{"id":"W4388184463","doi":"10.48550/arxiv.2310.20142","title":"Alternating Proximal Point Algorithm with Gradient Descent and Ascent Steps for Convex-Concave Saddle-Point Problems","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saddle point; Mathematics; Convex function; Function (biology); Gradient descent; Minimax; Proximal Gradient Methods; Concave function; Regular polygon; Convergence (economics); Mathematical analysis; Sequence (biology); Saddle; Applied mathematics; Mathematical optimization; Combinatorics; Geometry; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00315217,0.001727169,0.001786432,0.0009677694,0.0006332144,0.001164852,0.00193642,0.001781036,0.00263901],"category_scores_gemma":[0.006793696,0.0008720584,0.001084798,0.0007726954,0.001596833,0.001329369,0.002440227,0.002855217,0.001039399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008216998,"about_ca_system_score_gemma":0.001848962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001790419,"about_ca_topic_score_gemma":0.001374285,"domain_scores_codex":[0.9989406,0.0005834016,0.00004942445,0.0001184017,0.0002323645,0.00007586156],"domain_scores_gemma":[0.9979348,0.001333131,0.0001248022,0.0001542851,0.0003358638,0.0001171496],"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":[0.0001788614,0.0001002246,0.0005105437,0.0001992377,0.0001046491,0.0001517912,0.0001587978,0.8341178,0.002751454,0.1003501,0.00273323,0.0586434],"study_design_scores_gemma":[0.00001697102,0.00003673548,0.00002975829,0.000008438715,0.00000505115,0.00002103681,0.000006830739,0.9846126,0.0005179663,0.01405306,0.0006844081,0.000007195198],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002726143,0.00008210646,0.9962294,0.00008361869,0.00001705908,0.00003153106,0.000008276877,0.00009875983,0.0007230666],"genre_scores_gemma":[0.2236436,0.0003272333,0.7695369,0.0001823849,0.00009778724,0.0005019581,0.000147551,0.0002824631,0.005280222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00315217,"threshold_uncertainty_score":0.01667053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07343936484998281,"score_gpt":0.1911477443131127,"score_spread":0.1177083794631298,"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."}}