Reading training examples...done
Training set properties: 104 features, 90 rankings, 8163 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.527100
Iter 1: .........*(NumConst=1, SV=1, CEps=527.1000, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=353.0609, QPEps=0.0000)
Iter 3: .........*(NumConst=3, SV=3, CEps=591.9260, QPEps=0.0001)
Iter 4: .........*(NumConst=4, SV=3, CEps=1069.9566, QPEps=0.0000)
Iter 5: .........*(NumConst=5, SV=5, CEps=375.0729, QPEps=0.0000)
Iter 6: .........*(NumConst=6, SV=4, CEps=200.7911, QPEps=0.3733)
Iter 7: .........*(NumConst=7, SV=7, CEps=155.6275, QPEps=0.0001)
Iter 8: .........*(NumConst=8, SV=5, CEps=114.1638, QPEps=34.5214)
Iter 9: .........*(NumConst=9, SV=5, CEps=87.0376, QPEps=15.5592)
Iter 10: .........*(NumConst=10, SV=6, CEps=95.7121, QPEps=15.0276)
Iter 11: .........*(NumConst=11, SV=6, CEps=50.5103, QPEps=19.3954)
Iter 12: .........*(NumConst=12, SV=6, CEps=109.8621, QPEps=9.7033)
Iter 13: .........*(NumConst=13, SV=6, CEps=68.5448, QPEps=19.9329)
Iter 14: .........*(NumConst=14, SV=6, CEps=49.1631, QPEps=21.6351)
Iter 15: .........*(NumConst=15, SV=6, CEps=24.9489, QPEps=10.0901)
Iter 16: .........*(NumConst=16, SV=6, CEps=40.0741, QPEps=8.3138)
Iter 17: .........*(NumConst=17, SV=6, CEps=24.9365, QPEps=9.8560)
Iter 18: .........*(NumConst=18, SV=6, CEps=17.8627, QPEps=5.7142)
Iter 19: .........*(NumConst=19, SV=6, CEps=22.7286, QPEps=8.8625)
Iter 20: .........*(NumConst=20, SV=5, CEps=12.9203, QPEps=3.4918)
Iter 21: .........*(NumConst=21, SV=6, CEps=19.0628, QPEps=5.7662)
Iter 22: .........*(NumConst=22, SV=5, CEps=9.5761, QPEps=3.8931)
Iter 23: .........*(NumConst=23, SV=5, CEps=8.0985, QPEps=2.9317)
Iter 24: .........*(NumConst=24, SV=6, CEps=12.1850, QPEps=3.3979)
Iter 25: .........*(NumConst=25, SV=6, CEps=8.1089, QPEps=2.6889)
Iter 26: .........*(NumConst=26, SV=6, CEps=5.4036, QPEps=2.6500)
Iter 27: .........*(NumConst=27, SV=6, CEps=3.2014, QPEps=1.5094)
Iter 28: .........*(NumConst=28, SV=7, CEps=3.1679, QPEps=0.8612)
Iter 29: .........*(NumConst=29, SV=7, CEps=2.4413, QPEps=1.2117)
Iter 30: .........*(NumConst=30, SV=5, CEps=2.5789, QPEps=0.8449)
Iter 31: .........*(NumConst=31, SV=5, CEps=1.5032, QPEps=0.7496)
Iter 32: .........*(NumConst=32, SV=6, CEps=1.0310, QPEps=0.4776)
Iter 33: .........*(NumConst=33, SV=7, CEps=1.1865, QPEps=0.3956)
Iter 34: .........*(NumConst=34, SV=8, CEps=0.6380, QPEps=0.1791)
Iter 35: .........*(NumConst=35, SV=7, CEps=1.2200, QPEps=0.2263)
Iter 36: .........*(NumConst=36, SV=8, CEps=0.6422, QPEps=0.2917)
Iter 37: .........*(NumConst=37, SV=8, CEps=0.7112, QPEps=0.2897)
Iter 38: .........*(NumConst=38, SV=8, CEps=0.5837, QPEps=0.2116)
Iter 39: .........(NumConst=38, SV=8, CEps=0.1848, QPEps=0.2116)
Final epsilon on KKT-Conditions: 0.21164
Upper bound on duality gap: 0.00021
Dual objective value: dval=0.27629
Primal objective value: pval=0.27650
Total number of constraints in final working set: 38 (of 38)
Number of iterations: 39
Number of calls to 'find_most_violated_constraint': 3510
Number of SV: 8 
Norm of weight vector: |w|=0.18342
Value of slack variable (on working set): xi=259.49457
Value of slack variable (global): xi=259.67935
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5364.21371
Runtime in cpu-seconds: 0.25
Compacting linear model...done
Writing learned model...done