{"id":"W2125240990","doi":"10.1023/b:joco.0000038911.67280.3f","title":"On Approximate Graph Colouring and MAX-k-CUT Algorithms Based on the θ-Function","year":2004,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Technische Universiteit Delft; Universiteit Utrecht; Deutsche Forschungsgemeinschaft","keywords":"Mathematics; Combinatorics; Semidefinite programming; Approximation algorithm; Bounded function; Graph; Relaxation (psychology); Discrete mathematics; Maximum cut; Algorithm; Mathematical optimization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004712902,0.002084481,0.003425859,0.001994377,0.001620122,0.003695635,0.00573985,0.003328848,0.008145647],"category_scores_gemma":[0.02858309,0.00124251,0.001771945,0.004694732,0.002865822,0.01050966,0.004007641,0.004301536,0.001466171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003874093,"about_ca_system_score_gemma":0.00308674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005003412,"about_ca_topic_score_gemma":0.006365009,"domain_scores_codex":[0.9964935,0.001745567,0.0001622861,0.000506858,0.0007369123,0.0003548669],"domain_scores_gemma":[0.9790069,0.01536012,0.0008958541,0.002962847,0.00116262,0.0006116511],"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":[0.001499125,0.0003828559,0.002183439,0.0003795777,0.0001631293,0.00007483061,0.0002837303,0.6374008,0.002667702,0.1610929,0.009311582,0.1845603],"study_design_scores_gemma":[0.00005481324,0.00005638802,0.0003006271,0.00002795608,0.00003594034,0.00005012979,0.00004936893,0.8777672,0.000613706,0.1199626,0.001064422,0.00001667606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0391659,0.00155981,0.9497644,0.00113183,0.000173893,0.0001333939,0.0002001086,0.0009994453,0.006871257],"genre_scores_gemma":[0.3488642,0.001467961,0.6394535,0.0007520328,0.000320805,0.0004226658,0.000898855,0.0007315564,0.007088301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008145647,"threshold_uncertainty_score":0.02810866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223000613380427,"score_gpt":0.2180044899597861,"score_spread":0.2057744838259818,"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."}}