{"id":"W4414229121","doi":"10.1109/msp.2025.3557958","title":"Quadratic Transform for Fractional Programming in Signal Processing and Machine Learning: A unified approach for solving optimization problems involving ratios","year":2025,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Advanced Control Systems Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Fractional programming; Quadratic programming; Signal processing; Maximization; Minification; Margin (machine learning); Optimization problem; Key (lock); Radar","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000779457,0.0004382757,0.0005541431,0.0004543628,0.0004576855,0.0003824553,0.0001673688,0.000211181,0.000003888159],"category_scores_gemma":[0.00007452229,0.0004588766,0.00008397066,0.0006804185,0.00007032209,0.001030843,0.00001282066,0.0004412827,4.224183e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002460997,"about_ca_system_score_gemma":0.0002560216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006916683,"about_ca_topic_score_gemma":0.00003481393,"domain_scores_codex":[0.9976679,0.00004697666,0.0008411102,0.0005644072,0.0002544936,0.0006251023],"domain_scores_gemma":[0.9990546,0.0002661024,0.0002085066,0.00009658784,0.0002829973,0.00009125836],"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":[0.0001505703,0.00007236699,0.0001066836,0.004841849,0.00003905792,0.000001132438,0.0005765353,0.8229684,0.03265326,0.0000741279,0.00001730608,0.1384988],"study_design_scores_gemma":[0.002595908,0.0001426757,0.00001486806,0.0008954881,0.00007877976,0.00001130673,0.0001971792,0.9920573,0.002084283,0.0008266334,0.0006420926,0.000453453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003627916,0.004159012,0.9914334,0.00009206581,0.00006768461,0.002965699,0.000007980922,0.0004738513,0.0004375203],"genre_scores_gemma":[0.8351173,0.00002107634,0.1626477,0.00003370104,0.0001525265,0.001528647,0.0001025029,0.0001065966,0.0002898693],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8347546,"threshold_uncertainty_score":0.9997863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186079600272055,"score_gpt":0.2400353840145565,"score_spread":0.221427423987351,"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."}}