{"id":"W2096911290","doi":"10.1007/s10898-015-0397-x","title":"A relaxed-projection splitting algorithm for variational inequalities in Hilbert spaces","year":2015,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Goiás; University of British Columbia; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Mathematics; Variational inequality; Hilbert space; Subgradient method; Monotone polygon; Hyperplane; Monotonic function; Sequence (biology); Projection (relational algebra); Solution set; Strongly monotone; Projection method; Applied mathematics; Pure mathematics; Set (abstract data type); Dykstra's projection algorithm; Mathematical analysis; Mathematical optimization; Algorithm; Combinatorics","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":[0.002758758,0.001272882,0.001973591,0.0007519432,0.0006676918,0.001462281,0.002808572,0.002117814,0.008982886],"category_scores_gemma":[0.005600312,0.001271474,0.001378636,0.001010105,0.001189981,0.002387079,0.004011899,0.005189573,0.001505754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007469901,"about_ca_system_score_gemma":0.002282594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00251197,"about_ca_topic_score_gemma":0.002804177,"domain_scores_codex":[0.9988858,0.0004319688,0.00006980771,0.0001831237,0.000343445,0.00008590148],"domain_scores_gemma":[0.9981351,0.0009399942,0.00005963081,0.0002355369,0.0004560558,0.0001736488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007767623,0.0004838817,0.0007578242,0.000489831,0.0002336754,0.0001941543,0.0003210942,0.4109565,0.013906,0.1488608,0.01050865,0.4125108],"study_design_scores_gemma":[0.0000427397,0.00005906731,0.00004675764,0.00001418577,0.000008757537,0.00002411063,0.00001307445,0.9821386,0.0006816144,0.01598565,0.0009779503,0.000007505369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003905698,0.0001007324,0.9945021,0.0001260248,0.00006348758,0.00006603485,0.00004336283,0.0001562993,0.001036333],"genre_scores_gemma":[0.07345555,0.0001475817,0.9218321,0.0001442918,0.00009746128,0.0003063921,0.0003195554,0.0002247399,0.003472269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008982886,"threshold_uncertainty_score":0.03005075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02899715749163309,"score_gpt":0.2842935387154878,"score_spread":0.2552963812238547,"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."}}