008441278,0.001186745,0.0004495116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005082352,"about_ca_system_score_gemma":0.00126779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003622317,"about_ca_topic_score_gemma":0.0025411,"domain_scores_codex":[0.9994448,0.0001868642,0.00003380988,0.00009133172,0.0002102996,0.00003288126],"domain_scores_gemma":[0.999421,0.0003399517,0.00007357995,0.00002872447,0.0001173915,0.0000193247],"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":[0.00001697258,0.00002144026,0.0001731621,0.0001450403,0.00004021766,0.00006670169,0.00003784367,0.9559227,0.00297194,0.0105646,0.0005709337,0.02946846],"study_design_scores_gemma":[0.00000244577,0.00001459936,0.00002825723,0.000005002044,0.000003286213,0.00001103637,0.000002771262,0.9980106,0.0002997206,0.001030689,0.0005881934,0.00000345242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001530443,0.000122022,0.9970745,0.00005644844,0.00001470551,0.00001848046,0.000009582841,0.00004420458,0.001129526],"genre_scores_gemma":[0.1541523,0.0005523586,0.8406314,0.0001255127,0.00009100545,0.0003137343,0.000108009,0.00008641635,0.003939133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003622317,"threshold_uncertainty_score":0.007202506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09640393866883326,"score_gpt":0.395242406603361,"score_spread":0.2988384679345277,"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."}}