{"id":"W4401550494","doi":"10.1137/23m1552140","title":"MGProx: A Nonsmooth Multigrid Proximal Gradient Method with Adaptive Restriction for Strongly Convex Optimization","year":2024,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Division of Mathematical Sciences; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Fields Institute for Research in Mathematical Sciences","keywords":"Mathematics; Multigrid method; Regular polygon; Mathematical optimization; Convex optimization; Proximal Gradient Methods; Applied mathematics; Mathematical analysis; Geometry; Partial differential equation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009494319,0.0007080141,0.0009269296,0.0004713745,0.0003473834,0.0006246265,0.001214483,0.001141634,0.001540553],"category_scores_gemma":[0.001774653,0.000449639,0.0006946877,0.0005023733,0.000932432,0.0008883889,0.001784139,0.001493795,0.0005643793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003663716,"about_ca_system_score_gemma":0.001041824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001755228,"about_ca_topic_score_gemma":0.001828722,"domain_scores_codex":[0.9995978,0.0001556428,0.00001421909,0.00005203496,0.0001504992,0.00002976884],"domain_scores_gemma":[0.9994692,0.0002861421,0.00004754766,0.00007129034,0.00008100164,0.00004480255],"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":[0.0001154333,0.00008838579,0.0006980958,0.000241989,0.00008746718,0.0001763014,0.0001139314,0.8465865,0.01080228,0.04013107,0.003740911,0.0972177],"study_design_scores_gemma":[0.000008456958,0.00002396525,0.00003883155,0.000004991153,0.000002344937,0.00001632232,0.000003456301,0.9946235,0.0008191147,0.003343284,0.001111873,0.000003827189],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00337035,0.0001322447,0.9954443,0.0001249322,0.00003110253,0.00002955887,0.00001404698,0.0001571355,0.0006962694],"genre_scores_gemma":[0.1694637,0.0003538277,0.8256542,0.0002313741,0.00008520304,0.000248648,0.0001113544,0.0002243115,0.003627408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001755228,"threshold_uncertainty_score":0.005153596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679189818565932,"score_gpt":0.2575591347630491,"score_spread":0.2407672365773898,"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."}}