Reading training examples...done
Training set properties: 23 features, 90 rankings, 8124 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.488833
Iter 1: .........*(NumConst=1, SV=1, CEps=488.8333, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=479.9665, QPEps=0.0002)
Iter 3: .........*(NumConst=3, SV=2, CEps=792.7237, QPEps=0.0024)
Iter 4: .........*(NumConst=4, SV=3, CEps=2328.9900, QPEps=0.0033)
Iter 5: .........*(NumConst=5, SV=4, CEps=1148.7198, QPEps=0.0044)
Iter 6: .........*(NumConst=6, SV=5, CEps=751.8980, QPEps=0.0056)
Iter 7: .........*(NumConst=7, SV=6, CEps=1076.5952, QPEps=49.8828)
Iter 8: .........*(NumConst=8, SV=5, CEps=718.7192, QPEps=0.0678)
Iter 9: .........*(NumConst=9, SV=5, CEps=631.1863, QPEps=0.0001)
Iter 10: .........*(NumConst=10, SV=7, CEps=337.9252, QPEps=45.0238)
Iter 11: .........*(NumConst=11, SV=10, CEps=118.8549, QPEps=41.8631)
Iter 12: .........*(NumConst=12, SV=8, CEps=127.4140, QPEps=15.2087)
Iter 13: .........*(NumConst=13, SV=8, CEps=87.1612, QPEps=37.0994)
Iter 14: .........*(NumConst=14, SV=6, CEps=133.5315, QPEps=0.0000)
Iter 15: .........*(NumConst=15, SV=9, CEps=59.9387, QPEps=19.8621)
Iter 16: .........*(NumConst=16, SV=6, CEps=169.4705, QPEps=0.0000)
Iter 17: .........*(NumConst=17, SV=9, CEps=68.1294, QPEps=25.9658)
Iter 18: .........*(NumConst=18, SV=7, CEps=57.2500, QPEps=9.4574)
Iter 19: .........*(NumConst=19, SV=6, CEps=29.7512, QPEps=0.0000)
Iter 20: .........*(NumConst=20, SV=7, CEps=23.5761, QPEps=0.5960)
Iter 21: .........*(NumConst=21, SV=7, CEps=25.3042, QPEps=0.0000)
Iter 22: .........*(NumConst=22, SV=6, CEps=20.9961, QPEps=4.0302)
Iter 23: .........*(NumConst=23, SV=6, CEps=17.1006, QPEps=3.5766)
Iter 24: .........*(NumConst=24, SV=7, CEps=15.1780, QPEps=3.9425)
Iter 25: .........*(NumConst=25, SV=7, CEps=9.9447, QPEps=4.4766)
Iter 26: .........*(NumConst=26, SV=7, CEps=9.7948, QPEps=4.8392)
Iter 27: .........*(NumConst=27, SV=7, CEps=8.4356, QPEps=0.7665)
Iter 28: .........*(NumConst=28, SV=7, CEps=8.5004, QPEps=4.2059)
Iter 29: .........*(NumConst=29, SV=7, CEps=5.5527, QPEps=1.8389)
Iter 30: .........*(NumConst=30, SV=7, CEps=10.0652, QPEps=2.4484)
Iter 31: .........*(NumConst=31, SV=9, CEps=7.1138, QPEps=2.5764)
Iter 32: .........*(NumConst=32, SV=9, CEps=8.0472, QPEps=2.2300)
Iter 33: .........*(NumConst=33, SV=9, CEps=3.8426, QPEps=1.6949)
Iter 34: .........*(NumConst=34, SV=7, CEps=5.2197, QPEps=0.3911)
Iter 35: .........*(NumConst=35, SV=7, CEps=3.2025, QPEps=0.8393)
Iter 36: .........*(NumConst=36, SV=6, CEps=2.5261, QPEps=0.2106)
Iter 37: .........*(NumConst=37, SV=6, CEps=2.1886, QPEps=0.0000)
Iter 38: .........*(NumConst=38, SV=9, CEps=1.5232, QPEps=0.6024)
Iter 39: .........*(NumConst=39, SV=6, CEps=1.7008, QPEps=0.0000)
Iter 40: .........*(NumConst=40, SV=8, CEps=1.3775, QPEps=0.6376)
Iter 41: .........*(NumConst=41, SV=7, CEps=0.8898, QPEps=0.3647)
Iter 42: .........*(NumConst=42, SV=8, CEps=1.4996, QPEps=0.4392)
Iter 43: .........*(NumConst=43, SV=8, CEps=1.3189, QPEps=0.3937)
Iter 44: .........*(NumConst=44, SV=8, CEps=1.1110, QPEps=0.1125)
Iter 45: .........*(NumConst=45, SV=9, CEps=0.5511, QPEps=0.2270)
Iter 46: .........*(NumConst=46, SV=10, CEps=0.7759, QPEps=0.0376)
Iter 47: .........(NumConst=46, SV=10, CEps=0.4466, QPEps=0.0376)
Final epsilon on KKT-Conditions: 0.44658
Upper bound on duality gap: 0.44761
Dual objective value: dval=271.23602
Primal objective value: pval=271.68363
Total number of constraints in final working set: 46 (of 46)
Number of iterations: 47
Number of calls to 'find_most_violated_constraint': 4230
Number of SV: 10 
Norm of weight vector: |w|=2.37330
Value of slack variable (on working set): xi=268.44044
Value of slack variable (global): xi=268.86735
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3059.83706
Runtime in cpu-seconds: 0.76
Compacting linear model...done
Writing learned model...done