{"id":"W2065073968","doi":"10.5539/mas.v7n6p90","title":"On Solving Linear Fractional Programming Problems","year":2013,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"VIT University","keywords":"Fractional programming; Linear programming; Simplex algorithm; Decomposition method (queueing theory); Linear-fractional programming; Mathematical optimization; Decomposition; Mathematics; Computer science; Fuzzy logic; Algorithm; Nonlinear programming; Nonlinear system; Discrete mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001936222,0.0001083822,0.00008837419,0.00009283573,0.0001964283,0.0001830617,0.0001998966,0.00003663448,0.0001408834],"category_scores_gemma":[0.00003476649,0.00009626667,0.00002067334,0.0003220548,0.0001298124,0.0002546368,0.00003672671,0.000148322,0.0004401772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005730187,"about_ca_system_score_gemma":0.00001947685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002067137,"about_ca_topic_score_gemma":4.818708e-7,"domain_scores_codex":[0.9989352,0.000002243624,0.0001524316,0.0002138054,0.0003555693,0.0003407215],"domain_scores_gemma":[0.9996025,0.00003974378,0.00002183589,0.0001632967,0.00004801157,0.0001245951],"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":[0.000001771459,0.0001106953,0.00001070326,0.000101989,0.000008625741,5.653861e-7,0.0008976264,0.6335295,0.1213711,0.04127258,0.0002786716,0.2024162],"study_design_scores_gemma":[0.00008417272,0.000009601762,0.00001726807,0.00001387027,0.000001587871,0.000001187097,0.00002913149,0.985683,0.001535616,0.01183275,0.0006654365,0.0001263672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006838541,0.00001043013,0.9586315,0.00005358753,0.00009960867,0.0004298098,2.584169e-7,0.0005332426,0.03340304],"genre_scores_gemma":[0.9192563,0.000001458006,0.08037522,0.00007742485,0.00002988594,0.0001488876,0.000001234463,0.00001818696,0.00009135687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9124178,"threshold_uncertainty_score":0.5657734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013430967590886,"score_gpt":0.2232066346652236,"score_spread":0.2097756670743376,"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."}}