{"id":"W2171456339","doi":"10.1109/jsac.2004.833851","title":"A Lagrangean Relaxation and Subgradient Framework for the Routing and Wavelength Assignment Problem in WDM Networks","year":2004,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"South China University of Technology; University of Waterloo","keywords":"Subgradient method; Routing and wavelength assignment; Computer science; Mathematical optimization; Lagrangian relaxation; Heuristics; Heuristic; Wavelength-division multiplexing; Routing (electronic design automation); Relaxation (psychology); Computation; Algorithm; Computer network; Wavelength; 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":[0.0003489732,0.0001289444,0.0001472398,0.0001211772,0.0002646651,0.00006366368,0.0003136572,0.0001315627,4.907466e-7],"category_scores_gemma":[0.000284536,0.0001024324,0.00002031986,0.0005282972,0.00009432142,0.000104901,0.00005025599,0.001201406,2.811455e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284545,"about_ca_system_score_gemma":0.00001644925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007474359,"about_ca_topic_score_gemma":0.0003378931,"domain_scores_codex":[0.9991466,0.00005396114,0.0003309762,0.0001088811,0.00009045807,0.0002690677],"domain_scores_gemma":[0.9981634,0.001268776,0.00008920049,0.0003774044,0.0000549384,0.00004625536],"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":[0.00002945395,0.0001044496,0.002442921,0.00001239697,0.00004367642,0.000002487349,0.001000574,0.8554323,0.00008017806,0.05856892,0.00003392511,0.0822487],"study_design_scores_gemma":[0.001459534,0.0002252455,0.04063794,0.00134127,0.00003548323,0.00007750682,0.0006243082,0.8107424,0.00008706349,0.1436739,0.0007337611,0.0003616045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2378323,0.005912131,0.7476165,0.006867675,0.0001488654,0.001106608,0.000004193346,0.0002902724,0.0002214206],"genre_scores_gemma":[0.8672917,0.006934867,0.1255772,0.0000423609,0.0000361405,0.00009339843,0.000002510715,0.00002050141,0.000001299717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6294594,"threshold_uncertainty_score":0.5219578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033662191449393,"score_gpt":0.2620670664752454,"score_spread":0.2417304445607515,"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."}}