Reading training examples...done
Training set properties: 23 features, 90 rankings, 8128 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.495733
Iter 1: .........*(NumConst=1, SV=1, CEps=495.7333, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=460.2406, QPEps=0.0002)
Iter 3: .........*(NumConst=3, SV=2, CEps=733.9566, QPEps=0.0003)
Iter 4: .........*(NumConst=4, SV=3, CEps=1968.4281, QPEps=0.0005)
Iter 5: .........*(NumConst=5, SV=4, CEps=980.9190, QPEps=0.0086)
Iter 6: .........*(NumConst=6, SV=6, CEps=626.7641, QPEps=0.0008)
Iter 7: .........*(NumConst=7, SV=5, CEps=786.0521, QPEps=49.6042)
Iter 8: .........*(NumConst=8, SV=6, CEps=1653.6506, QPEps=40.9936)
Iter 9: .........*(NumConst=9, SV=6, CEps=810.8644, QPEps=48.7933)
Iter 10: .........*(NumConst=10, SV=6, CEps=522.1340, QPEps=36.0746)
Iter 11: .........*(NumConst=11, SV=7, CEps=327.3357, QPEps=47.2062)
Iter 12: .........*(NumConst=12, SV=7, CEps=221.5572, QPEps=27.7362)
Iter 13: .........*(NumConst=13, SV=9, CEps=178.8900, QPEps=48.6786)
Iter 14: .........*(NumConst=14, SV=8, CEps=116.0969, QPEps=9.1788)
Iter 15: .........*(NumConst=15, SV=7, CEps=101.9881, QPEps=24.0041)
Iter 16: .........*(NumConst=16, SV=8, CEps=74.0177, QPEps=33.7898)
Iter 17: .........*(NumConst=17, SV=7, CEps=102.3609, QPEps=10.6897)
Iter 18: .........*(NumConst=18, SV=7, CEps=83.5112, QPEps=22.5766)
Iter 19: .........*(NumConst=19, SV=7, CEps=48.9296, QPEps=17.2190)
Iter 20: .........*(NumConst=20, SV=7, CEps=42.8767, QPEps=7.3283)
Iter 21: .........*(NumConst=21, SV=6, CEps=31.9600, QPEps=11.4872)
Iter 22: .........*(NumConst=22, SV=7, CEps=21.3617, QPEps=6.4928)
Iter 23: .........*(NumConst=23, SV=7, CEps=33.3261, QPEps=10.6455)
Iter 24: .........*(NumConst=24, SV=6, CEps=15.9383, QPEps=0.0000)
Iter 25: .........*(NumConst=25, SV=6, CEps=15.5464, QPEps=0.0000)
Iter 26: .........*(NumConst=26, SV=7, CEps=10.4186, QPEps=3.4531)
Iter 27: .........*(NumConst=27, SV=7, CEps=15.6348, QPEps=1.2517)
Iter 28: .........*(NumConst=28, SV=8, CEps=15.1111, QPEps=3.9875)
Iter 29: .........*(NumConst=29, SV=8, CEps=14.6190, QPEps=1.3000)
Iter 30: .........*(NumConst=30, SV=8, CEps=5.6544, QPEps=2.2364)
Iter 31: .........*(NumConst=31, SV=7, CEps=5.9189, QPEps=1.6606)
Iter 32: .........*(NumConst=32, SV=7, CEps=6.5861, QPEps=1.0726)
Iter 33: .........*(NumConst=33, SV=7, CEps=5.6252, QPEps=2.5390)
Iter 34: .........*(NumConst=34, SV=7, CEps=4.7664, QPEps=0.9807)
Iter 35: .........*(NumConst=35, SV=7, CEps=2.3989, QPEps=0.4156)
Iter 36: .........*(NumConst=36, SV=7, CEps=2.9170, QPEps=1.1414)
Iter 37: .........*(NumConst=37, SV=6, CEps=2.9313, QPEps=0.0000)
Iter 38: .........*(NumConst=38, SV=7, CEps=2.4048, QPEps=1.1298)
Iter 39: .........*(NumConst=39, SV=7, CEps=1.0674, QPEps=0.4562)
Iter 40: .........*(NumConst=40, SV=6, CEps=1.9362, QPEps=0.0000)
Iter 41: .........*(NumConst=41, SV=6, CEps=1.0986, QPEps=0.5063)
Iter 42: .........*(NumConst=42, SV=7, CEps=0.8525, QPEps=0.3135)
Iter 43: .........*(NumConst=43, SV=7, CEps=1.3697, QPEps=0.3436)
Iter 44: .........*(NumConst=44, SV=5, CEps=0.9148, QPEps=0.1720)
Iter 45: .........(NumConst=44, SV=5, CEps=0.4428, QPEps=0.1720)
Final epsilon on KKT-Conditions: 0.44281
Upper bound on duality gap: 0.21284
Dual objective value: dval=136.20476
Primal objective value: pval=136.41760
Total number of constraints in final working set: 44 (of 44)
Number of iterations: 45
Number of calls to 'find_most_violated_constraint': 4050
Number of SV: 5 
Norm of weight vector: |w|=2.16541
Value of slack variable (on working set): xi=267.75616
Value of slack variable (global): xi=268.14622
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3264.41858
Runtime in cpu-seconds: 1.52
Compacting linear model...done
Writing learned model...done