Reading training examples...done
Training set properties: 49 features, 90 rankings, 8128 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.495733
Iter 1: .........*(NumConst=1, SV=1, CEps=495.7333, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1288.9181, QPEps=0.0000)
Iter 3: .........*(NumConst=3, SV=3, CEps=666.4240, QPEps=0.0005)
Iter 4: .........*(NumConst=4, SV=4, CEps=1572.0832, QPEps=0.0003)
Iter 5: .........*(NumConst=5, SV=4, CEps=750.1451, QPEps=0.0009)
Iter 6: .........*(NumConst=6, SV=5, CEps=454.4586, QPEps=0.0007)
Iter 7: .........*(NumConst=7, SV=6, CEps=676.0508, QPEps=0.0004)
Iter 8: .........*(NumConst=8, SV=7, CEps=412.0399, QPEps=0.0006)
Iter 9: .........*(NumConst=9, SV=7, CEps=168.4321, QPEps=3.8398)
Iter 10: .........*(NumConst=10, SV=8, CEps=179.5610, QPEps=9.3006)
Iter 11: .........*(NumConst=11, SV=7, CEps=259.5979, QPEps=1.5684)
Iter 12: .........*(NumConst=12, SV=7, CEps=142.2494, QPEps=0.0981)
Iter 13: .........*(NumConst=13, SV=7, CEps=181.9747, QPEps=41.2815)
Iter 14: .........*(NumConst=14, SV=10, CEps=70.6845, QPEps=24.3305)
Iter 15: .........*(NumConst=15, SV=8, CEps=148.5379, QPEps=2.1156)
Iter 16: .........*(NumConst=16, SV=7, CEps=92.1057, QPEps=0.0517)
Iter 17: .........*(NumConst=17, SV=7, CEps=81.4966, QPEps=0.0007)
Iter 18: .........*(NumConst=18, SV=7, CEps=96.7300, QPEps=0.0016)
Iter 19: .........*(NumConst=19, SV=7, CEps=63.9038, QPEps=12.4719)
Iter 20: .........*(NumConst=20, SV=7, CEps=84.8482, QPEps=3.4164)
Iter 21: .........*(NumConst=21, SV=8, CEps=52.3541, QPEps=0.0001)
Iter 22: .........*(NumConst=22, SV=8, CEps=43.4546, QPEps=15.3298)
Iter 23: .........*(NumConst=23, SV=8, CEps=48.8470, QPEps=0.0008)
Iter 24: .........*(NumConst=24, SV=8, CEps=35.6894, QPEps=9.1825)
Iter 25: .........*(NumConst=25, SV=9, CEps=34.4404, QPEps=4.1418)
Iter 26: .........*(NumConst=26, SV=9, CEps=34.8446, QPEps=9.8750)
Iter 27: .........*(NumConst=27, SV=8, CEps=31.1678, QPEps=3.1119)
Iter 28: .........*(NumConst=28, SV=7, CEps=21.1739, QPEps=0.0415)
Iter 29: .........*(NumConst=29, SV=7, CEps=28.1727, QPEps=4.6980)
Iter 30: .........*(NumConst=30, SV=7, CEps=15.7174, QPEps=0.0081)
Iter 31: .........*(NumConst=31, SV=8, CEps=42.0571, QPEps=0.0002)
Iter 32: .........*(NumConst=32, SV=7, CEps=15.6894, QPEps=5.3489)
Iter 33: .........*(NumConst=33, SV=8, CEps=19.4549, QPEps=7.2148)
Iter 34: .........*(NumConst=34, SV=7, CEps=22.6836, QPEps=0.2367)
Iter 35: .........*(NumConst=35, SV=8, CEps=12.4932, QPEps=3.3538)
Iter 36: .........*(NumConst=36, SV=10, CEps=16.1629, QPEps=4.8553)
Iter 37: .........*(NumConst=37, SV=10, CEps=13.6039, QPEps=1.0183)
Iter 38: .........*(NumConst=38, SV=9, CEps=13.2787, QPEps=3.9986)
Iter 39: .........*(NumConst=39, SV=9, CEps=10.5261, QPEps=0.0008)
