Reading training examples...done
Training set properties: 23 features, 180 rankings, 15573 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.987111
Iter 1: .........*(NumConst=1, SV=1, CEps=987.1111, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1019.0208, QPEps=0.0014)
Iter 3: .........*(NumConst=3, SV=3, CEps=2775.8228, QPEps=0.0011)
Iter 4: .........*(NumConst=4, SV=4, CEps=3230.8963, QPEps=0.0068)
Iter 5: .........*(NumConst=5, SV=5, CEps=3315.1464, QPEps=0.0066)
Iter 6: .........*(NumConst=6, SV=5, CEps=1370.7994, QPEps=0.0012)
Iter 7: .........*(NumConst=7, SV=6, CEps=1073.3380, QPEps=0.0009)
Iter 8: .........*(NumConst=8, SV=6, CEps=1591.7173, QPEps=48.5722)
Iter 9: .........*(NumConst=9, SV=9, CEps=3768.1432, QPEps=49.4957)
Iter 10: .........*(NumConst=10, SV=8, CEps=233.0409, QPEps=48.1891)
Iter 11: .........*(NumConst=11, SV=8, CEps=426.3881, QPEps=48.0424)
Iter 12: .........*(NumConst=12, SV=8, CEps=280.8180, QPEps=45.5176)
Iter 13: .........*(NumConst=13, SV=8, CEps=152.8165, QPEps=49.7133)
Iter 14: .........*(NumConst=14, SV=9, CEps=246.9655, QPEps=49.9332)
Iter 15: .........*(NumConst=15, SV=7, CEps=252.3364, QPEps=49.5317)
Iter 16: .........*(NumConst=16, SV=8, CEps=141.0222, QPEps=47.4397)
Iter 17: .........*(NumConst=17, SV=9, CEps=99.3123, QPEps=49.0380)
Iter 18: .........*(NumConst=18, SV=10, CEps=92.9887, QPEps=45.5868)
Iter 19: .........*(NumConst=19, SV=9, CEps=203.3673, QPEps=42.4443)
Iter 20: .........*(NumConst=20, SV=10, CEps=69.1050, QPEps=33.6635)
Iter 21: .........*(NumConst=21, SV=11, CEps=87.7800, QPEps=34.5012)
Iter 22: .........*(NumConst=22, SV=11, CEps=74.8009, QPEps=34.5430)
Iter 23: .........*(NumConst=23, SV=11, CEps=41.5565, QPEps=20.4794)
Iter 24: .........*(NumConst=24, SV=8, CEps=34.9759, QPEps=16.6458)
Iter 25: .........*(NumConst=25, SV=8, CEps=53.6482, QPEps=1.8388)
Iter 26: .........*(NumConst=26, SV=7, CEps=28.7470, QPEps=13.9831)
Iter 27: .........*(NumConst=27, SV=7, CEps=47.3107, QPEps=9.1435)
Iter 28: .........*(NumConst=28, SV=7, CEps=22.8797, QPEps=1.3738)
Iter 29: .........*(NumConst=29, SV=8, CEps=25.5864, QPEps=10.9210)
Iter 30: .........*(NumConst=30, SV=10, CEps=17.9728, QPEps=7.7248)
Iter 31: .........*(NumConst=31, SV=9, CEps=28.3147, QPEps=8.4166)
Iter 32: .........*(NumConst=32, SV=9, CEps=19.4589, QPEps=8.1444)
Iter 33: .........*(NumConst=33, SV=10, CEps=29.0656, QPEps=8.6540)
Iter 34: .........*(NumConst=34, SV=10, CEps=10.2317, QPEps=4.6158)
Iter 35: .........*(NumConst=35, SV=10, CEps=10.3822, QPEps=5.0910)
Iter 36: .........*(NumConst=36, SV=8, CEps=13.1924, QPEps=3.5093)
Iter 37: .........*(NumConst=37, SV=11, CEps=10.1272, QPEps=3.6758)
Iter 38: .........*(NumConst=38, SV=11, CEps=11.9270, QPEps=4.3404)
Iter 39: .........*(NumConst=39, SV=10, CEps=6.9974, QPEps=3.4661)
Iter 40: .........*(NumConst=40, SV=9, CEps=10.0161, QPEps=3.2190)
Iter 41: .........*(NumConst=41, SV=9, CEps=4.8198, QPEps=1.8021)
Iter 42: .........*(NumConst=42, SV=10, CEps=5.3054, QPEps=2.2485)
Iter 43: .........*(NumConst=43, SV=10, CEps=5.2429, QPEps=1.7591)
Iter 44: .........*(NumConst=44, SV=9, CEps=5.8910, QPEps=2.3937)
Iter 45: .........*(NumConst=45, SV=11, CEps=3.9097, QPEps=1.7108)
Iter 46: .........*(NumConst=46, SV=10, CEps=3.8404, QPEps=1.8306)
Iter 47: .........*(NumConst=47, SV=10, CEps=3.3746, QPEps=1.6572)
Iter 48: .........*(NumConst=48, SV=11, CEps=3.1824, QPEps=1.3103)
Iter 49: .........*(NumConst=49, SV=10, CEps=3.4708, QPEps=1.4479)
Iter 50: .........*(NumConst=50, SV=13, CEps=2.5631, QPEps=1.0957)
Iter 51: .........*(NumConst=51, SV=13, CEps=4.0078, QPEps=0.9654)
Iter 52: .........*(NumConst=52, SV=10, CEps=2.2514, QPEps=1.0349)
Iter 53: .........*(NumConst=53, SV=10, CEps=2.2940, QPEps=0.8587)
Iter 54: .........*(NumConst=54, SV=8, CEps=1.1111, QPEps=0.2499)
Iter 55: .........*(NumConst=55, SV=10, CEps=1.2147, QPEps=0.5083)
Iter 56: .........*(NumConst=56, SV=9, CEps=1.6091, QPEps=0.5284)
Iter 57: .........(NumConst=56, SV=9, CEps=0.9667, QPEps=0.5284)
Final epsilon on KKT-Conditions: 0.96675
Upper bound on duality gap: 1.05110
Dual objective value: dval=624.62803
Primal objective value: pval=625.67913
Total number of constraints in final working set: 56 (of 56)
Number of iterations: 57
Number of calls to 'find_most_violated_constraint': 10260
Number of SV: 9 
Norm of weight vector: |w|=1.50170
Value of slack variable (on working set): xi=623.64397
Value of slack variable (global): xi=624.55158
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5556.10073
Runtime in cpu-seconds: 16.79
Compacting linear model...done
Writing learned model...done