Reading training examples...done
Training set properties: 25 features, 90 rankings, 8146 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.497278
Iter 1: .........*(NumConst=1, SV=1, CEps=497.2778, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=359.8525, QPEps=0.0001)
Iter 3: .........*(NumConst=3, SV=3, CEps=1449.3859, QPEps=0.0013)
Iter 4: .........*(NumConst=4, SV=3, CEps=887.5812, QPEps=0.0003)
Iter 5: .........*(NumConst=5, SV=4, CEps=708.2182, QPEps=0.0007)
Iter 6: .........*(NumConst=6, SV=4, CEps=1122.6441, QPEps=43.0653)
Iter 7: .........*(NumConst=7, SV=5, CEps=289.2155, QPEps=43.4196)
Iter 8: .........*(NumConst=8, SV=5, CEps=225.6920, QPEps=38.8818)
Iter 9: .........*(NumConst=9, SV=6, CEps=179.5256, QPEps=34.7928)
Iter 10: .........*(NumConst=10, SV=7, CEps=125.2292, QPEps=2.9428)
Iter 11: .........*(NumConst=11, SV=7, CEps=120.2888, QPEps=43.6799)
Iter 12: .........*(NumConst=12, SV=6, CEps=62.3185, QPEps=26.3313)
Iter 13: .........*(NumConst=13, SV=8, CEps=85.7267, QPEps=13.6097)
Iter 14: .........*(NumConst=14, SV=6, CEps=99.4578, QPEps=30.8378)
Iter 15: .........*(NumConst=15, SV=6, CEps=56.7494, QPEps=26.2262)
Iter 16: .........*(NumConst=16, SV=7, CEps=42.9901, QPEps=3.7578)
Iter 17: .........*(NumConst=17, SV=7, CEps=61.5278, QPEps=17.4190)
Iter 18: .........*(NumConst=18, SV=6, CEps=19.9285, QPEps=1.8174)
Iter 19: .........*(NumConst=19, SV=7, CEps=33.6365, QPEps=9.4758)
Iter 20: .........*(NumConst=20, SV=7, CEps=21.6699, QPEps=3.9585)
Iter 21: .........*(NumConst=21, SV=8, CEps=26.9628, QPEps=7.2630)
Iter 22: .........*(NumConst=22, SV=7, CEps=14.3410, QPEps=7.1466)
Iter 23: .........*(NumConst=23, SV=8, CEps=12.5907, QPEps=0.2130)
Iter 24: .........*(NumConst=24, SV=7, CEps=16.6638, QPEps=5.8114)
Iter 25: .........*(NumConst=25, SV=8, CEps=8.7399, QPEps=3.8415)
Iter 26: .........*(NumConst=26, SV=7, CEps=7.5843, QPEps=1.0665)
Iter 27: .........*(NumConst=27, SV=7, CEps=9.2474, QPEps=0.0230)
Iter 28: .........*(NumConst=28, SV=7, CEps=5.4340, QPEps=0.4529)
Iter 29: .........*(NumConst=29, SV=7, CEps=5.4709, QPEps=0.1678)
Iter 30: .........*(NumConst=30, SV=8, CEps=3.8391, QPEps=0.0107)
Iter 31: .........*(NumConst=31, SV=8, CEps=7.6637, QPEps=0.2497)
Iter 32: .........*(NumConst=32, SV=9, CEps=4.0099, QPEps=0.0000)
Iter 33: .........*(NumConst=33, SV=9, CEps=3.7526, QPEps=0.0000)
Iter 34: .........*(NumConst=34, SV=9, CEps=4.1861, QPEps=0.0056)
Iter 35: .........*(NumConst=35, SV=8, CEps=2.6143, QPEps=0.0943)
Iter 36: .........*(NumConst=36, SV=10, CEps=1.9834, QPEps=0.8785)
Iter 37: .........*(NumConst=37, SV=10, CEps=2.5298, QPEps=0.8885)
Iter 38: .........*(NumConst=38, SV=8, CEps=2.2932, QPEps=0.9290)
Iter 39: .........*(NumConst=39, SV=8, CEps=1.5783, QPEps=0.7662)
Iter 40: .........*(NumConst=40, SV=9, CEps=0.7115, QPEps=0.1445)
Iter 41: .........*(NumConst=41, SV=9, CEps=2.3431, QPEps=0.0000)
Iter 42: .........*(NumConst=42, SV=9, CEps=1.5966, QPEps=0.3538)
Iter 43: .........*(NumConst=43, SV=9, CEps=0.8508, QPEps=0.3518)
Iter 44: .........*(NumConst=44, SV=9, CEps=1.0986, QPEps=0.3295)
Iter 45: .........(NumConst=44, SV=9, CEps=0.4791, QPEps=0.3295)
Final epsilon on KKT-Conditions: 0.47909
Upper bound on duality gap: 0.05749
Dual objective value: dval=29.01255
Primal objective value: pval=29.07004
Total number of constraints in final working set: 44 (of 44)
Number of iterations: 45
Number of calls to 'find_most_violated_constraint': 4050
Number of SV: 9 
Norm of weight vector: |w|=1.27909
Value of slack variable (on working set): xi=282.07645
Value of slack variable (global): xi=282.52000
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=2600.22796
Runtime in cpu-seconds: 0.36
Compacting linear model...done
Writing learned model...done