Reading training examples...done
Training set properties: 49 features, 180 rankings, 15673 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.998928
Iter 1: .........*(NumConst=1, SV=1, CEps=998.9278, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1529.2142, QPEps=0.0003)
Iter 3: .........*(NumConst=3, SV=3, CEps=1621.3966, QPEps=0.0005)
Iter 4: .........*(NumConst=4, SV=4, CEps=1564.6999, QPEps=0.0117)
Iter 5: .........*(NumConst=5, SV=4, CEps=1299.0451, QPEps=0.0010)
Iter 6: .........*(NumConst=6, SV=5, CEps=1158.9309, QPEps=0.0092)
Iter 7: .........*(NumConst=7, SV=5, CEps=1499.8324, QPEps=12.9313)
Iter 8: .........*(NumConst=8, SV=6, CEps=829.1273, QPEps=49.7694)
Iter 9: .........*(NumConst=9, SV=7, CEps=552.5803, QPEps=15.6454)
Iter 10: .........*(NumConst=10, SV=7, CEps=380.0811, QPEps=47.1722)
Iter 11: .........*(NumConst=11, SV=9, CEps=240.3624, QPEps=0.0152)
Iter 12: .........*(NumConst=12, SV=7, CEps=289.2862, QPEps=22.5735)
Iter 13: .........*(NumConst=13, SV=8, CEps=291.2521, QPEps=0.3777)
Iter 14: .........*(NumConst=14, SV=8, CEps=245.5845, QPEps=21.5999)
Iter 15: .........*(NumConst=15, SV=7, CEps=201.5304, QPEps=8.1932)
Iter 16: .........*(NumConst=16, SV=8, CEps=158.9859, QPEps=7.6895)
Iter 17: .........*(NumConst=17, SV=8, CEps=170.4093, QPEps=16.5634)
Iter 18: .........*(NumConst=18, SV=8, CEps=204.9819, QPEps=8.5376)
Iter 19: .........*(NumConst=19, SV=9, CEps=94.5393, QPEps=0.0005)
Iter 20: .........*(NumConst=20, SV=9, CEps=135.8527, QPEps=30.3011)
Iter 21: .........*(NumConst=21, SV=9, CEps=136.2447, QPEps=0.4461)
Iter 22: .........*(NumConst=22, SV=8, CEps=85.1893, QPEps=21.2740)
Iter 23: .........*(NumConst=23, SV=7, CEps=112.7287, QPEps=27.2847)
Iter 24: .........*(NumConst=24, SV=7, CEps=64.1088, QPEps=16.8250)
Iter 25: .........*(NumConst=25, SV=7, CEps=57.3558, QPEps=17.1356)
Iter 26: .........*(NumConst=26, SV=7, CEps=46.6817, QPEps=3.0214)
Iter 27: .........*(NumConst=27, SV=8, CEps=46.8103, QPEps=0.0090)
Iter 28: .........*(NumConst=28, SV=9, CEps=54.2485, QPEps=4.7690)
Iter 29: .........*(NumConst=29, SV=8, CEps=50.8363, QPEps=15.7773)
Iter 30: .........*(NumConst=30, SV=8, CEps=30.5983, QPEps=0.0444)
Iter 31: .........*(NumConst=31, SV=9, CEps=30.7349, QPEps=11.8664)
Iter 32: .........*(NumConst=32, SV=10, CEps=55.6994, QPEps=0.0002)
Iter 33: .........*(NumConst=33, SV=10, CEps=27.8446, QPEps=1.3834)
Iter 34: .........*(NumConst=34, SV=8, CEps=35.2183, QPEps=12.4140)
Iter 35: .........*(NumConst=35, SV=9, CEps=46.3194, QPEps=0.6391)
Iter 36: .........*(NumConst=36, SV=8, CEps=20.1374, QPEps=7.5694)
Iter 37: .........*(NumConst=37, SV=7, CEps=21.9825, QPEps=0.0047)
Iter 38: .........*(NumConst=38, SV=7, CEps=14.9877, QPEps=6.5263)
Iter 39: .........*(NumConst=39, SV=7, CEps=20.5962, QPEps=0.0029)
Iter 40: .........*(NumConst=40, SV=9, CEps=15.3343, QPEps=0.0000)
