Reading training examples...done
Training set properties: 19 features, 180 rankings, 15544 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=1.014872
Iter 1: .........*(NumConst=1, SV=1, CEps=1014.8722, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1039.4861, QPEps=0.0004)
Iter 3: .........*(NumConst=3, SV=3, CEps=2521.6492, QPEps=0.0006)
Iter 4: .........*(NumConst=4, SV=4, CEps=1847.7715, QPEps=0.0012)
Iter 5: .........*(NumConst=5, SV=4, CEps=1504.0525, QPEps=0.0013)
Iter 6: .........*(NumConst=6, SV=5, CEps=1090.6431, QPEps=0.0005)
Iter 7: .........*(NumConst=7, SV=5, CEps=1192.5754, QPEps=0.0224)
Iter 8: .........*(NumConst=8, SV=7, CEps=455.8605, QPEps=0.0107)
Iter 9: .........*(NumConst=9, SV=7, CEps=439.5758, QPEps=0.0141)
Iter 10: .........*(NumConst=10, SV=8, CEps=371.0623, QPEps=14.4945)
Iter 11: .........*(NumConst=11, SV=8, CEps=231.8875, QPEps=0.5091)
Iter 12: .........*(NumConst=12, SV=7, CEps=204.6942, QPEps=9.9583)
Iter 13: .........*(NumConst=13, SV=7, CEps=219.2633, QPEps=0.8522)
Iter 14: .........*(NumConst=14, SV=7, CEps=219.9109, QPEps=35.5573)
Iter 15: .........*(NumConst=15, SV=8, CEps=146.3503, QPEps=35.8809)
Iter 16: .........*(NumConst=16, SV=7, CEps=173.3086, QPEps=48.3065)
Iter 17: .........*(NumConst=17, SV=8, CEps=81.5224, QPEps=4.1101)
Iter 18: .........*(NumConst=18, SV=8, CEps=284.2138, QPEps=0.6650)
Iter 19: .........*(NumConst=19, SV=8, CEps=100.3761, QPEps=34.8231)
Iter 20: .........*(NumConst=20, SV=7, CEps=151.5732, QPEps=4.3343)
Iter 21: .........*(NumConst=21, SV=7, CEps=74.0408, QPEps=27.5479)
Iter 22: .........*(NumConst=22, SV=6, CEps=47.5948, QPEps=7.5662)
Iter 23: .........*(NumConst=23, SV=8, CEps=71.3481, QPEps=21.7531)
Iter 24: .........*(NumConst=24, SV=8, CEps=43.4121, QPEps=7.4109)
Iter 25: .........*(NumConst=25, SV=8, CEps=65.4065, QPEps=17.4819)
Iter 26: .........*(NumConst=26, SV=7, CEps=46.3703, QPEps=4.9938)
Iter 27: .........*(NumConst=27, SV=8, CEps=39.8799, QPEps=19.1260)
Iter 28: .........*(NumConst=28, SV=8, CEps=30.0428, QPEps=0.0001)
Iter 29: .........*(NumConst=29, SV=8, CEps=51.6570, QPEps=14.9473)
Iter 30: .........*(NumConst=30, SV=7, CEps=36.5178, QPEps=8.9211)
Iter 31: .........*(NumConst=31, SV=8, CEps=30.8840, QPEps=15.0075)
Iter 32: .........*(NumConst=32, SV=9, CEps=22.3693, QPEps=10.8218)
Iter 33: .........*(NumConst=33, SV=7, CEps=28.2859, QPEps=4.4134)
Iter 34: .........*(NumConst=34, SV=7, CEps=20.7279, QPEps=3.1915)
Iter 35: .........*(NumConst=35, SV=8, CEps=21.9605, QPEps=1.1156)
Iter 36: .........*(NumConst=36, SV=7, CEps=19.3260, QPEps=0.7745)
Iter 37: .........*(NumConst=37, SV=8, CEps=12.5458, QPEps=4.1758)
Iter 38: .........*(NumConst=38, SV=8, CEps=12.0692, QPEps=3.6785)
Iter 39: .........*(NumConst=39, SV=9, CEps=13.0651, QPEps=5.5820)
Iter 40: .........*(NumConst=40, SV=9, CEps=9.6818, QPEps=3.0233)
Iter 41: .........*(NumConst=41, SV=8, CEps=7.6110, QPEps=0.3002)
Iter 42: .........*(NumConst=42, SV=8, CEps=10.2335, QPEps=0.2869)
Iter 43: .........*(NumConst=43, SV=9, CEps=5.3273, QPEps=2.2772)
Iter 44: .........*(NumConst=44, SV=8, CEps=12.7677, QPEps=0.5080)
Iter 45: .........*(NumConst=45, SV=6, CEps=7.3352, QPEps=0.1925)
Iter 46: .........*(NumConst=46, SV=8, CEps=5.2434, QPEps=1.5753)
Iter 47: .........*(NumConst=47, SV=7, CEps=7.8269, QPEps=1.1541)
Iter 48: .........*(NumConst=48, SV=8, CEps=4.2991, QPEps=1.7027)
Iter 49: .........*(NumConst=49, SV=7, CEps=3.6887, QPEps=0.0001)
Iter 50: .........*(NumConst=50, SV=7, CEps=4.6666, QPEps=0.0209)
Iter 51: .........*(NumConst=51, SV=8, CEps=2.6212, QPEps=0.0000)
Iter 52: .........*(NumConst=52, SV=8, CEps=2.7601, QPEps=0.8967)
Iter 53: .........*(NumConst=53, SV=8, CEps=4.3516, QPEps=0.0000)
Iter 54: .........*(NumConst=54, SV=8, CEps=2.4486, QPEps=0.1101)
Iter 55: .........*(NumConst=55, SV=7, CEps=2.4300, QPEps=0.5714)
Iter 56: .........*(NumConst=55, SV=8, CEps=1.1901, QPEps=0.0001)
Iter 57: .........*(NumConst=56, SV=8, CEps=2.3847, QPEps=0.0000)
Iter 58: .........*(NumConst=57, SV=8, CEps=1.8156, QPEps=0.3529)
Iter 59: .........*(NumConst=58, SV=8, CEps=1.9922, QPEps=0.0010)
Iter 60: .........*(NumConst=58, SV=7, CEps=1.2182, QPEps=0.0179)
Iter 61: .........*(NumConst=57, SV=9, CEps=1.0787, QPEps=0.4514)
Iter 62: .........*(NumConst=57, SV=6, CEps=1.6501, QPEps=0.0041)
Iter 63: .........(NumConst=57, SV=6, CEps=0.9861, QPEps=0.0041)
Final epsilon on KKT-Conditions: 0.98614
Upper bound on duality gap: 0.04935
Dual objective value: dval=32.65399
Primal objective value: pval=32.70334
Total number of constraints in final working set: 57 (of 62)
Number of iterations: 63
Number of calls to 'find_most_violated_constraint': 11340
Number of SV: 6 
Norm of weight vector: |w|=1.35384
Value of slack variable (on working set): xi=634.75339
Value of slack variable (global): xi=635.73792
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1759.02062
Runtime in cpu-seconds: 0.28
Compacting linear model...done
Writing learned model...done