Reading training examples...done
Training set properties: 23 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=993.1220, QPEps=0.0007)
Iter 3: .........*(NumConst=3, SV=3, CEps=3684.5165, QPEps=0.0017)
Iter 4: .........*(NumConst=4, SV=4, CEps=3095.8873, QPEps=0.0081)
Iter 5: .........*(NumConst=5, SV=5, CEps=2401.7822, QPEps=0.0011)
Iter 6: .........*(NumConst=6, SV=6, CEps=1222.9456, QPEps=0.0038)
Iter 7: .........*(NumConst=7, SV=6, CEps=1779.3389, QPEps=41.3552)
Iter 8: .........*(NumConst=8, SV=6, CEps=430.7081, QPEps=0.0000)
Iter 9: .........*(NumConst=9, SV=6, CEps=329.7995, QPEps=30.5008)
Iter 10: .........*(NumConst=10, SV=7, CEps=264.0237, QPEps=49.8134)
Iter 11: .........*(NumConst=11, SV=7, CEps=247.8679, QPEps=43.7222)
Iter 12: .........*(NumConst=12, SV=6, CEps=123.8970, QPEps=0.0000)
Iter 13: .........*(NumConst=13, SV=7, CEps=125.6118, QPEps=47.1582)
Iter 14: .........*(NumConst=14, SV=8, CEps=106.8307, QPEps=48.9784)
Iter 15: .........*(NumConst=15, SV=6, CEps=122.5994, QPEps=0.0000)
Iter 16: .........*(NumConst=16, SV=7, CEps=174.4365, QPEps=32.1420)
Iter 17: .........*(NumConst=17, SV=6, CEps=85.5058, QPEps=3.5688)
Iter 18: .........*(NumConst=18, SV=7, CEps=41.7030, QPEps=18.3526)
Iter 19: .........*(NumConst=19, SV=6, CEps=75.8690, QPEps=0.0000)
Iter 20: .........*(NumConst=20, SV=6, CEps=36.9002, QPEps=0.0000)
Iter 21: .........*(NumConst=21, SV=6, CEps=23.3408, QPEps=7.3578)
Iter 22: .........*(NumConst=22, SV=6, CEps=52.7075, QPEps=0.0000)
Iter 23: .........*(NumConst=23, SV=6, CEps=28.3454, QPEps=0.0000)
Iter 24: .........*(NumConst=24, SV=7, CEps=14.2740, QPEps=3.5542)
Iter 25: .........*(NumConst=25, SV=5, CEps=15.8200, QPEps=4.4314)
Iter 26: .........*(NumConst=26, SV=7, CEps=11.3125, QPEps=5.4595)
Iter 27: .........*(NumConst=27, SV=6, CEps=9.4085, QPEps=2.2286)
Iter 28: .........*(NumConst=28, SV=6, CEps=11.9912, QPEps=0.6061)
Iter 29: .........*(NumConst=29, SV=5, CEps=5.2081, QPEps=0.0000)
Iter 30: .........*(NumConst=30, SV=6, CEps=7.0291, QPEps=1.3537)
Iter 31: .........*(NumConst=31, SV=6, CEps=8.0133, QPEps=1.0631)
Iter 32: .........*(NumConst=32, SV=6, CEps=2.7260, QPEps=0.0000)
Iter 33: .........*(NumConst=33, SV=7, CEps=2.9663, QPEps=0.0000)
Iter 34: .........*(NumConst=34, SV=7, CEps=3.9871, QPEps=0.0000)
Iter 35: .........*(NumConst=35, SV=7, CEps=1.6893, QPEps=0.1990)
Iter 36: .........*(NumConst=36, SV=7, CEps=2.5668, QPEps=0.4052)
Iter 37: .........*(NumConst=37, SV=7, CEps=3.0489, QPEps=0.0000)
Iter 38: .........*(NumConst=38, SV=7, CEps=1.1027, QPEps=0.3001)
Iter 39: .........*(NumConst=39, SV=6, CEps=1.7228, QPEps=0.1464)
Iter 40: .........*(NumConst=40, SV=7, CEps=2.3219, QPEps=0.5204)
Iter 41: .........(NumConst=40, SV=7, CEps=0.9158, QPEps=0.5204)
Final epsilon on KKT-Conditions: 0.91578
Upper bound on duality gap: 0.02440
Dual objective value: dval=19.38734
Primal objective value: pval=19.41174
Total number of constraints in final working set: 40 (of 40)
Number of iterations: 41
Number of calls to 'find_most_violated_constraint': 7380
Number of SV: 7 
Norm of weight vector: |w|=1.11936
Value of slack variable (on working set): xi=625.77992
Value of slack variable (global): xi=626.17528
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5506.53251
Runtime in cpu-seconds: 0.18
Compacting linear model...done
Writing learned model...done