Reading training examples...done
Training set properties: 19 features, 180 rankings, 15470 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.975389
Iter 1: .........*(NumConst=1, SV=1, CEps=975.3889, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=993.6316, QPEps=0.0007)
Iter 3: .........*(NumConst=3, SV=3, CEps=2387.0588, QPEps=0.0002)
Iter 4: .........*(NumConst=4, SV=4, CEps=1457.2788, QPEps=0.0004)
Iter 5: .........*(NumConst=5, SV=5, CEps=441.4285, QPEps=0.0005)
Iter 6: .........*(NumConst=6, SV=5, CEps=278.0667, QPEps=0.0012)
Iter 7: .........*(NumConst=7, SV=5, CEps=290.3036, QPEps=3.7080)
Iter 8: .........*(NumConst=8, SV=6, CEps=200.2290, QPEps=3.4642)
Iter 9: .........*(NumConst=9, SV=6, CEps=131.8909, QPEps=42.7491)
Iter 10: .........*(NumConst=10, SV=6, CEps=152.7316, QPEps=48.4996)
Iter 11: .........*(NumConst=11, SV=7, CEps=87.3285, QPEps=43.1666)
Iter 12: .........*(NumConst=12, SV=6, CEps=104.9371, QPEps=0.5549)
Iter 13: .........*(NumConst=13, SV=7, CEps=96.7452, QPEps=27.1610)
Iter 14: .........*(NumConst=14, SV=7, CEps=71.2416, QPEps=34.8598)
Iter 15: .........*(NumConst=15, SV=8, CEps=49.3147, QPEps=21.8068)
Iter 16: .........*(NumConst=16, SV=7, CEps=43.6019, QPEps=14.1768)
Iter 17: .........*(NumConst=17, SV=7, CEps=52.4689, QPEps=21.5389)
Iter 18: .........*(NumConst=18, SV=6, CEps=29.1111, QPEps=7.8978)
Iter 19: .........*(NumConst=19, SV=5, CEps=13.2314, QPEps=5.8568)
Iter 20: .........*(NumConst=20, SV=5, CEps=15.6285, QPEps=3.3225)
Iter 21: .........*(NumConst=21, SV=6, CEps=6.1901, QPEps=2.6715)
Iter 22: .........*(NumConst=22, SV=6, CEps=6.9508, QPEps=2.4415)
Iter 23: .........*(NumConst=23, SV=7, CEps=9.1356, QPEps=2.3075)
Iter 24: .........*(NumConst=24, SV=6, CEps=7.5424, QPEps=0.1555)
Iter 25: .........*(NumConst=25, SV=6, CEps=4.2845, QPEps=2.0338)
Iter 26: .........*(NumConst=26, SV=6, CEps=4.0892, QPEps=1.8708)
Iter 27: .........*(NumConst=27, SV=6, CEps=2.7138, QPEps=1.1746)
Iter 28: .........*(NumConst=28, SV=7, CEps=2.0187, QPEps=0.7876)
Iter 29: .........*(NumConst=29, SV=6, CEps=2.0650, QPEps=0.6308)
Iter 30: .........*(NumConst=30, SV=8, CEps=1.0489, QPEps=0.5064)
Iter 31: .........*(NumConst=31, SV=7, CEps=2.2526, QPEps=0.0651)
Iter 32: .........*(NumConst=32, SV=7, CEps=1.0005, QPEps=0.0799)
Iter 33: .........(NumConst=32, SV=7, CEps=0.7822, QPEps=0.0799)
Final epsilon on KKT-Conditions: 0.78219
Upper bound on duality gap: 0.00797
Dual objective value: dval=6.69638
Primal objective value: pval=6.70435
Total number of constraints in final working set: 32 (of 32)
Number of iterations: 33
Number of calls to 'find_most_violated_constraint': 5940
Number of SV: 7 
Norm of weight vector: |w|=0.83639
Value of slack variable (on working set): xi=634.68665
Value of slack variable (global): xi=635.45746
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3544.60480
Runtime in cpu-seconds: 0.21
Compacting linear model...done
Writing learned model...done