{"id":"W1638986631","doi":"10.1109/twc.2015.2443095","title":"Using Lagrangian Relaxation for Radio Resource Allocation in High Altitude Platforms","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subgradient method; Lagrangian relaxation; Mathematical optimization; Computer science; Multicast; Knapsack problem; Resource allocation; Column generation; Relaxation (psychology); Linear programming relaxation; Integer programming; Algorithm; Computer network; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002799862,0.000174709,0.0001807604,0.0003357077,0.0002677981,0.00004879787,0.000432076,0.0001578191,0.000006554455],"category_scores_gemma":[0.000007430063,0.0001997099,0.00006538312,0.000674809,0.00006644889,0.0003373867,0.000003003826,0.0002659908,0.00001832273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004325804,"about_ca_system_score_gemma":0.00006149524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001847332,"about_ca_topic_score_gemma":0.0009207448,"domain_scores_codex":[0.9989795,0.00003541199,0.0004130599,0.000191663,0.0001576427,0.0002227193],"domain_scores_gemma":[0.9985082,0.0001469823,0.00007690353,0.001029625,0.000133325,0.0001049342],"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":[0.00002161535,0.000161028,0.00003032732,0.00001838537,0.00002701229,9.71568e-8,0.0006814283,0.9836511,0.001150872,0.004383572,0.0001333797,0.009741181],"study_design_scores_gemma":[0.001085343,0.00003795636,0.0001817143,0.00005640064,0.00004437767,0.000003982042,0.0003249426,0.9915849,0.003276907,0.0006714524,0.002468246,0.0002637544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07073004,0.00011798,0.927074,0.0003556245,0.0001482792,0.0007383993,0.00005240883,0.0003036496,0.0004795814],"genre_scores_gemma":[0.9483415,0.0002301898,0.05051966,0.0000406646,0.00002753328,0.0005409276,0.0001713204,0.00005828856,0.00006984389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8776115,"threshold_uncertainty_score":0.8143933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0473254049316312,"score_gpt":0.2694695459046288,"score_spread":0.2221441409729976,"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."}}