Reading training examples...done
Training set properties: 19 features, 180 rankings, 15506 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.986667
Iter 1: .........*(NumConst=1, SV=1, CEps=986.6667, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=959.5858, QPEps=0.0008)
Iter 3: .........*(NumConst=3, SV=3, CEps=2395.3112, QPEps=0.0005)
Iter 4: .........*(NumConst=4, SV=4, CEps=1262.9406, QPEps=0.0009)
Iter 5: .........*(NumConst=5, SV=5, CEps=386.3997, QPEps=0.0002)
Iter 6: .........*(NumConst=6, SV=5, CEps=246.6107, QPEps=0.0006)
Iter 7: .........*(NumConst=7, SV=5, CEps=311.8043, QPEps=1.8748)
Iter 8: .........*(NumConst=8, SV=5, CEps=168.6084, QPEps=17.4441)
Iter 9: .........*(NumConst=9, SV=6, CEps=102.2866, QPEps=17.8416)
Iter 10: .........*(NumConst=10, SV=6, CEps=154.6060, QPEps=44.1037)
Iter 11: .........*(NumConst=11, SV=6, CEps=106.4496, QPEps=46.9451)
Iter 12: .........*(NumConst=12, SV=6, CEps=41.2016, QPEps=7.5348)
Iter 13: .........*(NumConst=13, SV=6, CEps=41.0502, QPEps=19.4440)
Iter 14: .........*(NumConst=14, SV=7, CEps=45.2588, QPEps=19.5075)
Iter 15: .........*(NumConst=15, SV=7, CEps=39.0984, QPEps=1.2019)
Iter 16: .........*(NumConst=16, SV=7, CEps=20.7014, QPEps=9.9633)
Iter 17: .........*(NumConst=17, SV=6, CEps=18.4838, QPEps=8.7140)
Iter 18: .........*(NumConst=18, SV=6, CEps=10.8545, QPEps=3.9426)
Iter 19: .........*(NumConst=19, SV=7, CEps=9.6540, QPEps=4.0870)
Iter 20: .........*(NumConst=20, SV=8, CEps=7.7917, QPEps=3.6287)
Iter 21: .........*(NumConst=21, SV=7, CEps=6.5369, QPEps=3.1178)
Iter 22: .........*(NumConst=22, SV=7, CEps=4.5833, QPEps=1.9109)
Iter 23: .........*(NumConst=23, SV=7, CEps=4.0388, QPEps=1.8964)
Iter 24: .........*(NumConst=24, SV=7, CEps=3.0561, QPEps=1.4672)
Iter 25: .........*(NumConst=25, SV=8, CEps=3.6735, QPEps=1.2824)
Iter 26: .........*(NumConst=26, SV=8, CEps=2.8922, QPEps=1.3886)
Iter 27: .........*(NumConst=27, SV=9, CEps=1.5956, QPEps=0.7734)
Iter 28: .........*(NumConst=28, SV=7, CEps=1.1632, QPEps=0.5740)
Iter 29: .........*(NumConst=29, SV=8, CEps=1.1904, QPEps=0.4903)
Iter 30: .........*(NumConst=30, SV=8, CEps=1.2564, QPEps=0.5523)
Iter 31: .........(NumConst=30, SV=8, CEps=0.7503, QPEps=0.5523)
Final epsilon on KKT-Conditions: 0.75033
Upper bound on duality gap: 0.00515
Dual objective value: dval=6.70811
Primal objective value: pval=6.71326
Total number of constraints in final working set: 30 (of 30)
Number of iterations: 31
Number of calls to 'find_most_violated_constraint': 5580
Number of SV: 8 
Norm of weight vector: |w|=0.83226
Value of slack variable (on working set): xi=636.43640
Value of slack variable (global): xi=636.69250
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3740.82801
Runtime in cpu-seconds: 0.22
Compacting linear model...done
Writing learned model...done