Reading training examples...done
Training set properties: 23 features, 90 rankings, 8151 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.536978
Iter 1: .........*(NumConst=1, SV=1, CEps=536.9778, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=537.9545, QPEps=0.0001)
Iter 3: .........*(NumConst=3, SV=3, CEps=808.2379, QPEps=0.0015)
Iter 4: .........*(NumConst=4, SV=3, CEps=2694.1465, QPEps=0.0001)
Iter 5: .........*(NumConst=5, SV=4, CEps=363.2637, QPEps=0.0008)
Iter 6: .........*(NumConst=6, SV=5, CEps=226.5200, QPEps=0.0003)
Iter 7: .........*(NumConst=7, SV=5, CEps=67.2779, QPEps=31.1287)
Iter 8: .........*(NumConst=8, SV=4, CEps=44.3295, QPEps=16.8877)
Iter 9: .........*(NumConst=9, SV=5, CEps=59.5194, QPEps=12.5177)
Iter 10: .........*(NumConst=10, SV=6, CEps=27.7990, QPEps=0.0001)
Iter 11: .........*(NumConst=11, SV=6, CEps=70.2240, QPEps=0.0000)
Iter 12: .........*(NumConst=12, SV=6, CEps=47.4857, QPEps=0.0001)
Iter 13: .........*(NumConst=13, SV=5, CEps=18.2128, QPEps=0.1652)
Iter 14: .........*(NumConst=14, SV=6, CEps=13.8650, QPEps=0.0000)
Iter 15: .........*(NumConst=15, SV=6, CEps=9.3594, QPEps=0.0000)
Iter 16: .........*(NumConst=16, SV=7, CEps=10.1498, QPEps=3.8252)
Iter 17: .........*(NumConst=17, SV=8, CEps=8.2136, QPEps=0.8157)
Iter 18: .........*(NumConst=18, SV=7, CEps=8.5615, QPEps=0.7249)
Iter 19: .........*(NumConst=19, SV=7, CEps=4.2966, QPEps=0.4468)
Iter 20: .........*(NumConst=20, SV=7, CEps=6.1522, QPEps=1.5281)
Iter 21: .........*(NumConst=21, SV=5, CEps=3.0949, QPEps=0.3846)
Iter 22: .........*(NumConst=22, SV=6, CEps=3.5689, QPEps=1.0234)
Iter 23: .........*(NumConst=23, SV=6, CEps=2.1905, QPEps=0.3194)
Iter 24: .........*(NumConst=24, SV=7, CEps=1.8474, QPEps=0.0000)
Iter 25: .........*(NumConst=25, SV=8, CEps=3.3269, QPEps=0.0000)
Iter 26: .........*(NumConst=26, SV=7, CEps=1.0655, QPEps=0.2327)
Iter 27: .........*(NumConst=27, SV=7, CEps=1.9737, QPEps=0.4959)
Iter 28: .........*(NumConst=28, SV=6, CEps=1.0902, QPEps=0.0071)
Iter 29: .........*(NumConst=29, SV=6, CEps=0.7816, QPEps=0.0000)
Iter 30: .........*(NumConst=30, SV=6, CEps=1.3718, QPEps=0.3011)
Iter 31: .........*(NumConst=31, SV=8, CEps=0.5544, QPEps=0.2381)
Iter 32: .........*(NumConst=32, SV=6, CEps=1.1490, QPEps=0.0000)
Iter 33: .........(NumConst=32, SV=6, CEps=0.5001, QPEps=0.0000)
Final epsilon on KKT-Conditions: 0.50007
Upper bound on duality gap: 0.00500
Dual objective value: dval=3.65743
Primal objective value: pval=3.66243
Total number of constraints in final working set: 32 (of 32)
Number of iterations: 33
Number of calls to 'find_most_violated_constraint': 2970
Number of SV: 6 
Norm of weight vector: |w|=0.55674
Value of slack variable (on working set): xi=350.24469
Value of slack variable (global): xi=350.74476
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3240.68778
Runtime in cpu-seconds: 0.13
Compacting linear model...done
Writing learned model...done