{"id":"W2088422447","doi":"10.1007/s10589-007-9095-z","title":"Dynamic updates of the barrier parameter in primal-dual methods for nonlinear programming","year":2007,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Line search; Interior point method; Mathematical optimization; Nonlinear system; Convergence (economics); Dual (grammatical number); Computer science; Residual; Nonlinear programming; Newton's method; Local convergence; Rate of convergence; Point (geometry); Newton's method in optimization; Algorithm; Function (biology); Linear programming; Mathematics; Iterative method; Key (lock); Path (computing)","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":[0.003082763,0.001170806,0.001351665,0.0006989441,0.0006344098,0.002134266,0.001889354,0.002037267,0.004118643],"category_scores_gemma":[0.01999647,0.001075481,0.0003820726,0.0007321551,0.00165047,0.003290031,0.002450853,0.004217129,0.0009232677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000823932,"about_ca_system_score_gemma":0.001322309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369245,"about_ca_topic_score_gemma":0.001616503,"domain_scores_codex":[0.999189,0.0004773473,0.00003396827,0.00007316386,0.0001722874,0.00005438099],"domain_scores_gemma":[0.9961488,0.002623724,0.0002379961,0.0003220094,0.0005085947,0.0001589053],"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":[0.0004733636,0.0002016575,0.0005542064,0.0003413288,0.00007049469,0.000117335,0.0002060948,0.6262041,0.004246267,0.2738016,0.005882092,0.08790141],"study_design_scores_gemma":[0.00003215302,0.00002123795,0.00003882773,0.00002046919,0.000005864968,0.00001731078,0.00001094853,0.974192,0.0005954034,0.0238189,0.001237332,0.000009536693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009731409,0.000619463,0.9860752,0.0004556632,0.0002039638,0.00003569999,0.00003253405,0.0001967454,0.002649221],"genre_scores_gemma":[0.5168874,0.0009868646,0.4721883,0.0003581087,0.0002308804,0.0003961032,0.000157672,0.000792466,0.008002249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004118643,"threshold_uncertainty_score":0.01630342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03415400168738806,"score_gpt":0.4312444473736649,"score_spread":0.3970904456862768,"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."}}