Iter 41: .........*(NumConst=41, SV=9, CEps=11.5385, QPEps=0.6381)
Iter 42: .........*(NumConst=42, SV=9, CEps=22.1390, QPEps=0.0005)
Iter 43: .........*(NumConst=43, SV=8, CEps=11.6458, QPEps=0.1181)
Iter 44: .........*(NumConst=44, SV=9, CEps=14.7048, QPEps=3.7401)
Iter 45: .........*(NumConst=45, SV=8, CEps=12.3960, QPEps=0.0072)
Iter 46: .........*(NumConst=46, SV=8, CEps=7.2097, QPEps=0.7502)
Iter 47: .........*(NumConst=47, SV=8, CEps=9.7732, QPEps=3.4815)
Iter 48: .........*(NumConst=48, SV=8, CEps=9.3161, QPEps=0.0002)
Iter 49: .........*(NumConst=49, SV=8, CEps=6.0254, QPEps=2.3638)
Iter 50: .........*(NumConst=50, SV=8, CEps=8.7628, QPEps=0.0588)
Iter 51: .........*(NumConst=51, SV=10, CEps=5.1853, QPEps=1.1709)
Iter 52: .........*(NumConst=52, SV=9, CEps=7.8855, QPEps=0.1870)
Iter 53: .........*(NumConst=53, SV=8, CEps=4.9775, QPEps=0.1872)
Iter 54: .........*(NumConst=54, SV=9, CEps=5.4829, QPEps=0.0011)
Iter 55: .........*(NumConst=55, SV=8, CEps=5.5416, QPEps=0.0004)
Iter 56: .........*(NumConst=56, SV=8, CEps=3.3190, QPEps=0.0004)
Iter 57: .........*(NumConst=57, SV=10, CEps=3.0929, QPEps=1.3447)
Iter 58: .........*(NumConst=58, SV=8, CEps=9.1460, QPEps=1.3800)
Iter 59: .........*(NumConst=58, SV=9, CEps=5.2873, QPEps=0.0000)
Iter 60: .........*(NumConst=59, SV=10, CEps=3.6877, QPEps=0.7294)
Iter 61: .........*(NumConst=57, SV=9, CEps=2.9084, QPEps=0.0001)
Iter 62: .........*(NumConst=58, SV=9, CEps=3.4510, QPEps=0.4079)
Iter 63: .........*(NumConst=57, SV=9, CEps=2.6345, QPEps=0.0000)
Iter 64: .........*(NumConst=57, SV=9, CEps=2.8567, QPEps=0.0002)
Iter 65: .........*(NumConst=58, SV=9, CEps=1.6326, QPEps=0.1513)
Iter 66: .........*(NumConst=58, SV=9, CEps=3.3120, QPEps=0.0000)
Iter 67: .........*(NumConst=58, SV=10, CEps=1.7837, QPEps=0.3720)
Iter 68: .........*(NumConst=58, SV=9, CEps=2.2593, QPEps=0.4110)
Iter 69: .........*(NumConst=58, SV=8, CEps=2.0996, QPEps=0.0033)
Iter 70: .........*(NumConst=58, SV=8, CEps=1.5683, QPEps=0.0012)
Iter 71: .........*(NumConst=58, SV=10, CEps=1.2516, QPEps=0.2925)
Iter 72: .........*(NumConst=58, SV=8, CEps=1.8903, QPEps=0.0000)
Iter 73: .........(NumConst=58, SV=8, CEps=0.9794, QPEps=0.0000)
Final epsilon on KKT-Conditions: 0.97936
Upper bound on duality gap: 0.02938
Dual objective value: dval=17.80645
Primal objective value: pval=17.83583
Total number of constraints in final working set: 58 (of 72)
Number of iterations: 73
Number of calls to 'find_most_violated_constraint': 13140
Number of SV: 8 
Norm of weight vector: |w|=1.30882
Value of slack variable (on working set): xi=564.99845
Value of slack variable (global): xi=565.97778
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=4794.68641
Runtime in cpu-seconds: 0.58
Compacting linear model...done
Writing learned model...done