Iter 40: .........*(NumConst=40, SV=9, CEps=12.4762, QPEps=1.1845)
Iter 41: .........*(NumConst=41, SV=9, CEps=11.7138, QPEps=2.9878)
Iter 42: .........*(NumConst=42, SV=8, CEps=13.9338, QPEps=3.3234)
Iter 43: .........*(NumConst=43, SV=8, CEps=5.5993, QPEps=1.9120)
Iter 44: .........*(NumConst=44, SV=9, CEps=10.2134, QPEps=0.2560)
Iter 45: .........*(NumConst=45, SV=8, CEps=6.4907, QPEps=1.8510)
Iter 46: .........*(NumConst=46, SV=8, CEps=7.7782, QPEps=0.0010)
Iter 47: .........*(NumConst=47, SV=7, CEps=5.1404, QPEps=2.1238)
Iter 48: .........*(NumConst=48, SV=8, CEps=5.6254, QPEps=0.0000)
Iter 49: .........*(NumConst=49, SV=8, CEps=4.1700, QPEps=0.0001)
Iter 50: .........*(NumConst=50, SV=9, CEps=3.6360, QPEps=0.4044)
Iter 51: .........*(NumConst=51, SV=9, CEps=4.7261, QPEps=1.4943)
Iter 52: .........*(NumConst=52, SV=8, CEps=2.9512, QPEps=0.0001)
Iter 53: .........*(NumConst=53, SV=8, CEps=3.5825, QPEps=0.0015)
Iter 54: .........*(NumConst=54, SV=8, CEps=2.5432, QPEps=1.2146)
Iter 55: .........*(NumConst=55, SV=7, CEps=2.2560, QPEps=0.0010)
Iter 56: .........*(NumConst=56, SV=8, CEps=3.6440, QPEps=0.8223)
Iter 57: .........*(NumConst=57, SV=9, CEps=2.0668, QPEps=0.2748)
Iter 58: .........*(NumConst=56, SV=8, CEps=1.8457, QPEps=0.0520)
Iter 59: .........*(NumConst=57, SV=8, CEps=1.5621, QPEps=0.0001)
Iter 60: .........*(NumConst=58, SV=8, CEps=1.3432, QPEps=0.0630)
Iter 61: .........*(NumConst=58, SV=10, CEps=1.7084, QPEps=0.2905)
Iter 62: .........*(NumConst=59, SV=9, CEps=3.3592, QPEps=0.0007)
Iter 63: .........*(NumConst=59, SV=9, CEps=1.3778, QPEps=0.5031)
Iter 64: .........*(NumConst=58, SV=9, CEps=1.9778, QPEps=0.4206)
Iter 65: .........*(NumConst=57, SV=9, CEps=0.7809, QPEps=0.0000)
Iter 66: .........*(NumConst=57, SV=8, CEps=1.4487, QPEps=0.0000)
Iter 67: .........*(NumConst=57, SV=9, CEps=0.7482, QPEps=0.0468)
Iter 68: .........*(NumConst=57, SV=9, CEps=2.2869, QPEps=0.3365)
Iter 69: .........*(NumConst=57, SV=6, CEps=0.8057, QPEps=0.0003)
Iter 70: .........*(NumConst=58, SV=6, CEps=0.6342, QPEps=0.0613)
Iter 71: .........*(NumConst=58, SV=7, CEps=0.8675, QPEps=0.0023)
Iter 72: .........(NumConst=58, SV=7, CEps=0.3812, QPEps=0.0023)
Final epsilon on KKT-Conditions: 0.38116
Upper bound on duality gap: 0.01909
Dual objective value: dval=14.40381
Primal objective value: pval=14.42290
Total number of constraints in final working set: 58 (of 71)
Number of iterations: 72
Number of calls to 'find_most_violated_constraint': 6480
Number of SV: 7 
Norm of weight vector: |w|=1.51240
Value of slack variable (on working set): xi=265.20343
Value of slack variable (global): xi=265.58459
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=2177.60549
Runtime in cpu-seconds: 0.21
Compacting linear model...done
Writing learned model...done