Reading training examples...done
Training set properties: 23 features, 180 rankings, 15549 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.945178
Iter 1: .........*(NumConst=1, SV=1, CEps=945.1778, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=933.9627, QPEps=0.0011)
Iter 3: .........*(NumConst=3, SV=3, CEps=3066.0169, QPEps=0.0037)
Iter 4: .........*(NumConst=4, SV=4, CEps=4340.1719, QPEps=0.0044)
Iter 5: .........*(NumConst=5, SV=5, CEps=2433.6722, QPEps=0.0014)
Iter 6: .........*(NumConst=6, SV=5, CEps=1212.8605, QPEps=0.0023)
Iter 7: .........*(NumConst=7, SV=6, CEps=1266.8536, QPEps=49.2675)
Iter 8: .........*(NumConst=8, SV=6, CEps=648.5537, QPEps=46.4332)
Iter 9: .........*(NumConst=9, SV=6, CEps=584.4524, QPEps=38.7967)
Iter 10: .........*(NumConst=10, SV=7, CEps=161.9343, QPEps=34.6984)
Iter 11: .........*(NumConst=11, SV=7, CEps=207.1794, QPEps=0.0000)
Iter 12: .........*(NumConst=12, SV=6, CEps=127.3915, QPEps=0.0000)
Iter 13: .........*(NumConst=13, SV=6, CEps=200.5136, QPEps=0.0000)
Iter 14: .........*(NumConst=14, SV=6, CEps=90.4807, QPEps=0.0000)
Iter 15: .........*(NumConst=15, SV=6, CEps=64.1705, QPEps=0.0000)
Iter 16: .........*(NumConst=16, SV=6, CEps=44.9604, QPEps=0.0000)
Iter 17: .........*(NumConst=17, SV=7, CEps=63.1382, QPEps=21.4316)
Iter 18: .........*(NumConst=18, SV=7, CEps=31.0096, QPEps=11.2082)
Iter 19: .........*(NumConst=19, SV=7, CEps=36.6654, QPEps=5.3043)
Iter 20: .........*(NumConst=20, SV=7, CEps=24.7128, QPEps=11.3700)
Iter 21: .........*(NumConst=21, SV=9, CEps=24.7389, QPEps=7.1819)
Iter 22: .........*(NumConst=22, SV=8, CEps=31.3291, QPEps=0.7400)
Iter 23: .........*(NumConst=23, SV=8, CEps=16.7455, QPEps=3.8345)
Iter 24: .........*(NumConst=24, SV=9, CEps=10.7207, QPEps=2.3281)
Iter 25: .........*(NumConst=25, SV=8, CEps=15.6898, QPEps=3.8610)
Iter 26: .........*(NumConst=26, SV=9, CEps=13.0359, QPEps=0.0000)
Iter 27: .........*(NumConst=27, SV=9, CEps=11.1386, QPEps=0.0000)
Iter 28: .........*(NumConst=28, SV=9, CEps=6.2837, QPEps=0.5380)
Iter 29: .........*(NumConst=29, SV=8, CEps=6.7707, QPEps=2.3214)
Iter 30: .........*(NumConst=30, SV=9, CEps=4.9117, QPEps=0.0000)
Iter 31: .........*(NumConst=31, SV=9, CEps=4.3580, QPEps=0.0000)
Iter 32: .........*(NumConst=32, SV=9, CEps=3.2864, QPEps=0.0000)
Iter 33: .........*(NumConst=33, SV=9, CEps=2.1424, QPEps=0.0000)
Iter 34: .........*(NumConst=34, SV=8, CEps=3.3837, QPEps=0.0000)
Iter 35: .........*(NumConst=35, SV=8, CEps=2.1195, QPEps=0.0000)
Iter 36: .........*(NumConst=36, SV=8, CEps=1.5149, QPEps=0.0000)
Iter 37: .........*(NumConst=37, SV=9, CEps=1.6766, QPEps=0.5990)
Iter 38: .........*(NumConst=38, SV=8, CEps=1.3237, QPEps=0.0000)
Iter 39: .........(NumConst=38, SV=8, CEps=0.7480, QPEps=0.0000)
Final epsilon on KKT-Conditions: 0.74796
Upper bound on duality gap: 0.02244
Dual objective value: dval=18.21603
Primal objective value: pval=18.23847
Total number of constraints in final working set: 38 (of 38)
Number of iterations: 39
Number of calls to 'find_most_violated_constraint': 7020
Number of SV: 8 
Norm of weight vector: |w|=1.14865
Value of slack variable (on working set): xi=585.21118
Value of slack variable (global): xi=585.95910
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5532.82778
Runtime in cpu-seconds: 0.31
Compacting linear model...done
Writing learned model...done