{"id":"W4231873779","doi":"10.23952/jnva.2.2018.2.07","title":"A Krasnoselskii-type algorithm for approximating solutions of variational inequality problems and convex feasibility problems","year":2018,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"African Capacity Building Foundation; Anhui University of Science and Technology","keywords":"Variational inequality; Mathematics; Regular polygon; Type (biology); Inequality; Applied mathematics; Mathematical optimization; Mathematical analysis; Geometry","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":[0.00183137,0.0007793984,0.001009003,0.0007393355,0.0006129494,0.0008837432,0.001327569,0.001767193,0.002185797],"category_scores_gemma":[0.003929331,0.0004768019,0.00081693,0.0006737756,0.001180167,0.001553101,0.001367673,0.001800583,0.0005237642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009632431,"about_ca_system_score_gemma":0.001379833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002063459,"about_ca_topic_score_gemma":0.0022453,"domain_scores_codex":[0.9993212,0.000241288,0.00004965843,0.000111666,0.0002324685,0.00004374458],"domain_scores_gemma":[0.9992031,0.0004632085,0.0000661883,0.00005051569,0.0001667829,0.00005013481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001975505,0.0001199004,0.0008384781,0.0002869049,0.00009620829,0.0001330554,0.0002334884,0.6207862,0.01072249,0.2566713,0.001182841,0.1087316],"study_design_scores_gemma":[0.00001005308,0.00004696456,0.00004944671,0.00001037852,0.000004934272,0.00002566468,0.00001011464,0.9861386,0.0007218387,0.01194859,0.001025931,0.000007345679],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004307644,0.00008277791,0.9942753,0.00005679522,0.00003253683,0.00003642171,0.000008956757,0.00004237355,0.001157195],"genre_scores_gemma":[0.1913766,0.0002407687,0.8029258,0.00008041133,0.00005809087,0.0003313637,0.0000937536,0.0000818637,0.004811353],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002185797,"threshold_uncertainty_score":0.009685338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05466155751586396,"score_gpt":0.3008211946683584,"score_spread":0.2461596371524944,"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."}}